Professor Saigal has held faculty positions at the Haas School of Business, Berkeley and the department of Industrial Engineering and Management Sciences at Northwestern University, has been a researcher at the Bell Telephone Laboratories and numerous short term visiting positions. He currently teaches courses in Financial Engineering. In the recent past he taught courses in optimization, and Management Science. His current research involves data based studies of operational problems in the areas of Finance, Transportation, Renewable Energy and Healthcare, with an emphasis on the management and pricing of risks. This involves the use of data analytics, optimization, stochastic processes and financial engineering tools. His earlier research involved theoretical investigation into interior point methods, large scale optimization and software development for mathematical programming. He is an author of two books on optimization and large set of publications in top refereed journals. He has been an associate editor of Management Science and is a member of SIAM, AMS and AAAS. He has served as the Director of the interdisciplinary Financial Engineering Program and as the Director of Interdisciplinary Professional Programs (now Integrative Design + Systems) at the College of Engineering.
Lawrence Seiford, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering and the Goff Smith Co-Director of the Tauber Institute for Global Operations at the University of Michigan, Ann Arbor.
Prof. Seiford’s research interests include:
Healthcare Quality Improvement
Banking & Finance
- Industrial Operations
Distribution & Logistics
- Operations Research Tools
Data Envelopment Analysis
Productivity And Efficiency Analysis
- Quality and Applied Statistics
Statistical Quality Control
Exploratory Data Analysis
- Risk Management
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.
Ramon Satyendra, PhD, is Associate Professor of Music Theory and Director of Graduate Studies in the School of Music, Theatre & Dance at the University of Michigan, Ann Arbor.
Professor Satyendra holds a doctorate from the University of Chicago in music theory and history. Before coming to Michigan, he taught at Yale University and the University of Chicago. He currently serves on the editorial boards of The Journal of Mathematics and Music, Intégral, and Analytical Approaches to World Music. Highlights of previous service to the field include Executive Committee of the Society of Music Theory, editorial board of Music Theory Spectrum, and editor of the Journal of Music Theory. Among his awards are the Merten Hasse Award in Mathematics from the Mathematical Association of America and the Clauss Prize for Teaching Excellence in the Humanities from Yale University. He is a three-time fellow of the Mannes Institute for Advanced Studies in Music Theory. Satyendra’s research interests include music and mathematics, late nineteenth-century music, jazz, South Asian music, and compositional theory. He plays piano, organ, tabla, and guitar and has published in Music Theory Spectrum, Music Analysis, Journal of Music Theory, American Mathematical Monthly, and elsewhere.
Lu Wei, DSc, is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan, Dearborn.
Prof. Wei studies the analytical properties of interacting particle systems relevant to both classical and quantum information theory.
Eric Michielssen, PhD, is Professor of Electrical Engineering and Computer Science, Director of the Michigan Institute for Computational Discovery and Engineering, and Associate Vice President for Advanced Research Computing. His research interests include all aspects of theoretical, applied, and computational electromagnetics, with emphasis on the development of fast (primarily) integral-equation-based techniques for analyzing electromagnetic phenomena. His group studies fast multipole methods for analyzing static and high frequency electronic and optical devices, fast direct solvers for scattering analysis, and butterfly algorithms for compressing matrices that arise in the integral equation solution of large-scale electromagnetic problems. Furthermore, the group works on plane-wave-time-domain algorithms that extend fast multipole concepts to the time domain, and develop time-domain versions of pre-corrected FFT/adaptive integral methods. Collectively, these algorithms allow the integral equation analysis of time-harmonic and transient electromagnetic phenomena in large-scale linear and nonlinear surface scatterers, antennas, and circuits. Recently, the group developed powerful Calderon multiplicative preconditioners for accelerating time domain integral equation solvers applied to the analysis of multiscale phenomena, and used the above analysis techniques to develop new closed-loop and multi-objective optimization tools for synthesizing electromagnetic devices, as well as to assist in uncertainty quantification studies relating to electromagnetic compatibility and bioelectromagnetic problems.
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.
Luis Ortiz, PhD, is Assistant Professor of Computer and Information Science, College of Engineering and Computer Science, The University of Michigan, Dearborn
The study of large complex systems of structured strategic interaction, such as economic, social, biological, financial, or large computer networks, provides substantial opportunities for fundamental computational and scientific contributions. Luis’ research focuses on problems emerging from the study of systems involving the interaction of a large number of “entities,” which is my way of abstractly and generally capturing individuals, institutions, corporations, biological organisms, or even the individual chemical components of which they are made (e.g., proteins and DNA). Current technology has facilitated the collection and public availability of vasts amounts of data, particularly capturing system behavior at fine levels of granularity. In Luis’ group, they study behavioral data of strategic nature at big data levels. One of their main objectives is to develop computational tools for data science, and in particular learning large-population models from such big sources of behavioral data that we can later use to study, analyze, predict and alter future system behavior at a variety of scales, and thus improve the overall efficiency of real-world complex systems (e.g., the smart grid, social and political networks, independent security and defense systems, and microfinance markets, to name a few).
Professor Gull works in the general area of computational condensed matter physics with a focus on the study of correlated electronic systems in and out of equilibrium. He is an expert on Monte Carlo methods for quantum systems and one of the developers of the diagrammatic ‘continuous-time’ quantum Monte Carlo methods. His recent work includes the study of the Hubbard model using large cluster dynamical mean field methods, the development of vertex function methods for optical (Raman and optical conductivity) probes, and the development of bold line diagrammatic algorithms for quantum impurities out of equilibrium. Professor Gull is involved in the development of open source computer programs for strongly correlated systems.
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.