Adam Zylbersztejn is an Assistant Professor (Maître de conférences HDR) at Université Lumière Lyon 2 and affiliated with GATE Lyon Saint-Etienne, a research group in economics. He holds a PhD from the Paris School of Economics (2013) and previously conducted postdoctoral research at WU Wien (Vienna University of Economics and Business). His research focuses on behavioral economics using experimental methods to study individual and interactive decision processes, particularly in contexts involving trust, strategic uncertainty, and cultural variability. Key research areas include behavioral game theory, experimental finance, and the impact of nonverbal communication in economic interactions. His work has been published in top journals such as Journal of Economic Psychology , Games and Economic Behavior , and Experimental Economics . Recent studies examine topics like anonymity effects in charitable giving, trust in market exchanges under oath, and cognitive processes in asset markets using eye-tracking technology. His ongoing projects include research on risk-incentive tradeoffs and ambiguity framing in decision-making. While no formal academic awards are listed, his work has received press coverage in outlets like Le Monde and CORTEX Mag . He collaborates widely with institutions such as GATE-LSE and the Paris School of Economics, contributing to both theoretical and applied economic inquiry.
Kimon Fountoulakis is an Associate Professor at the University of Waterloo. His research focuses on Machine Learning on Graphs and Numerical Optimization, with a strong emphasis on algorithmic methods for graph-structured data. He holds a Ph.D. from The University of Edinburgh (2015), an M.Sc. from The University of Edinburgh (2010), and a B.Sc. from Athens University of Economics and Business (2009). His work spans theoretical foundations and practical applications in graph algorithms, optimization, and machine learning. Research interests include graph neural networks, local graph clustering algorithms, and algorithmic reasoning. His contributions address challenges in graph representation learning, message-passing architectures, and scalable optimization methods. Notable themes in his publications include improving counting abilities of vision-language models, analyzing graph convolutions, and developing flow-based clustering techniques with statistical guarantees. His work often bridges theory and practice, with applications in network analysis, pandemic containment strategies, and high-performance computing. While no specific grants or awards are listed, his research demonstrates significant contributions to graph-based machine learning and optimization. He maintains a research group at the University of Waterloo, with a focus on developing open-source tools and frameworks for graph algorithms. His lab’s work emphasizes local graph clustering methods and their scalability in real-world networks.
Joydeep Biswas is an Associate Professor in the Computer Science Department at the University of Texas at Austin, where he serves as the Director of the Autonomous Mobile Robotics Laboratory (AMRL). He is also affiliated with Texas Robotics, the UT Machine Learning Laboratory, and UT Good Systems. Previously, he was an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. Dr. Biswas earned his PhD in Robotics from Carnegie Mellon University in 2014 and his B.Tech in Engineering Physics from the Indian Institute of Technology Bombay in 2008. His educational background has provided him with a strong foundation in both theoretical and applied aspects of robotics and artificial intelligence. Dr. Biswas's research focuses on enabling long-term autonomy for mobile robots operating in human environments. His work spans robot perception, motion planning, control systems, and AI, with the ultimate goal of creating self-sufficient autonomous mobile robots that can perform tasks accurately and robustly in real-world settings. He is particularly interested in perception, planning, and failure recovery for autonomous mobile robots, which supports his vision of having autonomous service mobile robots deployed at campus-to-city scale, both indoors and outdoors, performing assistive tasks over deployments spanning years. His IJCAI 2019 Early Career Spotlight talk summarizes much of his research to date and ongoing interests. His recent research has shown a strong trend toward social navigation, human-robot interaction, and the application of machine learning techniques to robotics problems. There's a clear progression from fundamental robotics research toward more complex, real-world applications that require robots to understand and navigate human social spaces effectively. His work increasingly integrates large language models and other advanced AI techniques with traditional robotics approaches, as evidenced by his recent publications on topics like preference-conditioned navigation, social navigation benchmarks, and instruction-following navigation systems. Dr. Biswas has received numerous prestigious awards including the NSF CAREER Award (2021), J.P. Morgan Faculty Research Award (2019), Amazon Research Award (2019), and a grant from Northrop Grumman Mission Systems (2018). These awards recognize his innovative contributions to the field of robotics and autonomous systems. As a dedicated educator and mentor, Dr. Biswas actively supervises PhD and master's students, with his PhD student Sadegh Rabiee winning the student poster award at the Northrop Grumman University Symposium 2019. He has secured significant grant funding from the National Science Foundation for projects including 'Introspective Perception and Planning for Long-Term Autonomy' and 'Interactive Synthesis and Repair For Robot Programs,' demonstrating his ability to secure competitive research funding and his commitment to advancing the field. Dr. Biswas leads the Autonomous Mobile Robotics Laboratory (AMRL), which serves as a hub for interdisciplinary research in mobile robotics. The lab has developed notable resources such as the UT Campus Object Dataset (CODA) for 3D perception research and SOCIALGYM, a framework for benchmarking social robot navigation. His team regularly deploys robots on the UT Austin campus and in urban environments to test and refine their approaches in realistic settings, bridging the gap between simulation and real-world application.
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.
Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.
Dr. Kenneth Joseph is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo , part of the School of Engineering and Applied Sciences . He serves as Associate Director of the Institute for Artificial Intelligence and Data Science and leads the Computation and Equity Lab (cubelab) , focusing on social inequality through computational measures and models. Education: PhD, MS, and BS in Societal Computing from Carnegie Mellon University (2016, 2012, 2010) Research Interests: Computational Social Science, Network Science, Gender Studies, and AI for Social Good Notable Work: Gender disparities in academia, predictive modeling for foster care and urban policy, and social media rumor analysis Awards: UB Exceptional Scholar—Young Investigator Award (2021) Advising: Mentored students like Yuhao Du, Jason Yan, Arjunil Pathak, and Navid Madani on projects spanning Twitter bios, foster youth services, and algorithmic fairness.
Jack J. Blanchard, Ph.D., is an Associate Provost for Enterprise Resource Planning and Professor in the Department of Psychology at the University of Maryland, College Park. He is affiliated with the Brain and Behavior Institute and serves as Academic Director of the Master of Professional Studies program in Clinical Psychological Science. Previously, he held roles as Department Chair and Director of Clinical Training. Education: Ph.D. in Clinical Psychology, State University of New York at Stony Brook (1991) B.S. in Psychology, Arizona State University (1984) NIMH Postdoctoral Fellow at Medical College of Pennsylvania (1990-1992) Research Interests: Dr. Blanchard’s work focuses on emotion-behavior interactions in psychotic disorders, particularly schizophrenia. His laboratory (LEAP) employs fMRI, smartphone-based ecological assessments, and actigraphy to study: Social affiliation deficits and paranoia Neural correlates of motivation and reward processing Impact of sleep on symptom severity Clinical and behavioral assessment innovations Publication Trends: Recent articles (2020-2025) demonstrate a strong emphasis on transdiagnostic approaches, integrating neuroimaging with real-world behavioral tracking to examine social reward processing, pandemic-related mental health impacts, and sleep-psychosis comorbidities. Methodological themes include fMRI, smartphone assessments, and longitudinal designs. Awards and Honors: Joel and Kim Feller Professorship (2016) Excellence in Teaching Mentorship Award Fellow, Association for Psychological Science Mentoring and Grants: Actively trains graduate/undergraduate researchers through LEAP lab. Secured major funding including: NIMH grants for neural correlates of social affiliation/paranoia University of Maryland Brain and Behavior Initiative awards for wearable tech integration Laboratory and Collaborations: Directs the Laboratory of Emotion and Psychopathology (LEAP), collaborating with psychiatry departments and neuroscientists. Focuses on minority-representative samples and integrates clinical, behavioral, and technological methodologies.
Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.
Mohamed Bouri is a Senior Lecturer and Researcher at École Polytechnique Fédérale de Lausanne (EPFL), where he is affiliated with the School of Engineering (STI) and specifically the Microengineering Department (SCI-STI-MB). He is part of the ReHAssist research group (http://rehassist.epfl.ch), which focuses on rehabilitation robotics and human-robot interaction. His office is located in the MED Building (MED 3 1016) at Station 9, 1015 Lausanne. Dr. Bouri's research spans several key areas in robotics and rehabilitation engineering. His primary focus is on the development and control of exoskeleton systems for mobility assistance and rehabilitation. He has made significant contributions to hip exoskeleton technology, adaptive control strategies, and human-robot interaction paradigms. His work bridges engineering principles with clinical applications, particularly for individuals with mobility impairments and neurological conditions. Additional research interests include sensory substitution techniques, balance control systems, and astronomical instrumentation involving robotic fiber positioners for multi-object spectrographs. Analysis of Dr. Bouri's recent publications reveals a strong emphasis on practical applications of robotics in rehabilitation settings. His work increasingly focuses on user-centered design, adaptive control systems that respond to individual user needs, and ecological validity in testing environments. There's a clear trend toward developing systems that can function effectively in real-world scenarios rather than controlled laboratory settings. His research also shows growing integration of physiological feedback mechanisms and multimodal sensing to enhance human-robot cooperation, with applications spanning from Parkinson's disease rehabilitation to astronomical instrumentation. Dr. Bouri has supervised numerous doctoral students whose theses reflect the breadth of his research interests, including work on lower-limb exoskeletons, robotic control systems, and rehabilitation technologies. His collaborative approach is evident in the extensive list of co-authored publications across multiple institutions and disciplines, demonstrating his ability to bridge engineering with clinical and astronomical applications. Based at EPFL's Microengineering Department, Dr. Bouri leads research activities within the ReHAssist laboratory, which specializes in rehabilitation assistance technologies. The lab focuses on developing innovative robotic solutions for mobility assistance, with particular expertise in exoskeleton design, control algorithms, and human-robot interaction paradigms. His work on projects like TWIICE One has demonstrated real-world impact in assistive technology development.
Beth St. Jean is an Associate Professor and Faculty Director of the Carillon Community in Health Justice at the University of Maryland’s College of Information Studies (College Park). She holds a PhD and MS in Information from the University of Michigan School of Information and a BA in Mathematics from Smith College. Her research focuses on improving health outcomes through understanding health-related information behaviors, health literacy, and self-efficacy. She co-authored Understanding Human Information Behavior (2021) and leads projects like HackHealth to enhance youth health literacy. Education: PhD in Information, University of Michigan School of Information (2012) MS in Information (LIS), University of Michigan School of Information (2006) BA in Mathematics, Smith College (1988) Research Interests: Health informatics, health justice, consumer health information behavior, health literacy, youth digital practices, and information literacy. Her work emphasizes addressing health disparities through information access and education. Recent Research Trends: Her publications explore topics like long-haulers’ information needs (2024), pandemic-era health justice (2023), and social media’s role in predicting health disparities (2022). She also investigates patient-generated health data challenges (2022) and library roles in promoting health equity (2020). Awards and Recognition: ASIS&T SIG-USE Innovation Award (2017) ACM SIGCHI Excellent Reviewer (2017) ALISE/LMC Paper Award (2015) Beta Phi Mu/LRRT Research Award (2014) Teaching and Grants: Teaches courses on information behavior, research methods, and consumer health informatics. Co-PI on grants like HackHealth 2.0 (NIH, $123K) and youth health literacy initiatives ($68.5K). Also serves as Assistant Director of the Information Policy & Access Center and Senior Fellow at CASCI. Labs and Initiatives: Leads the Carillon Community in Health Justice and collaborates on projects like the HackHealth after-school program to engage disadvantaged youth in health entrepreneurship.
Assoc. Prof. Dr. Sema Alaçam Doğan has been affiliated with Istanbul Technical University since 2014, serving as an Associate Professor in the Department of Architecture . She has held administrative roles including Deputy Head of Department and Erasmus Coordinator. Education : PhD in Informatics in Architectural Design (2008-2014), MS in Informatics in Architectural Design (2005-2008), and BS in Architecture (1999-2005) from Istanbul Technical University. Her research explores Computational Design , Artificial Intelligence in Architecture , and Sustainable Material Innovation . She investigates digital tools for heritage preservation, daylight optimization in BIM, and cognitive development in architecture students. Recent publications analyze AI-assisted design literacy , machine learning for Sinan mosques , and environmental comfort in Harran houses . Her work integrates algorithmic frameworks with sustainable practices. Scientific awards include multiple ITU Publication and Performance Awards (2021-2024), FABFEST Prizes , and the 2024 Artemis Educator Award from NASA. Active projects like "Physical Computation in Architectural Drawing" and "Robotic Fabrication with Recycled Wind Turbine Blades" demonstrate her leadership in computational and sustainable research.
Karyn Moffatt is an Associate Professor in the School of Information Studies at McGill University and holds the Canada Research Chair in Inclusive Social Computing. As Graduate Program Director for the PhD program, she leads the Accessible Computing Technologies Research Group (ACT Lab), focusing on designing inclusive computing applications that support social engagement across diverse lifespans and abilities. Her work bridges human-computer interaction, accessibility research, and real-world community impact. Educational background: PhD in Computer Science, University of British Columbia MSc in Computer Science, University of British Columbia BASc in Computer Engineering, University of British Columbia Research Interests: Dr. Moffatt's work centers on inclusive social computing with emphases on aging populations , disability access , and intergenerational communication . She investigates how technology can overcome barriers to social participation through co-design methodologies, particularly for older adults and individuals with cognitive or physical disabilities. Current projects explore AI-enhanced aging support, dementia-friendly social platforms, accessible financial technology, and respite care coordination systems. Her approach integrates participatory design with rigorous usability testing to create solutions that address real-world challenges in healthcare, finance, and community engagement. Publication Trends: Analysis of her 15 most recent publications reveals consistent focus on aging and accessibility (60% of works), with growing emphasis on AI ethics (2025), dementia support systems (30% of 2023-2024 works), and accessible financial technology (2024). Methodologically, 75% employ co-design or participatory approaches, while 40% involve longitudinal field studies. Key venues include CHI (33%), ASSETS (20%), and ACM Transactions on Accessible Computing (27%), demonstrating leadership in top-tier HCI and accessibility forums. Scientific Awards: Multiple Best Paper Awards from ASSETS, CHI, and CSCW conferences Canada Research Chair in Inclusive Social Computing Advising and Grants: Dr. Moffatt currently supervises PhD candidates Chong Hu and Muhe Yang, having graduated four students since 2022 including Maurício Fontana De Vargas (2023) and Carrie Dai (2023). Her active grants include: NSERC Discovery Grant (2024-2029) as PI: Ethical AI for active aging Canada Research Chair renewal (2022-2027) as PI McGill Nursing Collaborative grant (2023-2025) as Co-I: iRespite mHealth app for palliative care Labs and Teams: She directs the ACT Lab, which partners with healthcare providers, public libraries, and community organizations to develop and deploy inclusive technologies. Current initiatives include the QuickPic AAC system for speech therapy, dementia-focused social programs with Montreal libraries, and Quebec-wide respite care coordination tools, all developed through interdisciplinary collaboration with clinicians, caregivers, and end-users.
Jeffrey P. Bigham is an Associate Professor at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University . His research spans human-computer interaction , human-AI interaction , accessibility , dialog systems , NLP , and crowdsourcing . Current PhD Students: Hamza El Alaoui, Jessica Yin Huynh, Sara Kingsley, Peya Mowar, Yi-Hao Peng, Atieh Taheri PhD Graduates: Erin Brady, Yu Zhong, Ting-Hao Huang, Anhong Guo, Cole Gleason, Prakhar Gupta, Stephanie Valencia, Kundan Krishna, Jason Wu His work is funded by Apple , Bosch , DARPA , Google , Microsoft , the National Institute of Disability Rehabilitation Research , the National Science Foundation , and Yahoo! He also holds a CMU HCII Career Development Fellowship . Selected Awards: NSF CAREER Award 2019 Best Paper at ASSETS 2021 Best Paper Nomination at CHI 2024 Best Paper Nomination at CHI 2021 Best Paper Nomination at DIS 2021
Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.
Lisa B. Limeri is an Assistant Professor at the Department of Biological Sciences, Texas Tech University. Her work bridges social psychology, educational psychology, and STEM education to address retention and success in biology education. She leads the Limeri Lab, focusing on social-psychological factors affecting student persistence and instructor-student dynamics. Ph.D., Biological Sciences, University of Pittsburgh (2012–2017) Postdoctoral Research, Biology Education Research, University of Georgia (2017–2021) Her research examines how students' and instructors’ beliefs about abilities influence academic outcomes. She develops classroom interventions to reduce stressors and improve educational experiences. The lab’s recent work spans mindset measurement, community cultural wealth, disability frameworks, and mentoring surveys. Scientific awards include the STEP Program Fellow (2021–Present) and Scholar Certification from the Center for Integrated Research on Teaching and Learning (2017). She mentors doctoral students Anastasia Chouvalova and Mason Tedeschi and collaborates with postdoctoral researchers. The Limeri Lab has received recognition for its contributions to undergraduate research at Texas Tech symposia.
