Dr. Saptarshi Sengupta is an Assistant Professor in the Department of Computer Science at San José State University (SJSU), leading the Machine Intelligence and Complex Systems (MICoSys) Lab. He advises the ACM student club at SJSU and holds a 'Alien of Extraordinary Ability' visa (Einstein Visa) from USCIS. His work focuses on resilient cyber-physical systems, risk analysis, and deep learning applications in healthcare and industrial systems. Education: Ph.D. in Electrical Engineering, Vanderbilt University M.S. in Electrical Engineering, Vanderbilt University B.Tech. in Electronics & Communication Engineering, West Bengal University of Technology Research Interests: Cyber-Physical Systems Security Healthcare AI for Cancer and Chronic Disease Prediction Battery Prognostics and Energy Systems Machine Learning for Complex Systems Analysis Key Achievements: Dr. T.M.A. Pai Gold Medal Award for Healthcare AI contributions Recipient of multiple best paper awards at international conferences Author of over 30 peer-reviewed publications Labs & Teams: Leads the MICoSys Lab, developing AI solutions for healthcare diagnostics, industrial prognostics, and smart infrastructure systems. Collaborations include interdisciplinary projects with biomedical and engineering domains.
Professor Danielle Matthews is a Professor of Psychology at the University of Sheffield's School of Psychology and an academic at the Interdisciplinary Centre of the Social Sciences (ICOSS). Her research focuses on how children learn language, particularly pragmatic development (e.g., communicative repair, referential adaptation) and the impact of deafness and socio-economic factors on language acquisition. She has pioneered interventions to support communication in deaf infants and families from disadvantaged backgrounds, collaborating with organizations like the BBC’s Tiny Happy People initiative. Research Interests: Pragmatic abilities in communication, language development in deaf children, socio-economic influences on language, and word learning mechanisms. Grants: Nuffield Foundation, BBC Education, Leverhulme Trust, and UKRI GCRF grants for projects on language interventions and communication development. Teaching: Undergraduate courses on Developmental Psychology and Pragmatic Development; postgraduate supervision and ethics training. Her work emphasizes practical interventions, such as video-based communication strategies for parents of deaf infants and randomized controlled trials to improve caregiver responsiveness. She has also explored how conversational experience shapes pragmatic skills and mitigates disparities linked to hearing loss or socio-economic status. Recent studies include investigations into the cognitive underpinnings of conversational skills in autistic children and the long-term mental health implications of early language delays. Her research bridges theory and practice, aiming to inform educational and clinical practices globally.
Aaron Anderson is a Research Fellow in the Department of Mathematics at the University of Pennsylvania. His work bridges model theory with combinatorics , particularly focusing on distal structures and their applications to logic and continuous logic. He collaborates with prominent researchers like Henry Towsner at UPenn and Michael Benedikt in logic and machine learning contexts. Anderson's research explores the intersection of mathematical logic , combinatorics , and machine learning . His publications on arXiv highlight advancements in understanding distal metric structures , NIP theories , and the logical foundations of learnable objects. Recent work extends distal regularity to continuous logic and random objects , demonstrating theoretical and practical implications across disciplines. His academic journey includes a Ph.D. at UCLA under Artem Chernikov, where he laid the groundwork for his current research. While no formal awards are listed, his contributions to combinatorial bounds and generic stability underscore his growing influence in the field. Anderson's work is supported by institutional frameworks like the Simons Foundation, and he actively engages in presenting his findings through talks and publications.
Raphaël Baur is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zürich, with affiliations to the ETH AI Center and OAT X. His research focuses on integrating cognitive science and reinforcement learning into architectural design, emphasizing evidence-based methodologies and educational tools. He develops frameworks for human-centered design and investigates how feedback mechanisms can enhance AI-driven decision-making in architecture. His work spans interdisciplinary areas, including the application of machine learning to architectural learnability, the design of educational tools like MazeMastery for high school curricula, and the creation of toolkits such as DesignMind to bridge cognitive science and spatial design. Key publications highlight trends in reinforcement learning with human feedback, evidence-based architectural pedagogy, and the cognitive foundations of architectural practice. Baur’s contributions aim to advance both theoretical and applied aspects of design through computational and behavioral insights. Labs/Teams: Active member of the ETH AI Center and OAT X, collaborating on projects at the intersection of AI, education, and spatial design.
Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.
Jeffrey Heinz is a Professor at Stony Brook University , holding a joint appointment in the Department of Linguistics and the Institute for Advanced Computational Science . He has been at Stony Brook since 2017, following a decade at the University of Delaware. His research focuses on computational linguistics, formal language theory, grammatical inference, and phonology, with applications to robotics and artificial intelligence. He earned his Ph.D. in Linguistics from UCLA in 2007. Heinz’s work bridges theoretical linguistics and computational methods, emphasizing the learnability of linguistic patterns through formal models. He has contributed to understanding phonological typology, reduplication, and the mathematical foundations of language learning. His research has been published in Science , Phonology , and Machine Learning , among others. He was honored with the 2017 Early Career Award from the Linguistic Society of America for his contributions to computational learning theory in linguistics. He teaches advanced courses in computational phonology and linguistics, including a course at the LSA Summer Institute. He actively organizes academic sessions and serves on steering committees for conferences like ICGI. His interdisciplinary approach integrates linguistics with computer science, robotics, and mathematical logic. Award highlights include: 2017 Early Career Award (Linguistic Society of America) He advises students in linguistics and computational fields, though specific names are not listed here. His research labs and collaborations involve computational linguistics and robotics projects, such as stress pattern databases and grammatical inference benchmarks.
Joanna McGrenere is a Professor in the Department of Computer Science at the University of British Columbia . Her research focuses on Human-Computer Interaction , Personalized User Interfaces , and Universal Usability , particularly for older adults and users with aphasia . She has extensively published on cross-device learnability , collaborative environments , and adaptive interface design . Research Interests: Human-Computer Interaction Personalized User Interfaces Universal Usability Interactive Technologies for Aging Populations Recent Publications address: real-time feedback in online meetings (2025), AI support for interviews (2025), financial technology for older adults (2025), ambient social systems (2025), computer-mediated self-disclosure (2025), digital home health assessments (2024), and intergenerational VR communication (2024). Key trends show increasing focus on age-inclusive design , context-aware interfaces , and interruption management . Collaborations include: Leah Findlater (University of Washington), Andrea Bunt (University of Manitoba), Karyn Moffatt (McGill University), and Kellogg S. Booth (University of British Columbia). Her work appears in journals like ACM Transactions on Accessible Computing and International Journal of Human-Computer Studies .
Ezer Rasin is a Senior Lecturer in the Department of Linguistics at Tel Aviv University (TAU), where he also leads the TAU Phonological Computation Lab. He holds a PhD in Linguistics from MIT (2018), an MA and BSc in Linguistics and Mathematics from TAU (both prior to his PhD). Before joining TAU, he was a postdoctoral researcher in Leipzig University's IGRA program. His research focuses on theoretical and computational phonology, learnability, and formal linguistics. Key areas include phonological opacity, optimality theory, and the interaction between phonology and morphosyntax. His work bridges empirical phonology with computational modeling, addressing questions about language acquisition and formal grammatical architecture. Recent publications examine challenges to size-based parallel analyses in Hebrew vowel deletion, opacity phenomena in Gua and Akan, and computational approaches to phonological learning. He has contributed to journals like Linguistic Inquiry and Journal of Language Modelling , and co-edited proceedings for NELS and other conferences. Rasin teaches courses on phonological opacity, computational phonology, and the phonology-morphology interface at TAU. His research also involves developing audio databases for endangered languages like Judeo-Baghdadi Arabic, reflecting his commitment to computational methods in linguistic documentation.
Cristina Guardiano is a Full Professor at the University of Modena and Reggio Emilia, Department of Communication and Economics. Her research focuses on syntax, historical linguistics, and the Parametric Comparison Method (PCM), exploring syntactic variation across languages to reconstruct linguistic phylogenies. She specializes in formal syntax, differential object marking, and the intersection of syntax with computational and genetic data analysis. Key Affiliations: Dipartimento di Comunicazione ed Economia, University of Modena and Reggio Emilia Research Themes: Syntactic parameters, language evolution, Romance dialectology, genetic-linguistic correlations Guardiano's work integrates theoretical linguistics with interdisciplinary approaches, including collaborations with geneticists and computational linguists. Notable contributions include studies on the syntax of Italiot Greek, Sicilian dialects, and the application of parametric syntax to historical language classification. She leads initiatives like the TerraLing database to systematize cross-linguistic syntactic data. Her teaching includes advanced courses on linguistics, parametric syntax, and the analysis of language evolution. She emphasizes critical evaluation of Large Language Models (LLMs) and their implications for linguistic theory.
Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.
Josien Pluim is a Full Professor of Medical Image Analysis at Eindhoven University of Technology (TU/e), where she leads the Medical Image Analysis group and serves as vice-dean of the Department of Biomedical Engineering. She also holds a part-time professorship at the University Medical Center Utrecht. Her research is centered at the intersection of artificial intelligence and clinical medicine, with strong affiliations to EAISI (Eindhoven Artificial Intelligence Systems Institute) and the EAISI Health initiative. Her academic background includes a Master's in Computer Science from the University of Groningen (1996), specializing in Scientific Computing and Imaging, followed by a PhD (2001) from the Image Sciences Institute at UMC Utrecht on multimodality image registration using mutual information. She advanced from assistant to associate professor at UMC Utrecht before joining TU/e as a Full Professor in 2014, with a concurrent part-time appointment at UMC Utrecht since 2015. Pluim’s research interests span medical image analysis, including image registration, segmentation, detection, and deep learning, with clinical applications in neurology and oncology. She investigates both methodological development and real-world clinical translation. Recent work emphasizes generative AI for synthetic data, robustness in deep learning models, and super-resolution techniques for brain MRI. Her publications reveal a strong trend toward addressing data scarcity, generalization, and evaluation in medical AI, particularly through simulation and diffusion models. She has co-authored over 250 peer-reviewed papers and is recognized with prestigious fellowships: Fellow of the MICCAI Society IEEE Fellow Pluim has served in leadership roles across the academic community, including Associate Editor for journals such as IEEE Transactions on Medical Imaging , IEEE TBME , and Medical Image Analysis . She has chaired major conferences like WBIR 2006 and MICCAI 2010, and served on the Executive Board of the MICCAI Society. She actively supervises research and educational projects, including team challenges and capstone courses in medical image analysis. Her group is involved in significant collaborative research, such as the EU-funded openGTN project, which supports PhD training in generative models for medical imaging. She also contributes to scientific advisory boards, including the Hanarth Fonds.
Josien PW Pluim is a Professor of Medical Image Analysis at Eindhoven University of Technology (TU/e) and a part-time Professor at the University Medical Center Utrecht . She leads the Medical Image Analysis group and serves as vice-dean for the Department of Biomedical Engineering at TU/e. Her academic background includes a PhD in Multimodality Image Registration from UMC Utrecht (2001) and a Master’s in Scientific Computing and Imaging from the University of Groningen (1996). Her research spans methodology development and clinical applications in Image Registration Segmentation Detection Machine/Deep Learning with a focus on neurology and oncology. Recent publications highlight advancements in generative AI for medical modalities, self-supervised surgical models , and robust image registration . She has co-authored over 250 peer-reviewed papers and served as Associate Editor for journals like IEEE TMI and Medical Image Analysis . Honors include: Fellow of the MICCAI Society IEEE Fellow She has held leadership roles in conferences (MICCAI, SPIE) and contributes to UN Sustainable Development Goals through medical imaging innovations.
Padakandla Arun is a Professor in the Communication Systems department at EURECOM. His research focuses on quantum information theory, network communication, and coding theory, with recent work on multi-terminal quantum channels and machine learning applications in quantum measurement systems. Affiliation: EURECOM - Communication Systems Academic Rank: Professor Research Interests Prof. Padakandla investigates quantum communication protocols, classical-quantum hybrid systems, and structured code design for multi-user channels. His work bridges information theory with quantum mechanics, emphasizing achievable rate regions and measurement simulation techniques. Scientific Contributions Key trends in his publications include: Quantum MAC and interference channel analysis PAC learning frameworks for POVM hypothesis classes Algebraic structures in network information theory Contact Email: Arun.Padakandla@eurecom.fr
Jiří Šíma is a senior scientist at the Department of Theoretical Computer Science, Institute of Computer Science, Czech Academy of Sciences. He holds the academic title of Research Professor (DrSc.) and has been a key researcher at ICS CAS since 1994. He has also served as head of the department (2010–2012, 2021–2023) and has held external lecturing positions at Charles University, Masaryk University, and Czech Technical University. His educational achievements include a CSc. (Ph.D.) in 1993, an Associate Professor qualification (doc.) and RNDr. in 2000, and a DrSc. in 2009 from the Slovak University of Technology. These qualifications reflect his deep expertise in theoretical computer science and neural networks. Šíma's research focuses on the theoretical foundations of neural computation, including the computational power of analog and spiking neural networks, energy complexity in deep learning models, formal language recognition by neural automata, and complexity theory. His work bridges theoretical computer science and artificial intelligence, with a strong emphasis on mathematical rigor and computational models. The 15 most recent publications highlight a consistent trend in analyzing the computational capabilities and energy efficiency of neural networks. His recent work (2020–2024) centers on energy complexity in fully-connected and convolutional networks, while earlier work explores analog neuron hierarchies, hitting sets for branching programs, and the limitations of spiking neurons. The research spans subfields such as formal languages, computational complexity, dynamical systems, and neurocomputing, demonstrating a cohesive and long-term research trajectory in theoretical machine learning. Best ICS Paper Award (2024) Best ICS Paper Award (2021) Second/Third Best ICS Paper Award (2019) Best ICS Paper Award (2018) Otto Wichterle Award (2003) Award of the CAS for young scientists (1998) Šíma has been principal investigator on multiple Czech Science Foundation grants, including LEDNeCo (2025–2027), AppNeCo (2022–2024), and FoNeCo (2019–2021). He has also served on grant evaluation panels and scientific councils, including at the Czech Science Foundation and Charles University. Although no formal students are listed, he has collaborated extensively with researchers such as J. Cabessa, P. Vidnerová, S. Žák, and P. Orponen. He is actively involved in the academic community, serving on program committees for major conferences such as ICANN, ICONIP, SOFSEM, and MFCS. His work is primarily conducted within the Department of Theoretical Computer Science at ICS CAS, a leading research group in theoretical computer science in the Czech Republic.
John Archibald is a Professor at the School of Languages, Linguistics and Cultures, University of Victoria, specializing in second and third language phonology. His work bridges phonological theory with empirical studies on multilingual acquisition, focusing on feature mapping, transfer effects, and input processing. PhD, University of Toronto Fellow of the Royal Society of Canada (2020) Research interests include L2/L3 phonology , language learnability , and multilingual mental representation . His theoretical work addresses Plato’s Problem (input poverty), Orwell’s Problem (input resistance), and Escher’s Problem (illusory phonological perception). Recent publications analyze Contrastive Hierarchy applications, pitch accent acquisition , feature dependency , and phonological redeployment . Awards include the Canadian Linguistic Association Lifetime Achievement Award (2021) and SSHRC-funded L3 phonology research . He co-edits Contemporary Linguistic Analysis and teaches courses on linguistics, child language acquisition, and advanced L2/L3 research.
