The Ronald and Maxine Linde Institute of Economic and Management Sciences is Caltech's hub for interdisciplinary research and education in the social sciences, with concentrations in economics, finance, and entrepreneurship. Linde Institute researchers strive to gain deeper insight into the mathematical, social, psychological, and even neurobiological principles behind business and economic interactions and behaviors. In their classroom and research interactions with Linde Institute faculty, students learn the analytical and conceptual tools needed for careers in economics and business as managers and entrepreneurs.
Established in 2011 thanks to the extraordinary generosity of Caltech benefactors Ronald Linde (MS '62, PhD '64) and his wife, Maxine Linde, The Linde Institute has since grown in scope to include the Center for Social Information Sciences (CSIS) and the Center for Theoretical and Experimental Social Sciences (CTESS). As of 2024, The Linde Institute also is affiliated with the new Ronald and Maxine Linde Center for Science, Society, and Policy (LCSSP), formerly known as the Caltech Center for Science, Society, and Public Policy (CSSPP).
Careers in Science Policy
Linde center for science, society, and policy (lcssp) workshop.
Research Initiatives
Student Opportunities
I am a professor of Economics and William H. Hurt Scholar in Caltech’s Division of the Humanities and Social Sciences. Before joining Caltech, I was a postdoc at Stanford. I completed my PhD in Economics at The Ohio State University.
Research Interests : Experimental Economics, Decision Theory, & Microeconomic Theory
Contact Me : [email protected]
HSS DIVISION
Curriculum vitae, kirby @caltech.edu, united states, (626) 395-4094.
The economics option provides students with an understanding of the basic principles underlying the functioning of economic institutions. It offers a modern quantitative approach seldom available at the undergraduate level. The emphasis on economic principles and modern methodology provides students with an excellent preparation for graduate study in economics, as well as for professional work in the fields of business, law, economics, and government.
The option is sufficiently flexible so that students can combine their pursuit of economics with studies in engineering, mathematics, or science. The core of the option consists of an economic theory component, a data analysis component, an applied microeconomic component, and a macroeconomic/growth component. Students are strongly encouraged to supplement this core with additional electives in economics, political science, and mathematics.
Economics Coursework at a Glance
Ec 135. Economics of Uncertainty and Information. An analysis of the effects of uncertainty and information on economic decisions. Included among the topics are individual and group decision making under uncertainty, expected utility maximization, insurance, financial markets and speculation, product quality and advertisement, and the value of information.
Graduate Degree in Computing + Mathematical Sciences
The Computing and Mathematical Sciences (CMS) PhD program is a unique, new, multidisciplinary program at Caltech involving faculty and students from computer science, electrical engineering, applied math, economics, operations research, and even the physical sciences. The program sets high standards for admission and graduation, and boasts a broad collection of world-class faculty (any faculty at Caltech from any of the areas above can advise students).
Disciplines across the information sciences are experiencing an unprecedented convergence. As different areas interact, new fields are emerging. For example, combining Computer Science with...
...Optimization and Statistics has led to machine learning, "big data," and the field of data science. ...Control and Electrical Engineering has led to the smart grid, smart buildings, and the internet of things. ...Physics has led to quantum computing and quantum information theory. ...Economics has led to algorithmic game theory, privacy, and the field of network science. ...Biology and Electrical Engineering has led to bioinformatics, molecular programming, and biomolecular circuits.
Because of this convergence, a new intellectual core is emerging in the information sciences. The core contains material from a spectrum of disciplines: algorithms, networks, machine learning, statistics, optimization, signal processing, and the underlying mathematics. But each area is enriched by the broader context. For instance, the study of algorithms now encompasses the traditional discrete problems of computer science, the continuous problems of applied mathematics, as well as worst-case and average-case perspectives.
The CMS PhD program is designed around the new information science core. This core provides the ideal foundation for future applications across the sciences, engineering, and beyond. Our approach requires the mastery of the following ways of thinking about information science:
- Interpret "information" and "computation" broadly. We study mechanisms that communicate, store, and process information. These structures might be etched in silicon and called hardware or written in code and called software. But the same mechanisms may be expressed in nucleotides and called DNA. They also arise in our society, where they are called social networks or markets. Our view encompasses all of these manifestations.
- Algorithmic thinking is the foundation. The modern world demands the ability to think algorithmically. Algorithms are not just the basis for advanced computer systems, but they help us understand biological organisms and auction design and more.
- Data is central. Data is being collected at an unprecedented speed and scale. Every area of science and society will be transformed as researchers learn to use this data to develop and test new hypotheses. To unlock this potential, we need to develop reliable algorithms for extracting information and making decisions based on data.
- Seek rigor and relevance. The CMS Program focuses on the theoretical core of information science. We believe that principled and rigorous methods provide the only solid basis for progress. But we also insist on research that is relevant to applications, and we train students to work at the interface of information science and other disciplines.
Students may select a research adviser from any of the 30+ faculty affiliated with the CMS Department, including specialists in Applied & Computational Mathematics, Biological Engineering, Computation & Neural Systems, Computer Science, Control & Dynamical Systems, Economics, Electrical Engineering, Mechanical Engineering, Philosophy, and Physics.
Graduate Program Details and Requirements
Requirements for the Computing and Mathematical Sciences graduate program are listed in the current Caltech Catalog .
Further details and advice can be found here: Navigating the Ph.D. Options in CMS
Graduate Options Administrator
Maria Lopez [email protected] (626) 395-3034
Graduate Option Representative
Yisong Yue Computing and Mathematical Sciences Option Representative
Application Requirements
Applicants must have completed a bachelor's degree or the equivalent before beginning graduate study. Applicants who already hold a Ph.D. degree will not be considered for a second Ph.D. degree. Transcripts from each college or university attended, three letters of recommendation, a CV, and the applicant's statement of purpose are required components of the application and are carefully and equally weighed during the evaluation process. GRE tests (general and advanced subject) are not required and in most options scores will not be considered for admission. Most of the funding sources require work authorization. As a consequence, matriculation into the PhD program requires evidence of work authorization, unless special compensation can be arranged with the admitting option. Applicants are expected to read, write and speak English and comprehend the spoken language. Although not required for admission, for applicants whose native language is not English or who have not received a degree from a university or college where English is the primary language of instruction, it is important to demonstrate a strong capability in English. This can be done by self-reporting scores from the Educational Testing Service (TOEFL), Pearson Test of English Academic (PTE Academic), the Cambridge Examinations and the International English Language Testing System (IELTS), or other services that provide a certified English-language proficiency examination.
- Application Checklist
- Application Deadlines
- Supplemental Documents
- Transcripts and Letters of Recommendation
Social Science
PHILOSOPHY AND GOALS OF THE SOCIAL SCIENCE UNDERGRADUATE CORE
The social science core curriculum initiates students in the study of how humans organize, decide, govern, and allocate their resources. It teaches students how to analyze and reason about individual behavior, markets, and other institutions. Our curriculum covers methods, as well as substance. The issues facing our students in the future will be different from those that are current, but the analytical principles and methodology needed to understand those problems will likely remain the same, albeit improved. Thus, the social science core curriculum provides students with the knowledge and tools to, in the words of the catalog, “navigate the societal, political, and economic factors that influence, and are influenced by, their work.”
The objectives of the social science core curriculum can be broken into three broad categories of courses: introductory courses that teach basic principles; methods courses that seek to transmit skills and analytical tools; and courses exposing students to substantive ideas and problems in the social sciences. More concretely: “Fundamental ideas and principles” classes expose students to a broad and introductory overview of basic ideas in economics, psychology, and political science. “Methodology and analysis” courses focus on giving students the theoretical, empirical, and data science tools to analyze problems. They cover the theoretical modeling tools most commonly used in the social sciences, as well as statistical and econometric techniques that are needed to analyze data. “Substantive problems in social science: Individuals, institutions, and markets” courses expose students to an array of substantive questions in social science, from resource allocation via markets and prices, the workings of political institutions, the consequences of poor governance, the psychological basis of human behavior, and an understanding of financial markets.
SOCIAL SCIENCE GRADUATE PROGRAM
The Caltech Ph.D. program in Social Science is highly interdisciplinary, integrating economics, political science, quantitative history, econometrics, and finance. It makes extensive use of mathematical modeling, laboratory experiments, and econometric techniques. Research in the social sciences program at Caltech strongly emphasizes the understanding and analysis of the relationships between individual incentives, collective behavior, political and economic institutions, and public policy.
Areas of Research
- Experimental Economics and Experimental Political Science . Caltech social scientists were among the early pioneers in the field of laboratory experimentation and Caltech has maintained a strong leadership position and research presence in the field ever since. Examples of the kinds of laboratory studies the faculty are engaged in include the behavior and design of auctions and market-like mechanisms, public goods provision and related topics in public economics, the economics of networks and matching, decision theory, inter-personal bargaining, behavioral economics, committee decision making, and electoral competition. Many of our faculty engage in laboratory experimentation as part of their research agendas in economics and political science (Agranov, Camerer, Nielsen, Palfrey, Saito, Shum, Sprenger). Considerable laboratory experimentation also focuses upon the workings of financial markets, and seeks to elucidate basic principles that underlie the pricing of assets, trading, and information aggregation in these markets. Many of these experiments are conducted through the use of net-worked computers (see Facilities) in the William D. Hacker Social Science Experimental Laboratory (SSEL) and the Laboratory for Experimental Economics and Political Science (LEEPS). The real world provides another setting for experimental research outside the laboratory, and Caltech social scientists have conducted field experiments involving a wide variety of topics, ranging from decision making in organizations, social networks, and the behavior of different cultural groups ranging from college students, to urban Americans to African villagers.
- Economic Theory and Game Theory. Caltech has a strong research group in economic theory, which, together with rigorous training in statistics and econometrics forms the backbone of the core curriculum for the PhD program. Theoretical research at Caltech has played a key role in the design and practical implementation of new institutions that more efficiently allocate scarce resources and provide public goods. Some of this work has had important public policy applications. Prominent examples include the design of FCC auctions to allocate the electromagnetic spectrum for telecommunication, and the market for allocating and trading permits for pollution emissions in the Los Angeles basin. Much of this theoretical design research is complemented by experimental studies that provide a testbed for competing designs. There is an active group of faculty and graduate students working in the areas of the optimal design of contracts and markets (Cvitanic), the economics of information (Pomatto, Tamuz), decision theory (Pomatto, Rangel, Saito), game theory (Palfrey, Pomatto, Saito, Tamuz) and matching Social Science and Research. There are several active programs for inter-action between our theory faculty in the social sciences and the faculties of computer science and applied mathematics. This is formally organized around two interdisciplinary centers, the Lee Center for Advanced Networking and the Social and Information Science Laboratory (SISL), with the latter offering a bi-weekly seminar coordinated between the computer science department and the social sciences faculty and featuring speakers in economics, computer science, game theory and related disciplines. There are many informal connections that reinforce the formal connections, including research collaborations between faculty and graduate students in these different areas.
- Political Economy and Political Science. Caltech has a long tradition of strength in research that spans the boundary of the economics and political science disciplines. Research in political economy at Caltech continues to be a major strength of the program and provides a natural bridge that unites the faculty in economics, political science, and quantitative history. The focus of research in political economy and political science at Caltech emphasizes rigorous theoretical modeling drawing heavily upon techniques from economic theory and game theory, combined with empirical studies using highly sophisticated quantitative analyses of a wide variety of data sources: experimental, survey, field, voting, and historical data. Ongoing political economy research areas of the current faculty include: the interacting forces of bargaining, voting, and communication in committees, legislatures, bureaucracies, and assemblies (Agranov, Gibilisco, Hirsch, Katz, Lopez-Moctezuma, Palfrey); the Voting Technology Project, a joint Caltech-MIT research venture, established in 2000 to evaluate and improve the performance and reliability of U.S. balloting technology, registration systems, election administration, redistricting, and election law (Alvarez, Katz); political forces affecting judicial behavior (Hirsch, Shum), strategic voting in multi-candidate and multi-stage elections (Alvarez, Kiewiet, Palfrey), the politics of inequality and redistribution (Agranov, Palfrey) and several areas of comparative and international politics, including studies of the causes and consequences of corruption, domestic unrest, and international conflict (Gibilisco, Lopez-Moctezuma).
- Financial Economics . Caltech has built a small but very active research group in finance. The researchers in this group are working on a range of topics in mathematical finance, asset pricing and dynamic contracting (Cvitanic), behavioral finance (Camerer) and economic history of financial crises (Janas, Rosenthal). There is a regular seminar series in finance that features distinguished researchers from around the world. Caltech faculty outside the finance group itself are also engaged in empirical research in financial economics. These include experimental areas of study and research studies of asset markets (Camerer), interest rate policy making by the Federal Open Market Committee (Lopez-Moctezuma), and online credit markets (Xin).
- Behavioral Economics. Research in behavioral economics at Caltech overlaps all of the above groups. Laboratory experimental research discovers interesting behavioral anomalies and can also test theoretical models designed to account for such anomalies. On the theoretical side, much of the game-theoretic and decision-theoretic research at Caltech is motivated by experimental observations, leading to extensions or modifications of standard models. These extensions in turn suggest experimental designs that are then implemented in the laboratory by our faculty and graduate students. Faculty research in political behavior (Alvarez, Katz, Kiewiet) and behavioral finance (Camerer) are complementary and add strength more generally to understanding social behavior. We also have on our faculty a small but very active group conducting research at the boundary of biology, psychology, and the social sciences (Adolphs, Camerer, Mobbs, O’Doherty, Rangel). This group offers a separate PhD option focused on the behavioral neuroscience of decision making (see the catalog entry for “Social and Decision Neuroscience”). Utilizing fMRI brain-imaging, eye-tracking, and other biological measurement technologies, this group, often in collaboration with other social science faculty and graduate students, has begun to explore the neural foundations of decision making in individual choice, game theoretic, and market settings.
- Quantitative History. Just as with the theoretical, experimental, and empirical work using contemporary data, historical research conducted at Caltech employs mathematical modeling and sophisticated statistical techniques to attack a wide range of historical questions. Historical research conducted at Caltech addresses questions that cut across economics, political science, political economy, and finance, often combining archival work with state-of-the art statistical techniques. These include the development of capital and credit markets (Hoffman, Janas, Rosenthal), the evolution of wealth distribution from 1800 to the present (Rosenthal), the causes of recurrent financial crises in capitalist economies (Hoffman, Janas, Rosenthal), the determinants of economic and technological growth, the development of and financing for public institutions (Dennison, Hoffman, Janas), and the roots of the divergence between east and west Europe (Dennison).
- Applied microeconomics and econometrics. Empirical research in social science blends creative collection and analysis of field data with rigorous application of tools and methods from econometrics and statistics. We offer courses and supervise students in these areas. A number of faculty members specialize in empirical work in a number of fields, including finance (Xin), industrial organization (Shum, Xin), political economy (Alvarez, Gibilisco, Katz, Kiewiet, Lopez-Moctezuma, Sherman, Shum), and behavioral economics (Camerer). Methodological areas of specialization include econometrics (Sherman, Shum, Xin), causal inference (Alvarez, Katz, Sherman, Shum), machine learning (Alvarez, Camerer, Katz, Shum, Xin), and Bayesian statistics (Katz).
Physical Facilities
Caltech has two state-of-the-art onsite laboratories for experimental research in economics, political science, game theory, decision theory, and financial economics: The Laboratory for Experimental Economics and Political Science (LEEPS, established 1987) and The Hacker Social Science Experimental Laboratory (SSEL, established 1998).
Job Market Candidates
Placement directors.
| SS Option Representative | SDN Option Representative |
|---|---|
| Professor Omer Tamuz Email: Tel: (626) 395-4064 | Professor John O'Doherty Email: Tel: (626) 395-5981 |
Placement Coordinator
Division Option Manager , HSS Undergraduate Options, and Social Science Graduate Programs Tel: (626) 395-4206 Fax: (626) 405-9841
Below is information on the candidates who are on the job market in the current academic year. Click on a candidate's name to link to that person's profile with more detailed information. For placement history of graduates of the HSS PhD programs, please see the alumni listings page .
2024–2025 candidates will be viewable in September
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Social Sciences
The Social Sciences program of the California Institute of Technology - Caltech offers the opportunity for highly motivated and quantitatively oriented students to pursue interdisciplinary research in areas common to economics, political science, political economy, history, psychology, anthropology, law, and public policy.
California Institute of Technology - Caltech Multiple locations Pasadena , California , United States Top 0.1% worldwide Studyportals University Meta Ranking 3.9 Read 30 reviews
A foundational belief of the Social Sciences program of the California Institute of Technology - Caltech is that a wide variety of social phenomena are best understood as the consequence of intelligent decisions by individuals pursuing their own ends. Caltech social scientists have established that such decisions can be modeled and that conclusions concerning social events should be based on observable and measurable parameters of those theories.
Caltech was one of the pioneering research institutions to first use laboratory experimentation in the study of economics and political science, and HSS remains one of the top departments—if not the top—in the world in this field. Under faculty supervision, graduate students are major users of HSS research centers, including the Social Science Experimental Laboratory (SSEL) and the Caltech Brain Imaging Center (CBIC).
Graduate students in the social sciences PhD program are encouraged to begin largely independent research early in their graduate career. Many of the research projects involve direct collaboration between members of the faculty and graduate students.
Students at Social Sciences program of the California Institute of Technology - Caltech will get:
- a strong background in economics, political science, and econometrics;
- a solid understanding of the technical tools, which themselves require an understanding of different theoretical and empirical approaches, needed to carry out research at the frontier of the social sciences
- a demonstrated record of independent and high quality research; and
- an ability to collaborate and communicate across different fields in the social sciences.
Programme Structure
Courses include:
- Economics,
- Political science,
- Political economy,
- History,
- Psychology,
- Anthropology,
- Public policy.
Key information
- 36 months
Start dates & application deadlines
- Apply before 2024-12-15 00:00:00
Disciplines
Academic requirements.
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Student insurance
Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:
- Additional medical costs (i.e. dental)
- Repatriation, if something happens to you or your family
- Home contents and baggage
We partnered with Aon to provide you with the best affordable student insurance, for a carefree experience away from home.
Starting from €0.53/day, free cancellation any time.
Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at California Institute of Technology - Caltech and/or in United States, please visit Student Insurance Portal .
Other requirements
General requirements.
- Statement of Purpose
- Three letters of recommendation from individuals familiar with your academic and/or work performance are required for all applicants.
- Transcripts
- Resume or CV
Tuition Fee
International, living costs for pasadena.
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
In order for us to give you accurate scholarship information, we ask that you please confirm a few details and create an account with us.
Scholarships Information
Below you will find PhD's scholarship opportunities for Social Sciences.
Available Scholarships
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Phd in Economics at CalTech
How is it like to be a PhD in Economics at CalTech? And why does it seem to be an unattractive location compared to other top schools? Also I was quite surprised that it ranked outside of the top 50 in terms of department but T15 on US news…
Eric Mazumdar
Assistant Professor, CMS & Economics
Assistant Professor in Computing and Mathematical Sciences & Economics
My research interests lie at the intersection of machine learning and economics. I am broadly interested in developing the tools and understanding necessary to confidently deploy machine learning algorithms into societal systems. This requires understanding the theoretical underpinnings of learning algorithms in uncertain, dynamic environments where they interact with strategic agents--- including people and other algorithms. Practically, I apply my work to problems in intelligent infrastructure, online markets, e-commerce, and the delivery of healthcare.
I am the recipient of a NSF Career Award aimed at studying the strategic interactions that arise in Societal-Scale Systems as well as a Research Fellowship for Learning in Games from the Simons Institute for Theoretical Computer Science . My work is supported by NSF, DARPA, and Amazon research grants.
Throughout my work, I use tools and ideas from statistical machine learning, optimization, stochastic control, dynamical systems, and game theory. Some of the topics addressed by my recent work include strategic classification, learning behavioral models of human decision-making from data, min-max optimization, learning in games, multi-agent reinforcement learning, distributionally robust learning, and learning for control.
Prior to Berkeley, I received an SB in Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) , where I had the opportunity to work with in the Laboratory for Multiscale Regenerative Technologies as well as in the MIT Computational Biology Group in CSAIL .
Office Hours
I keep office hours during academic quarters on Tuesdays from 4-5 pm PT in my office (ANB 216). I am available during this time for discussions with any students/postdocs who would like to meet. Feel free to just drop in; however, emailing ahead of time to is preferred in case I am traveling.
Publications
Preprints/under review.
Rethinking Scaling Laws for Learning in Strategic Environments Tinashe Handina, Eric Mazumdar [PDF]
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar , Asuman Özdaglar, Adam Wierman [PDF]
Convergent First-Order Methods for Bilevel Optimization and Stackelberg Games Chinmay Maheshwari, Shankar Sastry, Lillian Ratliff, Eric Mazumdar [PDF]
Journal Articles
On Finding Nash Equilibria (and only Nash equilibria) in Zero-Sum Continuous Games Eric Mazumdar , Michael I. Jordan, S. Shankar Sastry under submission [PDF]
On Gradient-Based Learning in Continuous Games Eric Mazumdar , Lillian J. Ratliff, S. Shankar Sastry SIAM Journal on Mathematics for Data Science (SIMODS), 2020 [PDF]
Inverse Risk-Sensitive Reinforcement Learning via Gradient Methods Lillian Ratliff, Eric Mazumdar IEEE Transactions on Automatic Control (TAC), 2020 [PDF]
Refereed Publications
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar , Asuman Özdaglar, Adam Wierman Conference on Neural Information Processing Systems (NeurIPS), 2023 [PDF]
Distribution Shifts of Strategic Interacting Agents via Coupled Gradient Flows Lauren Conger, Franca Hoffman, Eric Mazumdar , Lillian Ratliff Conference on Neural Information Processing Systems (NeurIPS), 2023 [PDF]
Coupled Gradient Flows for Strategic Non-Local Distribution Shift Lauren Conger, Franca Hoffman, Eric Mazumdar , Lillian Ratliff L4DC Workshop, International Conference on Machine Learning (ICML), 2023 [PDF]
Algorithmic Collective Action in Machine Learning Moritz Hardt, Eric Mazumdar , Celestine Mendler-Dünner, Tijana Zrnic (α-β ordering) [PDF] International Conference on Machine Learning (ICML), 2023 [PDF]
Designing System Level Synthesis Controllers for Nonlinear Systems with Stability Guarantees Lauren Conger, Sydney Vernon, Eric Mazumdar Conference on Learning for Dynamics and Control (L4DC), 2023
Synthesizing Reactive Test Environments for Autonomous Systems: Testing Reach-Avoid Specifications with Multi-Commodity Flows Apurva Badithela, Josefine B. Graebener, Wyatt Ubellacker, Eric V. Mazumdar , Aaron D. Ames, Richard M. Murray International Conference on Robotics and Automation (ICRA), 2023 [PDF]
Decentralized, Coordination- and Communication-Free Algorithms for Learning in Structured Matching Markets Chinmay Maheshwari, S. Shankar Sastry, Eric Mazumdar Conference on Neural Information Processing Systems (NeurIPS), 2022 [PDF]
Nonlinear System Level Synthesis for Polynomial Dynamical Systems Lauren Conger, Jing Shuang (Lisa) Li, Eric Mazumdar , Steven Brunton IEEE Conference on Decision and Control (CDC), 2022 [PDF]
Langevin Monte Carlo for Contextual Bandits Pan Xu, Hongkai Zheng, Eric Mazumdar , Kamyar Azizzadenesheli, Anima Anandkumar International Conference on Machine Learning (ICML), 2022 [PDF]
Fast Distributionally Robust Learning via Min-Max Optimization Yaodong Yu*, Tianyi Lin*, Eric Mazumdar* , Michael I. Jordan International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [PDF] (* denotes equal contribution)
Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization Chinmay Maheshwari, Chih-Yuan Chiu, Eric Mazumdar , S. Shankar Sastry, Lillian J. Ratliff International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 [PDF]
Who Leads and Who Follows in Strategic Classification? Tijana Zrnic*, Eric Mazumdar* , S. Shankar Sastry, Michael I. Jordan Conference on Neural Information Processing Systems (NeurIPS), 2021 [PDF] (* denotes equal contribution)
Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games Tanner Fiez, Lillian J. Ratliff, Eric Mazumdar , Evan Faulkner, Adhyyan Narang Conference on Neural Information Processing Systems (NeurIPS), 2021 [PDF]
High Confidence Sets for Trajectories of Stochastic Time-Varying Nonlinear Systems Eric Mazumdar , Tyler Westenbroek, Michael I. Jordan, S. Shankar Sastry IEEE Conference on Decision and Control (CDC), 2020 [PDF]
Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning Tyler Westenbroek, Eric Mazumdar , David Fridovich-Keil, Valmik Prabhu, Claire J. Tomlin, S. Shankar Sastry IEEE Conference on Decision and Control (CDC), 2020 [PDF]
Expert Selection in High Dimensional Markov Decision Processes Vicenc Rubies Royo, Eric Mazumdar , Roy Dong, Claire J. Tomlin, S. Shankar Sastry IEEE Conference on Decision and Control (CDC), 2020 [PDF]
On Approximate Thompson Sampling with Langevin Algorithms Eric Mazumdar* , Aldo Pacchiano*, Yi-an Ma*, Peter L. Bartlett, Michael I. Jordan International Conference on Machine Learning (ICML), 2020 [PDF] (* denotes equal contribution)
Feedback Linearization for Unknown Systems via Reinforcement Learning Tyler Westenbroek*,David Fridovitch-Keil*, Eric Mazumdar* , Shreyas Arora, Valmik Prabhu, Claire Tomlin, S. Shankar Sastry International Conference on Robotics and Automation (ICRA), 2020 [PDF] (* denotes equal contribution)
Policy Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games Eric Mazumdar , Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2020 [PDF]
Local Nash Equilibria are Isolated, Strict Local Nash Equilibria in ‘Almost All’ Zero-Sum Continuous Games Eric Mazumdar and Lillian Ratliff IEEE Conference on Decision and Control (CDC), 2019 [PDF]
Convergence Analysis of Gradient-Based Learning in Continuous Games Benjamin, Chasnov, Lillian Ratliff, Eric Mazumdar , Samuel A. Burden Conference on Uncertainty in Artificial Intelligence (UAI), 2019 [PDF]
On the Analysis of Cyclic Drug Schedules for Cancer Treatment using Switched Dynamical Systems Margaret Chapman, Eric Mazumdar , Ellen Langer, Rosalie Sears, Claire Tomlin IEEE Conference on Decision and Control (CDC), 2018 [PDF]
Gradient-based Inverse Risk-Sensitive Reinforcement Learning Eric Mazumdar , Lillian Ratliff, S. Shankar Sastry IEEE Conference on Decision and Control (CDC), 2017 [PDF]
To Observe or Not to Observe: Queuing Game Framework for Urban Parking Lillian Ratliff, Chase Dowling, Eric Mazumdar , Baosen Zhang IEEE Conference on Decision and Control (CDC), 2016 [PDF]
Understanding the Impact of Parking on Urban Mobility via Routing Games on Queue–Flow Networks Daniel Calderone, Eric Mazumdar , Lillian Ratliff, S. Shankar Sastry IEEE Conference on Decision and Control (CDC), 2016 [PDF]
Mathematical Framework for Activity-Based Cancer Biomarkers Gabriel Kwong, Jaideep Dudani, Emmanuel Carrodeguas, Eric Mazumdar , Miriam Zekvat, Sangeeta N. Bhatia Proceeding of the National Academy of Science (PNAS), 2015 [PDF]
Workshop Publications
Policy Gradient Has No Convergence Guarantees in Linear Quadratic Dynamic Games Eric Mazumdar , Lillian J. Ratliff, Michael I. Jordan, S. Shankar Sastry Workshop on Smooth Games and Optimization NeurIPS, 2019 [PDF]
Learning Feedback Linearization by Model-Free Reinforcement Learning Tyler Westenbroek*, David Fridovitch-Keil*, Eric Mazumdar* , Claire Tomlin, S. Shankar Sastry Workshop on Generative Modeling/Model-based Reasoning International Conference on Machine Learning (ICML), 2019 (* denotes equal contribution)
Zaiwei Chen (co-advised with Adam Wierman) Laixi Shi (co-advised with Adam Wierman) Kishan Panaganti Badrinath (co-advised with Adam Wierman)
Lauren Conger (co-advised with John Doyle) Tinashe Handina (co-advised with Adam Wierman) Yizhou Zhang
Prospective Students & Postdocs
Prospective graduate students.
I will be accepting graduate students applying during the 2022/23 academic cycle. If you are interested in my research, you can list me as a faculty of interest in your application to CMS or CDS. Unfortunately, due to the large number of applications I cannot respond to individual emails about applications.
Prospective Postdoctoral Fellows
I will be recruiting Postdoctoral fellows in CMS or HSS to begin in 2023. More details will be posted here in the coming months, however, if there is an exceptionally good research fit with my group, please feel free to send an email with your CV/resume and a short paragraph highlighting research fit.
Caltech Courses
Cms/cs/ee/ids 144 - network economics, winter 2023, 2024, cms/ec 248 - topics in learning and games, fall 2022, 2023.
This is an advanced topics course intended for graduate students with a background in optimization, linear systems theory, probability and statistics, and an interest in learning, game theory, and decision making more broadly. We will cover the basics of game theory including equilibrium notions and efficiency, learning algorithms for equilibrium seeking, and discuss connections to optimization, machine learning, and decision theory. While there will be some initial overview of game theory, the focus of the course will be on modern topics in learning as applied to games in non-cooperative settings. We will also discuss games of partial information and stochastic games as well as hierarchical decision-making problems (e.g., incentive and information design).
UC Berkeley Courses
Ds 102 - data, inference, and decisions, spring/fall 2019.
Data Science 102 is a capstone class for the Data Science Major at Berkeley. Its goal is to develop the probabilistic foundations of inference in data science and building a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. We covered topics including frequentist and Bayesian decision-making, permutation testing, false discovery rate control, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, multi-armed bandits, Thompson sampling, optimal control, Q-learning, differential privacy, and an introduction to machine learning tools including decision trees, neural networks and ensemble methods. I participated in the development of this course starting back in Spring 2019, and was a Graduate Student Instructor for the first semester of this course in Fall 2019. This included creating course content (including homeworks, exams, discussions, coding projects, and labs), as well as leading both a discussion and lab section.
IEOR 290 - Data Analytics and the IoT: Machine Learning for Operations with Human Data Sources
Spring 2018.
IEOR 290 was an introductory graduate-level class in the Industrial Engineering and Operations Research department on machine learning on data from human data sources and reasoning about the human, social, and ethical implications of making decisions based on such analyses. This course covered the theoretical tools for the analysis of data and human agents in cyber-physical systems. Concepts will included optimization, game theory, differential privacy, behavioral methods, statistical estimation, and utility function learning, with a focus on applications in a variety of Internet of Things systems, such as the energy grid, new transportation services, and database privacy. Throughout, we emphasized the underlying mathematical tools required to understand the current research in each of these fields. I was a Graduate Student Instructor for this class and also participated in the development of this course.
Learning in the Presence of Strategic Agents: Dynamics, Equilibria, and Convergence Keller Colloquium, October 2021, (invited). Pasadena, CA
Learning with Strategic Agents: Dynamics, Equilibria, and Convergence Center for Human-Compatible AI, July 2021, (invited). Virtual
Approximate Thompson Sampling with Langevin Algorithms RISE Retreat, May 2020 (invited). Virtual
Designing Learning Algorithms for Competitive Settings Center for Human-Compatible AI, March 2020 (invited). Berkeley, CA
On the Analysis and Design of Gradient-Based Learning Algorithms in Games ONR MURI Review Workshop, April 2019. George Mason University, VA
Towards Better Gradient-Based Learning Algorithms in Games Machine Learning Seminar, November 2018 (invited). University of Washington, WA
Fundamental Issues with Gradient-Play in Games ONR MURI Review Workshop, April 2018. Seattle, WA
Learning Agent Preferences via Inverse Risk-Sensitive Reinforcement Learning NSF Review Workshop, January 2017. Arlington, VA
Data Analytics for Parking Management and Congestion Reduction NSF Review Workshop, November 2015. Arlington, VA
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| Zhang, Shiyu (2022) Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ahsc-p250. This thesis consists of three papers, two studying the effectiveness of policy interventions curbing the opioid crisis, and one studying the value of network ties in the Chinese bureaucracy. The two chapters on the opioid crisis are coauthored with Daniel Guth, a fellow Caltech graduate student. The first chapter studies the effectiveness of the OxyContin reformulation in reducing opioid misuse and overdose. Purdue Pharma reformulated OxyContin in 2010 to make it more difficult to abuse. Previous research argued that OxyContin misuse fell dramatically and OxyContin users switched directly to heroin. Using a novel and fine-grained source of all oxycodone sales from 2006-2014, we show that the reformulation led users to substitute from OxyContin to generic oxycodone and the reformulation had no overall impact on opioid or heroin mortality. In addition, the chapter finds that generic oxycodone, instead of OxyContin, was the driving factor in the transition to heroin in recent years. These findings highlight the important role generic oxycodone played in the opioid epidemic and the limited effectiveness of a partial supply-side intervention. The second chapter studies the spatial spillover effect of Prescription Drug Monitoring Programs (PDMPs). PDMPs seek to potentially reduce opioid misuse by restricting the sale of opioids in a state. This chapter examines discontinuities along state borders, where one side may have a PDMP and the other side may not. We find that electronic PDMP implementation, whereby doctors and pharmacists can observe a patient's opioid purchase history, reduces a state's opioid sales but increases opioid sales in neighboring counties on the other side of the state border. We also find systematic differences in opioid sales and mortality between border counties and interior counties. These differences decrease when neighboring states both have PDMPs, which is consistent with the hypothesis that the differences were caused by cross-border opioid shopping. Our work highlights the importance of understanding the opioid market as connected across counties or states, as we show that states are affected by the opioid policies of their neighbors. The third chapter examines the value of patronage ties at lower levels of Chinese bureaucracy. A growing literature shows that connection with the right higher-level politicians is beneficial for advancements in the Communist Party of China. In this chapter, I use a self-collected data set to examine the value of patronage ties in the city committees, a previously overlooked but important level of the Chinese government. I present empirical evidence that the party secretaries are involved in the appointment of committee members. But upon departure, the party secretaries' career success does not improve the committee members' future promotion likelihood. This work highlights that the value of interpersonal connection in China is highly dependent on which level of the government is under inspection.
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The HSS PhD program in the social sciences offers the opportunity for highly motivated and quantitatively oriented students to pursue interdisciplinary research in areas common to economics, political science, political economy, history, psychology, anthropology, law, and public policy.. A foundational belief of the program is that a wide variety of social phenomena are best understood as the ...
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This thesis consists of three papers, two studying the effectiveness of policy interventions curbing the opioid crisis, and one studying the value of network ties in the Chinese bureaucracy. The two chapters on the opioid crisis are coauthored with Daniel Guth, a fellow Caltech graduate student. The first chapter studies the effectiveness of ...
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EDUCATIONStanford University Graduate School of Business 2014-2019 Ph.DinMarketing California Institute of Technology 2010-2014 B.S.inComputerScience,B.S.inBusiness,Economics,andManagement
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