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).



(1/31/2024)  I was unable to update my webpage for approximately a year, due to some server configuration issue. I plan to eventually update it, now that access is restored. As of this posting, I have only updated it somewhat.
Research interests
My research is theoretical machine learning and probabilistic inference problems on graphs. In particular, I am interested in fundamental informationtheoretic 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.
CV
Teaching
ICSI 501: Numerical linear algebra (Spring 2022)
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)
News

(12/31/2023)  I am grateful to have received an NSF CAREER award! Link

(2023)  Two journal papers accepted (more details to come).

(4/22/2022)  Local Limit Theorems for Approximate Maximum Likelihood Estimation of Network Information Spreading Models accepted to ISIT 2022 (conference).

(1/18/2022)  Sample Complexity Bounds for PAC Learning of Quantum Measurement Classes accepted to AISTATS 2022 (conference).

(11/9/2021)  Fundamental Limits of Deep Graph Convolutional Networks for Graph Classification accepted to IEEE Transactions on Information Theory (journal).

(10/18/2021)  Submitted a paper on sample complexity of PAC learning in a quantum setting (conference).

(10/4/2021)  Local Central Limit Theorems for Approximate Maximum Likelihood Estimation
of Network Information Spreading Models submitted (conference). This is a sole author paper.

(10/4/2021)  Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiments (https://arxiv.org/abs/2109.10937) submitted (conference).

(5/4/2021)  On Dimension in Graph Convolutional Networks for Distinguishing Random Graph Models accepted to ISIT 2021 (conference).

(4/28/2020)  Struct2Graph: A graph attention network for structure based predictions of proteinprotein interactions submitted (journal).

(3/31/2020)  The Power of Graph Convolutional Networks to Distinguish Random Graph Models (short version) accepted to ISIT 2020 (conference).

(3/11/2020)  Compression of Dynamic Graphs Generated by a Duplication Model accepted to Algorithmica (journal).

(2/13/2020)  The Role of Dimension in Graph Convolutional Networks submitted (conference).

(1/15/2020)  The Power of Graph Convolutional Networks to Distinguish Random Graph Models (short version) submitted (conference).

(12/18/2019)  A Deep Learning Architecture for Metabolic Pathway Prediction accepted to Bioinformatics (Oxford University Press) (journal).

(11/25/2019)  Toward Universal Testing of Dynamic Network Models accepted to Algorithmic Learning Theory 2020 (conference).

(10/15/2019)  The Power of Graph Convolutional Networks to Distinguish Random Graph Models submitted (journal).

(9/24/2019)  A Deep Learning Architecture for Metabolic Pathway Prediction submitted (journal).

(9/20/2019)  Toward Universal Testing of Dynamic Network Models submitted (conference).

(4/5/2019)  Compression of Preferential Attachment Graphs accepted to ISIT 2019 (conference).

(1/30/2019)  Goodness of Fit Testing for Dynamic Networks submitted (conference).

(1/14/2019)  Inferring Temporal Information from a Snapshot of a Dynamic Network accepted to Nature Scientific Reports (journal).

(11/16/2018)  Asymmetry and Structural Information in Preferential Attachment Graphs accepted to Random Structures and Algorithms (journal).

(10/1/2018)  I will be joining the Department of Electrical Engineering and Computer Science at the University of Michigan as a research fellow.

(8/6/2018)  Compression of Dynamic Graphs Generated by a Duplication Model accepted to the 2018 Allerton Conference on Communication, Control,
and Computing.

(8/6/2018)  Network Archaeology via Epidemic Processes: The Case of Growing Trees accepted to the 2018 Allerton Conference on Communication, Control,
and Computing.

(7/12/2018)  Entropy and Optimal Compression of Some General Plane Trees accepted to ACM Transactions on Algorithms (journal).

(7/8/2018)  Network Archaeology via Epidemic Processes: The Case of Growing Trees submitted (conference).

(7/8/2018)  Compression of Dynamic Graphs Generated by a Duplication Model submitted (conference).

(6/4/2018)  Lossless Compression of Binary Trees with Correlated Vertex Names accepted to IEEE Transactions on Information Theory (journal).

(1/18/2018)  Asymmetric Renyi Problem accepted to Combinatorics, Probability, and Computing (journal).

(12/22/2017)  TIMES: Temporal Information Maximally Extracted from Structure accepted to WWW 2018, Lyon, France.

(11/8/2017)  Gave an invited talk in the Purdue University Statistics Department probability seminar.

(11/3/2017)  Gave an invited talk in the University of Michigan Computer Science and Engineering theory seminar.

(9/19/2017)  Entropy and Optimal Compression of Some General Plane Trees submitted (journal).

(9/14/2017)  Gave an invited talk in the Johns Hopkins Applied Mathematics and Statistics departmental seminar.

(9/7/2017)  Large Deviations for Increasing Subsequences of Permutations and a Concurrency Application accepted to IFIP Performance 2017 (conference).

(7/25/2017)  Submitted Large Deviations for Increasing Subsequences of Permutations and a Concurrency Application (conference version of the Sigmetrics MAMA workshop paper).

(7/12/2017)  Submitted Structural Information and Compression of ScaleFree Graphs (conference version of Asymmetry and Structural Information in Preferential Attachment Graphs; contains more results on optimal compression algorithms).

(5/31/2017)  Submitted Asymmetry and Structural Information in Preferential Attachment Graphs (journal paper).

(5/18/2017)  Submitted TIMES: Temporal Information Maximally Extracted from Structure (conference paper).

(5/13/2017)  Large Deviations for Increasing Subsequences of Permutations and a Concurrency Application accepted for presentation at the Sigmetrics Mathematical performance Modeling and Analysis (MAMA) workshop.

(4/27/2017)  Large Deviations for Increasing Subsequences of Permutations and a Concurrency Application (extended abstract) submitted.

(4/1/2017)  Entropy of Some General Plane Trees accepted to ISIT 2017.

(4/1/2017)  Recovery of Vertex Orderings in Dynamic Graphs accepted to ISIT 2017.

(1/6/2017)  Submitted Recovery of Vertex Orderings in Dynamic Graphs (conference paper).

(1/3/2017)  Submitted Entropy of Some General Plane Trees (conference paper).

(11/20/2016)  I will be teaching a halfsemester course on analytic combinatorics and applications, starting March 2017.

(11/7/2016)  Fundamental Bounds for Sequence Reconstruction from Nanopore Sequencers to appear in IEEE Transactions on Molecular, Biological, and MultiScale Communications, Special Issue on Biological Applications of Information Theory

(11/5/2016)  Profiles of PATRICIA Tries accepted to Algorithmica, 76(4), 167.