Potential Grad students:

Please excuse the "canned" nature of this advice. I receive several hundred inquiries such as yours every  year, and this turns out to be the most convenient way to respond.

[Cribbed from Lera Boroditsky's page!]
Should I email you to let you know I am applying?

No. Really. This advice is specific to me (other professors may want you to get in touch). The only way I consider applications is through our organized admissions process. I won’t be able to look at any materials that you send me directly. So please, put that good proactive energy into your official application.  I take admissions very seriously and I will read your official application very closely once I receive it through our admissions process.  There is no reason to email me before you apply and no advantage to doing so. Unfortunately, I typically do not have time to read or reply to individual emails about admissions.

Can I come visit your lab/call you to discuss my application?

In the interest of fairness, I ask everyone to just apply and go through the same application process. I do not set up special meetings or phone calls with folks before they apply because it would not be fair to all of the other people who apply who don't get a special meeting or phone-call. So, please just apply. If you are selected by the admissions committee, you will be invited to our departmental open house, where you'll get to meet all the folks in the department and spend time with the graduate students and learn a great deal about what we all do here and what life here is like. So, please apply, and if the admissions committee selects you, you will be invited. Unfortunately my lab can't accommodate visits otherwise.

[END Crib from Lera's page!]

First, I can't accept new students on an individual basis - you have to go through the regular admissions process. Information on this and our department can be obtained from:


Please read the requirements first!

We turn away a large number of highly qualified people each year, so if you don't get in, it isn't necessarily your fault! Our strongest competitors are schools like Stanford, Berkeley, U. Washington, CMU and MIT. So, again, our students are the cream of the crop.

If you are interested in what I do, that is, Cognitive Science, you should also consider the Cognitive Science department at UCSD which has quite a few computational people, Virginia de Sa (Neurocomputational models of learning and vision, Brain Computer Interfaces), Jeff Elman (Neural networks models of Language and Development), Jim Hollan (HCI), Ed Hutchins (HCI, distributed cognition), David Kirsh (HCI and AI), and Angela Yu (computational cognitive neuroscience).

A third alternative is the Interdisciplinary Ph.D. Program in Cognitive Science, of which I am currently the Director. You can enter this program from any of eight different departments on campus, including ours and Philosophy.

By the way, here are a few good sites for "going to grad school" advice:

The illustrated guide to a Ph.D.

Phil Agre's "Advice for Undergraduates Considering Graduate School.

Often cited as "the best" advice, is Maries desJardins' article "How to succeed in Graduate School: A guide for students and advisors"

Here is David Chapman's "How to do research at the MIT AI Lab," which is more widely applicable than its title would suggest:

And here is a short list of concise advice by Wanda Pratt.

Most of these have further links. Please let me know if any links are broken!

I have included here a list of other schools, along with my research interests.

Starting with UCSD [N.B. This information is really, really old!!! I haven't had time to update it in a long time! Ok, I slightly updated it 08/02/2017]

I am the main person doing Cognitive Science in my department. There are people here doing machine learning  and cognitive science in my department (CSE), Electrical  and Computer Engineering (ECE), Cognitive Science, Biology, Philosophy, Neuroscience and Medicine. Also, Terry Sejnowski is here (he is in most departments on campus, at least it seems that way).  There's probably others I have forgotten to mention.

I do work in several areas, with the main goal being trying to understand the neural basis for the phenomenon. The list is pretty spread out. We have some nice results (see my publications page).

Another major interest is multi-layer Independent Components Analysis (mainly due to my excellent former graduate student, Honghao Shan), and novel approaches to ICA that are biologically plausible.

Another major interest is in recurrent nets. I have done work (or my students have done work) in learning simple procedures (especially arithmetic), face and emotion recognition, central pattern generators (in particular, the lobster stomatogastric ganglion, but more recently, the leech swim circuit), image compression, nonlinear dimensionality reduction, active selection of training examples, an area that for me has now morphed to "selective attention", information retrieval, speech recognition with emphasis on adapting to speaking rate, word sense disambiguation, etc.

Again, most of what I have done lately is face and object processing, with an emphasis on two approaches: Neural nets, and Bayesian models.

Here's part of my research statement:

"My research is strongly interdisciplinary. It concerns using neural networks and other formal approaches as computational models applied to problems in Cognitive Science & Artificial Intelligence, Engineering and Biology. The major thrust of my research over the review period has been in cognitive modeling of perceptual phenomena, as well as experimental approaches to testing those models in humans.  My ultimate goal is to understand how the mind works, given that the only "hardware" it has to run on is our brains.  I am committed to the view that there is a rich and productive interaction between computer science and related disciplines.  On the one hand, I believe that computer science has much to offer to the understanding of cognitive and biological sciences.  Computer science offers not only methodological tools for studying complex phenomena, but provides a theoretical basis for understanding computation as it may appear in other domains. At the same time, I believe that the study of "real world phenomena" offers a challenge to computer science. Attempting to understand the computational underpinnings of domains such as face and object recognition in humans can stimulate the development of new theories and techniques which enrich computer science."

Other CSE people:

David Kreigman is one of the most widely cited experts on the subject of face recognition, a crucial component of vision-based security systems for human-computer interaction as well as homeland security purposes. In object recognition, David revolutionized the field with his work on the geometry of curved surfaces to recognize objects from their occluding contours, silhouettes and aspects.

[Gone to Amazon] Charles Elkan has done excellent work in cost sensitive learning, clustering algorithms, finding sequences in biological databases, and genetic algorithms applied to computational finance. He caused a great deal of controversy with his paper on "The paradoxical success of fuzzy logic."

Sanjoy Dasgupta has developed some of the first efficient, provably correct algorithms for canonical statistical tasks (especially related to clustering) on high dimensional data sets.

[Gone to Cornell Tech] Serge Belongie is working on content-based image and video retrieval--a new way to search and analyze the output from the growing number of digital cameras everywhere (since the volume of images and video far outstrips anyone's ability to log all the video).

Mohan Paturi has analyzed the basins of attraction in Hopfield networks, threshold circuit complexity, and has been working on fast learning algorithms.

New to our department in the last few years are Yoav Freund, who co-discovered AdaBoost, one of the most widely used machine learning algorithms in recent years, and Lawrence Saul, co-inventor of Local Linear Embedding, which is a new and effective way to find the low dimensional manifold that high dimensional data "lives in." Both of these researchers have many other interests; I have just referred to what they are most famous for.

Also new is Julian McAuley, whose has wide interests; some of them are in Computer Vision and Recommender systems!

Terry Sejnowski is here at the Salk Institute, and has a wide range of interests, from modeling the hippocampus, to face recognition to speech recognition to motion perception to schizophrenia.

I listed the Cog Sci folks above.

[Appears to have disappeared into industry]: In ECE, Gert Lanckreit is a new addition in machine learning, a former student of Mike Jordan's. Nuno Vasconcelos studies  statistical approaches to computer vision. Pamela Cosman  works on image processing and data compression, Alon  Orlitsky works on compression, machine learning, and  speech recognition, Tony Sebald on neural networks for control, Sing Lee, Clark Guest, and Shaya Feinman work on optoelectronic implementations of neural nets,  Ken Kreutz-Delgado works on applications of neural nets to robotic control, especially the inverse kinematics problem. Mohan Trivedi is a world-famous expert on machine vision, intelligent systems, and robotics.

[Now emeritus}: In Biology, Bill Kristan has done extensive work in modeling invertebrate systems, especially "Behavioral Choice": how a simple invertebrate system "decides" what to do next. We worked together on leech swimming through our shared grad student, Adam Taylor.

In Psychology, there are a number of folks who use computational methods to understand how the brain works: Don MacLeod, Ed Vul, Adam Aaron, and John Serences.

People in the Med school have used neural nets for diagnosing eye problems. Larry Squire works on memory and has done some modeling of the hippocampus.

Other schools (quite out of date!):

Toronto: Geoff Hinton, Radford Neal, Rich Zemel, (who am I forgetting?)...

MIT (Brain and Cognitive Sciences Dept.) has Josh Tenenbaum, Tommi Jaakkola (I could have placed him in the wrong department).  Also, Tommy Poggio.  Mike Jordan, while he was at MIT, claimed that among him, Tommy Poggio and others, their students and postdocs, there are 40-50 researchers at MIT working on learning, and so MIT is the center of the machine learning Universe, just as it is the center of the Universe for everything else.

U Mass Amherst (COINS): Andy Barto reinforcement learning (now retired).

University of Alberta: Rich Sutton reinforcement learning

CMU (Psych or CS Dept.): Dave Touretzky, Dean Pomerleau, Scott Fahlman Dave Plaut, Mike Lewicki, Tom Mitchell, and many more.

U. of Rochester: Robbie Jacobs, Brain and Cognitive Sciences. James Allen, Comp Sci.

Indiana U. (CS or Linguistics Depts.): Michael Gasser (language production), Bob Port (speech recognition, recurrent networks), John Krushke (human categorization) [this list is out of date - there are more…]

U. of Colorado Boulder (CS Dept.): Mike Mozer and Randy O'Reilly.

Johns Hopkins: Paul Smolensky

Colorado School of Mines: Lori Pratt

Brandeis: Jordan Pollack (language, recurrent nets)

Columbia: Larry Abbott, Eero Simoncelli,

Stanford: Daphne Koller, Andrew Ng, and Bernard Widrow CS/EE.

Cal Tech: Carver Mead, Christof Koch (Computational Neural Systems)

USC: Stefan Schaal, Bart Mel, Michael Arbib, Ladan Shams,...

UCIrvine: (There are now a LOT more people than this list:) Pierre Baldi, Eric Mjolsness, Padhraic Smyth, Mark Steyvers,...

UCLA: Adnan Darwiche, Mike Dyer, Stefano Soatto, Demetri Terzopoulos,...

UCSC (Psych): Alan Kawamoto (language), in CS, Dave Haussler (Learning theory, bioinformatics applications), Manfred Warmuth.

McMaster University: Sue Becker

Berkeley has Peter Bartlett, Jerry Feldman, David Forsyth, Marti Hearst, Mike Jordan, Daniel Klein, Jitendra Malik, Stuart  Russell, Bob Wilensky.

Good luck!