Andrea Voyer is a Professor of Sociology at Stockholm University , focusing on social norms, boundaries, and their role in shaping social inequality , particularly in the context of immigrant integration , class disparities, and ethnic inequality. She employs both qualitative and computational methods , including large-scale text analysis and cultural theory, to explore these themes. Her research has received funding from Riksbankens Jubileumsfond , the Swedish Research Council , the National Science Foundation (USA) , and the Russell Sage Foundation . Voyer serves as a program coordinator for master's programs in sociology and applied social research, and leads the department's research groups in qualitative methods , computational sociology , and cultural sociology . She is also an editorial board member for Sociological Theory and American Journal of Cultural Sociology , and contributes to public discourse on immigration, segregation, and social norms. Her recent publications highlight the use of computational techniques to analyze immigrant incorporation through shifts in mainstream discourse, the cultural processes of inequality during the COVID-19 pandemic , and the evolving role of class distinction-making in etiquette literature. These works bridge cultural theory with empirical analysis, offering actionable insights for addressing social inequality.
Swiss Federal Institute of Technology in LausanneSwitzerland
Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.
Dr. Diyi Yang is an Assistant Professor in the Computer Science Department at Stanford University. She leads the Social and Language Technologies (SALT) Lab, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). Her research focuses on socially aware natural language processing, large language models (LLMs), and human-AI interaction, aiming to improve human-human and human-computer communication through socially grounded AI systems. Education: Ph.D. in Language Technologies Institute, Carnegie Mellon University (2013–2019) B.S. in ACM Honored Class, Shanghai Jiao Tong University (2009–2013) Research Interests: Dr. Yang’s work bridges computational social science and NLP. She explores how AI can understand social contexts in language use and develop systems that respect cultural norms, ethical standards, and human values. Her lab’s projects include AI companions for skill training (e.g., Rehearsal Dialects), norm-aware LLMs (NormBank), and frameworks for human-AI collaboration (Co-Gym). Recent efforts address bias in AI, societal impacts of LLMs, and ethical evaluation of human-AI systems. Awards & Honors: 2024: Sloan Research Fellowship, ONR Young Investigator Award 2023: Adamic-Glance Young Distinguished Award (ICWSM), Kavli Fellow (NAS) 2022: NSF CAREER Award, Microsoft Research Faculty Fellow 2020: IEEE AI’s 10 to Watch Advising & Grants: Dr. Yang advises over 10 PhD students and postdocs, co-leading projects on LLM evaluation (SWE-bench/SWE-smith), human-AI ethics, and culturally aware NLP. Her research is supported by NSF, Amazon, DARPA, Google, and Stanford’s HAI initiative. Labs & Teams: The SALT Lab collaborates across disciplines, with projects spanning computer science, linguistics, and social sciences. Current initiatives include developing AI tools for mental health support (AI Partner & Mentor) and auditing societal impacts of LLMs.
Laurel MacKenzie is an Associate Professor in the Department of Linguistics at New York University (NYU), affiliated with the Faculty of Arts and Science. She specializes in variationist sociolinguistics, dialectology, and language change, with a focus on English and French varieties. Her work integrates quantitative analysis of speech data to explore intra-speaker variation and language evolution. She co-directs the NYU Sociolinguistics Lab and leads the NSF-funded NYC Individual Differences Corpus project, alongside the Our Dialects initiative, an online atlas of British English dialects. Education: PhD in Linguistics, University of Pennsylvania (2012) BA in Linguistics and French, University of California, Berkeley (2006) Research Interests: Morphological and syntactic variation Regional dialects of English and French Linguistic pedagogy and public engagement Recent Projects: Recent work includes publications on participle leveling in English, sociolinguistic replication studies, and grammatical variation analysis. She has also collaborated on dialect mapping tools and consulted for media projects on language change and accents. Awards: No awards explicitly listed in provided texts. Labs/Teams: NYU Sociolinguistics Lab (Co-Director) Our Dialects Project (Academic Lead)
Jerrica Ty Rowlett serves as an Assistant Professor of Communication and Language Studies within the College of Arts and Sciences at Bryant University, where she examines digital communication's societal implications through research and teaching in digital culture and social media dynamics. Education Ph.D., Florida State University M.A., Clemson University B.A., Georgetown College Her research program investigates identity formation, civic engagement, and digital inequity through critical analysis of social media platforms. She explores how digital environments shape interpersonal relationships, political discourse, and community building while addressing power dynamics in online spaces, with particular attention to abusive contexts, gender representation, and emotional experiences. Analysis of her publication record (2015-2024) reveals a consistent trajectory examining social media's evolving societal impact. Early work focused on educational applications and youth culture, progressing to sophisticated analyses of political meme culture, relationship dissolution in abusive contexts, and emotional contagion online. Her scholarship demonstrates increasing attention to social justice dimensions within digital communication. Scientific Awards No awards documented in current sources Advising and Grants Student advising details and research grant information are not specified in available materials, though her extensive publication record indicates active scholarly mentorship and research productivity. Labs and Teams No dedicated research laboratories or formal collaborative teams are mentioned in the provided information.
Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.
Sara Ng is a Visiting Assistant Professor in the Department of Linguistics at Western Washington University. She holds a PhD in Linguistics from the University of Washington (2024) and an MS in Computational Linguistics (2023), alongside a B.A. in Linguistics and B.S. in Applied Mathematics from the University of Utah (2017). Her research focuses on computational models of prosody, speech perception, and their integration with speech technologies like automatic speech recognition (ASR). She investigates how prosody conveys pragmatic meaning, influences conversational dynamics, and impacts listeners with hearing impairments. Her work bridges computational methods and linguistic theory, addressing challenges in clinical and technological applications. Education PhD in Linguistics, University of Washington, 2024 MS in Computational Linguistics, University of Washington, 2023 B.A. in Linguistics (Honors), B.S. in Applied Mathematics, University of Utah, 2017 Research Interests Ng’s research explores computational linguistics, prosody modeling, speech technology, and hearing impairment studies. She develops methods to leverage prosodic cues for tasks like punctuation prediction and investigates the impact of hearing loss on speech perception. Grants & Awards 2024: Nominated for UW Excellence in Teaching Award 2023: Excellence in Linguistic Research Graduate Fellowship 2017: University of Utah Top Scholar Award Teaching Ng teaches courses in phonetics, computational linguistics, and linguistics for honors students. She emphasizes pedagogical innovation and inclusivity, with experience as an instructor of record and teaching assistant at both Western Washington University and the University of Washington. Labs & Collaborations She collaborates with the TIAL Lab (University of Washington), Phonetics Lab, Hearing Aid Laboratory (Northwestern University), and CLILLAC-ARP (Université Paris Cité). Her work intersects with clinical and engineering domains, addressing real-world applications of speech technology.
Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.
Robert JOLIET is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics and Management from HEC-ULg Management School (Belgium) and has held various academic leadership roles, including Director of Academic Development and Academic Director of the PGE (MSc in Management). His research focuses on finance, portfolio management, SRI (Socially Responsible Investing), and corporate finance, with a particular emphasis on international expansion, capital structure, and sustainable investment strategies. Education: 2008: Ph.D. in Economics and Management, HEC-ULg Management School, Belgium 2004: Master of Advanced Studies (DEA), Management Sciences (Finance), University of Liège, Belgium 2002: Master of Science in Business Engineering (Finance), University of Liège, Belgium Research interests span SRI fund dynamics, corporate social responsibility valuation, international debt policies, and market liquidity. Notable contributions include analyzing the impact of SRI mutual funds on corporate governance and environmental practices, and exploring the stability of sovereign debt structures in the Eurozone. Awards include the 2005 FORTIS Award (Third Prize) and the 2003 E.G. de Barsy Doctoral Fellowship. He has published extensively in journals like Economics Letters and Journal of Banking & Finance . Academic leadership roles include directing programs and departments at IÉSEG, alongside coordinating the Finance Track and managing academic development initiatives. His teaching focuses on financial data analysis, active portfolio management, and investment strategies in capital markets. He is a member of the LEM (Laboratoire d'Économie Moderne) research group and contributes to interdisciplinary research in finance and economics.
Massachusetts Institute of TechnologyUnited States
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.
Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.
Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.
David Williamson Shaffer is the Sears Bascom Professor of Learning Analytics and Vilas Distinguished Achievement Professor of Learning Sciences at the University of Wisconsin-Madison's Department of Educational Psychology. He is also a Data Philosopher at the Wisconsin Center for Education Research. His work focuses on merging statistical and qualitative methods to model complex human collaboration. Shaffer holds an AB in History and East Asian Studies from Harvard University (1987), and MS/PhD in Media Arts and Sciences from MIT (1996/1998). Before academia, he was a teacher, curriculum developer, and game designer. His research emphasizes Quantitative Ethnography —a methodology combining big data analysis with cultural interpretation. Key contributions include Epistemic Network Analysis (ENA) and over 250 publications, including influential books like How Computer Games Help Children Learn (2006) and Quantitative Ethnography (2017). Awardees of the EU Marie Curie Fellowship (2008) and Spencer Foundation Fellowship (2003), Shaffer's work has been presented globally at conferences like Learning Analytics & Knowledge and Computer Supported Collaborative Learning. His interdisciplinary approach bridges education, data science, and humanities, addressing challenges in authentic STEM experiences, collaborative learning analytics, and culturally responsive pedagogy.
Bo Bottomley is a Researcher affiliated with the School of Law at the University of East Anglia. She holds a Doctor of Philosophy (PhD) and is a member of the Migration Research Network. Her work focuses on migration and refugee studies, particularly examining how state policies impact human rights and refugee integration. She has contributed peer-reviewed analyses in high-impact journals such as the Journal of Refugee Studies . Bottomley’s research bridges legal frameworks, social policy, and global migration dynamics. Education: Doctor of Philosophy (PhD) from an unspecified institution (listed in her profile). Research Interests: Migration Studies, Refugee Law, Human Rights, and the intersection of state policies with international law. She critically evaluates how state actions influence refugee rights and societal inclusion, emphasizing structural inequalities and legal accountability. Publications: Her 2022 book review in Journal of Refugee Studies highlights her engagement with contemporary debates on refugee rights and state responsibilities. This work reflects her broader focus on systemic barriers faced by displaced populations. Labs/Teams: Active member of the Migration Research Network, contributing to collaborative projects on migration law and policy.
Samuel J. Gershman is a Professor of Psychology at Harvard University, affiliated with both the Department of Psychology and the Center for Brain Science. He directs the Computational Cognitive Neuroscience Lab (CCNLab), where he investigates how the brain acquires richly structured knowledge about the environment and uses this knowledge to guide adaptive behavior. Gershman received his B.A. in Neuroscience and Behavior from Columbia University in 2007 and his Ph.D. in Psychology and Neuroscience from Princeton University in 2013, followed by postdoctoral training in the Department of Brain and Cognitive Sciences at MIT (2013-2015). His research spans computational neuroscience, cognitive psychology, and machine learning. His primary research interests include learning, memory, decision making, and computational neuroscience. Gershman's work integrates behavioral, neuroimaging, and computational techniques to understand cognitive processes. He has made significant contributions to understanding memory systems, reinforcement learning, and the computational principles underlying human cognition. Analysis of Gershman's recent publications reveals a strong focus on computational approaches to understanding cognitive processes, with particular emphasis on memory systems, decision-making mechanisms, and the intersection of artificial intelligence with cognitive neuroscience. His work often bridges theoretical computational models with empirical neuroscience data, exploring how the brain implements efficient cognitive algorithms. Gershman actively mentors graduate students and postdoctoral researchers, with current advisees working on diverse projects spanning computational modeling, neuroimaging, and behavioral experiments. His lab investigates topics ranging from dopamine signaling to social cognition using a combination of theoretical and experimental approaches. The CCNLab, which Gershman directs, brings together researchers from psychology, neuroscience, computer science, and related fields to explore the computational principles of cognition. The lab utilizes a range of methodologies including behavioral experiments, neuroimaging, computational modeling, and theoretical analysis to address fundamental questions about how the mind works.
