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Ho-Joon Lee

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Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.

Figure 1. Proteomics and metabolomics analysis of IL-3 activation and cell cycle (Lee et al., Cell Reports 2017). (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.

 

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.

Romesh Saigal

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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

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Professor Seiford’s research interests are primarily in the areas of quality engineering, productivity analysis, process improvement, multiple-criteria decision making, and performance measurement. In addition, he is recognized as one of the world’s experts in the methodology of Data Envelopment Analysis. His current research involves the development of benchmarking models for identifying best-practice in manufacturing and service systems. He has written and co-authored four books and over one hundred articles in the areas of quality, productivity, operations management, process improvement, decision analysis, and decision support systems.

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.

Ramon Satyendra

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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

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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

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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

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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.

Santiago Schnell

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Dr. Schnell works at the interface between biophysical chemistry, mathematical and computational biology, and pathophysiology. As an independent scientist, his primary research interest is to use mathematical, computational and statistical methods to design or select optimal procedures and experiments, and to provide maximum information by analyzing biochemical data. His laboratory deals with the following topics:

(i) Development and implementation of mathematical, computational, and statistical methods to identify and characterize reaction mechanisms.

(ii) Investigate and test performance design of experiments or standards to quantify, interpret and analyze biochemical data.

(iii) Development of new algorithms and software to analyze biochemical data.

The key objective of my research is to create suitable standards and appropriate support of standards leading to reproducible results in the biochemical sciences. Reproducibility is central to scientific credibility. Meta-research has repeatedly shown that accurate reporting and sound peer-review do not by themselves guarantee the reproducibility of scientific results. One of the leading causes of poor reproducibility is limited research efforts in quantitative biology and chemometrics. In my laboratory, we are developing new ways to assess the reproducibility of quantitative findings in the biochemical sciences.

As a team scientist, Dr. Schnell’s research interest is to investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. His collaborators are primarily basic scientists who focus on the identification of molecular, biochemical or developmental mechanisms associated with diseases. To this end, Dr. Schnell’s expertise plays a central role in the identification of these mechanisms. Using mathematical and computational models, Dr. Schnell can formulate several hypothetical model mechanisms in parallel, which are compared with independent experimental data used to construct the models. The resulting comparisons are then independent between models, and any models that satisfy statistical measures of similarity will be used to make predictions, which will be tested experimentally by his collaborators. The model validated by the experiments will be considered the mechanism capable of explaining the behavior of the systems.