Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.
Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
Moe Z. Win is the Robert R. Taylor Professor at the Massachusetts Institute of Technology (MIT), specializing in wireless communications, optical communications, and space communications systems. His research bridges theoretical and applied domains, including quantum sensing, network localization, and signal processing. B.S.E.E., Texas A&M (1987) M.S.E.E. & Ph.D., University of Southern California (1989, 1998) Recent work focuses on quantum-enhanced positioning, machine learning for localization, and next-generation (xG) non-terrestrial networks. He leads research at the Quantum neXus Laboratory (QX Lab), Wireless Information & Network Sciences Lab, and Laboratory for Information and Decision Systems. His career spans the Jet Propulsion Laboratory (1987-1995) and AT&T Research Laboratories (1998-2002). Key methodologies include soft information fusion, variational quantum sensing, and robust beam tracking for terahertz communications.
Subhransu Maji is an Associate Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, and the co-director of the Computer Vision Lab. He is also affiliated with the Center for Data Science and holds a part-time role as an Amazon Scholar. His research focuses on high-level visual recognition algorithms and interdisciplinary applications in ecology and astronomy. He has received prestigious awards including the NSF CAREER Award (2018), Best Paper at WACV 2015, and the Google Graduate Fellowship (2008). Education: PhD in Computer Science from UC Berkeley (2011), BTech from IIT Kanpur (2006). Prior roles include Research Assistant Professor at Toyota Technological Institute at Chicago (2012-2014). Research Interests: Computer Vision Machine Learning AI Applications in Ecology and Astronomy 3D Shape Understanding Climate Science Grants and Funding: Supported by NSF, NASA, Climate Change AI, and industry grants from Facebook, NVIDIA, Adobe, and Dolby. Current projects include satellite imagery analysis for ecology and material science applications using deep learning. Labs and Teams: Leads the Computer Vision Lab, collaborates with interdisciplinary teams on ecological monitoring (e.g., bird migration tracking via radar data) and material property prediction (e.g., zeolite adsorption modeling).
Peter Henderson is an Assistant Professor at Princeton University with joint appointments in the Department of Computer Science and the School of Public and International Affairs. He is affiliated with the Center for Information Technology Policy (CITP), Princeton Language and Intelligence Initiative (PLI), Center for Statistics and Machine Learning (CSML), and Program in Law & Public Policy (PLAW). J.D./Ph.D., Stanford University, 2023 M.Sc., McGill University and Montréal Institute for Learning Algorithms Henderson's research focuses on the critical intersection of artificial intelligence and law, with particular emphasis on AI safety, methods to improve reasoning in foundation models, interdisciplinary approaches to law and AI, and AI governance. His work spans language-grounded reinforcement learning, alignment techniques, strategic decision-making in legal contexts, and public interest artificial intelligence. He investigates how legal frameworks can guide the development of AI systems that benefit society while addressing potential harms. Henderson's recent publications reveal a strong trajectory toward addressing the safety, governance, and legal implications of foundation models. His work combines rigorous technical AI research with deep legal analysis, particularly examining how intellectual property law interacts with AI development, regulatory pathways that balance innovation with harm prevention, and the role of law in shaping responsible AI deployment. A significant thread throughout his research is the development of better evaluation methodologies for AI systems, especially in critical domains like law. SEAS Excellence in Teaching Award (2025) Henderson leads the Princeton Law+Language, AI, & Society (POLARIS) Lab, where he advises students and researchers working at the intersection of AI and legal studies. His research has been supported through collaborations with government agencies, including work with the IRS on audit selection algorithms. His findings have informed practical applications in government efficiency and equity, as well as legal system improvements. Henderson runs the POLARIS Lab at Princeton, which focuses on developing AI systems that work for the public interest, particularly in legal contexts. His team develops sequential decision-making systems for government efficiency, foundation models capable of reasoning about law, and improved safety evaluation frameworks for AI systems. The lab maintains strong connections with legal practitioners and policymakers to ensure research has real-world impact.
Amy Bruckman is a Regents’ Professor and Senior Associate Chair in the School of Interactive Computing at Georgia Institute of Technology. As a Fellow of The ACM and member of the SIGCHI Academy , she contributes to social computing research focusing on online collaboration, cross-cultural understanding, and content moderation. Her 2022 book "Should You Believe Wikipedia? Online Communities and the Construction of Knowledge" explores digital knowledge creation. Education: Ph.D. from MIT Media Lab (1997), B.A. in Physics from Harvard (1987) Research Affiliations: GVU Center, IRI People and Technology Her work addresses social computing challenges including: AI ethics in human-AI interaction Algorithmic bias in content moderation Epistemic justice in online communities Comparative platform analysis (Reddit, Discord, TikTok) Grassroots technology movements Recent publications examine: AI support tools for online counseling Community-driven research frameworks Racial bias mitigation in toxic language detection Designing teachable social media experiences Human trafficking intervention ethics Scientific recognition includes: ACM Fellowship SIGCHI Academy membership She has contributed to: Georgia Tech's Computing Education initiatives Research ethics frameworks for Human-Computer Interaction
Todd Sorensen is an Associate Teaching Professor at the University of California, Merced (UC Merced), affiliated with the School of Social Sciences, Humanities & Arts and the Economics and Business Management department. He holds fellowships at IZA (Institute of Labor Economics) and GLO (Global Labor Organization). Prior to UC Merced, he served as an Associate Professor at the University of Nevada, Reno (2014–2023) and an Assistant Professor at UC Riverside (2007–2014). His professional journey includes a visiting role as Visiting Associate Professor at UC Merced before his current appointment in 2022. Education: PhD in Economics, University of Arizona, 2007 Research Interests: Todd's work focuses on labor market structures , exploring topics like monopsony power and wage-setting dynamics; immigration , including its demographic and policy implications; and discrimination , particularly in criminal sentencing and labor markets. His research has been published in journals like ILR Review , Labour Economics , and the Journal of Population Economics , with findings cited in the U.S. Economic Report of the President. Scientific Awards: Fellow at IZA (2022–present) Fellow at GLO (2022–present) Advising & Grants: Todd has advised students across various levels but no specific advisees are listed. His grants and funding sources are not detailed in the provided text. He collaborates with interdisciplinary teams and maintains affiliations across UC campuses through his and his spouse’s academic careers. Labs/Teams: Engaged with research networks at IZA and GLO, focusing on labor market policies and migration studies. Active in collaborative projects analyzing immigration trends, labor market competition, and discrimination metrics.
Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at the University of Chicago Booth School of Business and a Professor of Economics and the Peter de Florez Professor of EECS at the Massachusetts Institute of Technology . His work bridges machine learning , behavioral science , and computational medicine , focusing on social problems like discrimination , poverty , and health equity . Research Interests : Behavioral economics, algorithmic fairness, poverty, AI in healthcare, and policy evaluation. Teaching : Courses on Artificial Intelligence and Algorithmic Solutions to Human Problems. Publications : Over 150 papers in journals like Science , Quarterly Journal of Economics , and Nature Medicine , with recent work on AI-driven healthcare disparities and behavioral economics. Scientific Awards : MacArthur ‘Genius’ Grant, Infosys Prize, ‘Top 100 Thinker’ (Foreign Policy Magazine), ‘Young Global Leader’ (World Economic Forum). Organizations : Co-founder of ideas42 (behavioral science non-profit), J-PAL (randomized trials in development), and Dandelion Health (healthcare data for AI). Serves on the MacArthur Foundation board.
Ji Zhu is the Susan A. Murphy Collegiate Professor of Statistics at the University of Michigan, Department of Statistics. He holds affiliations with the Michigan Institute for Data Science (MIDAS) and the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP). His research focuses on statistical machine learning, network analysis, and health science applications. Education: B.Sc. in Physics (Peking University, 1996), M.Sc. and Ph.D. in Statistics (Stanford University, 2000 and 2003). Notable awards include the NSF CAREER Award (2008), Fellowships from the ASA (2013) and IMS (2015), and recognition as a Web of Science Highly Cited Researcher (2014–2020). Research interests span statistical methodologies for networks, survival analysis, and high-dimensional data. He co-authored influential papers on community detection, network cross-validation, and latent space models. Current editorial roles include Editor-in-Chief of the Annals of Applied Statistics and Action Editor for the Journal of Machine Learning Research. Advising: Supervised over 50 students and postdocs, many now in academia and industry. Notable former advisees include Tianxi Li (University of Minnesota), Yuan Zhang (Ohio State University), and Weijing Tang (Carnegie Mellon University). Labs/Teams: Active in interdisciplinary projects at MIDAS and MiCHAMP, focusing on healthcare analytics and predictive modeling for diseases like hepatitis and cardiovascular outcomes.
Alison Ledgerwood is a Professor in the Department of Psychology at the University of California, Davis, and Principal Investigator of the Attitudes and Group Identity Lab. Her research examines how social context shapes attitudes and preferences, with a focus on open and inclusive scientific practices. Ph.D., Social Psychology, New York University (2008) M.A., Psychology, New York University (2006) B.A., Psychology, Amherst College (2003) Her research explores psychological distance , framing effects , and system justification theory , while methodologically advancing preregistration , collaborative science , and equity in publishing . She also investigates group identity dynamics and implicit/explicit bias measurement . Recent publications highlight her work on racial bias methodology , open science reform , and contextual framing . Awards include the 2024 Distinguished Service to the Society award, 2021 UC Davis Advising and Mentoring Award, and 2017 SPSP Service to the Field award. 2024 Distinguished Service to the Society, SPSP 2021 UC Davis Graduate Advising and Mentoring Award 2017 Service to the Field, SPSP Hellman Fellowship (2010-2011), UC Davis UC Davis Chancellor's Fellow APS Fellow SESP Fellow Ledgerwood advises on scientific integrity through roles like Chair of the Transparency and Openness Promotion (TOP) II Guidelines Task Force and Anti Colorism and Eurocentrism in Methods and Practices (ACEMAP) Task Force. Her lab fosters contextual evaluation studies and psychological distance frameworks .
Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.
Benjamin Brooks is a Professor in the Department of Economics and the College at the University of Chicago, serving as Director of Graduate Studies. He holds a Ph.D. from Princeton University (2014), an M.A. from Princeton (2010), and a B.A. in Mathematics and Quantitative Economics from Tufts University (2008). His research focuses on microeconomic theory, mechanism design, auction theory, repeated games, and computational game theory. Brooks has published extensively in top journals such as the American Economic Review, Econometrica, and the Journal of Political Economy. His work often explores informationally robust mechanisms and strategic behavior in auctions and stochastic games. Brooks has received grants from the National Science Foundation for projects on mechanism design and stochastic game algorithms. He is an Associate Editor of the American Economic Review: Insights, Theoretical Economics, and Economic Theory. His teaching spans game theory, price theory, and advanced topics in economic theory at both undergraduate and graduate levels. Brooks has also contributed to software tools like BCESolve and SGSolve for analyzing Bayesian correlated equilibria and stochastic games. Research Highlights: Contributions to auction design under incomplete information, robust mechanism design, and algorithmic solutions for stochastic games. Awards: Excellence in Refereeing Award (2017), NSF grants (2015-2025), and Fellow of the Society for the Advancement of Economic Theory (2024).
Stephen Billings is an Associate Professor of Finance at the Leeds School of Business, University of Colorado Boulder, where he holds the Global Real Estate Capital Markets Chair position. He serves as the faculty director of the MS in Real Estate program and research director for the CU Real Estate Center. His work spans multiple research centers including the Center for Research on Consumer Financial Decision Making and the Michael A. Klump Center for Real Estate. Billings received his PhD in Economics from the University of Colorado, a MS in urban planning from the University of North Carolina, and a BS in economics from Georgetown University. Prior to joining CU Boulder in 2016, he was faculty at the University of North Carolina Charlotte with appointments in both economics and public policy, where he also served as faculty director for the real estate center. His research focuses on the intersection of housing, schools, neighborhoods and crime, addressing critical policy issues including the impact of public investment in light-rail transit on neighborhoods, school segregation's influence on academic achievement and crime, neighborhood gentrification causes, lead-based paint in low-income housing, and the effects of sea level rise and natural disasters on neighborhood sorting and household finances. Billings employs sophisticated analysis of large micro-datasets with studies based on data and policies in Charlotte, NC and Denver, CO. His work connects urban economics with public policy, demonstrating how neighborhood environments shape individual outcomes across multiple dimensions. Billings' recent publication record demonstrates a consistent focus on neighborhood effects, school choice impacts, crime economics, and housing market dynamics. His work frequently examines the intersection of race, housing, and economic outcomes, with particular attention to policy implications. He has developed innovative methodological approaches to studying residential amenities and neighborhood spillovers, with increasing attention to climate change impacts on housing markets. His research appears in top journals including The Quarterly Journal of Economics, American Economic Review: Insights, and Journal of Urban Economics. As an educator, Billings teaches undergraduate and masters classes in real estate finance and economics, as well as doctoral and masters level courses on cost-benefit analysis and statistical methods. He has extensive consulting experience conducting economic impact studies, including assessments of North Carolina's public higher education system, affordable housing needs in Colorado communities, and the economic effects of marijuana legalization on property markets. His practical policy work complements his academic research, creating a bridge between theoretical insights and real-world applications. Billings leads significant research initiatives through the CU Real Estate Center, where he directs studies examining the impact of business improvement districts and other urban development policies. His work bridges academic research with practical policy applications, making substantial contributions to our understanding of how housing, education, and neighborhood environments shape individual outcomes and community development. His December 2023 memo on the HUD Officer Next Door Program exemplifies his engagement with current housing policy issues.
Atsuko Tanaka is an Associate Professor in the Department of Economics at the University of Calgary. Her research focuses on labor markets, health economics, and public policy, particularly examining topics such as female labor supply, skill investment, and pension reforms. She holds a Ph.D. in Economics from the University of Wisconsin-Madison (2013), alongside advanced degrees from Cornell University and the University of Tokyo. Education Ph.D. Economics, University of Wisconsin-Madison (2013) M.S. Economics, University of Wisconsin-Madison (2010) M.S. Applied Economics and Management, Cornell University (2007) B.A. Agricultural Economics, University of Tokyo (2004) Research Interests Tanaka’s work explores how health shocks, financial constraints, and policy interventions influence labor market outcomes. She investigates topics such as post-educational skill accumulation via college loans, strategic medical treatment choices under reimbursement reforms, and the macroeconomic effects of pension system transitions. Her research combines rigorous econometric methods with real-world administrative data to inform evidence-based policy. Awards and Grants SSHRC Connection Grant (2022, co-PI: Apostolos Serletis) National Science Foundation Extreme Science Grant (2021) SSHRC Insight Development Grant (2016) Susan Jane Blake Kocin Award, University of Wisconsin-Madison (2011) Grants and Collaboration Her research has been supported by major grants, including SSHRC and NSF funding. She collaborates with scholars on projects like analyzing physician behavior under reimbursement reforms, auditing firm dynamics, and international aid allocation strategies. Her work bridges theoretical models with empirical evidence to address complex socio-economic challenges.
