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Antoine BosselutView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Antoine Bosselut is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the EPFL NLP group. His research focuses on developing AI reasoning agents that can model, represent, and reason about human and world knowledge, with applications in health, education, and global fairness. His research interests span multiple critical areas in modern AI: LLM Representations of Knowledge: Understanding what language models know and how they represent that knowledge internally Reasoning Algorithms: Developing methods to improve LLMs' reasoning capabilities through symbolic systems, neuroscience, and cognitive science Large-scale AI Development: Creating open-source foundation models with multilingual capabilities AI Democratization: Ensuring equitable access to AI technologies across different cultural and regulatory contexts His recent publications reveal a strong focus on the intersection of language models and cognitive science, with multiple studies examining how LLMs align with human cognition. He's also deeply engaged in practical applications of NLP technology, particularly in education and multilingual settings, as evidenced by his work on evaluating AI's impact on higher education and developing culturally-aware language models. His scientific achievements have been recognized with several prestigious awards: Outstanding Paper Award at NAACL 2025 ELLIS Scholar designation in 2024 Outstanding Paper Award at ACL 2023 Inclusion in Forbes 30 Under 30 list for Science & Healthcare in 2021 Bosselut actively mentors numerous PhD students across diverse NLP research areas and has secured significant recognition for his work in both academic and media circles. His lab has been featured in major publications including Communications of the ACM, The Atlantic, and Quanta Magazine, highlighting the societal impact of his research on commonsense reasoning and AI capabilities.

Robert WestView profile

Associate Professor

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +4 more

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.

Adriana KovashkaView profile

Associate Professor

• Computer Vision
• Machine Learning
• Natural Language Processing
• +7 more

Adriana Kovashka is an Associate Professor at the University of Pittsburgh , affiliated with the School of Computing and Information and serving as Department Chair . Her academic journey began with BA degrees in Computer Science and Media Studies from Pomona College (2008) and a PhD in Computer Science from The University of Texas at Austin (2014). Joined Pitt’s faculty in January 2015 NSF CAREER awardee (2021) Google Faculty Research Award recipient Dr. Kovashka’s research spans Computer Vision , Machine Learning , and Natural Language Processing , focusing on visual rhetoric, weak multimodal supervision, and domain adaptation. She pioneered techniques for analyzing political imagery, developing robust object detection frameworks, and exploring the intersection of visual and textual persuasion through large-scale annotated datasets. Her recent work emphasizes geographic diversity in vision-language systems, audio-visual fusion for domain generalization, and shape-texture bias mitigation in CNNs. Key publications include groundbreaking studies on symbolic reasoning, multimodal dialogue systems, and ethical AI applications in education. Scientific honors include: NSF CRII Award (2016) NSF CAREER Award (2021) Pitt CRDF Award (2016, 2018) Best Paper at ECV Workshop (2021) Google Faculty Research Award (2016, 2018) Dr. Kovashka actively mentors students in multimodal learning projects and collaborates with interdisciplinary teams on NSF-funded initiatives. She co-organizes workshops like the first CVPR workshop on advertisement understanding and leads research groups exploring human-AI co-learning systems.

Jiaxuan YouView profile

Assistant Professor

• Graph Neural Networks
• Relational Data
• Machine Learning Systems
• +9 more

Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.

Fei FangView profile

Associate Professor

• Artificial Intelligence
• Multi-Agent Systems
• Machine Learning
• +5 more

Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .

Pranav AnandView profile

Professor

• Semantics
• Pragmatics
• Syntax-Semantics Interface
• +5 more

Pranav Anand is a Professor in the Department of Linguistics at the University of California, Santa Cruz (UCSC). He currently serves as the Faculty Director of the Humanities Institute at UCSC since July 2023. His research focuses on the interplay between context, interpretation, and grammatical perspective, particularly in areas like de re/de se contrasts, evaluative predication, and indexical shift. He has contributed to studies on narrative structures, evidential restrictions, and the syntax-semantics interface in sluicing. Dr. Anand has taught a variety of courses including Ling 119: Narratives , Ling 231: Semantics A , and special topics like Invented Languages: From Elvish to Esperanto . His work bridges theoretical linguistics with computational methods, evidenced by collaborations in projects such as the Santa Cruz sluicing dataset and analyses of political discourse in online commentary. His research has been published in journals like Linguistics and Philosophy , Language , and Discourse and Society , with a focus on semantics, pragmatics, and narrative linguistics. He has also contributed to computational linguistics initiatives, including the development of annotated corpora for sentiment analysis and argumentation studies. Dr. Anand's academic contributions span both theoretical exploration and applied computational linguistics, reflecting his interdisciplinary approach to understanding language structure and usage.

Wei PengView profile

Professor

• Health Communication
• Human-Computer Interaction
• Media Psychology
• +7 more

Dr. Wei Peng is a Professor in the Department of Media and Information at Michigan State University, specializing in health communication technology design and human-computer interaction. Her research explores behavior change mechanisms through interactive media. Ph.D. in Communication from University of Southern California (2006) Key research areas include: Psychological and social effects of interactive media AI-driven health promotion systems Conversational agents for wellness Digital games for physical activity Mobile health applications Combating health misinformation through technology Her 2024-2021 publications demonstrate expertise in AI ethics, health gamification, and technology-based behavior change. Grant collaborations include: National Science Foundation (NSF) - Sensor-enabled family routines for child obesity prevention Robert Wood Johnson Foundation (RWJF) - Exergame development Scientific recognition includes multiple ICA Top Paper Awards and MSU Teacher-Scholar honors. She actively serves on editorial boards of: Journal of Communication Health Communication Games for Health Journal BMC Public Health Currently advising in AI applications for health misinformation and persuasive technology research.

Shipra AgrawalView profile

Associate Professor

• Optimization
• Machine Learning
• Multi-Armed Bandits
• +12 more

Shipra Agrawal is an Associate Professor at the Department of Industrial Engineering and Operations Research, Columbia University, with affiliations to the Data Science Institute and the Department of Computer Science. Her research bridges optimization and machine learning, focusing on decision-making in uncertain environments. PhD in Computer Science from Stanford University (2011) Researcher at Microsoft Research India (2011–2015) Her work addresses online optimization , reinforcement learning , and game theory , aiming to develop algorithms that balance exploration and exploitation for long-term goals. Applications include internet advertising , revenue management , and resource allocation . Recent publications examine dynamic pricing models, regret bounds in reinforcement learning, and convex knapsack optimization. Her research has been supported by NSF CAREER , Google Faculty Research , and Amazon Research Awards . NSF CAREER Award CMMI-1846792 (2019) Google Faculty Research Award (2017) Amazon Research Award (2017) She has advised PhD students who now hold positions at institutions like Google DeepMind, Amazon, and Facebook. Agrawal serves as an associate editor for Management Science , INFORMS Journal on Optimization , and Journal of Machine Learning Research , and co-chaired major conferences such as COLT 2024 and AISTATS 2025.

Benjamin BrooksView profile

Professor

• Microeconomic Theory
• Mechanism Design
• Auction Theory
• +3 more

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).

Nicole Koschate-FischerView profile

Professor

• customer insights
• digital marketing
• pricing and product management
• +3 more

Nicole Koschate-Fischer is Professor and holds the GfK Chair for Marketing Intelligence at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where she currently serves as Dean of Research at the Faculty of Business, Economics and Social Sciences. She is spokesperson for the interdisciplinary research focus "Customer Insights" and founding director of the Experimental Lab for Business Insights Nuremberg (ELAN). Previously, until September 30, 2024, she served as spokesperson for the Institute of Marketing and head of the Master's in Marketing program. Professor Koschate-Fischer earned her doctorate and habilitation from the University of Mannheim after completing a degree in business administration and psychology. Her academic journey includes membership in the interdisciplinary Collaborative Research Center "Concepts of Rationality, Decision Behavior, and Economic Modeling" at Mannheim, along with research stays as visiting scholar at University of Florida and University of Texas at Austin. She has held professorships at Technical University of Kaiserslautern and HEC Paris, and served as visiting professor at University of Vienna and Central University of Finance and Economics in Beijing. Research Interests Professor Koschate-Fischer's research spans customer insights , digital marketing , pricing and product management , and marketing analytics . Her work investigates consumer behavior in digital contexts, brand partnerships, life event impacts on purchasing decisions, and the psychological aspects of pricing. She employs experimental research methods and mediation analysis to understand causal relationships in consumer decision-making processes. Her publication record demonstrates consistent high-impact research in leading journals including Journal of Marketing, Journal of Marketing Research, and Journal of the Academy of Marketing Science. Recent work focuses on digital communication phenomena like emoji usage in electronic word-of-mouth, conversational agent interactions, and the application of behavioral pricing principles in charitable contexts. Professional Recognition Full member of the Bavarian Academy of Sciences and Humanities Member of the Board of Academic Trustees of the Center for Advanced International Marketing Knowledge (AiMark) Editorial board member of Journal of International Marketing Professor Koschate-Fischer regularly serves as reviewer for leading international academic journals and presents her research at both academic and practitioner-oriented conferences. Her work receives funding from organizations including the German Research Foundation (DFG), and she maintains active collaborations with renowned companies across various industries. She has directed the Experimental Lab for Business Insights Nuremberg (ELAN) since its founding, creating a hub for interdisciplinary customer insights research that bridges academic rigor with practical business applications.

Emily WallView profile

Assistant Professor

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +3 more

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Virginia LeavellView profile

Assistant Professor

• Organisational Anticipation
• Digital Prediction Technologies
• AI and Autonomous Systems
• +4 more

Virginia Leavell serves as Assistant Professor in Organisational Theory and Information Systems at Cambridge Judge Business School, University of Cambridge. Her research investigates how organisations anticipate technological change through digital prediction systems, AI, and infrastructure transformation, drawing on her decade-long background in political organising for non-profits and labour groups across the US and Thailand. Her academic credentials include: BA in Interdisciplinary Studies, Georgetown University MA in Sociology, University of California Santa Barbara PhD in Technology Management, University of California Santa Barbara Leavell's scholarly work centers on organisational anticipation of technological futures, employing ethnographic and social network analysis methods to examine digital transformation in critical infrastructure contexts. She explores how predictive technologies reshape organisational structures and actions before technological change fully materializes, with particular focus on water systems, urban planning, and labor industries where digital twins and simulation technologies create new governance challenges. Analysis of her 2017-2024 publications reveals consistent examination of prediction performativity across sectors, demonstrating how digital models influence physical realities in infrastructure management. Her research trajectory shows increasing emphasis on sociomaterial dimensions of anticipatory organising, with recent work interrogating the equalization of human and technological agency in digital transformation processes. No scientific awards were specified in source materials. Leavell actively contributes to the Organisational Theory and Information Systems subject group at Cambridge Judge Business School, which engages cross-disciplinary leadership themes. Information regarding student advising and research grants remains unspecified in available documentation.

Lorraine (Xiang) LiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Lorraine (Xiang) Li is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh’s School of Computing and Information (SCI). Her research focuses on the intersection of natural language processing, commonsense reasoning, knowledge representation, and machine learning, particularly in designing probabilistic models and evaluation methods for implicit commonsense knowledge in language. Li holds a PhD from the University of Massachusetts, Amherst, and previously worked as a young investigator with the Mosaic team at AI2. She has an M.S. in Computer Science from the University of Chicago, where she conducted research at TTIC. Her work emphasizes advancing AI’s ability to reason contextually and generate robust, human-like understanding through probabilistic frameworks. Key research themes include bias detection in reasoning models, iterative model editing, domain adaptation with LLMs, and evaluating commonsense through probabilistic measures. Her recent publications explore challenges like confirmation bias in chain-of-thought reasoning and geographical robustness in object recognition. Li actively contributes to the NLP community, serving on program committees for ACL, EMNLP, NAACL, and ARR. Though no formal awards are listed, her prolific publication record reflects her impact in AI research. She currently leads research in procedural knowledge models (e.g., Plasma) and long-tail knowledge generation, advancing foundational AI methodologies.

Khin Than WinView profile

Professor

• Health Informatics
• Digital Health
• Persuasive Technology
• +7 more

Professor Khin Than Win is a leading academic in health informatics and digital health at the University of Wollongong (UOW), holding appointments as Professor in the School of Computing and Information Technology, Head of Postgraduate Studies, and Deputy Head (Research). She also serves as Academic Program Director for UOW's Master of Health Informatics and Graduate Certificate in Health Analytics programs. Her research focuses on applying information technology to healthcare, particularly in behavior change support systems, persuasive technology, and ethical AI applications. She has supervised over 20 PhD students and holds leadership roles including Deputy Chair of UOW's Health and Medical Research Ethics Committee, and membership in international committees like the Persuasive Technology Steering Committee. Education: MBBS from Rangoon University, Master's and PhD in IT from Assumption University (Bangkok) and UOW (Australia) Research Interests: Health data analytics, AI in healthcare, privacy/security of health systems Leadership: Program/General Chair roles at ACIS and Persuasive Technology conferences Awards: Best Paper Awards (2023, 2018) Her extensive funding portfolio includes ARC grants and NHMRC projects, totaling over 24 funded initiatives. Current research explores blockchain in medical passports, AI ethics, and culturally tailored health interventions.

• Multimodal Discourse Analysis
• Systemic Functional Linguistics
• Digital Humanities
• +6 more

Professor Kay O'Halloran serves as Chair Professor and Head of Department of Communication and Media within the School of the Arts at the University of Liverpool since August 2019. She concurrently holds the position of Co-Director for the Digital Media and Society Institute (DMSI), demonstrating significant leadership across academic and research domains. Her career spans prestigious institutions including Curtin University (2013-2019) and National University of Singapore (1998-2013), where she directed the Multimodal Analysis Lab and served as Deputy Director of the Interactive & Digital Media Institute. PhD from Murdoch University (1996) Postdoctoral position at Martin Luther University (1997-1998) Visiting Distinguished Professor at Shanghai Jiaotong University (2017-2020) Professor O'Halloran's research focuses on multimodal discourse analysis, particularly the interaction of language with visual and mathematical resources. Her pioneering work in systemic functional multimodal discourse analysis (SF-MDA) has significantly impacted mathematics education and digital communication studies. Current research emphasizes digital tools for multimodal analysis and mixed methods approaches to big data analytics, addressing contemporary challenges in misinformation and public health communication. Her recent publications reveal strong thematic concentration in pandemic-related communication analysis, misinformation resistance mechanisms, and multimodal argumentation across social and cultural contexts. The 15 most recent articles demonstrate consistent application of multimodal frameworks to urgent societal issues including public health crises, political discourse, and digital literacy challenges. Founding editor of Routledge Studies in Multimodality book series (57+ volumes) Over 150 publications in leading international journals 50+ plenary/keynote presentations globally Competitive funding from National Research Foundation, MOE Singapore, US Air Force, ARC, NHMRC, and others Professor O'Halloran has directed interdisciplinary research teams comprising social scientists, computer scientists, and designers. Her leadership extends to developing commercial multimodal analysis software (Multimodal Analysis Image and Video) adopted internationally. Current projects include Eurovision 2023 wellbeing evaluation and COVID-19 misinformation immunity development, demonstrating continued relevance to contemporary societal challenges.