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Erhan Bayraktar

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Erhan Bayraktar, PhD, the holder of the Susan Smith Chair, is a full professor of Mathematics at the University of Michigan, where he has been since 2004. Professor Bayraktar’s research is in stochastic analysis, control, applied probability and mathematical finance. He has over 120 publications in top journals in these areas.

Professor Bayraktar is recognized as a leader in his areas of research: he is a corresponding editor in the SIAM Journal on Control and Optimization and also serves in the editorial boards of Applied Mathematics and Optimization, Mathematics of Operations Research, Mathematical Finance. His research has been also been continually funded by the National Science Foundation; in particular, he received a CAREER grant.

Professor Bayraktar has also been devoting his time to teaching and synergistic activities: Professor Bayraktar has been the director of the Risk Management and Quantitative Finance Masters program since its inception in 2015. As one of the two organizers of the Financial/Actuarial Math seminar which brings about 10-15 speakers every academic year and he has also organized several international workshops in stochastic analysis for finance and insurance in Ann Arbor.

Areas of interest: Mathematical finance, applied probability, stochastic analysis, stochastic control, optimal stopping.

Danny Forger

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Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.

Mehrdad Simkani

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Mehrdad Simkani, PhD, is Professor of Mathematics, College of Arts and Sciences, at the University of Michigan, Flint.

Prof. Simkani’s current research is in the area of rational approximation in the complex domain. For example, he investigates the convergence of rational function series on the extended complex plane.

Jun Zhang

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Jun Zhang, PhD, is Professor of Mathematics and Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Zhang develops algebraic and geometric methods for data analysis. Algebraic methods are based on theories of topology and partially ordered sets (in particular lattice theory); an example being formal concept analysis (FCA). Geometric methods include Information Geometry, which studies the manifold of probability density functions. He interests include mathematical psychology and computational neuroscience, broadly defined to include neural network theory and reinforcement learning, dynamical analysis of nervous system (single neuron activity and event-related potential), computational vision, choice-reaction time model, Bayesian decision theory and game theory.

Shravan Veerapaneni

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Dr. Veerapaneni’s research group develops fast and scalable algorithms for solving differential and integral equations on complex moving geometries. Application areas of current interest include large-scale simulations of blood flow through arbitrary confined geometries, electrohydrodynamics of soft particles and heat flow on time-varying domains.


Mahesh Agarwal

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Mahesh Agarwal is Associate Professor of Mathematics and Statistics at the University of Michigan, Dearborn.

Prof. Agarwal’s is primarily interested in number theory, in particular in p-adic L-functions, Bloch-Kato conjecture and automorphic forms. His secondary research interests are polynomials, geometry and math education, Machine Learning, and healthcare analytics.

Indika Rajapakse

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Indika Rajapakse, PhD, is Assistant Professor of Computational Medicine & Bioinformatics, Medical School, with a secondary appointment in Mathematics, College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Rajapakse’s research is focused on the dynamics of genome organization in human cells, with emphasis on gaining a deeper understanding of how the cell cycle guides cell fate determination. Prof. Rajapakse and his team are developing genomic and imaging technologies for determining the natural dynamics of the cell cycle and building a data guided mathematical foundation. Their long term goal is to develop strategies for direct reprogramming of normal and abnormal cells

Martin J. Strauss

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Martin J. Strauss, PhD, is Professor of Mathematics, College of Literature, Science, and the Arts and Professor of Electrical Engineering and Computer Science, College of Engineering, in the University of Michigan, Ann Arbor.

Prof. Strauss’ interests include randomized approximation algorithms for massive data sets, including, specifically, sublinear-time algorithms for sparse recovery in the Fourier and other domains.  Other interests include data privacy, including privacy of energy usage data.

Jeffrey C. Lagarias

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Jeffrey C. Lagarias is the Harold Mead Stark Collegiate Professor of Mathematics in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Lagarias’ research interests are diverse. His initial training was in analytic and algebraic number theory. After receiving his PhD in 1974, he worked at Bell Laboratories and AT &T Labs until 2003, on problems in many pure and applied fields. Besides number theory, Prof. Lagarias has made contributions in harmonic analysis (wavelets and fractals), mathematical optimization (interior point methods), discrete geometry (tilings and quasicrystals), ergodic theory, low-dimensional topology (complexity of unknotting), and theoretical computer science.

At Michigan Prof. Lagarias has been active in the number theory group over the last few years, with additional work in other fields. His last 25 postings on the arXiv were in: Number Theory (16), Dynamical Systems (3), Classical Analysis and ODE?s (3), Metric Geometry (1), Optimization and Control (1), Spectral Theory (1). His doctoral students typically work on their own topics. Some have worked in topics in number theory: integer factorial ratios, character sum estimates, Diophantine equations with two separated variables; Others have worked in topics in discrete geometry: packings of regular tetrahedra, rigidity of circle configurations.


Anna Gilbert

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My research interests include mathematical analysis, probability, networking, and algorithms. I am especially interested in randomized algorithms with applications to harmonic analysis, signal and image processing, computer networking, and massive datasets.