Job Candidates
Xue Chen
Thesis Information
Title: Using and Saving Randomness
Adviser: David Zuckerman
Institution: University of Texas at Austin
Graduation Date: April 2018
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SIGACT Membership No.: 7912706
Candidate Bio:
Xue is a postdoc in Northwestern University. Before that, he obtained PHD at University of Texas at Austin, advised by Prof. David Zuckerman. He is broadly interested in randomized algorithms and derandomization. Specific areas include algorithms for big data --- sparse recovery and fast Fourier transform, foundations of machine learning, and pseudorandomness.
Keywords: Algorithms for big data
Greg Bodwin
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Title: Sketching Distances in Graphs
Adviser: Virginia Vassilevska Williams
Institution: MIT
Graduation Date: June 2018
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"Hi! I’m Greg Bodwin, a researcher in theoretical computer science. I finished my PhD in 2018, split between MIT and Stanford, advised in both places by Virginia Vassilesvska Williams. Currently, I’m a postdoc at Georgia Tech with Santosh Vempala.
I’m seeking a job that will let me continue my ongoing research program, either through a tenure-track assistant professorship or with a research-focused industry position. In the past I’ve been lucky to work seriously with both mathematicians and computer scientists, and I am fully comfortable with either flavor of research and either type of job."
Keywords: Graph Theory
Adam Hesterberg
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Title: Closed Quasigeodesics, Escaping from Polygons, and Conflict-Free Graph Coloring
Adviser: Erik Demaine
Institution: MIT
Graduation Date: June 2018
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Seven years of teaching undergrad- and grad-level graph theory, complexity theory, and other math and CS to high schoolers at Canada/USA Mathcamp, with a preference for inquiry-based learning over lectures. Some undergrad and grad TAing. Assorted research in computational complexity, graph theory, and computational geometry: see my CV.
Keywords: algorithms and data structures
Sivakanth Gopi
Thesis Information
Title: Locality in Coding Theory
Adviser: Zeev Dvir
Institution: Princeton University
Graduation Date: June 2018
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SIGACT Membership No.: 9185199
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Sivakanth Gopi (called "Gopi" by friends and colleagues) is a postdocotoral researcher in the Algorithms group at Microsoft Research Redmond. He received a PhD in Computer Science from Princeton University in 2018, advised by Zeev Dvir; the title of his dissertation was "Locality in coding theory". His main research interests are in coding theory and its connections to pseudorandomness, complexity theory and cryptography. He is also involved with the DNA Storage project, erasure coding in Azure storage and Project Laplace (differential privacy) at Microsoft. He enjoys spending leisure time learning new things, staying fit, exploring nature and talking to his family.
Keywords: Coding theory
Saranurak Thatchaphol
Thesis Information
Title: Dynamic algorithms: new worst-case and instance-optimal bounds via new connections
Adviser: Danupon Nanongkai
Institution: KTH Royal Institute of Technology
Graduation Date: September 2018
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SIGACT Membership No.: 1921369
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Thatchaphol Saranurak is a research assistant professor at Toyota Technological Institute at Chicago. His main research interest is efficient graph algorithms, in particular for dynamic and distributed models. Thatchaphol completed his PhD at KTH Royal Institute of Technology in 2018 where he was supervised by Danupon Nanongkai. His dissertation focused on showing new connections between dynamic data structures and to other areas, including approximation algorithms, local algorithms, streaming algorithms, fine-grained complexity theory, and combinatorics.
Keywords: Algorithms and data structures
Anirbit Mukherjee
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Title: Mathematics of Deep Learning
Adviser: Amitabh Basu
Institution: Johns Hopkins University
Graduation Date: January 2019
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I did my undergraduate in physics at the Chennai Mathematical Institute (CMI), India and my masters in theoretical physics at the Tata Institute of Fundamental Research (TIFR), India. After a few years of doing research in Quantum Field Theory, eventually I found my true calling in mathematics. Now I am completing my Ph.D. in applied mathematics at the Johns Hopkins University (JHU) under the guidance of Prof. Amitabh Basu. During my Ph.D. I got intrigued by this ongoing rise of deep-learning and I went on to prove many theorems about the neural function space and popular deep-learning algorithms.
Keywords: Deep Learning
Canonne Clément
Thesis Information
Title: Property Testing and Probability Distributions: New Techniques, New Models, and New Goals
Adviser: Rocco Servedio
Institution: Columbia University
Graduation Date: October 2019
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SIGACT Membership No.: 8042312
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I am currently a Goldstine fellow at IBM Research. Before that, I spent two years as a Motwani postdoctoral fellow in the Stanford Theory Group. Even before, I obtained my Ph.D. from the Computer Science department of Columbia University, where I was advised by Prof. Rocco Servedio. Long ago, in a distant land, I received a M.Sc. in Computer Science from the Parisian Master of Research in Computer Science, and an engineering degree from one of France's "Grand Schools," the Ecole Centrale Paris.
Keywords: computational learning
Biaoshuai Tao
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Title: Complexity, Algorithms, and Heuristics of Influence Maximization
Adviser: Grant Schoenebeck
Institution: University of Michigan
Graduation Date: April 2020
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Biaoshuai Tao is currently working toward the Ph.D. degree with the Computer Science and Engineering Division, University of Michigan, Ann Arbor, MI, USA, and he is expected to graduate in spring, 2020. His research interests mainly include the interdisciplinary area between theoretical computer science and economics, including social network analyses, resource allocation problems and algorithmic game theory. Before joining the University of Michigan, Biaoshuai was employed as a project officer at Nanyang Technological University in Singapore from 2012 to 2015, and he received the B.S. degree in mathematical science with a minor in computing from Nanyang Technological University in 2012.
Keywords: computational economics
Tanmay Inamdar
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Title: Covering and Clustering Problems with Outliers
Adviser: Kasturi Varadarajan
Institution: The University of Iowa
Graduation Date: May 2020
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Tanmay Inamdar is a fifth year PhD candidate at the University of Iowa, advised by Prof. Kasturi Varadarajan. His research is mainly focused on approximation algorithms. He is also interested in computational geometry and distributed algorithms.
Keywords: Approximation Algorithms
Tongyang Li
Thesis Information
Title: Quantum algorithms for machine learning and optimization
Adviser: Andrew M. Childs
Institution: University of Maryland
Graduation Date: May 2020
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Tongyang Li is a Ph.D. candidate at the Department of Computer Science, University of Maryland. He received B.E. from Institute for Interdisciplinary Information Sciences, Tsinghua University and B.S. from Department of Mathematical Sciences, Tsinghua University, both in 2015; he also received a Master degree from Department of Computer Science, University of Maryland in 2018. He is a recipient of the IBM Ph.D. Fellowship, the NSF QISE-NET Triplet Award, and the Lanczos Fellowship. His research focuses on designing quantum algorithms for machine learning and optimization.
Keywords: Quantum computing