Margaret L. Brandeau
Margaret L. Brandeau is Coleman F. Fung Professor of Engineering and Professor of Medicine (by Courtesy) at Stanford University. Her research focuses on the development of applied mathematical and economic models to support health policy decisions. Her recent work has focused on HIV prevention and treatment programs, programs to control the spread of hepatitis B virus, and preparedness plans for bioterror response. She has published extensively in the areas of health policy modeling, operations management, management science applications, and bioterrorism preparedness planning. She is a Fellow of INFORMS, and has received the President’s Award from INFORMS (recognizing important contributions to the welfare of society), the Pierskalla Prize from INFORMS (for research excellence in health care management science), the Award for Excellence in Application of Pharmacoeconomics and Health Outcomes Research from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), and a Presidential Young Investigator Award from the National Science Foundation, among other awards. Professor Brandeau earned a BS in Mathematics and an MS in Operations Research from MIT, and a PhD in Engineering-Economic Systems from Stanford University.
Creating Impact with Operations Research in Health
OR-based analyses have the potential to improve decision making for many important problems in healthcare. However, scholars – particularly junior scholars – often avoid working on practical applications in health because promotion and tenure processes tend to value theoretical studies more highly than applied studies. This talk discusses the speaker’s experiences in using OR to inform and influence decisions in health and provides a blueprint for researchers who wish to find success by taking a similar path. We also suggest how journals, funding agencies, and senior academics can encourage such work by taking a broader and more informed view of the potential role and contributions of OR to solving health care problems.
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Computational Thinking, Inferential Thinking and Big Data
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in “Big Data” is apparent from their sharply divergent nature at an elementary level—in computational science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, algorithm weakening as a tool for trading off the speed and accuracy of inference and the theoretical study of lower bounds that embody computational and statistical constraints.
Stevens Institute of Technology
Bill Rouse is the Alexander Crombie Humphreys Chair within the School of Systems & Enterprises at Stevens Institute of Technology and Director of the Center for Complex Systems and Enterprises. He is also Professor Emeritus, and former Chair, of the School of Industrial and Systems Engineering at the Georgia Institute of Technology. His research focuses on understanding and managing complex public-private systems such as healthcare delivery, urban systems and national security, with emphasis on mathematical and computational modeling of these systems for the purpose of policy design and analysis. Rouse has written hundreds of articles and book chapters, and has authored many books, including most recently Modeling and Visualization of Complex Systems and Enterpries (Wiley, 2015), Understanding and Managing the Complexty of Healthcare (MIT Press, 2014), Economic Systems Analysis and Assessment (Wiley, 2011), People and Organizations: Explorations of Human-Centered Design (Wiley, 2007), Essential Challenges of Strategic Management (Wiley, 2001) and the award-winning Don’t Jump to Solutions (Jossey-Bass, 1998). He has edited or co-edited numerous books including Engineering the System of Healthcare Delivery (IOS Press, 2010), The Economics of Human Systems Integration (Wiley, 2010), Enterprise Transformation: Understanding and Enabling Fundamental Change (Wiley, 2006), Organizational Simulation: From Modeling & Simulation to Games & Entertainment (Wiley, 2005), the best-selling Handbook of Systems Engineering and Management (Wiley, 1999, 2009), and the eight-volume series Human/Technology Interaction in Complex Systems (Elsevier). Among many advisory roles, he has served as Chair of the Committee on Human Factors of the National Research Council, a member of the U.S. Air Force Scientific Advisory Board, and a member of the DoD Senior Advisory Group on Modeling and Simulation. He has been designated a lifetime National Associate of the National Research Council and National Academies. Rouse is a member of the National Academy of Engineering and has been elected a fellow of four professional societies — Institute of Electrical and Electronics Engineers (IEEE), the International Council on Systems Engineering (INCOSE), the Institute for Operations Research and Management Science (INFORMS), and the Human Factors and Ergonomics Society (HFES). Rouse received his B.S. from the University of Rhode Island, and his S.M. and Ph.D. from the Massachusetts Institute of Technology.
Understanding and Managing the Complexity of Healthcare
The overall nature of the healthcare system is considered. It is argued that this enterprise is best modeled as a complex adaptive system. The stakeholders in this enterprise and their interests and objectives are outlined. This provides the basis for discussion of five case studies. The first presents an analysis of the complexity of healthcare using information theoretic metrics. The second case study uses production-learning theory to determine how efficient the system would have to be to keep healthcare costs from rising faster than GDP. The third derives providers’ optimal response to Medicare price controls. The fourth case study develops a multi-level model of the healthcare enterprise and uses this model to project the economic benefits of employer-based prevention and wellness programs. The fifth cases study considers how providers in New York City have responded to the Affordable Care Act.
Alfred Z. Spector recently retired as Vice President of Research and Special Initiatives at Google. There, he was responsible for research at Google and also Google’s open source, university relations, internationalization, and various education initiatives. He was also the executive engineering lead for Google.Org. Previously, Dr. Spector was vice president of strategy and technology at IBM’s Software Business, and prior to that, he was vice president of services and software research across IBM. He was also founder and CEO of Transarc Corporation, a pioneer in distributed computing, and was a professor of computer science at Carnegie Mellon University. Beginning in 2004, Dr. Spector has been the lead proponent of “CS+X” – a short-hand for the need to infuse computer science into the study and practice of every discipline, X. Dr. Spector received his Ph.D. in computer science from Stanford and a bachelor’s degree in applied mathematics from Harvard. He is a member of the National Academy of Engineering and a Fellow of the IEEE, the ACM, and the American Academy of Arts and Sciences. Dr. Spector is also the recipient of the 2001 IEEE Computer Society’s Tsutomu Kanai Award for work in scalable architectures and distributed systems. Dr. Spector was elected a Fellow of American Academy of Arts and Sciences in 2009.
Empiricism and Optimization in the World of Big Data
In its first decades, computer science combined mathematical analysis (e.g., the study of computability and algorithms) and engineering (e.g., abstraction, encapsulation, and re-use). However, empiricism became an equally important 3rd leg in the 1980’s. This happened due to the (1) growth in computer usage and data availability, (2) the exponential growth in communications, computation, and storage capabilities, (3) progress in machine learning and optimization, and (4) significant economic and scientific rewards. This presentation will discuss the growth and benefits of empirical computer science to-date but will focus on key challenges moving forward, particularly considering the advantages, and consequences, of various forms of optimization. In particular, I will discuss open questions regarding big data and Artificial Intelligence, issues in big data and science (including a discussion of the role of the hypothesis), and some fascinating, if not problematic, societal implications.
Mihai Anitescu is a Senior Computational Mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory and a Professor in the Department of Statistics at the University of Chicago. His research interests are in the areas of numerical optimization, computational science, numerical analysis and uncertainty quantification. He has used techniques from these areas to key applications in power grid and related infrastructure, nuclear engineering, materials science, geosciences, chemistry, chemical engineering, signal processing. He has co-authored more than 100 peer-reviewed papers in scholarly journals, book chapters, and conference proceedings, and he is on the editorial board of Mathematical Programming A and B, SIAM Journal on Optimization, Optimization Methods and Software, SIAM Journal on Scientific Computing, and SIAM/ASA Journal on Uncertainty Quantification.
Getting more out of a modern power grid: the role of mathematical modeling and optimization
The electrical power grid (the electricity transmission and distribution system) is one of the most complex engineering achievements of the 20th century. It is also at the center of massive changes in the way we create and consume energy. Such changes are brought about by many drivers, including an increasing use of renewable energy and natural gas. Moreover, the power grid exhibits persistent conceptual difficulties that, while generally successfully contained by engineering practice, have never been fully resolved. In this talk we discuss some of these difficulties and the important role that mathematical modeling and optimization can play in solving them. We argue that in some cases a change of the problem framework may be desirable and that this may be made while keeping the solution computationally achievable. We outline a number of existing and emerging fundamental research challenges and discuss some recent promising avenues in the area. A distinguishing feature of power grid applications is that optimization is ubiquitous and that it must accommodate simultaneously multiple complexity drivers. These include not only discrete variables, nonconvexity, or stochasticity but also ordinary and — with the increased usage of natural gas — partial differential equations. We discuss the productivity and performance implications of this fact for the modeling and computational environments.
Alper Atamturk is a Professor of Industrial Engineering and Operations Research at the University of California – Berkeley. He received his Ph.D. from the Georgia Institute of Technology in 1998 with a major in Operations Research and minor in Computer Science. His current research interests are in discrete optimization and optimization under uncertainty with applications to energy, finance and operations interface, cancer therapy, and defense. He serves on the editorial boards of Discrete Optimization, Journal of Risk, Mathematical Programming C, Networks, and Operations Research. He served on the organizing committees of INFORMS, IPCO, MIP and few others. He served as vice chair-integer programming of the INFORMS Optimization Society during 2008-2009. Dr. Atamturk is a US Department of Defense National Security Fellow.
Conic Integer Optimization
In the last 25 years we have experienced significant advances in conic optimization. Polynomial interior point algorithms that have earlier been developed for linear optimization have been extended to second-order cone optimization and semi-definite optimization. The availability of efficient algorithms for convex conic optimization spurred many novel optimization and control applications in diverse areas ranging from medical imaging to statistical learning, from finance to truss design. However, the advances in convex conic optimization and linear integer optimization have until recently not translated into major improvements in conic integer optimization, i.e., conic optimization problems with integer variables. In this talk we will review the recent progress in conic integer optimization. We will discuss cuts, lifting methods, and conic reformulations for improving computations for general as well as special structured problems and connections to submodular optimization for the 0-1 case. We will present applications of conic integer optimization in probabilistic optimization, portfolio optimization, location/inventory optimization with risk pooling.
JOHN GLASER, executive vice president at Cerner, is responsible for driving technology and product strategies, interoperability and government policy development. Glaser has devoted his career to furthering health care through innovation. He is committed to helping clients maximize their investment in HIT. Prior to joining Cerner, Glaser was CEO of the Health Services business unit of Siemens Healthcare, where he was responsible for heading Siemens’ global healthcare IT business. Cerner acquired Siemens Health Services in February 2015. Formerly, Glaser was vice president and chief information officer at Partners HealthCare, Inc. Previously, he was vice president of information systems at Brigham and Women’s Hospital. Glaser is a well-respected industry thought leader. He was the founding chair of the College of Healthcare Information Management Executives (CHIME), he’s the past president of the Healthcare Information & Management Systems Society (HIMSS), and has served on numerous boards including the eHealth Initiative, the National Alliance for Health Information Technology, and the American Medical Informatics Association (AMIA). Additionally, Glaser is a fellow of HIMSS, CHIME, and the American College of Medical Informatics. He is also a former senior advisor to the Office of the National Coordinator for Health Information Technology (ONC). Glaser has published more than 150 articles and three books on the strategic application of information technology in health care, including the most widely used textbook on the topic which is Healthcare Information Systems: A Practical Approach for Health Care Management. Glaser is on the faculty of the Wharton School at the University of Pennsylvania, the Medical University of South Carolina and the Harvard School of Public Health. He received his PhD in health care information systems from the University of Minnesota.
The Advent of the Intelligent Electronic Health Record
We’ve made great progress in embedding the Electronic Health Record (EHR) in our healthcare processes, with use reaching unprecedented rates. Now, we’re poised to take it the next level with the intelligent EHR. The intelligent EHR will look very similar to the traditional system – one can still look up patient results and history and write prescriptions but the application will move past transactional functions.
The intelligent EHR will be characterized by sophisticated and flexible decision support, rules engines, process monitoring engines, intelligent displays of important patient data, access to knowledge resources, the ability to collect data from multiple care settings through a health information exchange, and tools that enable provider collaboration.
The advent of the intelligent EHR will be necessary if healthcare is to effectively address challenges such as those generated by payment reform and managing the care of chronically ill populations.
Sanjay Mehrotra is the director of Center for Engineering at Health at Northwestern University. He received his PhD in Operations Research from Columbia University. Mehrotra is widely known for his methodology research in optimization that has spanned from linear, convex, mixed integer, stochastic, multi-objective, distributionally robust, and risk adjusted optimization. His healthcare research includes topics in predictive modeling, budgeting, hospital operations, and policy modeling using modern operations research tools. He is the immediate past chair of the INFORMS Optimization Society. He has also been a INFORMS vice-president representing Chapters/Fora. He is the current Healthcare Department editor of Institute for Industrial Engineering journal IIE-Transactions, and also held the role of Optimization Department editor for the same journal.
Optimizing Healthcare and Using Healthcare to Motivate the Development of New Optimization Models, Methods, and Tools
Healthcare globally is a significantly under-optimized system. Policies are determined based on legislated priorities, and decisions are often made without scientific rigor. There is a growing interest in optimal resource utilization, while achieving greater equity and access in healthcare. Solutions require a trans-disciplinary collaborative approach, where members of INFORMS community are making significant contributions by developing increasingly realistic data-driven modeling approaches to promote evidence based decision making and informing policy changes. The need to bring greater realism to the decision models also motivates new methodological developments that can then benefit application in areas other than health.
The central consideration in developing innovative strategies to improve the health system is to save and improve patients’ quality of life. This must be balanced against risks and cost to individuals and society. It leads to problems with multiple objectives, and input from multiple experts weighing in on these objectives. The parameters of the functions modeling the objectives and constraints are uncertain as model recommendations have implications on an unknown future.
In this presentation, after briefly reviewing the global healthcare landscape, we will focus on a few specific examples from our research illustrating how close interactions with transplant surgeons and nephrologists led to the development of alternative strategic models for improving geographical disparity in waiting time for kidney transplant; consideration of a budgeting problem arising in diabetes prevention programs provided insights towards developing new concepts of weight-robustness in multi-objective decision making; and the need for solving realistic staffing and scheduling problems under demand uncertainty led to the development a highly efficient computational tool for solving a general class of stochastic mixed integer programs.
Kavita Ramanan is a professor at the Division of Applied Mathematics at Brown University. Previously, she has also been a Professor at the Mathematical Sciences Department at Carnegie Mellon University and a Member of Technical Staff at the Mathematical Sciences Research Center at Bell Laboratories. Her research lies in the area of probability theory, stochastic processes and their applications, including the study of stochastic networks that arise in telecommunications and operations research. She has served on numerous editorial boards including the Annals of Probability, Annals of Applied Probability, Mathematics of Operations Research and Queueing Systems. She is a recipient of the Erlang Prize of the INFORMS Applied Probability Society (2006), was elected fellow of the IMS (Institute for Mathematics and Statistics) in 2013 and was an IMS Medallion Lecturer in 2015.
Stochastic Networks: Scaling Limits, Performance Analysis and Optimization
Stochastic networks are ubiquitous and arise in diverse fields including telecommunications, service systems for call centers and health care, computer networks and biological systems. These networks are typically too complex to admit an exact analysis. However, it is often possible to obtain tractable approximations of both transient and equilibrium behavior that can provide key insight into network performance. These include both deterministic or fluid approximations that describe mean behavior and diffusion approximations that capture stochastic variability. The accuracy of these approximations in a suitable network parameter regime can be rigorously justified through “limit theorems”. While the mathematical methods required to justify these approximations are fairly well developed for some classes of single-server networks that use so-called head-of-the-line scheduling policies, new approaches are required to analyze many other classes of networks that are of relevance for applications such as large-scale load-balancing networks used, for example, in distributed memory machines and web servers. We provide a survey of these mathematical methods and the associated scaling limits, with an emphasis on recent developments, and illustrate through a number of concrete examples how these approximations can be used to develop new algorithms and optimize network design.
University of Wisconsin- Madison
Stephen J. Wright Professor of Computer Sciences at the University of Wisconsin-Madison. His research is on computational optimization and its applications to many areas of science and engineering. Prior to joining UW-Madison in 2001, Wright was a Senior Computer Scientist at Argonne National Laboratory (1990-2001), and a Professor of Computer Science at the University of Chicago (2000-2001). He has served as chair of the Mathematical Optimization Society and as a Trustee of the Society for Industrial and Applied Mathematics (SIAM). He is a Fellow of SIAM. In 2014, he won the W.R.G. Baker award from IEEE. Wright is the author or coauthor of widely used text / reference books in optimization including “Primal Dual Interior-Point Methods” (SIAM, 1997) and “Numerical Optimization” (2nd Edition, Springer, 2006, with J. Nocedal). He has published widely on optimization theory, algorithms, software, and applications. Wright is editor-in-chief of the SIAM Journal on Optimization and has served as editor-in-chief or associate editor of Mathematical Programming (Series A), Mathematical Programming (Series B), SIAM Review, and Applied Mathematics and Computation.
Optimization Techniques in Data Analysis
Optimization perspectives have provided valuable insights into machine learning and data analysis problems, and optimization formulations have led to practical algorithms with good theoretical properties. In turn, the rich collection of problems arising in learning and data analysis is driving new fundamental research in optimization, reviving interest in well established techniques and stimulating developed of new methods. We discuss research on several areas of learning and data analysis, including regression / classification, signal and image reconstruction, and manifold learning, in each case describing problem areas in which optimization algorithms have been developed and applied successfully.
Mark C. Reed, Ed.D.
Saint Joseph’s University
Mark C. Reed, Ed.D., began his tenure as the 28th president of Saint Joseph’s University, and the first lay president in the University’s 164-year history, on July 1, 2015. Formerly, Dr. Reed served as senior vice president and chief of staff at Fairfield University, in Fairfield, Conn.
A 1992 graduate of St. Joseph’s Preparatory School in Philadelphia, Reed received a B.S. in mathematics from Fairfield University in 1996, a master of education in secondary educational administration from Boston College in 1999, an MBA from Fairfield in 2002, and a doctorate of education in higher education management from the University of Pennsylvania in 2008.
During his 15-year career at Fairfield and prior to his most recent position as senior vice president and chief of staff, Reed served as the institution’s interim vice president for university advancement; vice president for administration and student affairs; vice president for student affairs; associate vice president and dean of students; and dean of students. He is a past president of the Jesuit Student Affairs Association, and also taught mathematics as an adjunct faculty member at Fairfield. He recently received Fairfield’s Distinguished Faculty/Administrator Award.
Preceding his career in higher education, Dr. Reed worked as a teacher and administrator in Catholic secondary education.
Ali A. Houshmand, Ph.D.
Glassboro, New Jersey
Dr. Ali A. Houshmand became Rowan University’s seventh president in June 2012 after serving approximately six years as provost/senior vice president, CEO and interim president. His vision and leadership have set the University on a path of unprecedented transformation, most recently earning Rowan regard as New Jersey’s second comprehensive research university.
During his tenure, the University has earned the reputation of being entrepreneurial in its planning, able to quickly implement change to respond to market and State needs. Rowan focuses on cost, quality, access and economic impact in all of its major decisions. Driven and supported by creative partnerships with business and industry, Rowan is meeting the demand for an educated workforce and contributing significantly to southern New Jersey’s expanding economy. Rowan’s proximity to three major U.S. cities in the midAtlantic region enhances the possibilities of these relationships.
In just the past three years under Dr. Houshmand’s leadership, Rowan opened Cooper Medical School of Rowan University and integrated the School of Osteopathic Medicine from the former University of Medicine and Dentistry of New Jersey. Rowan is now just the second university in the nation to offer M.D. and D.O. degree programs.
Dr. Houshmand remains steadfast in believing the University must stay loyal to its core mission: top-quality, affordable, undergraduate education. To that end, he committed to not raising undergraduate tuition beyond the cost of inflation during his tenure.
Dr. Houshmand is an active partner in economic development. A hallmark of this commitment is the Rowan Boulevard project—a $300-million collaboration of private developers, the Borough of Glassboro and the University to create an educational and economic corridor that is reinventing the historic downtown and changing the definition of “town-gown” relations.
He has broadened this philosophy to make the University more entrepreneurial, with a goal of increasing research and academic offerings at the intersection of science, technology, business, engineering and medicine. Plans are under way to expand the University’s technology park and introduce several Ph.D. programs.
Determined to address the severe shortage of access to high-quality undergraduate education in New Jersey’s southern half, Dr. Houshmand has committed to increase Rowan’s enrollment from 15,000 to 25,000 students by 2023, increase annual research funding from $25 million to $100 million and increase Rowan’s operating budget from $400 million to $1 billion, making Rowan one of the region’s most important economic engines.
Dr. Houshmand earned his bachelor’s and master’s degrees in mathematics and mathematical statistics from the University of Essex, United Kingdom. He earned a second master’s degree and a doctoral degree in industrial and operations engineering from the University of Michigan, Ann Arbor. He then worked as a staff analyst for United Airlines, developing large-scale optimization and forecasting models. Leaving industry for academia, he joined the University of Cincinnati and, later, Drexel University, where he taught and held several academic administrative positions before coming to Rowan.
Stephen K. Klasko, M.D., MBA
President and CEO
Thomas Jefferson University and Jefferson Health System
Stephen Klasko is President of Thomas Jefferson University and CEO of Jefferson Health System after serving as CEO of USF Health and dean of the college of medicine at University of South Florida.. A board certified OB-GYN, he is bridging the art and science of medicine and healthcare information technology through an entrepreneurial-academic model. After receiving his M.D, and completing his obstetrics and gynecology residency, he completed his M.B.A. at the Wharton School of Business, University of Pennsylvania.
At Jefferson he leads an academic medical center that consistently ranks among the top academic health systems in the country with 19,000 employees, five hospitals and over 2,000,000 patient visits. In 2015, he led the merger of Thomas Jefferson University and Health System and Abington Health Network in one of the nation’s first shared governance, “hub and hub” academic medical center mergers.
Over the last several years, he has led the development of the first medical school choosing students based on emotional intelligence, led the team that built the country’s largest assessment of technical and teamwork competence center and created an innovative primary-care-driven, patient-centric, Medicare-based accountable care model within the country’s largest retirement community and created Jeff Connect: Medicine Without Walls, a unique telehealth and unscheduled care innovation entity.
He has been on the boards of several national non-profit hospital systems and is currently on the corporate board of Teleflex (TFX: NYSE) a global medical device company. He also has recently been named as a trustee of Lehigh University, one of the country’s leading academic institutions. He has written extensively on the need to change the “DNA of healthcare” by transforming the selection and education of health professionals. To that end, he has received over two million dollars in grants researching the biases affecting physicians’ willingness to accept change. He has written over 200 peer reviewed articles and books including “The Phantom Stethoscope” and “Mamas Don’t Let Your Babies Grow Up to Be OB-GYNs”
Dr Klasko is married to Colleen Wyse, a former Conde Nast publishing executive and now a vice president at Visit Philly and has three amazing children, Lynne a public health professional at Moffitt Cancer Center, David, an actor in New York City and Jill, a marketing director in Tampa, Florida.
John D. Simon
John D. Simon l is the 14th president of Lehigh University. He became president in July 2015. President Simon previously was executive vice president and provost of the University of Virginia, where he oversaw the academic activities of more than 20,000 undergraduate and graduate students as well as 2,200 faculty. Under his leadership, the University of Virginia launched several important programs including UVA’s Data Science Institute, created a new physical presence in Asia and established an Endowment for the Arts, among many other notable accomplishments. Before arriving at the University of Virginia, John served as vice provost of academic affairs at Duke University and chairman of Duke’s chemistry department. He received his B.A. in Chemistry from Williams College and Ph.D. from Harvard University. He is a fellow of the American Physical Society and the American Association for the Advancement of Science. He is the author of over 250 scientific papers and 3 textbooks. His most recent research has focused on the chemical properties of pigments preserved in the fossil record.