Shmuel Friedland has worked in several areas in mathematics, such as: matrix theory and its applications, dynamical systems, and matchings in graphs. He is now primarily interested in theoretical and computational aspects of tensors and their applications to quantum information.
Dhruv Mubayi works primarily on extremal and structural questions on graphs and hypergraphs, with applications to computer science. Much of his work involves the use of probabilistic and algebraic methods. His focus in the past five years lies in Turan and Ramsey type questions, particularly on hypergraphs. Specific recent projects include: the forbidden intersection problem in extremal set theory, the supersaturation phenomenon in extremal graph and hypergraph theory, extending the spectral theory of quasirandom graphs to hypergraphs, and using semi-random, or nibble methods in hypergraph coloring.
Lev Reyzin's work focuses on foundational questions in computational and statistical learning theory, but he is more broadly interested a variety of topics, ranging from practical issues in machine learning to theoretical computer science. His research also intersects with various areas of combinatorics and optimization. Specific recent projects include work on noise-tolerant algorithms, active and interactive learning, network optimization, boosting and ensemble methods, multiarmed bandits, and graph coloring problems.
Andrew Suk works in discrete and computational geometry and extremal combinatorics. Much of his work is based on Ramsey and Turan-type problems on geometric objects, with applications to graph drawing, graph theory, combinatorial number theory, and approximation algorithms. Specific projects include intersection graphs, extremal problems in topological graph theory, problems around the Erdos-Szekeres convex polygon theorem, and Ramsey's theorem for hypergraphs.
Gyorgy Turan works in theoretical computer science, artificial intelligence and related areas in discrete mathematics and logic. Recently he worked on combinatorial, probabilistic, algorithmic and belief revision aspects of propositional Horn formulas, and on experimental aspects of commonsense reasoning. In the past he also worked on complexity theory (circuit complexity, decision trees, proof complexity) and computational learning theory (query learning, theory revision, learnability in logic).
Jan Verschelde's research interests are mainly computational algebraic geometry, with a focus on numerical and symbolic methods to solve polynomial systems. He is also interested in mathematical software, parallel algorithms, and supercomputing.
Faculty with Related Interests
|Name (Department)||Research interests|
|Rafail Abramov (MSCS)||statistical description of chaotic dynamical systems with many conserved quantities, information theory-based predictability, fluctuation-dissipation theorem and its geophysical applications, numerical methods|
|Gerard Awanou (MSCS)||numerical analysis of partial differential equations|
|Tanya Y. Berger-Wolf (CS)||application of discrete modelling and analysis techniques to various areas of computational biology|
|Daniel J. Bernstein (CS)||computational number theory, computational commutative algebra, cryptography, computer security|
|Bhaskar DasGupta (CS)||bioinformatics, computational biology, neural networks, machine learning, optical networks, combinatorial algorithms|
|Piotr Gmytrasiewicz (CS)||artificial intelligence, multi-agent systems, intelligent agents, knowledge representation, uncertainty reasoning, automated decision-making, decision and game theories, intelligent coordination and communication, cognitive modeling|
|David Nicholls (MSCS)||numerical analysis and applied PDE|
|Robert H. Sloan (CS)||algorithms and complexity theory, especially applied to artificial intelligence problems, security, computer science education|
|Negar Soheili Azad (IDS)||design and analysis of algorithms, algorithms for convex optimization, optimization in machine learning, complexity and computation|
|Theja Tulabandhula (IDS)||artificial intelligence, operations research and their applications to multiple scientific and business domains|
|Xinhua Zhang (CS)||convex models for learning predictive representations|
|Brian Ziebart (CS)||statistical machine learning, robotics, artificial intelligence, assistive technologies|
|Floyd B. Hanson||computational stochastic control, computational finance, computational biomedicine, stochastic manufacturing systems, scientific supercomputing, stochastic bioeconomics, asymptotics, industrial mathematics, numerical analysis|
|Richard G. Larson||applications of Hopf algebras to control theory and data mining, structure of Hopf algebras and quantum groups, apptrcations of algebra to computer science|
|Glenn K. Manacher||computer algorithms, computer language design|
|Vera Pless||coding theory, combinatorics|
Current and Former Postdocs
|Name||Years at UIC||Present position|
|Li Wang||2015 - present|
|John Lenz||2011 - 2016|
|Sonja Petrović||2008 - 2011||Assistant Professor at IIT|
|Vladimir Trifonov||2007 - 2008||Research Investigator at GNF|
|Amitava Bhattacharya||2005 - 2008||Fellow at the Tata Institute|
|Anton Leykin||2003 - 2006||Associate Professor at Georgia Tech|
|Yi Zhao||2002 - 2005||Associate Professor at Georgia State|
|Keith Mellinger||2001 - 2004||Professor at University of Mary Washington|
|Jennifer Wagner||2000 - 2003||Associate Professor at Washburn University|
|Name||Years at UIC||Present position|
|Martin Grohe||2000 - 2001||Professor at RWTH Aachen|
|Robert Grossman||1988 - 2010||Professor at the University of Chicago|
|Jeffrey Leon||1971 - 2008||deceased|
|Wolfgang Maass||1982 - 1993||Professor at Technische Universität Graz|
|Uri Peled||1984 - 2009||deceased|
|Jeremy Teitelbaum||1990 - 2008||Dean and Professor at the University of Connecticut|