Speakers

Biography
Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor's Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines.

He has made several contributions to the theory of deep learning, and developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the analysis of circadian rhythms in biology. He has written four books and over 300 peer-reviewed articles.

He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is an Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.

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Biography
Brian Athey, Ph.D. is the Michael Savageau Collegiate Professor and Founding Chair of the Department of Computational Medicine and Bioinformatics in the U-M Medical School, where he also has appointments as a Professor of Psychiatry and of Internal Medicine. He has previously served as Director of Academic and Clinical Informatics for the School. Brian also serves as Founding Co-director of the U-M wide Michigan Institute for Data Science (MIDAS). Brian is the Principal Investigator of one of the most well established NIH Pre-Doctoral Training Programs in Bioinformatics in the US. He has served as the Principal Investigator on many seminal efforts such as the NIH Visible Human Project, DARPA Virtual Soldier Project, and the NIH National Center for Integrative Biomedical Informatics. He is a founder of the Michigan Institute for Clinical and Health Research (MICHR), where he served for 11 years as Director of the Biomedical Informatics of its Clinical and Translational Science Award (CTSA). He was Co-founder and later CSO of the tranSMART Foundation, a data integration and analytics platform utilized by the pharmaceutical industry globally. Brian’s recent research is the creation and use of bioinformatics pipelines, epigenomics, Electronic Health Records (EHRs), and machine learning methods to radically improve the efficacy of Pharmacogenomics, and is considered a pioneer researcher in the new field of “Pharmacoepigenomics”. From 2010-2016, he served as Chair of Scientific Advisory Board (SAB) of Assurex Health, Inc. (Mason, OH), the leading psychiatric pharmacogenomics company in the world, recently acquired by Myriad Genetics, Inc. (Salt Lake City, UT). He has consulted extensively for the Defense Advanced Research Projects Agency (DARPA), the NIH Office of the Director, the NIH Chief Information Officer. Since 2017, Brian has served as an advisor to the Chinese University Hong Kong, Shenzhen (CUHK-CZ). He is an elected Fellow of the American College of Medical Informatics (FACMI).

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Roger Brockett has contributed widely to control theory and applied mathematics, including early work on linear systems, frequency domain stability theory, differential geometric methods in nonlinear control, the computation of the Volterra series, a geometric approach to the sufficient statistics problem in nonlinear estimation, feedback linearization and feedback stabilization, robot kinematics and dynamics, formal languages for motion control, hybrid systems, computational problems related to tensor ranking and integrable systems, quantum control, and optimal control of Markov processes. His research and teaching has been recognized with awards from the IEEE, ASME, Society for Industrial and Applied Mathematics (SIAM), and AACC and, most recently, International Federation of Automatic Control. He is a Fellow of IEEE, SIAM, and American Mathematical Society and a member of the National Academy of Engineering. As a Harvard faculty member, he served on the faculty council for several years, initiated long-standing introductory-level courses in design and developed major funding for group efforts in robotics and computer vision. He retired from his teaching position in 2012 after nearly 50 years of lecturing while advising more than 60 Ph.D. students at Harvard University, Massachusetts Institute of Technology, and Brandeis University. His 1970 textbook on linear systems has recently been reprinted in the SIAM Classic Series.

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Biography
James Douglas Engel’s PhD work in the von Hippel lab was devoted to understanding the thermodynamic behavior of methylated DNA. As a postdoc with Norman Davidson and Tom Maniatis, they generated the first genomic DNA libraries from which we cloned the chicken alpha- and beta-globin loci. They collaboratively were the first to show that DNaseI “hypersensitive sites” in chromatin were coincident with transcriptional regulatory elements. They discovered the first erythroid gene enhancer and subsequently discovered that enhancers function by looping through chromatin to directly interact with promoters. They discovered the GATA transcription factor gene family, and showed that GATA3 loss of function led to embryonic lethality. They later found that conditional GATA3 loss of function led to complete abolition of the T cell lineage. They pioneered the study of mutant transgenic BACs and YACs to study human globin gene regulation and through those studies identified the first gamma-globin repressor. they showed that inactivation of that repressor led to unprescedented induction of fetal globin, a goal for the development of therapeutics to treat sickle cell disease and beta-thallasaemia for several decades. They continue to improve on compounds that exhibit greater gamma-globin induction efficacy.

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Biography
Leon Glass attended Brooklyn College and did graduate work at the University of Chicago. Before moving to McGill University, where he is currently a Professor of Physiology and the Isadore Rosenfeld Chair in Cardiology, he held postdoctoral positions at the University of Edinburgh, the University of Chicago, and the University of Rochester. He has been a visiting professor at the University of California, San Diego, Harvard Medical School, and Boston University. Dr. Glass is a Fellow of the Royal Society of Canada, the American Physical Society, SIAM, and the American Association for the Advancement of Science. He is the recipient of a Guggenheim Fellowship, the Prix Jacques-Rousseau for interdisciplinary research from ACFAS, and the Arthur T. Winfree Prize from the Society for Mathematical Biology. Dr. Glass' research focuses on the applications of mathematics to study biological function and rhythms in the cardiovascular system, dynamics and control in genetic and neural networks, and visual perception. He is the coauthor (with Michael Mackey) of "From Clocks to Chaos: The Rhythms of Life" (Princeton, 1988), which has been translated into Russian, Chinese and Portuguese, and (with Daniel Kaplan) of "Understanding Nonlinear Dynamics (Springer-Verlag, 1995).

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Biography
Gemunu Gunaratne is a Professor and the Chairman of the Department of Physics at the University of Houston. He received his Ph.D. from Cornell University and conducted post-doctoral research at the University of Chicago before assuming the position at the University of Houston. He proposed the use of periodic orbits and their eigenvalues to characterize strange attractors and showed how dynamical invariants such as fractal dimensions and expansion rates can be derived from them. In spatio-temporal patterns, he invented a class of measures, referred to as the “disorder function,” to quantify the level of pattern irregularity, and used them to describe the dynamics of pattern relaxation. He was a member of the “Chicago” group that first established anomalous scaling properties of hard-turbulence. Gunaratne’s group analyzed “stylized facts” in financial markets, and showed that they follow from a stochastic process with variable diffusion. He has addressed several biologically related problems including establishing linear response as a possible means to establish the strength of osteoporotic bone and developing models to study animal locomotion. More recently, he has focused on model-free control algorithms for coupled networks and turbulent flows.

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Biography
Alfred O. Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is also the Co-Director of the University’s Michigan Institute for Data Science (MIDAS). His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has served as President of the IEEE Signal Processing Society and as a member of the IEEE Board of Directors. He has received numerous awards for his scientific research and service to the profession including several best paper awards, the IEEE Signal Processing Society Technical Achievement Award in 2013 and the 2015 Society Award, which is the highest career award bestowed by the IEEE Signal Processing Society. He has received a Rackham Distinguished Faculty Achievement Award in 2011 and the 2017 Stephen S. Attwood Excellence in Engineering Award, from the University of Michigan. Alfred Hero’s recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical signal processing, and machine learning. Of particular interest are applications to social networks, network security and forensics, computer vision, and personalized health.

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Biography
Dr. Lander enjoys doing “systems” biology, an activity that fuses experimental biology with mathematics, physics, engineering and computer science, in the search for fundamental design principles that explain the complexity of life. He has focused on how cells accurately know their locations in space; how tissues and organs stop growing at precisely-determined sizes; and how selection for control of these processes opens the door to combinatorial fragility, wherein combinations of small changes (e.g. in gene expression) can lead to catastrophic failures (e.g. birth defects).

Dr. Lander received his B.S. degree in Molecular Biophysics and Biochemistry from Yale in 1979, followed by an M.D. degree and a Ph.D. in neuroscience from the University of California, San Francisco in 1985. After postdoctoral research in neurobiology at Columbia University, he joined the faculty of the Massachusetts Institute of Technology in 1987, in the Departments of Brain & Cognitive Sciences and Biology. After receiving tenure, he moved to UC Irvine, where he is currently the Donald Bren Professor of Developmental & Cell Biology, and holds joint appointments in the Departments of Biomedical Engineering and Logic & Philosophy of Science. At UCI, Dr. Lander founded and directs the Center for Complex Biological Systems, designated a National Center for Systems Biology by the NIH, to foster interdisciplinary research, training, and outreach at the interface between biology and the physical, computational, and engineering sciences. He also co-directs UCI’s Cancer Systems Biology Center, and is an Associate Director of the NSF-Simons Center for Multi-scale Cell Fate Research (MathBioSys).

Dr. Lander’s honors include a David and Lucille Packard Fellowship for Science and Engineering; an NIH-Javits Neuroscience Investigator Award; and a RARE Champion of Hope in Science Award, as well as election to membership in the American Society for Clinical Investigation and fellowship in the American Association for the Advance of Science. Dr. Lander has served on the editorial boards of the Journal of Cell Biology, PLoS Biology and BMC Biology, and as a member of multiple NIH grant review panels, including the Modeling and Analysis of Biological Systems Study Section, which he chaired. He is a member of the Clinical Advisory Board and Board of Directors of the Cornelia de Lange Syndrome Foundation, and advocates globally for systems biology through visiting appointments at the University of Tsukuba, in Japan, and National Taiwan University, as well as through teaching in short courses and workshops at various locations in the U.S., Europe and Asia.

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Biography
In the PhD in molecular and cell biology (University of Washington, Seattle, WA, USA), Lindsey trained in development of gene and cell therapies for skeletal muscle disease, primarily testing the feasibility of using cell reprogramming in autologous therapy that can regenerate skeletal muscle. In the postdoctoral work (University of Michigan, Ann Arbor, MI, USA) Lindsey trained in areas of immunology and metabolism, studying how obesity affects immune cells in mouse and human adipose tissue. In humans, how immune cells such as blood monocytes and tissue macrophages respond in disease settings is yet ill-defined. In postdoctoral and current work at the University of Michigan, Lindsey use high-throughput and genomics technologies with computational tools to quantify and better define these cells and their sub-populations, which may have very different—even opposing—functions. Lindsey also pursue approaches for human immune cell reprogramming that can produce therapeutically beneficial cells in disease settings.

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Biography
Gil Omenn is the founding director of the Center for Computational Medicine & Bioinformatics and the Harold T. Shapiro Distinguished University Professor of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health at the University of Michigan, Ann Arbor, MI, USA. His research is focused on cancer proteomics and bioinformatics, especially the role of mRNA and protein splice isoforms, a remarkable development of the evolution of multi-cellular organisms with multi-exon gene structures. He leads the global Human Proteome Project (www.hupo.org). He previously worked on biochemical genetics of the brain, cancer prevention, health promotion and disease prevention for older adults, and science and health policy. He is an author of 592 publications and editor/author of 18 books.

He was a White House Fellow at the U.S. Atomic Energy Commission, Associate Director of the White House Office of Science & Technology Policy and the Office of Management & Budget, and President of the American Association for the Advancement of Science. He is an honorary faculty of Peking Union Medical College and a member of the Peking University/University of Michigan Joint Institute. He advised the Shanghai Jiao Tong University. He participated in the 2016 CUHK IAS Workshop on Mathematics of the Genome. He is an elected member of the National Academy of Medicine, the American Academy of Arts and Sciences, and the American College of Physicians. He received the Walsh McDermott Medal from the National Academy of Sciences and the David Rogers Award from the Association of American Medical Colleges. He holds the BA from Princeton, MD from Harvard, and PhD in Genetics from the University of Washington. He has three children and six grandchildren. He is a musician and tennis player.

Website: www.ccmb.med.umich.edu/omenn

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Indika Rajapakse is an Associate Professor of Department of Computational Medicine and Bioinformatics, and Department of Mathematics at the University of Michigan, Ann Arbor, MI, USA. Received his B.Sc. degree in electrical engineering and Ph.D. in Applied Mathematics. After postdoctoral work at Fred Hutchinson Cancer Research Center with Mark Groudine joined the faculty at the University of Michigan. His lab is developing genomics and imaging technologies for studying the dynamics of genome organization. A major focus of his lab is to gain a deeper understanding of how the cell cycle guides cell fate determination and also develop strategies for direct reprogramming of normal and abnormal cells. His lab is a part of the Smale Institute. URL: https://rajapakse.lab.medicine.umich.edu/

Biography
Dr. Ried received his M.D. degree in 1989 from the Max-Planck-Institute for Medical Research in Heidelberg, Germany, after cloning the b-myosin heavy chain gene and developed the first definitive test for carrier detection of Duchenne muscular dystrophy based on fluorescence in situ hybridization (FISH). Dr. Ried was awarded a three-year postdoctoral fellowship from the German Research Society (Deutsche Forschungsgemeinschaft), which he spent at the Department of Cytochemistry and Cytometry of the University of Leiden, the Netherlands (Dr. van der Ploeg), the Department of Human Genetics, Yale University, New Haven, CT (Dr. David C. Ward), and the Department of Human Genetics of the University of Heidelberg (Dr. Thomas Cremer).

Dr. Ried is one of the pioneers in the development of FISH for the detection of human genetic diseases and cancer, which are exemplified in (i) first in demonstrating that formalin-fixed archived tissue sections can be analyzed using comparative genomic hybridization. He and his colleagues were first in conceptualizing the use of arrayed nucleic acids for comparative genomic hybridization and quantitative DNA and RNA analysis (U.S. Patent # 20030157537). While in Dr. Thomas Cremer’s Laboratory, Dr. Ried was first in demonstrating that formalin-fixed archived tissue sections can be analyzed using comparative genomic hybridization. In fact, this was the first time that retrospective comprehensive screening of tumor genomes became possible, and it precipitated an extensive series of activities in the cancer cytogenetic community in the study of chromosomal aberrations and the correlation of such abnormalities with clinical outcome. Dr. Cremer, Dr. Ried and his colleagues were first in conceptualizing the use of arrayed nucleic acids for comparative genomic hybridization and quantitative DNA and RNA analysis (U.S. Patent # 20030157537). This paved the way for the construction of array-based comparative genomic hybridization and gene expression arrays, tools that are now central to genome research and genetic diagnostics. Dr. Ried’s identification of consistent copy number increases of 3q in cervical cancer was a first. It led to the first patent that was awarded to the National Human Genome Research Institute (then National Center for Human Genome Research, US Patent # 5919624). In 1996, Dr. Ried developed spectral karyotyping, now known more widely by its acronym SKY. He and his colleagues utilized an entirely novel approach for the visualization of FISH experiments. In contrast to conventional epifluorescence filter technology, SKY employs an imaging interferometer linked to a CCD camera to distinguish multiple and overlapping fluorochromes. Today, SKY has been adapted by more than 350 routine cytogenetic diagnostics laboratories all over the world and has become an essential tool for the identification of constitutional and acquired chromosomal aberrations. SKY-based chromosome analysis has been used in close to 1300 publications. Dr. Ried has organized advanced molecular cytogenetic courses at Cold Spring Harbor Laboratories dedicated to educate scientists around the world in this technology.

Dr. Ried’s laboratory now focuses on the role of chromosomal aneuploidy on gene expression in cancer cells, and on how aneuploidy affects nuclear structure. Dr. Ried has published more than 300 peer-reviewed articles, organized two different courses at Cold Spring Harbor Laboratories, serves on numerous editorial boards and as an editor, and as a reviewer for 33 scientific journals and 26 national and international funding agencies. He serves as the Chairman of CancerNet of the German National Genome Research Network, the major funding source for cancer related genome research in Germany. In 2006, Dr. Ried delivered a keynote address at the 20th NIH Intramural Research Festival. Dr. Ried is a member of the steering committee of two Centers of Excellence at the NCI and Associate Professor at Washington University, Washington, D.C., and a Visiting Professor at the University of Göttingen, Germany. In 2000, Dr. Ried received the ASBMB-Amgen Award of the American Society of Biochemistry and Molecular Biology, and was elected an honorary member of the German Society of General and Visceral Surgery in 2009.

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Biography
Stephen Smale entered the University of Michigan in 1948. Initially, he was a good student, placing into an honors calculus sequence taught by Bob Thrall and earning himself As. However, his sophomore and junior years were marred with mediocre grades, mostly Bs, Cs and even an F in nuclear physics. However, with some luck, Smale was accepted as a graduate student at the University of Michigan's mathematics department. Yet again, Smale performed poorly in his first years, earning a C average as a graduate student. It was only when the department chair, Hildebrandt, threatened to kick Smale out that he began to work hard. Smale finally earned his Ph.D. in 1957, under Raoul Bott.

Smale began his career as an instructor at the college at the University of Chicago. In 1958, he astounded the mathematical world with a proof of a sphere eversion. He then cemented his reputation with a proof of the Poincaré conjecture for all dimensions greater than or equal to 5, published in 1961; in 1962 he generalized the ideas in a 107-page paper that established the h-cobordism theorem.

After having made great strides in topology, he then turned to the study of dynamical systems, where he made significant advances as well. His first contribution is the Smale horseshoe that started significant research in dynamical systems. He also outlined a research program carried out by many others. Smale is also known for injecting Morse theory into mathematical economics, as well as recent explorations of various theories of computation.

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Biography
Gary Stormo is one of the founders of the field of Computational Biology and has contributed to several areas within that field. He is best known for his work on modeling the specificity of protein-DNA interactions, including the introduction of "weight matrices" to represent short DNA and RNA regulatory motif patterns. His work remains the basis for most motif discovery and analysis programs today. Stormo and his coworkers developed a widely influential formalization of weight matrices in terms of Shannon information theory, providing the mathematical foundation for regulatory motif discovery. He was the first to develop computational methods for discovering motifs in unaligned DNA data, and one of the pioneers of expectation-maximization (EM) algorithms in this field. He did significant subsequent development of this work, improving its statistical footing and increasing its power using comparative genome analysis. His most recent work in this area focuses on obtaining optimal free energy models from high-throughput sequencing data. A related topic was the development of novel methods to predict the specificity of transcription factors based only on their protein sequences.

Stormo was also influential in the fields of RNA structure prediction and protein-coding gene finding. He was the first to take a quantitative approach to RNA structure prediction by comparative sequence analysis based on information theory methods and introduced innovative new methods for RNA folding capable of predicting more complex structures. He also developed methods to predict RNA regulatory motifs that are composed of a combination of sequence and structure constraints. He also introduced methods using dynamic programming techniques to make optimal (highest scoring) predictions of protein coding genes in genome sequences.

In addition to the purely computational work he has invented several new experimental approaches for obtaining quantitative data about protein-DNA interactions, including recent advancements that allow for measurements of the cooperativity between proteins and the effects of epigenetic modifications to the DNA sequences.

Stormo received his BS degree from the California Institute of Technology and his PhD from the University of Colorado. He stayed at the University of Colorado for many years, rising to the rank of Professor, before moving to Washington University School of Medicine in 1999 where he is currently the Joseph Erlanger Professor of Genetics and a member of the Edison Family Center for Genome Sciences and Systems Biology.

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Biography
Michael (Xiaohua) Xuan is the Founder and Chairman of UniData Technology, a company based in Shanghai, focusing on big data/AI technology and incubation. He also co-founded eBao Technology, an innovative core business system software company for insurance industry. Prior to that, he had worked at Hewlett Packard in USA for eight years in developing large-scale circuit simulation algorithms and commercial software. Michael Xuan holds Ph. D degree in Mathematics from UC Berkeley and MS degree in Computational Mathematics from Zhejiang University in China.

Micahel Xuan is Vice Chairman CSIAM (Chinese Soceity of Inudstrial and Applied Mathematics) and Adjunct Proefessor/member of Academic Committee of Big Data Institute in Fudan University. He is also the executive Director of Smale Institute of Mathematics and Computation.

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Yuan Yao received the B.S.E and M.S.E in control engineering both from Harbin Institute of Technology, China, in 1996 and 1998, respectively, M.Phil in mathematics from City University of Hong Kong in 2002, and Ph.D. in mathematics from the University of California, Berkeley, in 2006. Since then he has been with Stanford University and in 2009, he joined as a Fellow of 100-Talent Program the Department of Probability and Statistics in School of Mathematical Sciences, Peking University, Beijing, China. He is currently an Associate Professor of Mathematics, Chemical & Biological Engineering, and by courtesy, Computer Science & Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China. His current research interests include machine learning and high dimensional data analysis, in particular topological and geometric methods, with applications in computational biology, computer vision, and information retrieval, etc.

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