Machine Learning (Theory)


A Bumper Crop of Machine Learning Graduates

Tags: Machine Learning jl@ 7:26 pm

My impression is that this is a particularly strong year for machine learning graduates. Here’s my short list of the strong graduates I know. Analpha (for perversity’s sake) by last name:

  1. Jenn Wortmann. When Jenn visited us for the summer, she had one, two, three, four papers. That is typical—she’s smart, capable, and follows up many directions of research. I believe approximately all of her many papers are on different subjects.
  2. Ruslan Salakhutdinov. A Science paper on bijective dimensionality reduction, mastered and improved on deep belief nets which seems like an important flavor of nonlinear learning, and in my experience he’s very fast, capable and creative at problem solving.
  3. Marc’Aurelio Ranzato. I haven’t spoken with Marc very much, but he had a great visit at Yahoo! this summer, and has an impressive portfolio of applications and improvements on convolutional neural networks and other deep learning algorithms.
  4. Lihong Li. Lihong developed the KWIK (“Knows what it Knows”) learning framework, for analyzing and creating uncertainty-aware learning algorithms. New mathematical models of learning are rare, and the topic is of substantial interest, so this is pretty cool. He’s also worked on a wide variety of other subjects and in my experience is broadly capable.
  5. Steve Hanneke: When the chapter on active learning is written in a machine learning textbook, I expect the disagreement coefficient to be in it. Steve’s work is strongly distinguished from his adviser’s, so he is guaranteed capable of independent research.

There are a couple others such as Daniel and Jake for whom I’m unsure of their graduation plans, although they have already done good work. In addition, I’m sure there are several others that I don’t know—feel free to mention others I don’t know in comments.

It’s traditional to imagine that one is best overall for hiring purposes, but I have substantial difficulty with that—the field of ML is simply to broad. Instead, if you are interested in hiring, each should be considered in your context.

One Comments to “A Bumper Crop of Machine Learning Graduates”
  1. jl says:

    Adam Smith notes that Penn State is hiring.

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