Lily L. Tsai is the Ford Professor of Political Science at the Massachusetts Institute of Technology (MIT) and founder/director of the MIT Governance Lab (MIT GOV/LAB). She previously chaired the MIT Faculty and focuses her research on governance, accountability, and political participation in developing contexts, particularly Asia and Africa. Her research explores how decentralization, democratic reforms, informal institutions, and economic development influence public goods provision. She also investigates the dual impacts of social capital and civil society on political outcomes, and analyzes political behavior in authoritarian and transitional systems through surveys, interviews, and field experiments. Recent publications span topics including collective governance, vaccine compliance in Uganda, anticorruption strategies in China, and the role of informal institutions in rural China. Tsai’s work appears in journals like American Political Science Review and World Development . Dogan Award for best book in comparative research (2007-08) She leads interdisciplinary projects such as the Compass Initiative (SHASS Education Innovation Fund) and The Science of Respect with MIT’s Brain and Cognitive Sciences department. MIT GOV/LAB, which she founded in 2014, emphasizes real-time empirical collaboration between researchers and practitioners to address governance challenges.
Dr. Kaya de Barbaro is an Associate Professor in the Department of Psychology at the University of Texas at Austin (College of Liberal Arts). She holds a Ph.D. from the University of California San Diego. Her research focuses on bridging computer science and developmental/clinical psychology, particularly maternal mental health and infant social-emotional development. She directs the Daily Activity Lab, which uses mobile/wearable sensors and machine learning to analyze real-world interactions, aiming to develop just-in-time interventions for new mothers. Key research areas include maternal-infant dynamics, physiological synchronization, and the impact of environmental chaos on development. She has pioneered methods for analyzing high-density data, such as Granger causality and machine learning algorithms to detect behaviors like crying and holding. Recent work emphasizes leveraging ecological momentary assessment surveys and 24-hour LENA audio recordings to understand proximal mechanisms of development. Dr. de Barbaro teaches Psychology 333D (Introduction to Developmental Psychology) and has developed curricula for both in-person and online formats. She is actively involved in training students through the Eureka program at UT Austin. Her lab collaborates on tools like chatbots for postpartum mental health and has published extensively on sensor-based methodologies in developmental science.
Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.
Andrew Connor is a Senior Lecturer in Archaeology and Ancient History at Monash University. He specializes in Ptolemaic and Roman Egypt, papyrology, and the social and religious history of ancient Egyptian communities. His research focuses on Egyptian temples during the Roman period, ancient economic practices, and the editing and publication of documentary papyri. Connor has particular expertise in the Fayum depression and Dakhleh Oasis regions. Connor's publications demonstrate a consistent focus on Egyptian religious institutions, economic systems, and textual evidence from archaeological sites. His recent work includes publications on temple administration, Greek papyri, and digital archaeology. He has received multiple teaching awards including the Faculty Citation for Outstanding Contribution to Student Learning (2017), Faculty Citation for Overseas Programs (2018), and Vice Chancellor's Citation for Outstanding Contribution to Student Learning (2018). Connor teaches courses on Greek and Roman history, archaeology, and ancient languages at both undergraduate and postgraduate levels. He serves as Honours Coordinator for the School of Philosophical, Historical, and International Studies.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Ceren Budak is an Associate Professor at the University of Michigan School of Information and holds a joint appointment as Associate Professor of Electrical Engineering and Computer Science in the College of Engineering. Her work bridges computer science, statistics, and social sciences through computational social science approaches. Her educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a Bachelors degree in Computer Science from Bilkent University in Turkey (2007). Prior to joining the University of Michigan faculty, she was a Postdoctoral Researcher at Microsoft Research New York. Professor Budak's research centers on computational social science, with particular emphasis on analyzing large-scale datasets to address questions with social, political, and policy implications. Her work spans several interconnected domains: News Media Production & Consumption (examining bias in news outlets and reader preferences), Social Movements & Media (using social media data to study collective action), Social Networks (understanding information diffusion processes), and Measuring and Promoting the Quality of Online Discussions (developing tools to improve online conversations). She teaches SI 608 (Networks) and SI 618 (Data Manipulation and Analysis) at the School of Information. Her publication record demonstrates consistent contributions to understanding how online information ecosystems operate, with recent work focusing on AI-human collaboration, misinformation dynamics, social movement framing, and the application of computational methods to political communication. Her research shows a clear trajectory from foundational work on social network diffusion to increasingly sophisticated analyses of contemporary information challenges. Among her service activities, she has served as Registration chair for COSN (ACM Conference on Online Social Networks) 2015 and as Program Committee Member for numerous prestigious conferences including WWW, ICWSM, WebSci, AAAI, and others. She has also been involved in organizing the MSR NYC Data Science Seminar Series and instructing the Microsoft Research Data Science Summer School.
Zhi Da is the Howard J. and Geraldine F. Korth Chair in Finance and Professor of Finance at the University of Notre Dame , Mendoza College of Business, Department of Finance. He completed his Ph.D. in Finance at Northwestern University’s Kellogg School of Management (2006), preceded by an M.Sc. in Financial Engineering from the National University of Singapore (2001) and a B.B.A. with First-Class Honors (1999) from the same institution. Holding editorial roles at Journal of Finance , Management Science , Review of Financial Studies and several other top journals, he is a leading voice in empirical finance research. Education Ph.D. in Finance, 2006 – Kellogg School of Management, Northwestern University M.Sc. in Financial Engineering, 2001 – National University of Singapore B.B.A. (1st Class Honors), 1999 – National University of Singapore Research Interests Zhi Da’s scholarship sits at the intersection of asset pricing , behavioral finance , and market microstructure . He investigates how investor attention, institutional trading, liquidity frictions, and information flows jointly determine the cross-section of expected returns. His work delves into retail margin trading, the role of pension-fund flows in exchange-rate dynamics, the informational content of SEC filings, and the efficiency of short-selling mechanisms. By combining large-scale data analytics, textual analysis, and structural modeling, he uncovers novel predictors of returns ranging from presidential approval ratings to real-time attention measures. Recent projects explore fractional trading ’s impact on price efficiency, hedging demand as a driver of intraday momentum, and the hidden effort problem in delegated portfolio management. These themes collectively advance our understanding of limits to arbitrage and the formation of extrapolative beliefs. Publication Landscape Spanning 2025 back to 2009, his 15 most recent articles in Journal of Finance , Review of Financial Studies , Management Science , Journal of Financial Economics , and Journal of Financial and Quantitative Analysis converge on three broad motifs: (1) micro-level trading frictions—liquidity costs, margin requirements, and short-selling constraints; (2) macro-finance linkages—exchange rates, fiscal policy, and global capital flows; and (3) information economics—attention allocation, media analytics, and regulatory disclosures. The collective evidence demonstrates that seemingly small trading or informational frictions aggregate into large, persistent cross-sectional return predictability. Honors and Awards 2017 William F. Sharpe Award for Best Paper, Journal of Financial and Quantitative Analysis Lead-article distinctions in Journal of Finance , Review of Financial Studies , and Management Science Featured coverage in SmartMoney and CNBC Teaching & Mentorship At Notre Dame’s Mendoza College, Professor Da teaches Investments (undergraduate and MBA) and Fixed Income Securities , integrating cutting-edge research insights into the curriculum. While specific advisees are not listed, his extensive co-author network (22+ recurring collaborators) attests to a vibrant mentoring environment. Laboratory & Data Resources He publicly distributes the NAT (Net Arbitrage Trading) dataset, a stock-quarter panel of arbitrage positions used in Chen, Da & Huang (2019). This resource has become a standard tool for researchers studying arbitrage capital movements.
Johan Gustav Bellika is a Professor at the Department of Clinical Medicine, UiT The Arctic University of Norway, based in Tromsø. His work bridges clinical medicine with health informatics, focusing on practical applications that improve healthcare delivery and patient outcomes. Current Position: Professor, Department of Clinical Medicine Institution: UiT The Arctic University of Norway Location: Tromsø, Norway Contact: johan.gustav.bellika@uit.no Professor Bellika's research spans several critical areas in modern healthcare. His primary focus is on health informatics with particular emphasis on medical data privacy, electronic health records, and the application of artificial intelligence in clinical settings. He has made significant contributions to understanding chronic pain management through technology, primary care research networks, and patient-centered care models. His work often involves large population studies such as the Tromsø Study, examining how digital health tools impact healthcare utilization and patient outcomes. His research demonstrates a consistent thread connecting technological innovation with practical clinical applications. Bellika has been instrumental in developing privacy-preserving architectures for healthcare data analysis, which enable researchers to gain insights from sensitive health information without compromising patient privacy. His work on federated learning frameworks represents cutting-edge approaches to analyzing medical data while maintaining strict privacy controls. Health Informatics and Medical Data Privacy Chronic Pain Management through Technology Primary Care Research Networks (PraksisNett) Electronic Health Records and Clinical Decision Support Patient-Centered Digital Health Solutions Federated Learning Applications in Healthcare Professor Bellika's publication record shows a strong trend toward interdisciplinary research that combines clinical medicine, computer science, and public health. His recent work increasingly focuses on the intersection of artificial intelligence and healthcare, particularly how machine learning can be applied to chronic conditions while maintaining rigorous privacy standards. The geographical scope of his research extends across Norway with particular emphasis on Northern Norwegian populations, providing valuable insights into healthcare delivery in Arctic and remote regions. His collaborative approach is evident through numerous co-authored publications with researchers across multiple institutions and disciplines. While specific awards aren't detailed in the available information, his extensive publication record in reputable journals suggests recognition within his field. Professor Bellika has been actively involved in several major research initiatives including the Tromsø Study and PraksisNett, Norway's nationwide practice-based research network. His work on privacy-preserving architectures for healthcare data has significant implications for how medical research can be conducted while respecting patient confidentiality. He appears to be particularly focused on translating research findings into practical tools for clinicians, as evidenced by his work on audit and feedback systems for antibiotic prescribing.
Peter A. Flach is Professor of Artificial Intelligence at the Intelligent Systems Laboratory, School of Computer Science, University of Bristol, where he has been faculty since 1997 and was promoted to Professor in 2003. He currently directs the UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence and serves as Vice-President of the European Association for Data Science (EuADS), having previously served as its President. His research centers on rigorous evaluation and improvement of machine learning systems, with seminal contributions to classifier calibration (including Beta Calibration and Precision-Recall-Gain curves), explainable AI frameworks, and data science methodology. He pioneered the extension of CRISP-DM to data science trajectories and developed Explainability Fact Sheets for systematic assessment of XAI approaches. His work bridges theoretical foundations with practical deployment in real-world systems. Analysis of his recent publications reveals a dominant focus on interpretable and reliable machine learning, with strong emphasis on performance evaluation metrics, model calibration techniques, and human-centered explainability. His research increasingly addresses healthcare applications through projects like SPHERE and clinical decision support systems, while maintaining core contributions to fundamental ML theory. His scientific recognition includes prestigious fellowships: Fellow of the European Lab for Learning and Intelligent Systems (ELLIS) Fellow of the European Association for Artificial Intelligence (EurAI) Professor Flach has secured major research funding including UKRI Centre for Doctoral Training grants and EU network funding (TAILOR). He leads extensive collaborations across Engineering, Population Health Science, and international institutions (Monash University, Polytechnic University of Valencia), plus over 20 industry partners in the Practice-Oriented AI CDT including LV= and QinetiQ. His work with the SPHERE project demonstrates successful translation of AI research into residential healthcare settings. He leads the Intelligent Systems Laboratory at Bristol, which develops influential open-source tools including the FAT Forensics Python toolbox for algorithmic fairness and the Explainability Fact Sheets framework. His lab maintains strong connections with the European AI community through ELLIS and EurAI, positioning Bristol as a hub for human-centered and methodologically rigorous AI research.
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.
Armin Falk is a Professor of Economics at the University of Bonn, leading the BonnEconLab since 2004 and co-founding the briq Institute on Behavior & Inequality. He holds affiliations with the IZA, DIW, CEPR, and Max-Planck-Institut. His research focuses on behavioral economics, exploring how social preferences like fairness and trust influence decision-making, and investigates early childhood development's role in inequality. Falk has received prestigious awards, including the Leibniz Prize and Yrjö Jahnsson Award. His work combines lab experiments, field studies, and representative data analysis. Education includes a PhD in Economics from the University of Zurich after studies in Economics, Philosophy, and History at the University of Cologne. Key research highlights include global climate action support studies (Nature Climate Change, 2024), socioeconomic disparities in child development (JPE, 2021), and global economic preferences surveys (QJE, 2018). He co-authored the book *Warum es so schwer ist, ein guter Mensch zu sein* to popularize behavioral economics. Awards include recognition for contributions to behavioral and development economics. Mentoring programs like *Balu und Du* aim to reduce educational inequality. His work bridges academic research with public discourse through media contributions and policy engagement.
Scott D. Landes is a Professor in the Sociology Department at Syracuse University's Maxwell School of Citizenship and Public Affairs. He holds affiliations with the Aging Studies Institute, Lerner Center for Public Health Promotion, and Center for Aging and Policy Studies. Landes earned his Ph.D. in Sociology from the University of Florida in 2014. His research focuses on disability mortality disparities, veteran health outcomes, and the intersection of disability with medical and social theory. Education: Ph.D. in Sociology, University of Florida, 2014 Research Interests: Landes investigates mortality trends for disabled adults and veterans, disability theory integration with life course frameworks, and data equity in disability measurement. His work addresses systemic undercounting of disability in federal surveys and explores racial/ethnic disparities in lifespan outcomes for individuals with intellectual/developmental disabilities (IDD). Recent Research Trends: His publications analyze pandemic impacts on IDD populations, combat veteran longevity, and diagnostic overshadowing in mortality reporting. Recent work critiques Washington Group disability measures' limitations and advocates for improved data collection methods. Awards: 2025 Excellence in Graduate Education Faculty Recognition Award O’Hanley Faculty Scholar (Syracuse University) NIH-funded research projects on aging policy and mortality disparities Advising & Grants: Recipient of multiple National Institutes of Health grants. Advises on disability data equity initiatives and participates in interdisciplinary teams addressing veteran health disparities. Supervises graduate research on IDD lifespan trajectories and pandemic vulnerability. Labs/Teams: Core member of Syracuse’s Aging Studies Institute and Lerner Center for Public Health Promotion. Collaborates with the Center for Aging and Policy Studies on federal health data projects. Engages with national disability advocacy groups through research briefs and policy consultations.
Moshe E. Ben-Akiva is the Edmund K Turner Professor at the Massachusetts Institute of Technology (MIT), affiliated with the School of Engineering and the Department of Civil and Environmental Engineering. He holds a B.S. from Technion-Israel Institute of Technology (1968), and M.S. and Ph.D. degrees in transportation systems from MIT (1971, 1973). His research focuses on transportation systems analysis, intelligent transportation systems, demand modeling, econometrics, and infrastructure management. He has been recognized with prestigious awards, including election to the National Academy of Engineering (2025) for contributions to transportation systems modeling and demand analysis. His work spans theoretical and applied domains, including agent-based microsimulation for freight logistics, tradable credit schemes for congestion management, and behavioral dimensions of transport decarbonization. Ben-Akiva collaborates with industry and policymakers to design sustainable mobility solutions. His notable publications include foundational texts on discrete choice analysis and stated preference elicitation. He advises on transportation policy, urban planning, and emerging mobility technologies such as automated vehicles and urban air mobility. Current research explores impacts of automated mobility-on-demand systems, real-time tolling strategies, and e-commerce delivery demand modeling. His team develops tools like SimMobility Freight, an agent-based urban freight simulator. He remains active in teaching, focusing on demand modeling and econometrics courses at MIT.
Christopher L. Foote is a Principal Economist and Policy Advisor at the Federal Reserve Bank of Boston, where he has worked since 2003. He also holds the title of Professor in the Department of Economics at Harvard University since 2012. Foote earned his BA from the College of William and Mary in 1987 and a PhD in economics from the University of Michigan in 1996. His academic career includes teaching positions at Harvard University (1996–2002) and visiting roles at MIT (2004–2006). His research focuses on labor economics, housing, and macroeconomics. Key areas include labor market polarization, mortgage debt patterns, housing market dynamics, geographic disparities in mortality, and economic impacts of population aging. His work frequently addresses policy implications for financial and labor market stability. Measuring employment through online panels Geographic mortality disparities Mortgage debt and foreclosure analysis Population aging effects Historical labor market discrimination Business cycle modeling Foote has received prestigious awards including the Regents Fellowship, Rackham Pre-doctoral Fellowship, and Alfred P. Sloan Doctoral Dissertation Fellowship. He has also served as Associate Editor and editorial board member for major economic journals. His work spans academic publications in top journals, book chapters, and numerous working papers that analyze economic trends and policy responses. Foote's contributions cover housing economics, labor market dynamics, and macroeconomic policy, with a focus on empirical analysis and real-world applications.
Margaret E. Roberts is a Professor in the Department of Political Science at the University of California, San Diego. She co-directs the China Data Lab at the 21st Century China Center and serves as an affiliate at the UC Institute on Global Conflict and Cooperation. Her academic appointments reflect her interdisciplinary approach combining political science, statistics, and computational methods. University of California, San Diego, Department of Political Science (Current) Co-director, China Data Lab at the 21st Century China Center Affiliate, UC Institute on Global Conflict and Cooperation Roberts earned her PhD in Government from Harvard University (2014), MS in Statistics from Stanford University (2009), and BA in International Relations and Economics from Stanford University (2009). Her educational background bridges political science, statistics, and computational methods, forming the foundation for her interdisciplinary research approach. Professor Roberts' research focuses on the intersection of political methodology and the politics of information, with specific expertise in automated content analysis and the politics of censorship and propaganda in China. Her work employs innovative methods including social media analysis, online experiments, and large-scale text analysis to understand how censorship and propaganda influence information access and political beliefs. She has made significant contributions to text-as-data methodologies, developing tools like the Structural Topic Model (stm) R package that have become widely used in social science research. Roberts' research portfolio demonstrates consistent focus on authoritarian information control, particularly in China, while expanding into broader applications of text analysis in political science. Her publications span top journals in political science, computer science, and interdisciplinary fields, reflecting the cross-disciplinary nature of her work. Goldsmith Book Award Best Book Award in the Human Rights Section Best Book Award in Information Technology and Politics Section Best Book Award of the last decade in the Political Communication Section of the American Political Science Association Chancellor's Associates Endowed Chair at UCSD Foreign Affairs Best Books of 2018 Professor Roberts has secured significant research funding supporting her work on Chinese censorship, propaganda, and text analysis methodologies. Her research has practical applications for understanding digital authoritarianism, content moderation, and the development of computational tools for social science research. She has mentored numerous students and collaborators, contributing to the next generation of scholars working at the intersection of political science and computational methods. Roberts also leads the China Data Lab, which serves as a hub for research on Chinese politics and society using digital methods.
