Conference Program
Please check back often as we update this page frequently

Program Overview

Wednesday, October 23, 2019 (136 Irving Street, Cambridge, MA; vehicle entrance at 200 Beacon Street, Somerville)

Welcome and Introductory Remarks..........................................................8:50 AM - 9:15 AM
Regina Barzilay Plenary......................................................................................9:15 AM - 10:00 AMCoffee Break..............................................................................................................10:00 AM- 10:30 AM
Panel on Evidence-Based Policy.................................................................10:30 AM - 11:30 AMPanel on Algorithmic Business.....................................................................11:30 AM - 12:30 PMLunch................................................................................................................................12:30 PM - 1:30 PM
Panel on Data Science and the Urban Environment.......................1:40 PM - 2:40 PMCoffee Break..................................................................................................................2:40 PM - 3:10 PM
Michael Jordan Plenary..........................................................................................3:10 PM - 3:55 PMPanel on Data-Driven Scientific Discovery..............................................3:55 PM - 4:55 PMClosing Remarks.........................................................................................................4:55 PM - 5:10 PM
Poster Session .............................................................................................................5:10 PM - 6:30 PM

Thursday, October 24, 2019 (Smith Campus Center, 10th Floor, 1350 Massachusetts Ave, Cambridge, MA)

Tutorial on Causal Inference....................................................................................9:15 - 12:00 PMLunch.......................................................................................................................................12:00 - 12:45 PM
Tutorial on Deep Learning........................................................................................12:45 - 3:30pmPublic Statistics Lecture.............................................................................................4:00 PM

Friday, October 25, 2019
(Science Center Hall C, 1 Oxford St, Cambridge, MA)

A detailed agenda for Friday can be found here.

Plenary Speakers

Regina Barzilay

Regina Barzilay is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing. Currently, Prof. Barzilay is focused on bringing the power of machine learning to oncology. In collaboration with physicians and her students, she is devising deep learning models that utilize imaging, free text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Prof. Barzilay is poised to play a leading role in creating new models that advance the capacity of computers to harness the power of human language data.

Regina Barzilay is a recipient of various awards including the MacArthur Fellowship, NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards in top NLP conferences. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship.

Prof. Barzilay received her MS and BS from Ben-Gurion University of the Negev. Regina Barzilay received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.

Michael Jordan

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. Prof. Jordan is a member of the National Academy of Sciences and a member of the National Academy of Engineering. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009.

Panel Sessions

Algorithmic Business

Yael Grushka Cockayne, Moderator

Visiting Associate Professor of Business Administration, Harvard Business SchoolAssociate Professor of Business Administration, Darden School of BusinessAssociate Professor Yael Grushka-Cockayne's research and teaching activities focus on data science, analytics, forecasting, decision analysis, project management, and behavioral decision-making.  Yael is an award-winning teacher and in 2014 was named one of "21 Thought-Leader Professors" in Data Science. At HBS Yael teaches the required Technology and Operations Management course and an elective course on Business Analytics. She has been teaching in the Harvard Business Analytics Program, powered by 2U, since 2018.  At the Darden School, Yael taught the core course in Decision Analysis and elective courses on Project Management and Data Science in Business. Yael's recent "Fundamentals of Project Planning and Management" Coursera MOOC had over 200,000 enrolled, across 200 countries worldwide.

Before starting her academic career, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management, she has served as a consultant to international firms in the aerospace and pharma industries. Yael is an Associate Editor at Management Science, Operation Research, and Decision Analysis.Education: B.Sc., Ben-Gurion University; MSc, London School of Economics; Ph.D., MRes, London Business School

Eva Ascarza

Eva Ascarza is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. As a marketing modeler, she uses tools from statistics, economics, and machine learning to answer relevant marketing questions. Her main research areas are customer analytics and customer management, with special attention to the problem of customer retention. She uses field experimentation (e.g., A/B testing) as well as econometric modeling and machine learning tools not only to understand and predict patterns of behavior, but also to optimize the impact of firms’ interventions. Her research has appeared in leading marketing journals including Marketing Science and Journal of Marketing Research. She received the 2014 Frank Bass award, awarded to the best marketing paper derived from a Ph.D. thesis published in an INFORMS-sponsored journal. Her research has been recognized as a Paul E. Green Award finalist in 2016 and 2017, and winner in 2018, awarded to the best article in the Journal of Marketing Research that demonstrates the greatest potential to contribute significantly to the practice of marketing research. In 2019 she received the Erin Anderson Award for Emerging Female Marketing Scholar and Mentor from the American Marketing Association. She was named a Marketing Science Institute Young Scholar in 2017 and serves on the editorial review board of several top marketing journals including Marketing Science, Journal of Marketing Research, Journal of Marketing, and Quantitative Marketing and Economics.

Jacomo Corbo

Jacomo is founder & chief scientist at QuantumBlack; he focuses on the problem of applying AI and other technology to improving the quality and scalability of QuantumBlack’s client delivery. He is passionate about helping organizations adopt AI at scale to radically improve their performance.He is also a Senior Fellow at the Wharton School at the University of Pennsylvania and was the Canada Research Chair in Information and Performance Management at the University of Ottawa between 2011 and 2015. Between 2006 and 2008, he was the Chief Race Strategist for the Renault F1 Team. He also holds a PhD in Computer Science from Harvard University.

Jeff Polzer

Jeff Polzer is the UPS Foundation Professor of Human Resource Management in the Organizational Behavior Unit at Harvard Business School. He studies teams across many settings to understand how interpersonal dynamics among team members can disrupt or enhance performance. He has taught a variety of courses in the MBA, Executive, and Doctoral Programs at HBS. He has also conducted executive training sessions for a variety of organizations including IBM, Novartis, Seagate, Jabil, Merrill Lynch, Royal Bank of Scotland, Citizens Bank, Bharti Airtel, Fresenius, Mercy Corps, and Ernst & Young.

Professor Polzer teaches a new elective MBA course called People Analytics, along with the doctoral course Micro Topics in Organizational Behavior. He has taught MBA courses such as Field Immersion Experiences for Leadership Development (FIELD), Leadership and Organizational Behavior, and Leading Teams.  He also taught the doctoral course Human Behavior and served as the faculty chair of Harvard’s Organizational Behavior PhD program. At HBS, he has received the Robert F. Greenhill Award for outstanding service as well as the Apgar Award for Innovation in Teaching. Before coming to Harvard, he taught courses in Organizational Behavior and Negotiations at the University of Texas at Austin and Northwestern University, where he won the Kellogg Graduate School of Management's Doctoral Teaching Award.

Professor Polzer has published his research in the Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly, Journal of Conflict Resolution, Journal of Personality and Social Psychology, Organizational Behavior and Human Decision Processes, Psychological Science, and Small Group Research. He also serves on the editorial board of ASQ.

A native of Wisconsin, Professor Polzer earned a B.S. in Finance and Economics from the University of Wisconsin-Stevens Point and an MBA from Texas Christian University in Fort Worth, Texas, where he worked for Burlington Northern Railroad as a marketing analyst. He received his Ph.D. in Organizational Behavior from the Kellogg Graduate School of Management at Northwestern University. He then taught and conducted research as an Assistant Professor at the University of Texas at Austin, and was a Visiting Scholar in the Program on Negotiation at Harvard University.

Pearl Pu

Pearl Pu currently leads  the HCI Group in the School of Computer and Communication Sciences at the Swiss Federal Institute of Technology in Lausanne (EPFL). Her early interests focused on novel interaction models for product search and recommendation in online environments. Lately she became more interested in applying HCI methods to healthcare technology. She is a member of the steering committee of the ACM International Conference on Recommender Systems, a distinguished speaker for ACM, and served on the editorial boards of several highly recognized scientific journals. She is a recipient of 14 Research Awards from the Swiss National Science Foundation, 3 Technology Innovation Awards from the Swiss Government, and a Research Career Award from US National Science Foundation. She also co-founded three startup companies, for which she received the 2008 Rising Star Award from Sina.com and the 2014 Worldwide Innovation Challenge Award from the French president.

Dan Wulin

Dan Wulin leads the Data Science & Machine Learning team at Wayfair, where the team works on a wide range of problems spanning business topics (marketing, recommendations, product merchandising and many more) and multiple data science disciplines. Dan is passionate about using technology to create business impact and the challenges that that entails. Prior to Wayfair, Dan received his BA from Columbia University in Mathematics and Physics and earned his Ph.D in theoretical physics from the University of Chicago. He spent time at the Boston Consulting Group before transitioning to a career in data science.

Data-Driven Scientific Discovery

Chris Stubbs

Christopher Stubbs is a Professor of Physics and of Astronomy, and is currently the Dean of Sciences at Harvard. His interests lie at the intersection of cosmology, particle physics, and gravitation. Stubbs received an International Baccalaureate diploma from the Tehran International School in 1975, a BSc in physics from the University of Virginia in 1981, and a PhD in physics from the University of Washington in 1988.  Stubbs has long been involved in data-intensive projects in astrophysics, including his participation in the discovery of the “dark energy” that is driving the accelerating expansion of the Universe.

Cora Dvorkin

Dr. Cora Dvorkin is an Associate Professor at the Department of Physics at Harvard University.

Prof. Dvorkin's is a theoretical cosmologist. Her research interests span questions related to inflation, dark matter, and neutrinos. To assess these questions, she uses data from the Cosmic Microwave Background and the large-scale structure of the universe.

Prof. Dvorkin is currently the co-leader of the Inflation analysis group for the proposed next-generation CMB-S4 experiment.She has been named the "2018 Scientist of the year" by the Harvard Foundation for "Salient Contributions to Physics, Cosmology and STEM Education". She has also been awarded a Radcliffe Institute Fellowship for 2018-2019 and a Shutzer Professorship at the Radcliffe Institute for the period 2015-2019.

Professor Dvorkin, born and raised in Buenos Aires, Argentina, received her Diploma in Physics from the University of Buenos Aires with honors. She earned her Ph.D. in the Department of Physics at the University of Chicago in 2011, where she won the "Sydney Bloomenthal Fellowship for "outstanding performance in research". She has conducted postdoctoral research at the School of Natural Sciences at the Institute for Advanced Study in Princeton (from 2011 to 2014) and at the Institute for Theory and Computation at the Center for Astrophysics at Harvard University (from 2014 to 2015), where she was both a Hubble Fellow and an ITC fellow.

Galit Lahav

Galit Lahav received her PhD in 2001 from the Technion, Israel Institute of Technology. In 2003, she completed her postdoctoral fellowship at the Weizmann Institute of Science in Israel. She then spent a year at Harvard’s Bauer Center for Genomics Research, and in 2004 joined the Department of Systems Biology at Harvard Medical School. In 2018 Lahav became the Chair of the Department of Systems Biology.

Lahav’s goal is to determine why human cancer cells often show different responses to the same treatment, and to identify new therapies that will increase the efficacy of anti-cancer drugs. Her research program works across traditional disciplinary boundaries. Her lab has pioneered computational and quantitative experimental approaches to studying the fate and behavior of human cells in disease and health at the single-cell level. Her work has yielded critical insights into the function and behavior of tumor-suppressing mechanisms and their role in cellular destiny.

Lahav has been recognized through several awards and honors including the Smith Family Award, Vilcek Prize for Creative Promise, and Excellence in Teaching and Mentoring awards. Lahav have established and organized leadership and management workshops for postdocs and faculty, as well as developed programs for advancing women in science.

Lahav is now leading a department that uses the power of systems thinking, across macro and micro scales, to unlock new insights into health and disease. Her goal is to establish new initiatives that promote the development of novel single-cell technologies as well as the analysis of vast amounts of data, to create new mathematical models and formulas that will let us move from observing biology to predicting and engineering it.

Venkatesh Murthy

Venkatesh Murthy was born in a small industrial town in south India called Neyveli. After getting a B.Tech. in Mechanical Engineering from the Indian Institute of Technology, Madras, he came to the United States with a vague idea of combining engineering and biology. A degree (M.S.E.) in Bioengineering from the University of Washington, Seattle led to his interest in neuroscience. A Ph.D. in Physiology & Biophysics at the University of Washington followed, and postdoctoral work at the Salk Institute for Biological Studies, La Jolla, solidified his path in neuroscience research. Venki came to Harvard University as an Assistant Professor in 1999 and is now the Raymond Leo Erikson Life Sciences Professor of Molecular & Cellular Biology (and a member of the Center for Brain Science). He is also the Chair of MCB and the co-Chair of the Graduate Program in Biophysics. He teaches an introductory neuroscience course and will teach a Gen Ed course titled “Artificial and Natural Intelligence” in 2020. His research aims to shed light on how collections of neurons in the brain process information and give rise to behaviors, with a current emphasis on the sense of smell. Beyond Harvard, Venki is also keenly interested in teaching and research initiatives that link Harvard and India. Research website: http://vnmurthylab.org

Matthew Schwartz

Matthew Schwartz's research is focused on expanding the boundaries of our current understanding of particle physics. This includes exploring the foundations and structure of quantum field theories, improving our ability to perform precision calculations in the Standard Model, and developing new methods for collider physics. Schwartz has contributed to diverse realms of particle physics, from quantum gravity to quantum chromodynamics. His textbook Quantum Field Theory and the Standard Model (Cambridge Univ. Press, 2013) is a standard text adopted in field theory courses worldwide.

A central element of Schwartz's current research is how perturbation theory can be used to explain non-perturbative physics. A key observation is that non-perturbative effects can be calculable if the expansion is reorganized in a clever way. An example of this is the effective field theory approach, which Schwartz has advanced and applied in many contexts. Another example is the instanton calculus, which Schwartz has developed for tunneling calculations in quantum field theory, producing new insights into the ultimate fate of our universe. A third example comprises factorization-violating effects associated with strong coupling in gauge theories. To make progress in this direction, Schwartz has brought new tools to bear on old problems, such as exploiting hidden symmetries associated with broken scale or Lorentz invariance, or ideas from effective field theory.

Another theme in Schwartz's research is developing new methods for precision calculations and new physics searches at colliders. Schwartz has produced the world's most precise calculation of a number of observables, including event and jet shapes. He has produced the first viable methods for finding highly energetic top quarks, measuring the electric charge of quark jets, discriminating quarks from gluons, measuring color flow, and removing contamination from secondary hadronic collsions. Recently, Schwartz has been bringing machine learning techniques to bear on collider physics problems. For example, he has demonstrated the efficacy of convolutional networks both for complex discrimination and for regression tasks relevant to the Large Hadron Collider. His work on modern machine learning exploits state-of-the-art developments in computer science to reshape the frontiers of particle physics.

Data Science and the Urban Environment

Susan Crawford, Moderator

Susan Crawford is the John A. Reilly Clinical Professor of Law at Harvard Law School. She is the author of Captive Audience: The Telecom Industry and Monopoly Power in the New Gilded Age, co-author of The Responsive City: Engaging Communities Through Data-Smart Governance, author of FIBER: The Coming Tech Revolution—and Why America Might Miss It, and a contributor to WIRED.com. She served as Special Assistant to the President for Science, Technology, and Innovation Policy (2009) and co-led the FCC transition team between the Bush and Obama administrations. She also served as a member of Mayor Michael Bloomberg’s Advisory Council on Technology and Innovation and Mayor Bill de Blasio’s Broadband Task Force. Ms. Crawford was formerly a (Visiting) Stanton Professor of the First Amendment at Harvard’s Kennedy School, a Visiting Professor at Harvard Law School, and a Professor at the University of Michigan Law School (2008-2010). As an academic, she teaches courses about cities, public leadership, technology, and communications policy. She was a member of the board of directors of ICANN from 2005-2008 and is the founder of OneWebDay, a global Earth Day for the internet that takes place each Sept. 22. One of Politico’s 50 Thinkers, Doers and Visionaries Transforming Politics in 2015; one of Fast Company’s Most Influential Women in Technology (2009); IP3 Awardee (2010); one of Prospect Magazine’s Top Ten Brains of the Digital Future (2011); and one of TIME Magazine’s Tech 40: The Most Influential Minds in Tech (2013). Ms. Crawford received her B.A. and J.D. from Yale University. She served as a clerk for Judge Raymond J. Dearie of the U.S. District Court for the Eastern District of New York, and was a partner at Wilmer, Cutler & Pickering (now WilmerHale) (Washington, D.C.) until the end of 2002, when she left that firm to enter the legal academy.

Joseph Allen

Dr. Joseph G. Allen is an assistant professor at the Harvard T.H. Chan School of Public Health and co-author of Healthy Buildings: How Indoor Spaces Drive Performance and Productivity, with John Macomber at Harvard Business School. He began his career conducting forensic health investigations of sick buildings in several hundred buildings across a diverse range of industries, including healthcare, biotechnology, education, commercial office real estate and manufacturing. At Harvard, Dr. Allen directs the Healthy Buildings program where he created ‘The 9 Foundations of a Healthy Building’. He is also the faculty advisor to the Harvard Healthier Building Materials Academy. He works with Fortune 100 companies on implementing Healthy Building strategies in their global portfolios and presents internationally on the topic of Healthy Buildings. His work has been featured widely in the popular press, including the Wall Street Journal, Harvard Business Review, National Geographic, Time, NPR, Newsweek, The Washington Post, Fortune and The New York Times. Dr. Allen is an Associate Editor of the Journal of Exposure Science and Environmental Epidemiology and an Associate Editor of the journal Indoor Air. He earned his Doctor of Science (DSc) and Master of Public Health (MPH) degrees from the Boston University School of Public Health, and a Bachelor of Science (BS) degree in Biology from Boston College. More information on his research can be found at: www.ForHealth.org

Lauren Bennett

Lauren Bennett leads the Spatial Analysis and Data Science software development team at Esri. In her role, she oversees the R&D of the ArcGIS analytical framework, which includes spatial and spatiotemporal statistics, raster and multidimensional analysis, machine learning and big data analytics. She directs releases of new spatial data science capabilities across a wide range of products and applications including desktop, enterprise and SaaS. Lauren received a BA in Geography from McGill University, an MS in Geographic and Cartographic Science from George Mason University, and her PhD in Information Systems and Technology from Claremont Graduate University.

Anita Berrizbeitia

Anita Berrizbeitia is Professor of Landscape Architecture and Chair of the Department of Landscape Architecture. Her research focuses on design theories of modern and contemporary landscape architecture, the productive aspects of landscapes, and Latin American cities and landscapes. She was awarded the 2005/2006 Prince Charitable Trusts Rome Prize Fellowship in Landscape Architecture. A native of Caracas, Venezuela, she studied architecture at the Universidad Simon Bolivar before receiving a BA from Wellesley College and an MLA from the GSD.

Berrizbeitia has taught design theory and studio, most recently at the University of Pennsylvania School of Design, where she was Associate Chair of the Department of Landscape Architecture. Her studios investigate innovative approaches to the conceptualization of public space, especially on sites where urbanism, globalization, and local cultural conditions intersect. She also leads seminars that focus on significant transformations in landscape discourse over the last three decades. From 1987 to 1993, she practiced with Child Associates, Inc., in Boston, where she collaborated on many award-winning projects.

She is co-author, with Linda Pollak, of Inside/Outside: Between Architecture and Landscape (Rockport, 1999), which won an ASLA Merit Award; author of Roberto Burle Marx in Caracas: Parque del Este, 1956-1961 (University of Pennsylvania Press, 2004), awarded the J.B. Jackson Book Prize in 2007 from the Foundation for Landscape Studies; and editor of Michael Van Valkenburgh Associates: Reconstructing Urban Landscapes (Yale University Press, 2009), which received an ASLA Honor Award. Her essays have been published in Daniel Urban Kiley: The Early Gardens (Princeton Architectural Press), Recovering Landscape (Princeton Architectural Press), Roberto Burle Marx: Landscapes Reflected (Princeton Architectural Press), CASE: Downsview Park Toronto (Prestel), Large Parks (Princeton Architectural Press), Retorno al Paisaje (Evren), and Hargreaves Associates: Landscape Alchemy (ORO Publishers), as well as in magazines such as A+U.

Fábio Duarte

Fábio Duarte is a research scientist at the MIT Senseable City Lab, where he manages projects including Underworlds, Roboat, City Scanner, as well as the data visualization team. Duarte has a background in urban planning and a PhD in communication and technology from the Universidade de São Paulo, Brazil. Duarte has been a visiting professor at the Yokohama University and Twente University, is a professor at PUCPR (Brazil) and has served as a consultant in urban planning and mobility for the World Bank. His most recent book is "Unplugging the city: the urban phenomenon and its sociotechnical controversies" (Routledge, 2018), and his papers have appeared in Urban Studies, Journal of Urban Technology, and Science Robotics.

Data Science and Evidence-Based Policy

Sara Bleich, Moderator

Sara Bleich is a Professor of Public Health Policy at the Harvard Chan School of Public Health in the Department of Health Policy and Management. She is also the Carol K. Pforzheimer Professor at the Radcliffe Institute for Advanced Study and a member of the faculty at the Harvard Kennedy School of Government. Her research provides evidence to support policy alternatives for obesity prevention and control, particularly among populations at higher risk for obesity. A signature theme throughout her work is an interest in asking simple, meaningful questions about the complex problem of obesity which can fill important gaps in the literature. Sara is the past recipient of an award for “most outstanding abstract” at the International Conference on Obesity in Sydney, Australia, an award for “best research manuscript” in the journal Obesity, and an award for excellence in public interest communication from the Frank Conference. Sara was recently appointed as a White House Fellow (2015-2016) where she was a Senior Policy Advisor to the U.S. Department of Agriculture and the First Lady’s Let’s Move initiative. She holds degrees from Columbia (BA, Psychology) and Harvard (PhD, Health Policy).

Bethany Hedt-Gauthier

Dr. Bethany Hedt-Gauthier is an Associate Professor of Global Health and Social Medicine (Harvard Medical School) and Biostatistics (Harvard Chan School). Her primary research interests include quantifying the health needs of and evaluating programs targeting marginalized populations, with a focus on global surgery research. She currently leads research related to provision of cesarean sections and outcomes at rural district hospitals in Rwanda. This work includes developing machine learning algorithms for image-based diagnosis of surgical site infections. Since 2012, Dr. Hedt-Gauthier has led a comprehensive research capacity building program at Partners In Health/Rwanda and has held faculty positions at the University of Rwanda and the University of Global Health Equity (Rwanda).

Tarun Khanna

Tarun Khanna is the Jorge Paulo Lemann Professor at the Harvard Business School. For over two decades, he has studied entrepreneurship as a means to social and economic development in emerging markets. At HBS since 1993, after obtaining degrees from Princeton and Harvard, he has taught courses on strategy, corporate governance and international business to MBA and Ph.D. students and senior executives.  For many years, he has served as the Faculty Chair for HBS activities in India and South Asia.

A summary of his work on emerging markets appeared in his 2010 co-authored book, Winning in Emerging Markets, and an example of his comparative work on entrepreneurship appears in his 2008 first-person analysis of China and India, Billions of Entrepreneurs, both published by Harvard Business Press and translated into many languages. In 2014, his piece, Contextual Intelligence, was a runner-up for the McKinsey Prize for the year’s best article in the Harvard Business Review. His latest book, Trust: Creating the foundations for Entrepreneurship in Developing Countries articulates why entrepreneurship has both higher reward and risk in emerging markets than in mature economies.

He was named the first director of Harvard’s University-wide Lakshmi Mittal and Family South Asia Institute in the fall of 2010.  The institute rapidly grew to engage over 150 faculty from across Harvard in projects embracing the pure sciences, social sciences and the humanities, and spanning the region from Afghanistan to Myanmar. A centerpiece of the Institute’s strategy is a deep local presence, anchored through offices in New Delhi and Lahore.

In this role, he currently teaches a popular university-wide elective course, Contemporary Developing Countries, where students work in multi-disciplinary teams to devise practical solutions to complex social problems.  The course is part of Harvard’s undergraduate general education core curriculum, and is rare in that it also attracts graduate students from across the university, engaging ‘sophomores to surgeons.’ An online version of the course is offered free on the edX platform and attracts tens of thousands of students from dozens of countries annually.

In 2007, he was nominated Young Global Leader (under 40) by the World Economic Forum; in 2009, elected as a Fellow of the Academy of International Business; in 2016, recognized by the Academy of Management as Eminent Scholar for Lifetime Achievement in the field of International Management.

Between 2015 and 2019, he was appointed to several national commissions by the Government of India, first to chair the effort to frame polices for entrepreneurship in India. More recently, he has been part of the commission to help select India’s Institutes of Eminence, an attempt to enhance India’s leading Universities for the future, and a new commission to enhance scientific literacy in the country.

Outside HBS, he serves on numerous for-profit and not-for-profit boards in the US and India, In the past decade, this included AES, a Washington DC headquartered global power company, and India-based Bharat Financial Inclusion Limited (BFIL), one of the world’s largest firms dedicated to financial inclusion for the poor.  Recently, he joined the board of inMobi, India’s first ‘unicorn,’ a global technology provider of enterprise platforms for marketers.  He is a co-founder of several entrepreneurial ventures in the developing world, spanning India, China, Southeast Asia and the Middle East. Recently, he co-founded Axilor, a vibrant incubator in Bangalore. In 2015, he was appointed a Trustee of Boston’s Museum of Fine Arts.

He lives in Newton, MA, with his wife, daughter and son.

Sema Sgaier

Dr. Sema Sgaier is Co-founder and Executive Director of Surgo Foundation, a privately funded action tank whose mission is combining a customer obsessed agenda with a thinking in systems to solve complex global development problems. Her work is focused on asking a simple, yet powerful, question: Why? She works at the intersection of behavior, data, and technology. Previously at the Bill & Melinda Gates Foundation, she led large-scale health programs in India and Africa. She is faculty at the Harvard T.H. Chan School of Public Health and the department of Global Health at the University of Washington. Her research interests include methodologies to understand human behavior, novel data systems and analytic approaches, and management practices to drive innovation within large-scale global health programs. She was selected as a Rising Talent by the Women’s Forum for the Economy and Society. She is on the board of the Bill & Melinda Gates Foundation’s Alumni Network. Sema has a PhD in neuroscience.


Causal Inference Tutorial

In this tutorial, we will provide an introduction to causal inference.  We will discuss what constitutes evidence across various disciplines and describe ideal study design principles.  We will explain the potential outcome framework for causal inference, the central role of randomization for identification and inference in randomized experiments, and then focus on observational studies distinguishing between study design strategies that aim to control for observed and for unobserved variation.  With both strategies, we will emphasize the use of modern matching and weighting methods for transparent, sample-bounded estimation of causal effects.  As time permits, we will also discuss sensitivity analyses to hidden biases and provide some practical considerations.  Attendees should have familiarity with regression approaches.

Sharon-Lise Normand

Sharon-Lise Normand, Ph.D., is S. James Adelstein Professor of Health Care Policy (Biostatistics) in the Department of Health Care Policy at Harvard Medical School and Professor in the Department of Biostatistics at Harvard School of Public Health.  Her research focuses on the development of statistical methods for health services and outcomes research, including the evaluation of medical devices, causal inference, provider profiling, evidence synthesis, item response theory, and latent variables analyses.  Her application areas include cardiovascular disease, severe mental illness, medical device safety and effectiveness, and medical technology diffusion.  Dr. Normand is Director of the Medical Device Epidemiology Network’s Methodology Center, and was Director of the Massachusetts Data Analysis Center (2002-2017).  She was a consultant to and served on the US FDA’s Circulatory System Devices Advisory Panel, and served on the Medicare Evidence Development and Coverage Advisory Committee for the US Centers for Medicare and Medicaid Services. Dr. Normand was the 2010 President of the Eastern North American Region of the International Biometrics Society and inaugural Vice Chair of the Patient Centered Outcomes Research Institute’s Methodology Committee (2010-2012).  She earned her Ph.D. in Biostatistics, M.Sc. and B.Sc. in Statistics, and completed a post-doctoral fellowship in Health Care Policy. In 2011, Dr. Normand was awarded the ASA Health Policy Statistics Section’s Long Term Excellence Award; in 2012, the American Heart Association’s Distinguished Scientist Award; in 2013, elected to the Society for Research Synthesis Methodology; in 2015, awarded the L. Adrienne Cupples’ Award for Excellence in Teaching, Research, and Service in Biostatistics from Boston University; in 2017 was awarded the American Heart Association Council on Quality of Care and Outcomes Research Outstanding Lifetime Achievement Award; and in 2018 was awarded Mosteller Statistician of the Year by the American Statistical Association Boston Chapter.

Jose Zubizarreta

Jose Zubizarreta, PhD, is an associate professor in the Department of Health Care Policy at Harvard Medical School and a faculty affiliate in the Department of Statistics at the Faculty of Arts and Sciences at Harvard University.  His work centers on the development of statistical methods for causal inference and impact evaluation to advance research in health care and public policy.  In his methodological work, Dr. Zubizarreta uses tools from modern optimization to develop new methods for randomized experiments and observational studies.  In his health care work, he is interested in assessing the quality of care provided by hospitals and physicians using health outcomes and operations measures.  His research interests also encompass comparative effectiveness research and health program impact evaluation.

Unsupervised Deep Learning

Unsupervised learning looks set to play an ever more important role for deep neural networks, both as a way of harnessing vast quantities of unlabelled data, and as a means of learning representations that can rapidly generalise to new tasks and situations. The central challenge is how to determine what the objective function should be, when by definition we do not have  specific target in mind. One approach is simply to attempt to 'learn everything' by building a generative model of the data distribution. This tutorial will focus in particular on autoregressive models, which have proved remarkably effective for many data types. We will also look at autoencoders, an important subclass of generative model, in which a compressed bottleneck of the network's latent representation is constructed, and which can be seen through the lens of the Minimum Description Length principle as a two-part coding scheme. We will consider self-supervised methods, which currently predominate in large-scale representation learning, and attempt to situate them between supervised and unsupervised learning. We will then discuss Generative Adversarial Networks (GANs) and explain how they can be used to infer representations as well as generate data. Time allowing, we will extend our discussion to the reinforcement learning setting, where the natural analogue of unsupervised learning is intrinsic motivation, and notions such as curiosity, empowerment and compression progress can be invoked as drivers of learning.

Alex Graves

Alex Graves completed a BSc in Theoretical Physics at the University of Edinburgh, Part III Maths at the University of Cambridge and a PhD in artificial intelligence at IDSIA with Jürgen Schmidhuber, followed by postdocs at the Technical University of Munich and with Geoff Hinton at the University of Toronto. He is now a research scientist at DeepMind. His contributions include the Connectionist Temporal Classification algorithm for sequence labelling (widely used for commercial speech and handwriting recognition), stochastic gradient variational inference, the Neural Turing Machine / Differentiable Neural Computer architectures, and the A2C algorithm for reinforcement learning.

Public Statistics Lecture

Ronald Wasserstein

Ronald L. (Ron) Wasserstein is the executive director of the American Statistical Association (ASA). Wasserstein assumed the ASA’s top staff leadership post in August 2007.

In this role, Wasserstein provides executive leadership and management for the association and is responsible for ensuring that the ASA fulfills its mission to promote the practice and profession of statistics. He also is responsible for a staff of 35 at the ASA’s headquarters in Alexandria, Va. As executive director, Wasserstein also is an official ASA spokesperson.

Prior to joining the ASA, Wasserstein was a mathematics and statistics department faculty member and administrator at Washburn University in Topeka, Kan., from 1984–2007. During his last seven years at the school, he served as the university’s vice president for academic affairs.

Wasserstein is a longtime member of the ASA, having joined the association in 1983, and has been active as a volunteer in the ASA for more than 20 years. He twice served as president of the Kansas-Western Missouri Chapter of the ASA. Wasserstein served as chair of two ASA sections—the ASA Section on Statistical Education and the ASA Section on Statistical Consulting. He also chaired the Council of Chapters Governing Board in 2006 and was a member of the ASA Board of Directors from 2001–2003.
Wasserstein is a Fellow of the ASA and American Association for the Advancement of Science. He was presented the John Ritchie Alumni Award and Muriel Clarke Student Life Award from Washburn University and the Manning Distinguished Service Award from the North American Association of Summer Schools.

Ron and his wife, Sherry, live in northern Virginia and enjoy movies, live theater, books, and doting on their children and grandchildren.

Subscribe to our mailing list
Stay informed about data science and data science events at Harvard University.
Sign Up now