Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)
Noah D. Goodman is Associate Professor of Psychology and Computer Science, and Linguistics (by courtesy) at Stanford University. He directs the Computation & Cognition Lab (CoCoLab) at Stanford, where he leads research on computational models of cognition, integrating logic and probability. His work spans cognitive psychology, linguistics, and computer science. Primary Appointment: Psychology Department By Courtesy: Computer Science Department and Linguistics Department Director: Computation & Cognition Lab (CoCoLab) Goodman's research focuses on computational models of cognition, with particular interest in probabilistic approaches to understanding human thought. His work integrates logic and probability to model concepts, categorization, intuitive theories, causal learning and reasoning, social cognition (including reasoning about others' goals, beliefs, and actions), cognitive development (especially acquisition of abstract knowledge), and natural language semantics and pragmatics. He has made significant contributions to the development of probabilistic programming languages as tools for cognitive modeling. His recent publications demonstrate a strong trend toward integrating probabilistic modeling with linguistic theory and social cognition. The articles span computational cognitive science, natural language processing, and artificial intelligence, with a consistent theme of using probabilistic frameworks to understand complex cognitive phenomena. Many papers explore how humans make inferences under uncertainty across different domains. Goodman teaches several courses at Stanford including Language and Thought (Psych 132), Computation and Cognition: the Probabilistic Approach (Psych 204/CS 428), Foundations of Cognition (Psych 205), and Introduction to Cognitive Science. He has also led seminars on topics ranging from natural and artificial intelligence to the science of meditation.
Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.
Professor Rebecca Lawson is a Professor of Neuroscience and Computational Psychiatry at the University of Cambridge, affiliated with the Department of Psychology and Bye-Fellow at Peterhouse College. Her research focuses on understanding how humans learn to make predictions under uncertainty, with applications to mental health conditions such as anxiety and depression. She leads the Prediction and Learning (PaL) Lab, which combines computational modeling, neuroimaging (e.g., 7T MRI), and behavioral experiments to study cognitive processes in typical and atypical populations. Key research interests include computational psychiatry, autism spectrum disorders, neuroimaging techniques, and the neurochemical basis of learning mechanisms. She has received significant funding, including a £4.3m Wellcome Mental Health Award and the Sir Henry Dale Fellowship. Notable contributions include advancing theories of neural gain and sensory expectations in autism, as well as studies on uncertainty processing in anxiety and depression. Professor Lawson holds academic awards such as the BNPA Lishmann Prize and the BAP Psychopharmacology Award. She actively contributes to public engagement, including initiatives like Knit-a-Neuron and involvement with PrideinSTEM. Her lab collaborates internationally, with projects like the CamRAA study investigating autism-anxiety overlaps and the Chemical and Brain Basis of Uncertainty (CBBU) study exploring pharmacological interventions. Education: PhD in Cognitive Neuroscience (University of Cambridge), BA (Hons) Psychology & Philosophy (University of Glasgow). Grants: £4.3m Wellcome Award, Parke-Davis Fellowship, Lister Institute Prize. Labs/Teams: Principal Investigator of the PaL Lab; collaborations with MRC Cognition and Brain Sciences Unit, Yale University, and Brown University.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
Gretchen A. Macht is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island, College of Engineering. She specializes in Human Systems Engineering, with research emphasizing team performance, sustainability, electric vehicles, solar energy systems, and voting systems. Her work bridges engineering, ergonomics, and public policy. Her research interests focus on understanding and optimizing human-centered systems. She investigates team communication and performance through fuzzy cognitive maps and entropy modeling, develops sustainable design practices for products and energy systems, and analyzes user behavior in electric vehicles and solar installations. A significant portion of her research is dedicated to election systems, where she uses simulation to improve accessibility, safety, and efficiency of in-person voting, particularly during public health crises. Her recent publications span topics such as EV charging behavior, solar installation safety, voting simulation, and team dynamics. These works reflect interdisciplinary trends combining systems engineering, data analytics, ergonomics, and sustainability science. She frequently employs simulation, naturalistic data analysis, and human factors methodologies. Dr. Macht has secured substantial grant funding from organizations including the U.S. Election Assistance Commission, National Science Foundation, MIT, and Democracy Fund. She serves as Principal Investigator (PI) or Co-PI on projects related to election systems (e.g., SMILE, URI VOTES), ergonomic redesign of protective gear, and community solar development. She leads the SIS Lab and the URI VOTES initiative, fostering collaborative research on simulation and system design. Her lab supports graduate students in conducting impactful research at the intersection of engineering and societal challenges. Notable grants include: PI, MIT: “Voting in Person - White Paper” (2023) PI, US EAC: “SMILE 2.0” (2022) PI, Democracy Fund: “URI VOTES Pocket Guide Tool” (2022) Co-PI, NSF: “Election Science: Convergence Accelerator Workshop Proposal” (2021) PI, US EAC: “SMILE: Simulation Models for an Immersive Learning Environment” (2021) She has advised several graduate students, including Mehrsa Khaleghikarahrodi, Nicholas D. Bernardo Jr., and Tim Jonas, whose research aligns closely with her focus areas in EVs, elections, and team performance.
Christopher M EARLS is a Professor in the Department of Psychology at Université de Montréal's Faculty of Arts and Sciences. He specializes in forensic psychology, psychopathology, and behavioral studies with a focus on sexual delinquency and juvenile prostitution. His research explores the intersections of psychopathy, criminal behavior, and mental health comorbidities. He teaches courses including Psychopathology: Introduction across undergraduate programs in psychology, social sciences, and cognitive neuroscience. His academic contributions span over two decades, with notable work on psychopathy assessment, parenting practices in non-criminal populations, and sexual behavior conditioning. Education Affiliations: Université de Montréal (primary) Teaching Programs: Baccalauréat en psychologie, Neuroscience cognitive, Social Work programs Dr. EARLS has directed 10+ theses and mémoires since 1998, focusing on forensic psychology topics like sexual delinquency correlates and psychopathy measurement validity. His work bridges clinical psychology with sociocultural factors, particularly in marginalized populations like sex workers and juvenile offenders. Research Highlights: Clinical assessment tools comparison (paper vs. digital psychopathy scales) Gender differences in psychopathy expression Impact of childhood trauma on sexual interest development Though no formal awards are listed, his extensive publication record reflects sustained contributions to forensic and clinical psychology research.
Andrés Buxó-Lugo serves as an Assistant Professor of Psychology at the University at Buffalo, where he directs the Language Processing and Computation Lab. His research investigates the cognitive mechanisms underlying language production, comprehension, and acquisition with a specialized focus on speech prosody—the rhythm, intonation, and intensity patterns in speech—and their role in human communication. His primary research interests include psycholinguistics, cognitive psychology, speech prosody, language production, language comprehension, language acquisition, and computational linguistics. He examines how listeners integrate diverse linguistic cues during speech processing, how individuals learn unfamiliar constructions like non-native pronunciations or novel prosodic patterns, and the cognitive basis of durational changes in speech. His work also explores how communicative context shapes prosodic production and how higher-level linguistic information aids prosodic structure parsing. Analysis of his 15 most recent publications (2019-2025) reveals consistent interdisciplinary work bridging cognitive science, linguistics, and computational modeling. Key trends include phonological representation studies, speech planning mechanisms, intonation adaptation across talkers, lexical representation structures, and the integration of input expectations in syntactic parsing. His research demonstrates significant methodological diversity, incorporating experimental paradigms, computational modeling, and acoustic analysis to unravel language processing complexities. As director of the Language Processing and Computation Lab at the University at Buffalo, Buxó-Lugo leads research initiatives focused on developing computational models of language processing while investigating the cognitive foundations of speech and prosody through empirical experimentation and theoretical innovation.
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Maria José Lisboa Brites de Azeredo is an Associate Professor with Habilitation at Lusófona University, Portugal. She serves as Vice-President of the Centre for Research in Applied Communication, Culture and New Technologies (CICANT) and coordinates the Media Literacy and Civic Cultures Lab (MeLCi Lab). She holds leadership roles in the Portuguese Association for Communication (SOPCOM) and the European Communication Research and Education Association (ECREA). Her academic career spans over two decades, including roles at institutions like Universidade Lusófona do Porto and Universidade Nova de Lisboa. Her education includes a PhD and Habilitation in Communication Sciences from Universidade Nova de Lisboa. She has completed specialized courses in digital education, intercultural dialogue, and pedagogical training from institutions like the University of Edinburgh and the Knight Center for Journalism in the Americas. Brites’ research focuses on youth journalism, audience studies, digital citizenship, and participatory methodologies. She has led projects such as the YouNDigital study (PTDC/COM-OUT/0243/2021) and the SMaRT-EU initiative. Her work has been recognized with awards including the Research Prize (2017, 2020, 2023) and the Gold Medal of Academic Value (2019). She coordinates the Master's Degree in Media and Information Literacy and Digital Citizenship at Lusófona University and has trained educators through initiatives like the Media in Action (MIA) project. Her publications span peer-reviewed journals, edited books, and conference proceedings addressing media literacy, generational media practices, and civic engagement.
Prof. Dr. Claudia Peus is a full Professor of Research and Science Management at the Technical University of Munich (TUM) School of Management , serving since May 2011. She holds executive roles as Founding Director of the TUM Institute for LifeLong Learning (TUM IL3) since December 2019 and Executive Vice President for Talent Management and Diversity since October 2017. Her career spans visiting positions at MIT's Sloan School of Management and Harvard University, alongside her habilitation (2011) and PhD (2005) from LMU Munich. Current affiliations: TUM School of Management, TUM IL3, RWI – Leibniz Institute for Economic Research Research focuses: Leadership development in the digital age, diversity in organizations, ethical leadership, and research organization management Recent research trends analyze digital leadership dynamics, gender-decoded recruitment practices, robotic leadership interfaces, and neurophysiological mechanisms in leadership behavior. Her 2025 work on moral careers and 2024 resource-based absorption theory highlight evolving themes in organizational psychology. Scientific honors include: Highly Cited Research Award, Leadership Quarterly (2016) Emerald Citations of Excellence (2016) Best Teaching Award, TUM School of Management (2014) Academy of Management Symposium Award (2010) Bayerische Landesbank Wissenschaftspreis (2006) Prof. Peus advises organizations on diversity and talent management. She was recognized as one of Germany's Most Inspiring Women (2020) and leads projects like FührMINT, ForGenderCare, and the TUM Neurophysiological Leadership Lab (NeLeLab).
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Fabian Fagerholm is an Assistant Professor in the Department of Computer Science at Aalto University. His work bridges software engineering, human-computer interaction, and empirical research methodologies. He actively participates in research groups such as Software and Service Engineering (SSE) and Human-Computer Interaction and Design (HCID). Fagerholm's research explores: Continuous experimentation in software development Developer cognition and mental models Agile methodologies and team dynamics Low-code platforms and end-user programming Software engineering education and pedagogy His publications reflect a strong empirical focus, with recurring themes of human factors in technical systems and educational innovation. He has received notable awards including: Journal of Systems and Software Best Paper Award (2018) EUROMICRO SEAA Distinguished Paper Award (2017) Teacher of the Year (2013) Nokia Foundation Scholarship (2013) Fagerholm contributes to software engineering infrastructure through tools for experimentation and boundary artifacts, enhancing collaboration in distributed teams.
Professor Simon Buckingham Shum is a leading academic in Learning Informatics at the University of Technology Sydney (UTS), serving as inaugural Director of the Connected Intelligence Centre since 2014. His work bridges Human-Computer Interaction, Educational Technology, and Learning Analytics to develop AI-driven tools like OnTask that enhance student belonging through personalized feedback. Key research areas: Human-Computer Interaction, Learning Analytics, AI in Education, Collective Intelligence Recent work focuses: LLM-augmented qualitative analysis, Belonging Analytics frameworks, and ethical AI implementation in education CIC received the 2024 Vice-Chancellor’s Student Experience Award for scaling OnTask across multiple faculties. His contributions span over 30 years, including founding roles at The Open University’s Knowledge Media Institute and innovations in Open Educational Resources (OER), SocialLearn, and FutureLearn platforms. Notable awards: 2024 Student Experience Award (OnTask), Fellow of the Royal Society of Arts (RSA), DEFI STRI Researcher at University of Cambridge Education: B.Sc. in Psychology (York), M.Sc. in Ergonomics (UCL), PhD in Human-Computer Interaction (York) He leads multidisciplinary collaborations with teams across Business, Law, and Education faculties, pioneering tools like Theme Explorer and Qreframer bot. His recent focus on the "metacrisis" reframes educational technology’s role in addressing global systemic challenges through transdisciplinary approaches.
Franz Heukamp is the Director General of IESE Business School at the University of Navarra and Professor in the Department of Decision Analysis. He holds the Antonio Valero Chair of Business Administration since 2016 and previously served as Vice Dean of the MBA program and Secretary General of IESE. His academic background includes a Ph.D. in Engineering from the Massachusetts Institute of Technology, an Ingénieur Civil des Ponts et Chaussées from Ecole Nationale des Ponts et Chaussées in Paris, and a Diplom-Ingenieur from Technische Universität München. Professor Heukamp's research focuses on behavioral decision-making with a special interest in neuroeconomics, examining how people make decisions under uncertainty, particularly regarding time-probability trade-offs. His work bridges business decision theory with neuroscience, as evidenced by his publications in journals like Management Science , European Neuropsychopharmacology , and British Journal of Pharmacology . In recent years, he has expanded his research interests to include artificial intelligence applications in management and sustainability. His recent scholarly output shows a progression from fundamental decision theory research to practical applications of AI in business leadership and management education. He has published extensively on risk preferences, intuition in decision making, and the neurological basis of economic choices, with increasing focus on how digital transformation and AI are reshaping management practices. As Director General, Heukamp has championed several strategic initiatives including internationalization efforts across North America, Latin America, and Central Europe, development of new degree programs like the Master in Management and Munich Executive MBA, and integration of digital learning technologies while maintaining the school's commitment to in-person educational excellence. Professor Heukamp is an active member of both the Institute for Operations Research and the Management Sciences and the Neuroeconomics Society, reflecting his interdisciplinary approach to business education and research. His leadership prioritizes supporting faculty research, advancing lifelong learning solutions for executives, and maintaining IESE's mission of service to society through academic innovation.
