OUT OF 3000 UNIVERSITIES IN AMERICA ABOUT 100 ARE WORTH CONSIDERATION FOR ADMISSION. SOME OF THESE ARE CATEGORISED BELOW
Public
Indiana University
Iowa State University
Michigan State University
The Ohio State University
The Pennsylvania State University
Purdue University
Rutgers, The State University of New Jersey
Stony Brook University-State University of New York
Texas A&M University
University at Buffalo, The State University of New York
The University of Arizona
University of California, Davis
University of California, Berkeley
University of California, Irvine
University of California, Los Angeles
University of California, San Diego
University of California, Santa Barbara
University of Colorado at Boulder
University of Florida
University of Illinois at Urbana Champaign
The University of Iowa
The University of Kansas
University of Maryland at College Park
University of Michigan
University of Minnesota, Twin Cities
University of Missouri-Columbia
University of Nebraska-Lincoln
The University of North Carolina at Chapel Hill
University of Oregon
University of Pittsburgh
The University of Texas at Austin
University of Virginia
University of Washington
The University of Wisconsin-Madison
Private
Brandeis University
Brown University
California Institute of Technology
Carnegie Mellon University
Case Western Reserve University
Columbia University
Cornell University
Duke University
Emory University
Harvard University
The Johns Hopkins University
Massachusetts Institute of Technology
New York University
Northwestern University
Princeton University
Rice University
Stanford University
Syracuse University
Tulane University
The University of Chicago
University of Pennsylvania
University of Rochester
University of Southern California
Vanderbilt University
Washington University in St. Louis
Yale University
June 30, 2007
Public and Private Univs Categorised
June 12, 2007
What is Faculty Scholarly Productivity Index?
The Faculty Scholarly Productivity Index™ (FSP Index) is a method for evaluating doctoral programs at Research Universities (both Carnegie Research Extensive and Research Intensive), based on a set of statistical algorithms developed by Lawrence Martin, PhD and Anthony Olejniczak. The FSP Index measures the annual productivity of faculty on several factors including:
* Publications (books and journal articles)
* Citations of journal publications
* Federal Research Funding
* Awards and Honors
The FSP analysis creates, at the discipline level, a scale based on the cumulative scoring of a program's faculty using these measures compared against national standards. Each program can then be compared to the national mean z-score.
Individual program scores can then be combined to demonstrate the quality of the scholarly work within broad fields as well as the entire university. When analyzed over time, a full picture of the direction of the program or university research can be realized.
By compiling the individual faculty activity into departmental indexes, one can truly assess the research strength of a university.
The FSP Index is unique in its focus on concrete data relating to faculty scholarly productivity. The FSP Index contains no reputational assessments, and is unrivaled in the depth and detail of its content - even compiling data in areas where other assessments have found difficulty (e.g. book publications).
The FSP Index is being produced on an annual basis. The 2004 and 2005 Reports are currently available. Order yours today!
The FSP Index is an ideal tool for use in:
* program assessment and evaluation
* strategic planning initiatives
* observing trends in faculty productivity
* and much more!
Click the links below to view selected FSP Index Data in individual disciplines within the 11 broad categories (Biological & Biomedical Sciences are separated for easier viewing).
1.Agricultural Sciences
2.Biological Sciences
3.Biomedical Sciences
4.Business
5.Education
6.Engineering
7.Health Science Professions
8.Humanities
9.Natural Resouces & Conservation
10.Physical Sciences & Mathematics
11.Public Administration & Social Service Professions
12.Social & Behavioral Sciences
June 11, 2007
SDSU Ranked No. 1 Small Research University
San Diego State University has been named the No. 1 small research university in the nation, according to a new ranking index based on the 2005 Faculty Scholarly Productivity Index (FSP Index).
The FSP Index, created by Academic Analytics, ranks universities based on the productivity of their faculty, including their publications, citations and financial and honorary awards won. Programs, not individual faculty, are rated and are aggregated to produce quantitative rankings of whole universities. The official ranking report was released this week.
Scholarly Productivity and Research
"This distinction reflects the solid work being done by faculty and staff at this premiere urban research university," said SDSU President Stephen L. Weber. "Scholarly productivity and research are expressions of our faculty's diligence to provide a learning environment that serves both our students and our community."
"Small research university" refers to universities which offer 14 or fewer Ph.D. programs. In 2005, the year from which the rankings are based, SDSU had a total of 10 Ph.D. programs offered jointly with University of California, San Diego; University of California, Santa Barbara; and Claremont Graduate University.
Read the Article...
Glittering univs are not churning gold
A majority of immigrants from India and five other nations who started one fourth of the technology and engineering companies in the United States in the last 10 years were not educated in elite institutions, says a study.
The findings were part of a new study conducted by researchers at Duke University and the University of California, at Berkeley, as a follow-up to a report released in January which showed that in 25.3 per cent of the companies started between 1995-2005, at least one key founder was foreign-born.
Nationwide, these immigrant-founded companies produced $52 billion in sales and employed 450,000 workers in 2005. The majority of these immigrant entrepreneurs came from India, Britain, China, Taiwan, Japan and Germany.
Highlights:
* More than half of the foreign-born founders of US technology and engineering businesses initially came to the United States to study. Very few came with the sole purpose of starting a company.
* Almost 40 per cent of immigrant founders entered the country because of a job opportunity, with only 1.6 percent entering the country with the sole purpose of entrepreneurship. They typically founded companies after working and residing in the United States for an average of 13 years.
* Technology centres with a greater concentration of immigrant entrepreneurs in their state averages include Silicon Valley (52.4 per cent), New York City (43.8 per cent), and Chicago (35.8 per cent). Three technology centres had a below-average rate of immigrant-founded companies: Portland (17.8 per cent), Research Triangle Park (18.7 per cent) and Denver (19.4 per cent).
Read the Article at-----
August 7, 2006
Some helpful remarks about popular universities
University Of Texas at Dallas
Very Few private jobs are available. Normal cost of Living.
University of Texas at Arlington
Very Few private jobs are available. Normal living costs.
NJIT
Few AID opportunities. Cost of living little higher.
Texas A&M University
Normal cost of Living. AID generally after one semester
Arizona State University
Less AID.
University of Arizona
Good AID after one Semester
Illinois Institute of Technology
Few AID Opportunities
SUNNY-Buffalo
Less AID.AID for Ph.D.
Ohio State University
AID generally after one semester.
University of South California
No aid for MS. PhD get AID in before venturing into it.
University of Cincinnati
Good aid Scene. Good Location. Most MS Students supported.
University of Illinois, Chicago
Aid requires good GRE/GPA.Good Location.
University of Florida
Less aid. Good Placements.
Clemson University
Fair aid Scene. Respected.
University of Missouri-Rolla
Fair aid Scene. Respected.
Wayne State University
Cheap. private work available.
Washington State University
Good aid Scene. Respected.Excellent Location
University of Wisconsin- Madison
Top Ranked University.
Portland State University
Good for VLSI.Good location.
University of Dayton
Good for Aerospace.
North Carolina State University
Excellent location.Ranked University.
Drexel University
Good location.
University of Utah
Good for EE/CS.Cheap.Respected
University of Houston
Good for Telecom.Excellent Location.
University of N. Carolina Charlotte
Fair AID scene.
July 20, 2006
Guidelines for applying to graduate schools based on past admissions statistics
Most students aspiring for graduate study apply without much prior knowledge about the University's selection policies or the performance of the earlier batch of students.
These STATISTICS are based on the performance of students over the past 5 years. University specifics have 3 headings namely :
suggestions
- broad suggestions about the graduate school
inside tips
- some specific features about the graduate school
requisites
- credentials of students who have made it to that graduate school in the past few years.
The CGPA is given on a scale of 10.Please adjust it accordingly if your undergraduate institution awards percentages or grade point avg. out of 4.
General Comments
These STATISTICS are compiled University wise and not separately for each branch as it would lead to confusion.
The rankings of certain Universities are very much different in different fields. Since these are general statistics these are best used along with University rankings for the particular field in which you propose to apply to graduate school.
Important note about GRE Score Conversion from Old to New Pattern
Two very rough indicators are given below
I VIEW
A score of 2000 in the old pattern GRE will be roughly equivalent to a score of 1300-1350 in the new GRE and a grade of 4.5 in the Essay section.
A score of 2200 in the old pattern GRE will be roughly equal to a score of
1450-1500 in the new GRE and a grade of 5.0 in the Essay section.
II VIEW
As a bench mark you can say that
1.Universities which were accepting a score of 2000 or above should be seeking candidates with a score of 1450 or above.
2.Universities falling under 1800 to 2000 would want a score of 1300 to 1450
3.For those falling in the group 1500 - 1800 a score of 1100 to 1300 will be desirable.
4. For those under 1500 a score above 1000 should do.
Caution. This is an intelligent guess.
Some univs. also ask for percentile scores.
A broad outline :
( based on subject wise rankings )
University Ranking General Tips
1- 5 Top ranked graduate schools generally give a lot of importance to academic performance as compared to GRE scores. Good under grad CGPA is a and impressive research credentials are a must. Best chances for the top 3 in a class .Others do not waste your time.
CGPA > 9.0 GRE > 2200 A research paper
10 - 25 Grad schools in this range are a safe bet for students with a decent academic record and GRE scores. Excellent GRE scores can make up for a slightly low CGPA.( 2250+ ).Research credentials do improve your chances.
CGPA > 8.0 GRE > 2000
CGPA > 7.5 GRE > 2150 good project work
30 - 50 Students with just OK academic record and good grades in courses specific to your field of interest stand a good chance. Good GRE scores are a boost and can certainly get you an admit ( inspite of weak acads ) but aid might be more tricky. Research credentials can make up for bad acads and get you aid .
CGPA > 7.0 GRE > 1800
CGPA > 6.0 GRE > 2000
50 - 80 Univs generally rated LOW. Getting an admit might be much easier but aid is generally a problem as funding is quite less. Comp Sci and Elec students have a decent chance of getting aid . Funding in other branches might be erratic. Research Credentials may prove to be inconsequential.
CGPA > 6.0 GRE > 1600
CGPA > 4.5 GRE > 1800
Beyond 100 Getting an admit should be easy . But expect no funding. You will have to finance yourself. Maybe except a Masters in Computer Science ask yourself whether it is worth it ??
SOME TERMS :
BP - Branch position RA - Research Asstship TA - Teaching Asstship
CGPA - Cumulative Grade Point Avg. ------> Look at applying process for conversion formulae Prof Let : writing to faculty
Penn State at University Park
Suggestions : A graduate school with lots of emphasis on GRE scores. A high GRE ( 2250 + ) can make up for low grades.
Inside Tips : Getting an application packet might be a spot of bother . you need to fill in a pre application form and mail it back. You might get a pre app bump ! Prof letters may work try it out .
Requisites :
CGPA > 8.5 & GRE > 2100
CGPA > 8.0 & GRE > 2250
Rensselaer Polytechnic Institute
Suggestions : A school reputed to be very good in pure sciences .Also very highly ranked for material sciences. A decent academic record and GRE score should see you through.
Inside Tips : Lots of importance given to pure science courses. Recommended for students wishing to pursue doctoral studies in Physics , optics etc. boasts of Professor Resnick ( of resnick and halliday fame ). Try to put your research credentials in an impressive manner. Prof letters have been known to help.
Requisites :
CGPA > 8.3 & GRE > 2100
Doctoral students in pure sciences : CGPA > 7.8 & GRE > 1900
Ohio State University
Suggestions : Decent academic record and GRE should see you through. A CGPA of 8 + and GRE of 1900 + should suffice. Good grades in courses pertaining to the field in which you are applying will be useful. App fee around $ 40
Inside Tips :Very persistent prof letters do pay off. you may have to write a couple of times before you get replies but keep at it. If you have good credentials Prof letters will give you an advantage over others with similar credentials. Aid decisions may come through a bit late ( by May ! or even June end )
Requisites :
CGPA > 8.0 & GRE > 2000
CGPA > 7.6 & GRE > 1900 with very good grades in relevant courses has made it through.
University of Minnesota - Twin Cities
Suggestions : A graduate school for you if you are considering a doctorate or a career in academics. An app fee of around $ 55 .The ranking of this graduate school is very high in some fields of study. Very good academics are required for those fields
Inside Tips : There are some not very well known areas of graduate study ...dig deeper into the website and hunt around for such programs .A CGPA of 8 + and GRE > 1900 should get you an admit. Prof letters are known to work but start early.
Requisites :
CGPA > 8.0 & GRE > 2000
CGPA > 7.2 & GRE > 2000 with some good project work ( for doctoral studies ).
John Hopkins University
Suggestions : Absolutely top school for Medicine and students interested in pursuing Bio technology , Bio engineering, medicinal physics , genetic engineering and related fields.
Inside Tips : Prof letters do work. Try to do a project in some field of on going research work. Try requesting faculty for their research publications if you are keen on the subject. Getting a RA should not be very difficult if you get an admission first.
Requisites :
CGPA > 8.0 & GRE > 2000 ( for above fields )
CGPA > 7.0 & GRE > 1800 ( other fields )
Virginia Tech
Suggestions : A graduate school whose rankings vary widely in different fields. A CGPA of 6.5 and a decent GRE score ( 2000 ) should get you an admission. App fee is quire less around $ 25
Inside Tips : Prof letters do work but mostly are non committal. An excellent place to head to if you can fund yourself. Getting an aid is slightly dicey and depends upon your field of study.
Requisites :
CGPA > 8.0 & GRE > 2100
CGPA > 6.5 & GRE > 2000
North Carolina State University ( NCSU)
Suggestions : A graduate school which insists on good academic performances. A CGPA > 8.0 should get you through. Moderate GRE scores suffice > 1800 .App fee around $ 40
Inside Tips : Prof letters certainly do work. Good SOPS and relevant research experience if put forward impressively and to the correct faculty member should get you an RA.
Requisites :
CGPA > 8.5 & GRE > 1800
CGPA > 8.1 & GRE > 2000
Good project work for getting an RA.
Rice University
Suggestions : A moderately ranked university with very high rankings in some fields like Bio medical engineering. Located in a relatively inexpensive place ( Houston ). A CGPA > 8.0 and GRE scores > 1900 should see you through. Students contemplating a doctorate stand very good chances of admission. App fee $ 25
Inside Tips : Prof letters work. Try writing to faculty members early. You can outdo people with better grades and GRE scores if you contact faculty members and impress them with your credentials !!
Requisites :
CGPA > 8.5 & GRE > 1800 and impressive prof letters
CGPA > 8.2 & GRE > 2000
University of Colorado Boulder
Suggestions : One of the most picturesque Universities. Check out the snaps on the net ( totally awesome ! ) .High GRE scores preferred around the 2200 mark. CGPA of around 8.4 should do. App fee around $ 40
Inside Tips : Getting an application packet might be a little tough . There is screening in the pre application stage. Prof letters do not work. You may try prof letters after getting an admission
Requisites :
CGPA > 8.5 & GRE > 2200
CGPA > 8.2 & GRE > 2100 and terrific research credentials
Duke University :
Suggestions : Good grades in your senior year ( ie 3rd year ) will help you . Academic performance in core courses is given a lot of credit . App fee around $ 50.
Inside Tips : Lots of importance given to undergraduate project work . If you have done some good project work in relevant fields of research you stand good chances of getting an R A .make sure that your SOP reflects your research abilities and try getting a very good letter of recommendation from your guide .
Requisites :
CGPA > 8.0 & GRE > 2000
CGPA > 7.5 & GRE > 2150
University of Pennsylvania :
Suggestions : The BSchool Wharton is located here. This makes it a good choice for students wishing to go in for programs like Industrial management and financial engineering. A CGPA > 8.3 and GRE scores > 2100 should see you through.
Inside Tips :Students contemplating doctoral are preferred for financial aid. Prof letters do not help at all. Try sending mails to the graduate secretary with your queries. You will mostly have to apply online.
Requisites :
CGPA > 8.4 & GRE > 2100
CGPA > 8.6 & GRE > 2000
University of Florida Gainsville
Suggestions : Well head here if you like the sun !!!!A CGPA > 8.4 and GRE scores > 2000 should suffice. Try prof letters. They work decently.
Inside Tips : Getting complete financial aid might be a bit of bother. You often end up getting tuition fee waiver or half a fellowship kind of stuff.
Requisites :
CGPA > 8.3 & GRE > 2000
Impressive SOPS and relevant research credentials for an RA.
University of Virginia
Suggestions : Ranked very highly in the fields of arts and economics. A CGPA of 8 and GRE scores > 2100 should suffice.
Inside Tips : Students wishing to move over to Economics or B Schools try applying here. Inter disciplinary programs especially in Operations management , financial engineering etc must be good.
Requisites :
CGPA > 8.5 & GRE > 2000
CGPA > 7.5 & GRE > 2200
Rutgers State University - New Brunswick
Suggestions : A CGPA around the 8.0 mark and GRE scores in excess of 2000 should suffice.
Inside Tips : Prof letters may be misleading here. Expected Q + V scores > 1350
Requisites :
CGPA > 8.2 & GRE > 2050
Iowa State University
Suggestions : A moderately high GRE score especially Q + V scores > 1400 . CGPA around 7.5 should suffice.
Inside Tips : Prof letters do work. Try them
Requisites :
CGPA > 7.8 & GRE > 2050
CGPA > 7.3 & GRE > 2160
Lehigh University
Suggestions : A decently ranked university . A CGPA of 8 + and a GRE score of 1900 + should be able to see you through. App fee around $ 40 .
Inside Tips : PhD strong ie you are sure to get an admit and aid if you are contemplating a PhD ( with decent credentials of course ). An economic place to be in. Prof lets are not known to work . Research credential are of importance . Be sure that you get an acknowledgement once you have applied or keep mailing for one .
Requisites :
CGPA > 8.2 & GRE >1950
CGPA > 7.6 & GRE > 2000 and some good project work .
Washington University in St Louis
Suggestions : A university where you stand a good chance with a CGPA of 7.3 and above . Not very particular about high GRE scores . 1800 + should be sufficient . Low app fee around $ 25
Inside Tips : You can get a Waiver on reporting your GRE and TOEFL scores ( save around $ 24 ) , try contacting the program coordinator . you may have to report only after you get an admit . Prof letters do work and you often get encouraging replies . Aid has been slightly dwindling of late.
Requisites :
CGPA > 7.7 & GRE > 1800
CGPA > 7.4 & GRE > 1940
Michigan State University
Suggestions : A cgpa of 7.6 or excess should hold u in good stead .
Inside Tips : A acad strong university . Good scores expected in Q + V sections of GRE ( Q + V > 1150 ) mostly .
Requisites :
CGPA > 7.5 & GRE > 2000
CGPA > 8.0 & GRE > 1750
University of Arizona
Suggestions : Yet another school which makes early decisions . A GRE score of 2000 and decent academic credentials should be sufficient .
Inside Tips : Lots of importance given to senior year grades especially in relevant courses . Good grades in such courses will make up for a relatively low all round CGPA . Research and practical credentials given a lot of importance .
Requisites :
CGPA > 7.4 & GRE > 2000
CGPA > 7.7 & GRE > 1850
University of Rochester
Suggestions : A highly ranked University in some fields . A GRE score of 1900 + and a CGPA around the 8.0 mark should help you get an admit.
Inside Tips : PhD strong . You stand very good chance s of getting an admit to doctoral programs . Prof letters are known to work . Try contacting faculty with similar research interests. Their replies are often very accurate about your chances of aid .
Requisites :
CGPA > 8.0 & GRE > 1900
CGPA > 7.6 & GRE > 2100
University of Delaware
Suggestions : A university whose rankings vary a lot with the field of specialization . A high CGPA required for fields in which it is ranked in top 20 else a CGPA of 7.2 + should suffice . App Fee around $ 40
Inside Tips : Check out the ranking before you apply . Not GRE strong and a GRE score of 1800 + should suffice . Good academic credentials necessary especially in core courses .
Requisites :
CGPA > 7.4 & GRE > 1850 ( ranked beyond 20 )
CGPA > 8.6 & GRE > 2000 ( ranked in top 20 )
University of New Mexico
Suggestions : A safe university for someone with CGPA of around 8.0 and a GRE score in excess of 1900 .A high GRE score can get you through with a CGPA of 7.2 + .App fee around $ 45
Inside Tips : A university which makes late decisions so app early . getting the app forms might a little trouble so preapp early to avoid delays. SOPS should be impressive.
Requisites :
CGPA > 8.0 & GRE > 1900
CGPA > 7.4 & GRE > 2050
Arizona State University
Suggestions : A CGPA of 7.5 + and a GRE score in excess of 1950 and you are home safe ( in most cases ! ) .
Inside Tips : A university which decides quite early . Offers may be made as early as January end for the fall . Aid decisions are often made after admissions . Prof letters play a big part in getting you aid after an admit . not very GRE strong . Write to faculty and concentrate a lot on your SOP to make your research potential clear .
Requisites :
CGPA > 7.8 & GRE > 2000
CGPA > 7.5 & GRE > 1950
University Of Massachusetts Amherst
Suggestions : A CGPA of 7.3 + and a GRE score in excess of 2000 and you are home safe ( in most cases ! ) . Sometimes higher grades and academic performance is preferred . App fee around $ 55
Inside Tips : A university where research credentials have a lot of importance . Good project work can get you an admit and an aid even if your grades are low . An excellent school for doctoral studies as most of the faculty are doctorates from M I T . Prof letters play a big part in getting you a research assistantship.
Requisites :
CGPA > 7.3 & GRE > 2040
CGPA > 7.8 & GRE > 1950
Auburn University
Suggestions : Ranked pretty low around the 70 mark in most departments. A good graduate school to land up in with a cgpa of around 7.0 - 7.4 . GRE scores around 1700 - 1900 should get you through . Score reporting and app fee are not waived . App fee ( around $ 25 )
Inside Tips : The funding here is quite good. Complete aid is around $ 18000 an year which is very good . You can also apply for a doctorate and later convert it into an M S once you get there .
Requisites :
CGPA > 7.3 & GRE > 1800
CGPA > 7.0 & GRE > 2000
C U N Y
Suggestions : A graduate school located in a slightly unsafe area . Good academic grades are expected . A CGPA of 8.0 should ensure an admit . App fee and score reporting fees are waived . around $ 45 anyway
Inside Tips : Located in NY . There are two main problems one is the high crime rate and the other is the expensive place .Worth going to only with full financial aid and even then the chances of your being able to save money is very less . The academic credentials required by this grad school will also get you into other more economic and safer graduate schools.
Requisites :
CGPA > 8.0 & GRE >1800
University of Connecticut
Suggestions : Ranked around the 65 mark . A CGPA of 7.6 and GRE scores of 1800 should see you through .Good grades in the senior year are a definite advantage .
Inside Tips : A good feature is that in most cases aid is almost guaranteed if you get an admission . You are assured of some form of financial assistance . Good SOPs are a must , please do not just cut and paste previous ones .
Requisites :
CGPA > 7.8 & GRE > 1900
CGPA > 7.2 & GRE > 2000
Kansas State University
Suggestions : A cgpa of 7.0 + and a GRE score of 1700 + should suffice in most cases . A university with pretty low intake of students . Ranks around the 75 mark .
Inside Tips : Suggested only for students who can afford to finance their graduate studies . Funds are usually low and aid is very difficult to come by .
Requisites :
CGPA > 7.2 & GRE >1830
CGPA > 6.8 & GRE >1740
Illinois Institute of Technology
Suggestions : Ranked around the 80 mark . A CGPA of 6.5 + and GRE scores in excess of 1750 should give you a fair chance of admission.
Inside Tips : Aid is generally not given in the first year for M S students . Suggested to students who can afford to finance at least a part of their graduate study . Chances of aid are good after a semester .
Requisites :
CGPA > 7.1 & GRE >1820
CGPA > 6.6 & GRE >1760
State University of New York Buffalo
Suggestions : Ranked in the vicinity of 55 - 60 in most fields . Very highly ranked in some specific fields . A cgpa around the 7.3 mark should be sufficient . App fee around $ 40
Inside Tips : An expensive place again . Think twice if you are going to finance your graduate studies . Good GRE scores can make up for some poor grades . Do not go to this graduate school with only partial aid if you do not have sufficient funds to back you up .
Requisites :
CGPA > 7.9 & GRE >1960
CGPA > 6.4 & GRE > 2100
Drexel University
Suggestions : Rankings range from late 40s to the 60s . A cgpa of 6.5 and GRE scores of 1960 have seen people through .
Inside Tips : Prof letters are known to work . Try writing if your grades are slightly low and you wish to check out your chances .A good graduate school for students contemplating a doctorate ( with low academic credentials ...)
Requisites :
CGPA > 7.5 & GRE > 1950
CGPA > 6.6 & GRE > 1940
University of Illinois Chicago
Suggestions : Ranking around 65 . Getting an admit should be pretty easy with decent academic credentials . An undergraduate cgpa of 7.2 should see you through .
Inside Tips : Prof letters do work . Getting aid might be a little difficult . If you can show enough funds accept this graduate school . Once you land up there it is pretty easy to get aid .Speaking personally to faculty should really help you in getting financial aid.
Requisites :
CGPA > 7.2 & GRE > 1890
CGPA > 6.8 & GRE > 2120
Michigan Technological University
Suggestions : A cgpa of 8.0 and GRE scores around 2050 should give you good chances but decisions have been erratic in the past . An app fee around $ 40 .
Inside Tips : Prof letters do not work . Getting a reply it self is very unlikely . Not very highly ranked but still applications are rare .Suggested only if you can fund your graduate studies . Our advice is to steer clear of this graduate school .
Requisites :
CGPA > 8.1 & GRE >2060
Oklahoma State University
Suggestions : A cgpa in the vicinity of 7.3 and GRE scores around 1800 should suffice . Ranked around the 100 mark
Inside Tips : Prof letters are not necessary. Partial financial aid is very common , suggested to students who can furnish some initial funds . SOPs should be original and should show research experience .
Requisites :
CGPA > 7.8 & GRE > 1810
CGPA > 6.4 & GRE > 2000
Mississippi State University
Suggestions : Ranked around the late 80s and 90s in several fields . A cgpa of 7.2 and GRE scores around 1800 should be sufficient for a student.
Inside Tips : A very good graduate school for mathematical programs like CFD and computing etc . Lots of importance given to undergraduate grades in math based courses. Prof letters can be of assistance.
Requisites :
CGPA > 7.4 & GRE > 1830
CGPA > 6.6 & GRE > 1780
University of Iowa
Suggestions : A cgpa in the vicinity of 7.3 and GRE scores around 1850 should suffice . Ranked around the 60 mark
Inside Tips : Admissions are relatively easy but financial aid is often hard to get. Suggested if you can afford to fund yourself for a semester .SOPs should be original and should show research experience .
Requisites :
CGPA > 7.3 & GRE > 1870
CGPA > 7.8 & GRE > 2000
New Jersey Institute of Technology
Suggestions : An undergraduate cgpa in the vicinity of 6.4 and GRE scores around 1650 should suffice . Ranked around the 95 mark . Some good project work can make up for low grades.
Inside Tips : Aid might not be available initially . However if you have sufficient funds go ahead ,you will invariably get some form of financial aid once you get there . Prof letters do work as in you might be promised financial assistance once you land up there . For prospective doctoral students with a cgpa of 6.0 should suffice.
Requisites :
CGPA > 7.1 & GRE > 1810
CGPA > 6.4 & GRE > 1650
Wayne State University
Suggestions : A cgpa in the vicinity of 6.8 and GRE scores around 1700 should suffice . Ranked around the 85 - 90 mark
Inside Tips : Prof letters are helpful . A safe graduate school for students with low undergraduate grades . Replies from the faculty indicate your chances of admission pretty well . If you are contemplating a doctorate a cgpa of 6.0 should suffice .
Requisites :
CGPA > 7.3 & GRE > 1860
CGPA > 6.1 & GRE > 1780
University of Maryland at Baltimore County
Suggestions : A cgpa in the vicinity of 7.2 and GRE scores around 1800 should suffice . Ranked around the 100 mark
Inside Tips : Multiple offers have been made in the past . So if students with better credentials than yours drop out the offer rolls down to you ..However decisions may come late .. ( around April end to May mid )
Requisites :
CGPA > 7.4 & GRE > 1860
University of Houston
Suggestions : Rankings vary from 30s to 70s so cannot say accurately . look up the ranking in your prospective department and apply accordingly
Inside Tips : If it is ranked in the top 30 then a CGPA of 8.4 and GRE scores of 1950 is required . More updates later !!
Requisites :
CGPA > 8.4 & GRE > 1980
University of Cincinnati
Suggestions : A cgpa in the vicinity of 7.2 and GRE scores around 1850 should suffice . App fee around $40
Inside Tips : It is easy to get a tuition fee waiver but full financial aid offers are not very common . Suggested to those who have enough funds to pay for living expenses . Prof letters do help but request for aid from early apping stages itself instead of requesting an admission which is quite easy to obtain.
Requisites :
CGPA > 7.2 & GRE > 1890
CGPA > 6.4 & GRE > 2050
Clemson University
Suggestions : A cgpa in the vicinity of 7.1 and GRE scores around 1950 should surely get you an admit . App fee $ 40 and ranked around 80 - 100
Inside Tips : A safe grad school . Multiple aid offers are very common . Lots of Indian students manage to get aid in this graduate school . Tuition fee waivers are also common . Prof letters might prove useful for aid decisions.
Requisites :
CGPA > 7.3 & GRE > 1900
University of Pittsburgh
Suggestions : A cgpa in the vicinity of 7.3 and GRE scores around 1950 should suffice for students willing to pursue doctoral studies . Application for masters M S is not all too common .
Inside Tips : Prof letters are very useful . Suggested for students willing to pursue doctoral studies . Students with cgpa of 8.4 and GRE > 2100 have been rejected for M S .
Requisites :
CGPA > 7.3 & GRE > 2030 ( for students interested in PhD )
University of Notre Dame
Suggestions : Ranking ranges from 30s to 60s . As said before go by the departmental rankings .
Inside Tips : Prof letters do not work as replies from faculty are all too rare. In highly ranked fields cgpa of 8.5 and GRE of 2050 may prove to be inadequate .
Requisites :
CGPA > 8.6 & GRE > 2100
July 12, 2006
Some Comments By Former Students On Their Berkeley Years
Will give you an idea about their profile
Karl Broman, Department of Biostatistics, Johns Hopkins University
There are five things that made my time at Berkeley Statistics wonderful: the faculty, the students, the computing facility, the university and the location. The faculty include the best statisticians and probabilists in the world; they are demanding but also generous and friendly. Berkeley statistics students are also the best in the world; they maintain a long tradition of excellence, hard work, collegiality and good parties. The department's computer system and computer sypport is excellent: it is the staff of the Statistical Computing Facility that one misses the most upon leaving Berkeley: the expert advice of staff members so giving of their time will not be found anywhere else. The university is known for excellent research in all areas, which is a real boon for the interdisciplinary scientist: there are a myriad of opportunities for forming stimulating and productive collaborations. Finally, the location: I've never been anywhere with such a pleasant climate, fabulous parks, great food and cool arts scene.
Babette Brumback (Department of Biostatistics, University of Washington)
I originally came to Berkeley as a PhD student in EECS, but for various reasons, including the familial atmosphere of the Statistics department and a stronger interest in math than engineering, I transferred to the Statistics department in 1991. Without much former training in statistics, I had a lot of learning to do before I could begin to think seriously about a dissertation topic. At first I considered the area of statistical genetics, but after a class project in David Brillinger's Time Series course, I became more interested in endocrinology. My project focused on the temporal modeling of reproductive hormones in female cows, but while gathering background information from endocrinology textbooks, I became more interested in the case of humans. Professor Brillinger referred me to Dr. Shanna Swan, then chief of the Reproductive Epidemiology Section at the State Department of Health in Berkeley, who happened also to hold a PhD from the Berkeley Statistics department. I joined her group of researchers involved in the Women's Reproductive Health Study (WRHS). A primary goal of WRHS was to measure reproductive hormones in clinical and nonclinical populations over long periods of time, in order to assess the normal range of variability of hormone profiles over time and across individuals, and also to address particular scientific hypotheses concerning early fetal loss. Professor John Rice, who was relatively new to the department at that time, agreed to supervise my dissertation on modeling these time series. I graduated in 1996 with a thesis entitled, "Statistical Modeling of Hormone Data".
After graduating, I spent three years as a postdoctoral fellow in the department of Biostatistics at Harvard and a summer on research at the department of Applied Math in Ghent, Belgium. Recently I began a new job as assistant professor of Biostatistics at the University of Washington in Seattle. These new experiences continue to remind me of the exceptionally high educational standards at Berkeley. A PhD in Statistics from Berkeley guarantees more than a strong statistical background; it also promises a highly developed capacity for critical thinking and independent work.
Sandrine Dudoit (Mathematical Sciences Research Institute, Berkeley, and Program in Molecular and Genetic Medicine, Stanford University)
When deciding where to pursue doctoral studies, what particularly attracted me to Berkeley were the broad interests of the outstanding faculty, the excellent Statistical Computing Facility, the friendly and international atmosphere of the Statistics Department, and the unique appeal of a city like Berkeley. I was not deceived by my first impressions and have only great things to say concerning my experience in Berkeley, both academically and personally.
Before coming to Berkeley, I had completed a B.Sc. in Mathematics and an M.Sc. in Probability and Statistics from Carleton University in Canada. My mathematical background was a very helpful preparation for the first year Ph.D. core courses and preliminary exams. I came to Berkeley with an interest in the applications of Statistics to Genetics and Molecular Biology. The courses I took early in my studies (Applied Statistics from David Freedman, Statistical Computing from Phil Spector, Statistical Genetics from Terry Speed, Consulting from John Rice) provided a very valuable foundation for my research. The weekly Statistical Genetics seminars organized by my advisor, Terry Speed, gave me a good overview of the different areas of application of Statistics to Biology and were very helpful in the choice of a thesis topic. I wrote my dissertation on the genetic mapping of complex human traits http://www.stat.berkeley.edu/~sandrine , a fascinating biological problem with interesting mathematical properties. It was a real pleasure to be a student of Terry's; I learnt a lot from working with him and his other students.
I have particularly enjoyed the lively and warm atmosphere of the Department. The five wonderful years I spent as a student in Berkeley made graduating a bitter-sweet moment.
I am now doing a postdoc on the analysis of gene expression data from DNA microarray experiments, jointly between the Mathematical Sciences Research Institute in Berkeley http://www.msri.org and Patrick Brown's lab http://cmgm.stanford.edu/pbrown/ at Stanford.
Bill Forrest (Department of Human Genetics, University of Pittsburgh)
I came to Berkeley Statistics by a fortunate coincidence. Near the end of my undergraduate stint as a mathematics major, I was looking at many graduate programs in mathematics. My favorite course had been an undergraduate class in probability theory. The instructor had spent some time in the eighties as a visitor in the Statistics Dept. at Berkeley, and he really talked it up as somewhere I should apply.
The idea was intimidating because I had only a vague idea of what statistics -- as opposed to probability -- was all about, and more so because I had never taken an actual statistics course. I applied, was admitted, loved my visit, and enrolled for the fall semester of 1992.
Graduate school is rough in any subject and especially at strong research institutions. This will be true of Berkeley or anywhere else you choose to attend. At least two factors make this so. First, people come to a course with hugely varied backgrounds. Some have already had nearly equivalent classes elsewhere, or picked up the material through independent study. Our first day in graduate probability, another first-year named Karl Broman and I found out that we were the only two in a class of about twenty who did not already have a masters degree in math or statistics. Accordingly, we had to work quite a bit just to keep our noses above the water. For the first time, I was turning in problem sets for which I could solve only half the exercises in a week. The second factor is that the level of expectation is raised. The standard for excellent work is what researchers active in the field consider impressive, not what's impressive for an undergraduate. Few graduate students look impressive early on. What helped make up for the depressingly hard work was the sense of camaraderie I found in the other graduate students. People worked hard, but the department was also a fun and supportive place. More experienced graduate students often took time to help out the newer ones with tough concepts. People hung around together outside of class, playing softball, going out to eat, holding picnics and parties, and working together as teaching assistants.
I survived my first couple of years in graduate school, during which I shored up my background and got an idea of where my long-term interests lay. Probability was not really in my future, judging by my waning interest and relative lack of ability. I mastered enough theory to pass my prelim exams, but that was the end. Instead, I started getting interested in a number of problems in applied statistics. I took a large number of courses in my years at Berkeley, and they have all been useful at one point or another. These included all the core prelim courses, time series, statistical computing, multivariate analysis, linear and generalized linear models, survival analysis, and several seminar courses on such things as statistical genetics and statistics in biotechnology. Taking a breadth of courses is important because you start to get a better idea of common themes and tricks that turn up consistently in modeling applications across fields, as well as mistakes that get repeated time and again. Gaining a wide perspective is also helpful because it gives you much greater career flexibility if you have dabbled in a number of fields and can sound a little competent in them.
I started working with Terry Speed on some questions in fisheries research during my third year. This grew into a student-advisor relationship which worked out very well. I was lucky in that he approached me after I asked quite a few questions in a fisheries statistics seminar. Getting paired up with an advisor can be a little tricky, especially if you are not really sure what will interest you. Conversely, it can be difficult to tell what's interesting without investing some serious research time in it. This was a bit of a problem when I started at Berkeley, but it is a problem which the department recognized and has taken steps to improve, holding special seminar days when the professors discuss their research at the introductory level and invite interested students to talk with them.
In applied statistics, many people get involved with research that matches their outside interests. One music aficianado did a thesis on statistical problems in music-related signal processing. A guy from Los Angeles wrote a thesis on spatial point-processes with applications to earthquake data. I'm originally from the Pacific Northwest of the U.S., and ended up with a thesis analyzing chinook salmon data gathered only a few kilometers from my childhood home.
Another important way of finding a research topic is to frequent the seminar series in Statistics and related departments so that you discover what sort of problems people are working on. Most of the applied projects I knew of involved some level of collaboration with people in other departments or outside the university altogether. Taking this out a bit more, working a semester as a research assistant can be a good way to learn more about a research project and how well you work with a professor without making a serious commitment.
Finally, consider taking a summer internship at some point in graduate school. This can be great experience in seeing how statistics gets integrated into practical problems, and can often give you an idea of what you want to do (or want to avoid) after graduation, in addition to finding interesting research projects. Again, the department was an excellent place to get internships because places that offer good summer jobs (e.g. Bell Labs, Genentech, Chiron, RAND, etc.) have many contacts with faculty in the department.
I graduated in six years, having earned a master's degree in 1994 and the Ph.D. in four additional years. I was a bit unsure what I wanted to do towards the end, though I should say that I was able to get job offers from both academia and industry, as did pretty much everyone who applied for such positions. I opted to make a lateral move and study statistical genetics as a postdoctoral fellow at the University of Pittsburgh, where I am now. I got the position by reading the want-ads in the American Statistical Association Bulletin, then starting an email correspondence. I work with a professor (also a Ph.D. in statistics) and a few of the other faculty designing and testing algorithms for a variety of problems in statistical genetics and molecular biology. Examples of the sorts of problems I work on are linkage analysis (localizing a disease gene to a small region of the genome), gene expression experiments (which study, roughly speaking, under what conditions certain genes' productions get adjusted "up" or "down"), problems in DNA and protein sequence alignment, and lots of relatively mundane but still fun statistical problems which pop up in clinical trials and data analysis.
Perhaps the most valuable thing about my education at Berkeley Statistics is that it gave me the skills to move into my current position, doing research in genetics, even though I have little background in the field. I've had to do additional reading to catch up with the research scientists who work here, but many of the statistical modeling approaches they take are similar to the ones I applied in my work on salmon populations, another friend's thesis on modelling auto traffic flow, and many others. The body of statistical techniques does not change that dramatically across most areas, and after getting a solid grounding in that body of knowledge at Berkeley, you will be well prepared to step into any number of fields.
Rudy Guerra (Rice University)
I was a graduate student in the Berkeley statistics department from 1987 to 1991.
Pre-Berkeley:
My hometown is San Antonio, Texas. I was born there, raised there, and obtained a B.S. in applied mathematics (1984) from University of Texas, in San Antonio (UTSA). At UTSA I had some very good mentors (Jerry Keating, Herb Silber, Hugh Maynard) who encouraged me to pursue graduate school in mathematics or statistics. In addition to believing that I had some aptitude at mathematics they also had a strong interest in seeing more Hispanic students pursue graduate school. I applied to many programs in mathematics and received several offers of admission. Since I was married with our first baby on the way it was very important for us to have financial aid. It was also very important to me to be in an environment that had a strong interest in minority students. At the time I wasn't aware of just how important it would be to graduate from a first-rate program. I also wasn't sure of what kind of mathematics I wanted to study. Not many people do as they enter graduate school. The Berkeley math department - via the Minority Opportunity Committee (MOC) - was very aggressive in recruiting me. In the end I felt that Berkeley satisfied all my needs. In particular, because the math and statistics departments are both large programs there would be a lot of opportunity to "look around" for an area of study. This is a huge advantage in attending a large program: there are many choices. Off I went to the Berkeley math department, with a view towards statistics.
Berkeley: Talk about culture shock! If you've never been to Berkeley, the town or the university, you should know that all the stories are probably true.
The town sits on the eastside of the San Francisco Bay and the university overlooks the Golden Gate bridge. The scenery is simply spectacular; both the town and university are full of interesting people from all over the world; the food is hard to beat, restaurants and markets alike; the train system, BART, allows for easy access to Oakland and San Francisco, both of which have many cultural and sporting events to offer; there are a lot of great book and music stores. The Bay Area is also very family oriented. With a relatively inexpensive lifestyle young families can always find something interesting and fun to do with kids, including visits to some unique parks around the Bay Area, science places, museums, walking trails, and beaches. Parents will also value the excellent public school systems in Berkeley and Albany and they will appreciate the multiculturalism in the area. Our family life in Berkeley was an experience that we will always treasure. I highly recommend married student housing - it's probably the cheapest way to go, as well!
I entered Berkeley as a mathematics graduate student, but ended up getting a Ph.D. in statistics (1992). I had a wonderful experience as a statistics graduate student. It is THE place to be if you want to study statistics. There were many classes available, all of which were interesting, challenging, and up to date on recent results. The computing facilities were great; they were modern and supported by several very knowledgeable and helpful staff members. The weekly seminars often brought in high profile statisticians and many of the topics were timely. But perhaps the best part of being a student at Berkeley is that you get to take classes from, interact with, and study under some of the most widely recognized names in statistics. Simply put, the Berkeley statistics faculty is second to none. My own experience includes taking classes from Peter Bickel, Leo Brieman, David Freedman, and Erich Lehmann., and having Terry Speed as my dissertation advisor. Each of these individuals has helped to shape the theory and/or practice of statistics. In most other departments you may have one or two people of this caliber And it is not true that research oriented professors are bad teachers! Some of the very best teachers that I have had are found at Berkeley. In general, the faculty have regular grant support and enjoy having students work with them on research funded projects. In my case I had NIH support through a MARC program to attend graduate school. Additionally, however, Terry Speed and Peter Bickel were instrumental in getting me additional support for a molecular biology project with Lawrence Livermore National Laboratory.
I also found it stimulating to work with fellow students from around the world who were very, very bright and keenly interested in statistics. Many of the students you meet at Berkeley go on to be leaders in the field. They also make for good friends and a social life. You will also find many students and faculty from other departments (e.g., biostatistics, epidemiology, genetics, engineering, economics, business) actively engaged in first-rate statistical research. This diversity adds a highly educational dimension to the program. Indeed, during my time there an informal genetics seminar developed in the statistics department with many attendees and speakers from other departments. This is now an important part of the graduate program along with a course in statistical genetics, which is just one of the many special topics courses that are taught throughout the years. Lastly, we received training in teaching by Roger Purves and advanced graduate students. Many departments do not offer such training, which is so important to those wanting an academic career.
Post-Berkeley:
Like many other graduating students from Berkeley I had several job prospects. Later I was told that the Berkeley letterhead automatically (well, almost automatically) causes one to pause and take a careful look at the applicant, many of whom are highly sought after. I took a tenure-track position in the department of Statistical Science at Southern Methodist University in Dallas, Texas. I am currently an associate professor and director of our Biostatistics Center. My main areas of research include statistical genetics and environmental science. As I look back over my (relatively young) career it's clear that my degree from Berkeley has played a very important role, especially in job interviews, establishing collaborations, and grant funding.
Closing Remarks:
The Berkeley statistics department has a history rich in excellence. It consistently leads rankings of statistics departments by maintaining a distinguished and prolific research record, a stimulating research environment with outstanding computing facilities, a broad and modern curriculum, and attracting first-rate faculty and the very best of graduate students. There are only a handful of departments that truly compare with Berkeley. However, Berkeley is still a special place. Knowing what I know now about the field I would still choose Berkeley for my graduate education. It is hard to imagine having had a better education.
Rafael A. Irizarry (Department of Biostatistics, Johns Hopkins University)
I began my studies as a mathematics major at the University of Puerto Rico. I decided to apply to Statistics and Probability graduate programs after taking a seminar in Basic Probability with Ani Adhikari during a minority summer math program at Berkeley. Since it was a nice place to live and given that it was ranked #1 among Statistics departments, I made Berkeley my first choice for graduate school and was fortunate enough to be accepted into the program. When I first began my graduate studies I was interested in Probability, but soon became more attracted to applications as a result of courses I took on applied statistics. During the end of my second year, I began looking for an advisor by visiting professors, all of whom had very interesting topics. The last one I visited, David Brillinger, asked my what my research interests were. I responded honestly and told him that I didn't really know. He then asked me what Iiked. I began to respond, "I'm a musician..." but before I had a chance to finish my sentence he said "You'll work on applications of statistics to music". After this direct order, I had no choice but to select him as my advisor. Prof. Brillinger urged me to take classes in music techology and luckily I found that Berkeley has a great center for music technology research (CNMAT). The researchers there were excited about having a statistician interested in their work and a collaboration quickly began. Two years later I finished my dissertation: Statistics and Music: Fitting a Local Harmonic Model to Musical Sound Signals. I enjoyed working on this topic and at the same time was able to learn some Time Series Theory, an experience that helped me land my present job.
http://biosun01.biostat.jhsph.edu/~ririzarr
Lei Li (Department of Statistics, Florida State University)
Berkeley is a place full of inspirations. It was really my luxury to be able to spend five years there as a graduate student.
Originally I was from Beijing, China (a neighborhood very close to the forbidden city). Before I came to Berkeley to study for my Ph.D degree, I had gotten my BS in mathematics and MS in probability and statistics from Beijing University. The main reason that I chose statistics seems quite random: when I was close to finishing my high school, I happened to meet a mathematics professor who told me that two great mathematicians----Kolmogorov and Wiener worked in this area. As you know, kids admire heros.
If you would spend some years at Berkeley, you will find more "living heros"---the professors there, who have all kinds of intelligence and virtues. You can develop and reshape yourself by borrowing whatever you like from these models.
The Ph.D program in statistics at Berkeley is very unique. Not only it provides a solid training in probability and theoretical statistics, but also has a large degree of research freedom. For example, my advisor---Professor Terry Speed helped me work on the DNA sequencing problem as my thesis topic. Through the collaboration with people in other units at Berkeley, I could feel the pulse of the current scientific research. I found it fun to work simultaneously in the two worlds---the theoretical one and the applied one, and try to link them. Of course I experienced self-doubts, frustrations and depressions, but Terry and other professors' wisdom, knowledge, and devotion to work had always been a source of power and courage to me. In fact, directly or indirectly every professor in the statistics department has influenced me a lot. I feel humbled by their intelligence, and feel honored by being helped and taken care of by them.
My fellow students at Berkeley were a galaxy of talents. They were the people whom I talked to every day. We shared life and study experiences, joke, and helped each other. Good memories. If you think you are good, then you should come to Berkeley, and become a member of it.
Another pleasant thing about Berkeley is the unbeatable weather. You can wear shorts or shirts all year around. Within hours' drive, you can go skiing, and see those unbelievable redwoods.
I found I had became a totally different person on the day I left Berkeley. The image of my professors at Berkeley is in my blood and cells. Until now, I still feel that I can breathe the Berkeley spiritual air. If you have the chance being admitted by Berkeley, don't miss it!
Vlada Limic (Department of Mathematics, Cornell University)
I was a Ph.D. student at Berkeley Statistics Department from 1994-98. The department was intellectually a very stimulating environment, and at the same time a very friendly place. My main interests were in probability, so the first year I took two topics courses in probability in addition to the first year Probability Theory and Theoretical Statistics courses. At the beginning of my second year, Professor David Aldous suggested an interesting research project, and soon afterwards became my thesis advisor. I learned a great deal from David through our collaboration, and from all the probabilists through a number of interesting courses and conversations. Many of my fellow graduate students are good friends, and we keep in touch. The lunch room was the main center of social activity, and there was a happy hour every Thursday afternoon. At Berkeley Statistics I realized how much I enjoyed doing research, teaching, and the rest of the academic life. After graduating I spent a year at UC San Diego, Mathematics Department as an NSF postdoc. Currently I have a 3-year position at Cornell University, Mathematics Department.
Mary Sara McPeek (Department of Statistics, University of Chicago)
How has my graduate education at Berkeley helped my career?
I am currently on the faculty of the Department of Statistics at the University of Chicago. The benefits of being here at the University of Chicago include a stimulating intellectual environment and constant interaction with top researchers, both within the Statistics Department and across disciplinary boundaries. The Statistics Department at University of Chicago is legendary for asking some of the toughest questions at seminars, but it is also an extremely friendly place. I am very fortunate to have outstanding graduate students whom I teach and with whom I work on research, and also outstanding post-docs to work with. Furthermore, the undergraduates here are a very hard-working and serious group, for the most part, and they are a privilege to teach. I credit my graduate education at Berkeley as being the critical factor that helped me to obtain a faculty position at the University of Chicago. (Aside: at least two of my senior colleagues here are also Berkeley graduates.)
What was my background when I arrived at Berkeley? What did I plan to work on at Berkeley, and how did this change over time?
I entered the PhD program at Berkeley with a very strong background in pure mathematics from my undergraduate education at Harvard. I knew less about statistics, though I had taken a few courses, for the most part very mathematical ones, in probability and statistics. When I was trying to decide where to go for graduate school and what to work on while there, I thought I might want to be a probabilist. On the other hand, while I didn't know much at all about applied statistics, I suspected that might be very interesting as well. Given my indecision, I wanted to pick a department where I could interact with as many of the top researchers in both major areas as possible, so I chose Berkeley. There were many different research paths I could have followed while a PhD student at Berkeley. I decided to work with Professor Terry Speed on applications of probability and statistics to genetics. That has turned out to be an extremely interesting and satisfying area of research, which only seems to get more active and exciting as time goes on.
How would I describe my experience as a student at Berkeley?
In hindsight, I think the most influential and beneficial aspect of my graduate education was my advisor, Terry Speed. I thought he did an excellent job of nurturing my intellectual development during the crucial transition from course work to research. Through his efforts, I had a lot of opportunities to work on research projects, to interact with and learn from other scientists, and to present my work, both in talks and in writing.
A second aspect of my graduate education that I felt was extremely beneficial was the interaction with my peers in the graduate program. I attended graduate school with a very bright, serious, and well- trained group of fellow students. I found the atmosphere among the students to be very interactive and friendly. I got very useful advice and the occasional extremely clever insight from them.
Overall, I found the department at Berkeley to be very accommodating of my intellectual goals and responsive to my needs. I found support to be generous, and there were many opportunities to become involved in a wide range of projects in probability and statistics. In summary, I would say I had a very positive experience in the PhD program in Statistics at Berkeley.
Ezra Nahum (Bank Paribas)
After an undergraduate degree in one of the Grandes Ecoles in France and a master's degree in Probability and Finance I decided to pursue my studies in the United States. Even though I was specialized in Mathematics applied to Finance up to this point, I wasn't sure that I wanted to keep on working in that area and was looking for a Ph.D. program whether it be in a Mathematics Department or a Statistics Department that would allow me to broaden my areas of interest.
I was accepted in a few prestigious schools and went for Berkeley mainly because it sounded cool to move to California and also because, contrary to its well known rival, was very close to San Francisco.
Well, for a start, my four years there were the most amazing of my life, I sometimes regret I didn't slow down in my research to enjoy that atmosphere an extra year or two.
The Department of Statistics at Berkeley is of the highest quality. Indeed, where in the world can you have an office in the same corridor as such superstars as Steve Evans, Jim Pitman, Terry Speed, David Brillinger, David Aldous, Peter Bickel, John Rice and many others. Academically, my fondest memory remains the Statistics 205 class that I took in my first year with Jim Pitman. This is the best class I have ever attended and the hours spent trying to solve the homework problems had that combination of pure frustration and intense hapiness (when I could find the solution). Up until I moved to Berkeley my training was very theoretical and the statistics classes I took there quenched my thirst for more "real" things.
All along I had a pretty good idea of what my research topic was going to be, namely some applications of probability theory to the pricing of exotic options. Even though there were no faculty concentrating on that aspect of applied mathematics, Steve Evans became very naturally my advisor as he already had a student working in that area and he is an expert on Stochastic Calculus and Brownian Motion. Him not being involved in Mathematical Finance research was maybe a bit scary at the start but turned out to be perfect as it allowed me to go in my own directions and forced me to rigorously put down the mathematical problems I encountered so that he could help me with them. Also I had the great opportunity to work with Jim Pitman and Marc Yor who are the top two world experts on Brownian Motion.
Not surprisingly, I am now a "quant" (quantitative researcher) for the Bank Paribas in London. It wasn't hard finding a job in this area given Berkeley's reputation overseas as much as in the States. But much more than allowing me to have a very succesful career, the department of Statistics in Berkeley provided tools that I use everyday. The Faculty there was always ready to help and spend some time with me. And on a personal note, I made amazing friends there both in the Department and outside that I keep in touch with very regularly.
Neil O'Connell (Hewlett Packard, Basic Research Institute in the Mathematical Sciences)
When I first went to Berkeley I had no idea what to expect, other than the fact that it was a top school, more down-to-earth than Stanford, nice climate, and a good place to do probability. I had just finished doing a maths degree and M.Sc. at Trinity College Dublin, and had decided on an academic career. I was lucky to be accepted at Berkeley (my maths GRE score was not great) and knew that I had been granted a fantastic opportunity. I wasn't wrong. Academically, and otherwise, it was a very formative time and a wonderful experience.
In my first year I took Stat 205 and 210, graduate courses in probability and theoretical statistics. I started doing 215, the applied statistics course, but dropped it very quickly after spending several hours trying to plot a sine-wave! Sheer laziness on my part, and in some ways I regret it. The probability course was fantastic, especially the weekly set of homework problems, which were so much fun to work on. I had planned to specialise in probability, and this course got me hooked. I still come across problems all the time which are similar in nature, and still get the same thrill when I find the right trick to solve them - there is always a trick, a right way of looking at things, I think this is one of the distinctive characteristics of doing research in probability.
My thesis topic had already taken shape by the end of the first year. That was a real stroke of luck. It emerged naturally through a series of chats with David Aldous, Steve Evans and Terry Speed. David Aldous had suggested several interesting problems to work on, and the one that grabbed me was on the genealogical structure of branching processes. This was largely because I could see by talking to Terry Speed about phylogenetic inference that a variant of David's proposal was precisely what was needed to make inference about the evolutionary history of a population given molecular data from the current population. It also had implications in the area of superprocesses, which I had been learning about from Steve Evans. I signed up to work with Steve, and followed through with these ideas. My interaction with faculty didn't stop there. What I was working on was also closely related to Ray-Knight theory and Brownian motion, which I was able to discuss with the world's leading experts in that area, Jim Pitman and Marc Yor (who visited Berkeley from Paris every summer).
One of the things that strikes me now, having been in academia for some time, is that we were incredibly well looked after by the faculty at Berkeley. These people have huge demands on their time and yet they were always available to listen and help out. I guess part of this is related to the fact that the student to faculty ratio at the Statistics Department in Berkeley is relatively low, which also meant that conditions for grad students were pretty good.
I now have a research position at Hewlett-Packard's Basic Research Institute in the Mathematical Sciences (BRIMS) www-uk.hpl.hp.com/brims/ which is located in Bristol, England. I still work in probability, although I have drifted through several different application areas since leaving Berkeley, from queueing networks to random matrices. I still feel the benefits of having done my PhD at Berkeley, in many ways. For snobs, there is no better credential, and of course that's useful. The main thing though is the feeling that I have been very well educated, and priviliged to have shared a coffee room with some of the world's smartest people.
Finally I guess I should say a few words about life in Berkeley. It's such a nice place to live! And very stimulating. The Department is small and cosmopolitan. I made friends there from all over the world and still keep in touch with many of them. My main activities were soccer (we always had a intramural team called the Bootstraps), volleyball, softball. I also co-organised great cocktail parties on the top floor of Evans Hall, with magical views of the Bay area. For this we made use of a little-known departmental fund which exists (note!) for the entertainment of graduate students.
James Reimann(Dept. of Biostatistics, Genentech Inc.)
I came to Berkeley with a four year degree in Statistics and Applied Mathematics at the University of Adelaide. Our program spent a great deal of time on applied statistics and computing skills. I had taken some pure mathematics (analysis and integration), but my background was more applied than theoretical. The department mostly taught in the Fisher (likelihood-based) tradition.
I wanted to take all the applied statistics courses I could, fill in some theory, find a dissertation project in applied statistics, and get a job in industry or biostatistics.
My plans were mostly unchanged. Since I had taken a lot of applied statistics courses in Australia, I ended up tutoring a number of the graduate applied courses instead of taking them for credit, because I had taken the material already. Of course, you learn at least as much tutoring a class as you do from taking it, so it worked out in the end. There seem to be a larger variety of applied statistics courses available now.
I based my preliminary exams on 215AB (Breiman/Freedman) and 210AB (Bickel/Evans), and then filled in with 248 (Brillinger), 205A (Aldous), 243/244 (Spector), 242B (Breiman). I also tutored 242A (twice), 248, and 215A, and one semester of Stat 20.
David Brillinger had been to a statististics-astronomy conference and talked with a Berkeley astronomer working with the MACHO collaboration (wwwmacho.anu.edu.au) who wanted to talk over statistical issued in their sky survey. One side aspect of the project, estimation of the oscillation period of variable stars, grew to become the topic of my dissertation. I had a topic but no advisor, so I talked with a few of the applied statistics professors and started working with John Rice, who had recently come from UC San Diego. I also talked with a couple other professors about their research areas, but I preferred a project in applied statistics rather than in theoretical statistics.
I took the statistical consulting series for a couple of semesters, which I recommend strongly for anyone interested in applied statistics or who plans to ever analyze data. The skills you learn - asking questions to define the problem, communicating with someone trained in a different discipline, deciding the complexity of analysis that is appropriate for the data, and writing up and communicating the results - are very valuable and complement the formal skills you learn in class.
I found a summer internship at Genentech by checking the jobs board outside Sara Wong's office, and ended up doing a second internship there another year. This raised my interest in working for a drug company, combining my two interests of industry and biostatistics. I sent my CV into a few biotechs in the Bay Area and kept in touch with Genentech. I was offered a full-time position at Genentech a couple of months before I finished my dissertation. Companies really prefer to hire people with experience, and doing internships in industry is a great way to get that experience while deciding if you are really interested in working in that field.
I have been at Genentech (www.gene.com) for 5 years now working on early- and late-stage clinical trials, pharmacokinetic analyses, and statistical consulting, and have taught classes within the company and at the school of biostatistics at Berkeley. I've worked on drugs for heart attacks, pediatric growth deficiency, diabetes, allergic asthma, macular degeneration, and rheumatoid arthritis. I've learnt a lot about medicine and biology, and about working in an interdisciplinary environment.
Berkeley has a large, distinguished faculty, which gives you a rich selection of classes and great choice in research projects. The intellectual life in the department and on campus is lively and you can see the world of statistics go by at the seminar series. The name of Berkeley carries a lot of weight with employers. The other students come from some of the best universities in the world, and there's nothing like competition to get you to work harder and achieve more. Whether you are interested in working in academia or industry, in theory or applications, Berkeley provides the resources to help you get there. Of course, the work is still yours to do, and you will get out of it what you put into it.
Anat Sakov, Technion, Israel Insitute of Technology
I must admit that I was very happy, when I saw the email regarding alumni writing on their Berkeley experience. I had a WONDERFUL experience, and this seems like a nice way to thank the department and its members for five great years.
I received my BA in Applied Mathematics from the Technion, Israel. During my BA I took an introductory Probability class and one semester in Statistics. I loved the classes, and applied to a master's program in Biostatistics, so the first two years of my Berkeley studies were in the Biostatistics department. Although I found the subject of Biostatistics very interesting, I decided for my Ph.D. to switch to the Statistics department, which I think was the BEST career choice I have made. Having a BA in applied Mathematics, I had less background in measure theory than my PhD fellows. However, I had more Statistics exposure because of my master's. I started my PhD in the fall of 1993, and took the Theoretical Statistics class with Peter Bickel and Rudy Beren. The class was challenging, but gave me a strong basis, and I still check my class notes every once in a while. The other class I took was Applied Statistics with David Freedman. I have learned a lot in the class. One of the papers we discussed in class was "Bootstrapping a regression Equation: some empirical results" by Freedman and Peters. We had to redo the simulation in the paper, and I enjoyed it a lot. That was probably the main reason for choosing the bootstrap as my research topic. After taking a class from Peter Bickel, I knew I wanted to work with him. His wealth of knowledge and Statistical intuition is unbelievable, and a very pleasant personality. Coincidently, Peter was working at the time on a paper with Goetze and van Zwet, on the m out of n bootstrap. For my thesis, I ended up working on the m out of n in hypothesis testing. I still work with Peter Bickel, but I miss our weekly meetings.
Over the years I took classes in probability, linear models, discrete data, time series, consulting and the statistical computing class by Phil Spector. Although, I have learned a lot in all the classes, Phil Spector's class was very important to my work since I am doing a lot of simulations.
Why did I enjoyed my years in Berkeley so much ? well, of course, the faculty. I still remember my first few weeks in Berkeley, where I was amazed to see on every door the name of an author of a well-known Statistics text or research book. Being in Berkeley, you meet many of the giants of Statistics in the last 50 years, and no words can describe the impact of this on your thinking, or the way you view Statistics. What makes it even better, is that everyone is very friendly, the atmosphere is informal and the students-faculty relationships are very good. As an indication for that see the big crowds coming to the Holiday party and the party after the first week of school, as well as to the department picnics. Another important factor is the friendly and helpful staff who make our life much easier. The department is very proud of its computing facility, and it should be. The help I received from the CSF personnel is invaluable.
For family reasons, after my graduation in 1998 I went back to Israel. Today I hold a postdoc position at the Technion. One year after I left, I came to visit the department, and it felt like coming back to my home. I cannot wait to my next visit !
Rick Schoenberg (Department of Statistics, UCLA)
Probably the greatest thing about the Berkeley Statistics Dept is that it is filled with the experts on nearly all important aspects of statistics, and these experts are surprisingly willing to lend you their wisdom and advice. This really was an important factor both in my decision to go to Berkeley and in the quality of my experience there. Statistics is a pretty large discipline, and as an entering student I didn't have a very good sense of what area I wanted to do my research in. Whether you end up doing pure probability, applied data analysis or anything in between, you really can't go wrong at Berkeley; Berkeley offers a kind of flexibility to the student that is truly unique in the world. In addition, learning these various subjects from the masters is an amazing experience. Looking back, I find it incredible that I was able to take probability classes from Pitman, Aldous, and Evans while learning applied statistics from Brillinger, Freedman, Speed, Rice, and Bickel. Even more incredible is how friendly these people all are.
Barathi Sethuraman (Statistical Engineering, Lifescan Inc.)
I graduated from Berkeley in '97 with a Ph.D in statistics under Terry Speed. I had joined Berkeley in '91after completing a Masters degree in Statistics from the Indian Institute of Technology (IIT) at Kanpur, India. I had planned to graduate with another Masters degree in Berkeley in Statistics because I wanted an education in a university in the U.S., and this was my opportunity to interact with some of the best minds in statistics (Lehmann, Bickel, Rice, Speed,...). After joining Berkeley, I decided to go on for a Ph.D. Not only did I enjoy the experience of belonging to Berkeley, I found the environment very stimulating. I got to apply the theory I had learned to some real-life problems. I took courses such as Statistical Models, Statistical Consulting, Multivariate Analysis, Time Series Analysis, Statistical Genetics, Estimation Theory. Since I work in industry, I find myself time and time again referring to my notes in these courses. I got most of my statistical foundation in the area of Applied Statistics in the Statistical Models course. This course involved critically reviewing papers on a variety of very interesting topics such as heart disease, cholera, cancer, census, DNA fingerprinting etc etc. I also found the course on Statistical Consulting very useful. The skills that I developed from that course are very useful to me in industry, where so-called "soft skills" are appreciated. Since I interact largely with non-statisticians, it is useful to have the skill to explain statistically complicated methods to non-statistician in simple terms.
As a student, I attended a number of conferences and workshops and presented my research at some of them. In addition, our department conducted weekly Neyman seminars, where scholars from a variety of disciplines presented their work. Terry also organized weekly seminars for Statistical applications in Genetics, in which his students gave presentations. Presentation skills are also extremely useful both in academics and industry.
After graduating from Berkeley, I have been working for a company called LifeScan, a Johnson and Johnson company, www.lifescan.com , which makes blood glucose meters. In addition to providing day-to-day project support for new products, and statistical support for improving our manufacturing processes, I am also involved in some strategic projects, such as standardizing statistical analysis for FDA submissions. I am also part of a group that will provide training in Statistics to the rest of the organization.
Hongyu Zhao (Epidemiology and Public Health, Yale University)
I chose Berkeley for my graduate study because of its reputation and the large number of faculty working on very different research topics. My undergraduate major was in probability and statistics, so I was thinking of working on mathematical statistics for my dissertation. However, after taking Terry Speed's course on Applied Statistics during my second year, I found myself more interested in statistical problems that are addressing real scientific questions. Terry was my dissertation advisor and we worked on modeling a fascinating biologic process: crossing-overs during meiosis. It was hard work but, more importantly, a lot of fun. I have benefited greatly from the experience of working with Terry and other faculty at Berkeley. Since I graduated, I have kept working on statistical problems in molecular biology and genetics, and I really enjoy my work. Sometimes, I wonder how much fun I would have missed if I had not come to Berkeley and had not taken Terry's Applied Statistics class.
http://info.med.yale.edu/eph/html/faculty_members/zhao.html
Xiaowen Zhou (Department of Mathematics, University of British Columbia)
Before coming to Berkeley, I was a graduate student in mathematics at Tufts university. I decided to transfer during my second year and was accepted by several schools. Since Berkeley was such a holy place to me when back in China, I headed for California without thinking twice.
During my first year at Berkeley, I found the course Stat 205 taught by Prof. Pitman fascinating. I was also interested in Prof. Evans' research and ended up working with him on something categorized as "Interacting measure valued Markov processes".
From my four years' experience, Berkeley's statistics department, with its world class reputation and setting, has the atmosphere of a small institute. The department and its staff are very supportive. You could have a beer with the reknowned faculty. One thing I appreciate a lot is that no matter what questions you have, you could always find a top notch probabilist or statistician who happens to know the subject in and out and is willing to spend some time with you. I appreciate it even more now I have left Berkeley.
Now a postdoctoral fellow at University of British Columbia, I am benefiting a great deal from not only the knowledge of probability and statistics I acquired from various courses, but also the way of finding, analyzing and solving mathematical problems I learned from different professors.
The academic reputation of Berkeley is also an asset. People, with a good chance, will show a bit more respect when you causally mention that you happen to be from this prestigious school even though they might know nothing about statistics.
May 30, 2006
Public and Private Universties
1. How do I find whether a University is Public or Private? is it
clearly mentioned on the University's website?
The only way is to check the web sites of the univs. If a university
carries the term 'STATE' in it's name it does not necessarily imply
that it is a state university.Select the univs where you want to go and
then check their web sites. Some of the univs are grouped and listed
below.
Public
Indiana University
Iowa State University
Michigan State University
The Ohio State University
The Pennsylvania State University
Purdue University
Rutgers, The State University of New Jersey
Stony Brook University-State University of New York
Texas A&M University
University at Buffalo, The State University of New York
The University of Arizona
University of California, Davis
University of California, Berkeley
University of California, Irvine
University of California, Los Angeles
University of California, San Diego
University of California, Santa Barbara
University of Colorado at Boulder
University of Florida
University of Illinois at Urbana Champaign
The University of Iowa
The University of Kansas
University of Maryland at College Park
University of Michigan
University of Minnesota, Twin Cities
University of Missouri-Columbia
University of Nebraska-Lincoln
The University of North Carolina at Chapel Hill
University of Oregon
University of Pittsburgh
The University of Texas at Austin
University of Virginia
University of Washington
The University of Wisconsin-Madison
Private
Brandeis University
Brown University
California Institute of Technology
Carnegie Mellon University
Case Western Reserve University
Columbia University
Cornell University
Duke University
Emory University
Harvard University
The Johns Hopkins University
Massachusetts Institute of Technology
New York University
Northwestern University
Princeton University
Rice University
Stanford University
Syracuse University
Tulane University
The University of Chicago
University of Pennsylvania
University of Rochester
University of Southern California
Vanderbilt University
Washington University in St. Louis
Yale University
GOOD LUCK
NBS
May 27, 2006
Accreditation of Universities
In USA students can choose to study at private or public schools, small colleges or large research universities.
Students may take courses full-time, part-time, on-campus, or on-line.
With all these choices, how can a prospective student decide? How does a prospective student know if the institution or program will deliver on its promises? Accreditation is one way to help narrow the selection process.
The United States does not have a ministry of education to approve curricula or courses of study. The US Department of Education primarily provides oversight at the post-secondary level for the distribution of federal funds in the form of student loans and grants.
In the United States, quality assurance in higher education rests with accreditation organizations, and in the case of public institutions, state boards for higher education which license or “charter” schools within the state borders.
How do I know if an accrediting organization is legitimate?
Most legitimate accrediting organizations in the United States are recognized by either the Council on Higher Education Accreditation (CHEA) (http://www.chea.org/) or
U.S. Department of Education (http://www.ed.gov/offices/ope/accreditat...
ABET is recognized by CHEA.
What is the significance of ABET or "Accreditation Board for Engineering Technology" - accreditation?
The criteria for accreditation are developed by a consortium of engineering societies which reflect current attitudes and expectations of what an engineering student should learn to become a fully functioning member of the engineering profession.
Therefore, the general public and employers of engineers know that graduates of an ABET accredited program have obtained an education in mathematics, basic sciences such as chemistry and physics, the humanities and the social sciences, engineering sciences, and engineering design which will enable them to practice competently.
In addition, professional registration at the state level requires applicants to be graduates of ABET accredited programs.
What is the process for accrediting an educational program?
ABET, in cooperation with its 31 professional and technical societies, has developed criteria, or standards, for the evaluation of educational programs. The criteria require that the program demonstrate that the graduates have mastered the knowledge and skills required and that the institution has in place a process for continuous improvement.
ABET has 4 commissions that administer the criteria, conduct the evaluations and accredit the programs:
Engineering Accreditation Commission (EAC)
Technology Accreditation Commission (TAC)
Computing Accreditation Commission (CAC)
Applied Science Accreditation Commission (ASAC)
IN THE LIGHT OF RECENT MOVES BY US GOVERNMENT IMPACTING INTERNATIONAL STUDENTS IT WOULD BE DESIRABLE IF THEY APPLY TO ACCREDITED PROGRAMS ONLY.
Source(s):
http://www.chea.org/
http://www.abet.org/
http://www.jointcommission.org
