Prof. Luke Zettlemoyer is an Adjunct Professor of Computer Science and Engineering at the University of Washington, with affiliations to the Department of Linguistics. He focuses on machine learning, natural language processing, and multimodal systems, contributing to advancements in large language models, ethical AI, and scalable architectures. His research addresses challenges in model alignment, generalization, and cross-domain integration. Key research interests include multimodal reward models, efficient tokenization strategies, and model optimization techniques. He has explored topics such as neural trajectories for robot learning, content-adaptive image processing, and ethical mitigation of verbatim data reproduction. His publications span 2023–2025, emphasizing practical applications of AI in robotics, vision-language systems, and scalable retrieval-based models. While no formal awards are listed, his work reflects significant contributions to foundational AI research.
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.
Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI
David B. Shmoys is the Laibe/Acheson Professor of Business Management & Leadership Studies at Cornell University's School of Operations Research and Information Engineering (ORIE) and holds a joint appointment in the Department of Computer Science. He is a leading researcher in approximation algorithms, stochastic optimization, and computational sustainability. His work emphasizes data-driven decision-making in enterprise and society, with applications ranging from healthcare logistics to environmental conservation. Shmoys has advised 27 Ph.D. students and served as an editorial board member for top journals like Operations Research and Mathematics of Operations Research. Education: Ph.D. in Computer Science, University of California, Berkeley (1984). Postdoctoral positions at MSRI and Harvard University, followed by faculty roles at MIT before joining Cornell. Research Interests: Design and analysis of approximation algorithms for NP-hard problems, stochastic optimization, scheduling, facility location, and inventory management. Applications include computational sustainability, epidemiological modeling, and political districting optimization. Articles Trends: Focus on optimization under uncertainty, computational sustainability, and real-world applications like air ambulance routing, bike-sharing systems, and gerrymandering analysis. Notable works include the 2018 Wagner Prize-winning paper on bike-sharing analytics and the 2013 Lanchester Prize-winning textbook on approximation algorithms. Awards: Lanchester Prize (2013), Daniel H. Wagner Prize (2018), Fellowships from ACM, INFORMS, and SIAM, NSF Presidential Young Investigator (1987). Advising & Grants: Over 27 advised Ph.D. students, with former students at MIT, Waterloo, Brown, and D-Wave. Active in grants related to data science and sustainability. Labs/Teams: Involved in the Institute of Computational Sustainability at Cornell, focusing on applying algorithms to environmental challenges. Collaborates with organizations like Motivate (bike-sharing) and Ornge (air ambulance services).
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.
Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu
Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.
Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.
Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.
Daniel Krutz is an Associate Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT) , with a secondary appointment as Courtesy Associate Professor at the University of Florida's Department of Computer & Information Science & Engineering. He directs the AWARE Lab , focusing on Autonomous Systems, Warfare Applications, and Software Engineering , supported by $6M+ in federal grants from NSF, NSA, DOD, and AFRL. His research spans AI/ML, Self-Adaptive Systems, Decision Support Systems, and Accessibility in Computing Education . He holds a PhD from Nova Southeastern University and has taught courses including Software Engineering Freshman Seminar and Capstone Research Project . He is an NSF CAREER Award recipient (2022) and former AFRL Fellow (2018). Educations: BS, St. John Fisher College MS, Rochester Institute of Technology PhD, Nova Southeastern University Labs: AWARE Lab (Autonomy, Warfare, and Engineering) Awards: NSF CAREER Award (2022) AFRL Research Faculty Fellowship (2018) Teaching: Undergraduate/Graduate courses in software engineering, accessibility, and AI/ML Research Interests: Focus on experiential learning in computing education, neuroevolutionary algorithms, and AI ethics. Recent projects address empathy-building interventions for inclusive software development and context-aware decision support systems . Grants & Funding: Over $6M secured since 2018 from NSF, DOD, and NSA for projects in AI/ML, cybersecurity, and adaptive systems. Current work explores AI-driven stock trading frameworks and accessibility education modules.
Darryl Jones is Professor at Griffith University's Centre for Planetary Health and Food Security, specializing in behavioral ecology and human-wildlife interactions. His research examines urban ecosystems, wildlife conflict resolution, and conservation strategies, with particular emphasis on bird ecology and road infrastructure impacts. Professor Jones has authored influential books including 'Feeding the Birds at Your Table' and 'A Clouded Leopard in the Middle of the Road' that bridge scientific research and public engagement. His work explores: Wildlife responses to urbanization Bird feeding practices and consequences Road ecology and mitigation design Human dimensions of conservation Koala-vehicle collision patterns Recent publications document COVID-19's impact on global bird feeding behaviors, analyze 20-year trends in koala road mortality, and evaluate LED streetlight effects on urban wildlife. He leads research on dingo population viability in Fraser Island and develops innovative wildlife monitoring technologies like the open-source WildWID RFID system. Professor Jones collaborates extensively with government agencies on conservation policy and has secured funding for projects examining wildlife crossing infrastructure effectiveness. His research informs evidence-based approaches to human-wildlife coexistence in rapidly changing landscapes.
Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Dr Kingsley Edney is an Associate Professor in International Relations of China at the University of Leeds' School of Politics and International Studies. He holds a PhD (International Relations) and MA (Political Science) from the University of Melbourne, along with a BA(Hons) in Politics and Anthropology from Victoria University of Wellington. Fluent in Chinese and with experience living in Shanghai and Beijing, his research focuses on China's non-material international security dynamics, including cultural diplomacy, propaganda, and ideological strategies to manage global information flows. His expertise includes Chinese soft power, the role of ideology in foreign policy, and the intersection of domestic political cohesion with international security objectives. He teaches undergraduate and master's courses on China's domestic and international politics. Current postgraduate researchers under his supervision are Jinru Huang, Yanyu Lu, and Hengfeng Zhao. Edney's research outputs span topics like public perceptions of China in the Anglosphere, Chinese youth nationalism, and environmental governance. His work often bridges theoretical frameworks with empirical analysis of China's evolving global engagement strategies. He maintains active academic engagement through platforms like Google Scholar and ORCID.
Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.
