Abram Magner

Assistant professor, Department of Computer Science, University at Albany, SUNY

Email: amagner at albany.edu
Education: Ph.D. in computer science, Purdue University (2015).

Research interests

My research is theoretical machine learning and probabilistic inference problems on graphs. In particular, I am interested in fundamental information-theoretic limits of learning/statistical inference and data compression/representation problems, generally involving networks, as well as efficient algorithms to achieve those limits. My recent work has concerned various notions of learning the mechanisms underlying the evolution of dynamic graphs, as well as formulating information theoretic limits of graph convolutional networks for graph representation learning.

I am looking for a PhD student.

If you're interested in pursuing a PhD in any of the areas described in my research interests, please feel free to send me an email, including your CV, transcripts, and any other information you think might be useful. I am particularly seeking students with mathematical maturity and an interest in applying mathematical methods to machine learning and graph problems. You should have a solid understanding of probability and the ability to write formal theorems and proofs.


Recent talks



ICSI 401: Numerical methods (Fall 2021)

ICSI 501: Numerical linear algebra (Spring 2021)

ICSI 401: Numerical methods (Fall 2020)

ICSI 521: Discrete mathematics with applications (Spring 2020)

ICSI 401/501: Numerical methods/numerical linear algebra (Fall 2019)