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Rediet Abebe, Harvard University

Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and a computer science Ph.D. candidate at Cornell University. Her research is broadly in the fields of algorithms and AI, with a focus on equity, justice, and social good concerns. Abebe holds an M.S. and a B.A. in mathematics from Harvard University, an M.A. in mathematics from the University of Cambridge, and an M.S. in computer science from Cornell University. She was recently named one of 35 Innovators Under 35 by the MIT Technology Review, in part for her work with MD4SG. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia. She co-founded and has been co-organizing the MD4SG initiative since fall 2016.


Wanyi Li, Stanford University

Wanyi Li is a Ph.D. candidate in operations management at Stanford University Department of Management Science and Engineering. She studies market design for environmental conservation. In particular, she builds theoretical frameworks for contracts and incentive schemes in the forestry sector. She is a recipient of the Stanford Interdisciplinary Graduate Fellowship (2018 - 2021). Prior to Stanford, she received her B.A. in Physics from Wellesley College and grew up in Xuzhou, China. She has been co-organizing the MD4SG Environment/Climate working group since spring 2020.


Irene Lo, Stanford University

Irene Lo is an assistant professor in the department Management Science & Engineering at Stanford University. She researches how to design matching markets and assignment processes to improve market outcomes. In her work, she focuses on public sector applications and socially responsible operations research. She received her undergraduate degree from Princeton University and her PhD in Industrial Engineering & Operations Research from Columbia University, and was a postdoctoral scholar in the Economics department at Stanford University. She has been co-organizing the MD4SG initiative since fall 2018.


Francisco J. Marmolejo Cossio, University of Oxford

Francisco J. Marmolejo Cossio is a Career Development Fellow in Computer Science at Balliol College within the University of Oxford. He is also a Research Fellow at Input Output Hong Kong (IOHK). His research lies at the intersection of Algorithmic Game Theory, Decentralised Consensus Protocols and Computational Learning Theory. His work has focused on establishing query-efficient protocols for equilibrium computation as well as understanding incentives in decentralised systems. Francisco holds a D.Phil. in Computer Science and an M.Sc. in Mathematics and Foundations of Computer Science from the University of Oxford, as well as a B.A. in Mathematics from Harvard University. Francisco is originally from San Luis Potosi, Mexico, and has been co-organizing the development working group since Spring 2019. Francisco has also been co-organizing the MD4SG initiative as a whole since Spring 2020.


Ana-Andreea Stoica, Columbia University

Ana-Andreea Stoica is a Ph.D. candidate at Columbia University. Her work focuses on mathematical models, data analysis, and policy implications for algorithm design in social networks. From recommendation algorithms to the way information spreads in networks, Ana is particularly interested in studying the effect of algorithms on people's sense of privacy, community, and access to information and opportunities. She strives to integrate tools from mathematical models—from graph theory to opinion dynamics—with sociology to gain a deeper understanding of the ethics and implications of technology in our everyday lives. Ana grew up in Bucharest, Romania, and moved to the US for college, where she graduated from Princeton in 2016 with a bachelor's degree in Mathematics. She has been co-organizing the MD4SG initiative since fall 2019.




Bryan Wilder, Harvard University

Bryan Wilder is a PhD student in computer science at Harvard University, where he is advised by Milind Tambe. His research focuses on optimization and machine learning for social good, with an emphasis on applications in public health. In particular, his work integrates techniques from discrete optimization, learning, and social networks to enable better decision-making under uncertainty and improve the quality of interventions offered to marginalized populations. Bryan is supported by a NSF Graduate Research Fellowship. He has been serving as head organizer of the MD4SG working groups since fall 2019 and was previously organizer of the MD4SG Inequality working group.



Tejumade Afonja, Saarland University

Tejumade Afonja Tejumade is a Graduate Student at Saarland University studying Computer Science. Previously, she worked as an AI Software Engineer at InstaDeep Nigeria. She holds a B.Tech in Mechanical Engineering from Ladoke Akintola University of Technology (2015) and worked on the Fabrication and Design of Robot Vacuum Cleaner for her undergraduate thesis which was published in Alexandria Engineering Journal hosted by Elsevier (2018). She’s currently a remote research intern at Vector Institute where she is conducting research in the areas of privacy, security, and machine learning under the supervision of Prof. Nicolas Papernot from the University of Toronto Tejumade is the co-founder of AI Saturdays Lagos, an AI community in Lagos, Nigeria focused on conducting research and teaching machine learning related subjects to Nigerian youths. She is also an Intel Software Innovator for Machine Learning in Nigeria and 2020 Google EMEA Women Techmakers Scholar.



Jessica Finocchiaro, University of Colorado, Boulder

Jessie Finocchiaro is a 4th year PhD student at the University of Colorado Boulder working with Dr. Rafael Frongillo. Her primary interests revolve around uncertainty in decision-making, and asks two primary questions: first, how can scoring rules and loss functions be designed so that we learn statistics of interest from resource-constrained systems. Second, how does uncertainty in machine learning algorithms and mechanism design affect different groups of people? She is currently a National Science Foundation Graduate Research Fellow, and is happy to talk to potential graduate students about paths to (and through) grad school.



Paul Gölz, CMU

Paul Gölz is a Ph.D. student in computer science at Carnegie Mellon University, where he is advised by Ariel Procaccia. He is interested in the mathematics of making joint decisions as a society. In particular, he studies novel means of political participation as well as resource allocation problems (such as kidney exchange and refugee resettlement). Within the area of civic participation, he has worked on liquid democracy, participatory budgeting, and recently on the theory and practice of sortition. Paul grew up in Germany and did his undergraduate at Saarland University before coming to the States. With Anson, he is organizing the new working group on Civic Participation this Fall.



Anson Kahng, CMU

Anson Kahng is a Ph.D. student in computer science at Carnegie Mellon University, where he is fortunate to be advised by Ariel Procaccia (currently at Harvard). He is interested in theoretical problems at the intersection of computer science and economics, particularly in computational social choice. In the space of civic participation, Anson has previously worked on liquid democracy and virtual democracy, and is currently working on participatory budgeting. He is also interested in the theory of deliberation.



Sara C. Kingsley, CMU

Sara C. Kingsley is a Ph.D. student in the computer sciences (HCI) at Carnegie Mellon University (CMU). Her research investigates online job, labor, and data markets for anti-competitively designed technology, and evaluates how outcomes, including access to economic opportunities, are determined. Beyond studying the future of work and data, her past research includes telecommunications and network engineering projects in Kenya, Ghana, Botswana, Namibia, Tanzania, and South Africa. Sara’s career started in public service; she has worked for the late U.S. Senator Edward M. Kennedy (of Massachusetts), President Barack Hussein Obama, and more recently, interned for U.S. Congressman Jim McGovern (of Massachusetts). Sara currently serves as a representative on CMU's graduate student legislative body, and is on the program committeefor AAAI 2020 social impact track. She has been co-organizing the MD4SG Data Economies working group since fall 2019. She previously co-organized the MD4SG Online Labor Markets working group.



Duncan McElfresh, University of Maryland, College Park

Duncan McElfresh Duncan is a PhD Candidate in Applied Math at the University of Maryland, College Park. His research centers on applications of computer science and optimization for matching and market design; recently he has worked on kidney exchange (with the United Network for Organ Sharing), public housing allocation (with the Los Angeles Homeless Services Authority), and blood donation (with Facebook).



Faidra Monachou, Stanford University

Faidra Monachou is a Ph.D. candidate in Operations Research at the Department of Management Science and Engineering at Stanford University, advised by Itai Ashlagi. Her work focuses on market design and matching theory, and aims at understanding the theoretical foundations of socioeconomic problems that arise in large-scale online markets. She is particularly interested in the study of bias and discrimination in the sharing economy, labor markets and education as well as the role of information design and learning in redesigning non-monetary matching markets. Before coming to Stanford, she graduated from National Technical University of Athens in Greece, where she studied Electrical Engineering and Computer Science. Faidra has been member of the MD4SG initiative since Fall 2018. She has been serving as a co-organizer of the MD4SG Bias, Discrimination, and Fairness working group since Fall 2019.



Chinasa Okolo, Cornell University

Chinasa Okolo is a Ph.D. student in the Department of Computer Science at Cornell University, co-advised by Bharath Hariharan and Nicki Dell. Her research leverages recognition techniques in computer vision to improve mobile healthcare in low-resource regions for the rapid diagnosis of infectious and tropical diseases. She also works on research projects to analyze the applications, implications, and perceptions of AI-enabled healthcare deployed throughout the Global South and North. Before coming to Cornell, Chinasa graduated from Pomona College with a degree in Computer Science. Her work has been supported by The National GEM Consortium, Oracle Corporation, and NANOG, and she has previously interned at Microsoft Research developing computational models and domain-specific computational tools for bacterial quorum sensing. Chinasa has been co-organizing the Development working group since Summer 2020.



Logan Stapleton, University of Minnesota

Logan Stapleton is a PhD student in computer science at University of Minnesota, advised by Professors Zhiwei Steven Wu and Maria Gini. His research applies machine learning and HCI methods to problems of algorithmic discrimination. Recently, he has been focused on methods of engaging community stakeholders in designing fair risk assessment algorithms in Ramsey County, MN and Allegheny County, PA. Logan received his bachelor's degree from Macalester College in 2018, where he studied philosophy and mathematics. He served as the social co-chair for the workshop MD4SG'20 and has served as a community engagement co-organizer, alongside the wonderful Lily Xu, since fall 2020.



Samuel Taggart, Oberlin College

Samuel Taggart is an assistant professor of computer science at Oberlin College. Before joining the department, he completed his doctoral study at Northwestern University in 2017 under the supervision of Jason Hartline. his research interests lie at the intersection of theoretical computer science and mathematical economics. Specifically, he is interested in the interplay between economic incentives and statistical learning and in obtaining theoretical performance guarantees for practical resource allocation protocols such as the first-price auction. He is also interested in how tools from theoretical computer science, AI, and economic design can help solve problems of social import. He has been serving as a co-organizer of the MD4SG Global Perspectives on Inequality working group since fall 2019.



Lily Xu, Harvard University

Lily Xu is a PhD student in computer science at Harvard University, advised by Milind Tambe. Her research applies online learning and game theory to wildlife conservation. She has focused on the prevention of illegal wildlife poaching, and her work on predictive models to prevent poaching is currently being field tested in over 20 protected areas around the world. Lily received her bachelor's degree from Dartmouth College in 2018, where she studied computer science and Spanish. Lily has served as a co-organizer of the MD4SG Environment and Climate working group since spring 2020 and also as a community engagement co-organizer since fall 2020.


Previous Organizers


Kira Goldner, Columbia University

Kira Goldner is a postdoctoral researcher at Columbia University hosted by Tim Roughgarden in the Computer Science department. Specifically, she is an NSF Mathematical Sciences Postdoctoral Research Fellow and a Data Science Institute Postdoctoral Fellow. She received her PhD in computer science and engineering from the University of Washington under the advisement of Anna Karlin, during which she was been supported by a 2017-19 Microsoft Research PhD Fellowship and a 2016-17 Google Anita Borg Scholarship. Her research is in algorithmic mechanism design, with work ranging from relaxing traditional behavioral and informational assumptions, maximizing revenue in settings motivated by practice, and applying mechanism design to social good. She co-founded and co-organized MD4SG from Fall 2016 to 2018.


Previous Leadership


Zoë Hitzig, Harvard University

Zoë Hitzig is a Ph.D. candidate in the Department of Economics at Harvard focusing on microeconomic theory, market design and public finance. Her current projects center on the provision of public goods and services. She is also interested in methodological questions arising out of the use of mechanism design in normatively complex settings. She is a 2019-2020 Graduate Fellow at the Edmond J. Safra Center for Ethics at Harvard, and has held internships at Microsoft Research NYC and Kensho Technologies. She co-organized the MD4SG Global Perspectives on Inequality working group during the 2019-2020 academic year.



Lily Hu, Harvard University

Lily Hu is a PhD candidate in Applied Mathematics and Philosophy at Harvard University. She works on topics in machine learning, algorithmic fairness, and (political) philosophy of technology. Her current time is divided between computer science-related research, where she studies theoretical properties and behaviors of machine learning systems as they bear on deployment in social and economic settings, and philosophical work, where she thinks about causal reasoning about categories like race, theories of discrimination, and what about current technological trends makes capitalism even more distressing. Lily received an A.B. in Mathematics from Harvard College and spent a year teaching English and Spanish history in Madrid on a Fulbright Fellowship. Her current work is supported by an NSF Graduate Research Fellowship. She served as an organizer of the MD4SG Bias, Discrimination, and Fairness working group during the 2018/19 academic year.



Moses Namara, Clemson University

Moses Namara is a Human-Centered Computing Ph.D. candidate at Clemson University, advised by Dr. Bart Knijnenburg. His research interests are broadly in the field of usable privacy and security, and human computer interaction . He uses interdisciplinary research methods from the fields of computer science, psychology and the social sciences to understand the principles behind technology users decision making processes, specifically to try and make decisions a little easier to make through effective policies, better designed and supportive technologies. He completed an MSc. in Computer Science from Clemson University and a BSc. in Computer Science from the University of Maryland, College Park. He is a recipient of the 2017 Facebook Emerging Scholar Award as support to his research. He was born and raised in Kampala, Uganda. He has been co-organizing the MD4SG Development working group since spring 2019.



Manish Raghavan, Cornell University

Manish Raghavan is a fourth-year Ph.D. candidate at Cornell University advised by Jon Kleinberg. He studies algorithmic decision-making and behavioral biases using techniques from theoretical computer science. In particular, he works on understanding the effects that algorithmic decision-making has on society, focusing on fairness in machine learning. His recent work seeks to understand the use of algorithms in the context of employment, with a focus on the algorithmic evaluation of job candidates. He received his B.S. from UC Berkeley in Electrical Engineering and Computer Science in 2016. His work is supported by fellowships from the NSF and Microsoft Research. He has been co-organizing the MD4SG Bias, Fairness, and Discrimination working group since fall 2019. He previously co-organized the MD4SG Online Labor Markets working group.



Daniel Waldinger, NYU

Daniel Waldinger is an empirical economist working at the intersection of market design, industrial organization, and public finance. His research has focused on the design of waiting lists for public housing and organ transplants. More broadly, Dan is interested in how to fairly and efficiently regulate housing and health care markets. Dan joined the NYU economics department as an assistant professor in Fall 2019 after spending a year as a post-doc at the NYU Furman Center. Dan received his PhD in economics from MIT, and his BA in mathematics and economics from University of Chicago. He served as an organizer of the MD4SG Housing working group during the 2018/19 academic year.