Shiyu Chang is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara , focusing on machine learning with applications in natural language processing and computer vision . He previously worked as a research scientist at the MIT-IBM Watson AI Lab alongside Prof. Regina Barzilay and Prof. Tommi Jaakkola, and earned both his B.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign , advised by Prof. Thomas S. Huang. Education : PhD, University of Illinois at Urbana-Champaign BS, University of Illinois at Urbana-Champaign His research centers on enhancing AI systems through human-AI interaction , aiming to improve interpretability , transferability , and adversarial robustness in LLMs. Recent work includes LLM watermarking defense , uncertainty decomposition , and self-denoised smoothing for model robustness. His publications span premier venues like ICML , NeurIPS , CVPR , and ACL , with recurring themes in diffusion models , LLM optimization , and ethical AI (e.g., hallucination detection, unlearning frameworks). He actively mentors students, several of whom are marked as advisees (☆) in his publications.
Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .
Igor Kriz is a Professor of Mathematics at the University of Michigan, specializing in algebraic topology. He is affiliated with the Department of Mathematics within the College of Literature, Science, and the Arts (LSA). His research focuses on advanced topics in algebraic topology, particularly stable homotopy theory and related areas. Kriz received his Ph.D. from Charles University in 1988. His academic journey has led him to become a prominent researcher in algebraic topology, with significant contributions to the field over several decades. Professor Kriz's primary research interests lie in algebraic topology , which studies topological spaces through algebraic invariants. He specializes in equivariant stable homotopy theory, Mackey functors, cobordism, and motivic homotopy theory . His work involves calculations of stable homotopy groups and other generalized homology theories, including Morava K-theories of classifying spaces of finite groups. He has made significant contributions to the study of operads and structures up to homotopy, with applications extending to differential geometry and physics, particularly string theory. His research often bridges multiple mathematical disciplines, creating connections between topology, algebra, and geometry. His recent publications (2022-2025) demonstrate a strong focus on equivariant topology and its connections to algebraic structures. Kriz frequently collaborates with researchers like P. Hu, P. Somberg, and others, producing work that explores the intersection of homotopy theory with representation theory and algebraic geometry. His research program shows consistent evolution from foundational work in stable homotopy to more recent applications in motivic contexts and topological Hochschild homology. Professor Kriz teaches both undergraduate and graduate courses at the University of Michigan. His teaching portfolio includes Math 425 (Introduction to Probability), Math 592 (Introduction to Algebraic Topology), and advanced graduate courses Math 695 and Math 696 (Algebraic Topology I and II). His course materials are regularly updated, reflecting his commitment to education in mathematical topology. Based in East Hall (room 3846) at the University of Michigan, Professor Kriz maintains an active research program while contributing to the academic community through teaching and mentorship. His work continues to advance our understanding of complex topological structures and their algebraic representations.
Emil Bjerrum-Bohr is an Associate Professor at the Niels Bohr Institute, University of Copenhagen, where he holds a position in the Theoretical high energy, astroparticle and gravitational physics department within the Faculty of Science. He is also affiliated with the Niels Bohr International Academy and leads the Computations of Amplitudes Group as a Lundbeck Foundation Junior Group Leader. Dr. Bjerrum-Bohr's research focuses on theoretical particle physics with particular emphasis on amplitude analysis and computations. His primary fields of research include amplitude analysis and computations, amplitudes and string theory, and quantum gravity. His current research explores relations between amplitudes from string theory and computation of amplitudes in Quantum Chromodynamics (QCD) for use at the Large Hadron Collider (LHC) at CERN. He has made significant contributions to understanding scattering equations and effective field theory in the context of gravitational physics. His recent publication record demonstrates a strong focus on gravitational scattering amplitudes, quantum gravity, and connections between string theory and particle physics. A notable trend in his research is the application of amplitude techniques to gravitational physics, particularly in the post-Minkowskian expansion framework which has implications for gravitational wave astronomy and black hole physics. His work bridges theoretical concepts with practical applications for collider physics. Scientific awards: Lundbeck Foundation Junior Group Leader As a Lundbeck Foundation Junior Group Leader, Dr. Bjerrum-Bohr oversees the Computations of Amplitudes Group, where he mentors junior researchers and collaborates with international colleagues on cutting-edge theoretical physics problems. His research has involved organizing academic meetings including the "Current Themes in High-Energy Physics and Cosmology" series (2013-2015) and Nordic Winter Schools on Cosmology and Particle Physics (2013, 2015). His work is conducted within the vibrant theoretical physics environment of the Niels Bohr Institute, which provides access to computational resources and collaborative opportunities with both experimental and theoretical physicists across multiple disciplines.
Olle Sköld is a Senior Lecturer at the Department of ABM (Archives, Libraries, and Museums) at Uppsala University. He is also the Head of the Department of ABM and holds an Associate Professor position in Library and Information Science. His research spans digital humanities, documentation practices, knowledge production, and digital preservation, with a particular focus on archaeological data processes and virtual world communities. Archaeological data documentation and reuse Digital game preservation Paradata theory and applications Information practices in social media His recent publications explore paradata in data papers, literacy challenges in research data management, and trust technologies like blockchain in digital archives. Olle is involved in the CAPTURE project, investigating documentation workflows for archaeological data. He has contributed to the Master's Programme in Digital Humanities at Uppsala University, blending digital humanities with library and information science education.
Heping Zhang is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health , with secondary appointments in the Child Study Center , Department of Statistics and Data Science , and Department of Obstetrics, Gynecology, and Reproductive Sciences . He directs the Collaborative Center for Statistics in Science (C²S²) and leads the Reproductive Medicine Network data coordinating center. Education: PhD in Statistics, Stanford University (1991) Postdoctoral Fellow, Mathematical Science Research Institute (1991) Research Focus : Zhang specializes in biostatistical methodology for genomic data analysis , clinical trials , and reproductive medicine . His work bridges genetics , mental health , and maternal-child health through innovative statistical approaches. Awards : 2023 Web of Science Highly Cited Researcher 2023 International Chinese Statistical Association Distinguished Achievement Award 2022 Institute of Mathematical Statistics Neyman Award and Lecture 2011 Royan Institute International Research Award 2011 Institute of Mathematical Statistics Medallion Award 2008 Harvard School of Public Health Myrto Lefokopoulou Distinguished Lecturer Professional Roles : He served as President of the International Chinese Statistical Association (2019) and Former Editor of the Journal of the American Statistical Association - Applications and Case Studies . His lab develops open-source software tools like ABESS , STREE , and modSaRa for genomic and clinical data analysis.
Shigeru Uesugi is Professor in the School of Creative Science and Engineering, Faculty of Science and Engineering, Waseda University. He directs interdisciplinary research that fuses mechanical engineering, human–robot interaction, and philosophy of technology to create co-creative interfaces, sports-assist devices, and mediated communication systems. Education: Ph.D. in Engineering, Waseda University Research Interests: Uesugi’s work centres on Human Interface & Interaction Design , Mediated Communication , and Being Design . He explores how technology can extend human bodily perception and foster co-creative relationships between users and machines, merging insights from robotics, sports science, and philosophy. Recent articles analyse embodied interaction in shared virtual spaces, remote collaboration via tangible interfaces such as networked “Lazy Susan” tables, and full-body rocking systems that create a sense of togetherness over distance. His publications reveal a steady focus on designing hardware–software hybrids that make remote partners feel co-present. Scientific Awards: 14th Okawa Thesis Award, Honorable Mention (2004) Human Interface Society Paper Award (2004) Grants & Projects: JSPS KAKENHI (A) “Comprehensive research on the relationship between scientific/technological innovation and human society” (2023-2026) JSPS KAKENHI Challenging Research “Transformations of intentionality and responsibility via artifacts” (2022-2025) JSPS KAKENHI “Fascia tension-propagation suit for motor-function enhancement” (2020-2023) Labs & Teams: He leads the Human-being Design Engineering Laboratory whose members develop co-creative interfaces, sports-training devices, and rehabilitation tools. The group maintains the website www.wesugi.mech.waseda.ac.jp and collaborates with the Waseda Research Institute for Science and Engineering.
Helmut Leder is a Professor at the University of Vienna, specifically within the Faculty of Psychology's Department of Cognition, Emotion, and Methods in Psychology. He serves as Head of the Vienna Cognitive Science Hub, integrating interdisciplinary approaches to study aesthetic experiences and cognitive processes. His academic profile includes teaching courses in General Psychology, Cognitive Psychology, and Neurosciences, alongside supervising master's and doctoral thesis seminars focused on perception and neuroaesthetics. Key Research Areas : Empirical Aesthetics, Cognitive Psychology, Visual Perception, Cross-Cultural Psychology, Urban Art Impact, Mental Imagery Studies Methodological Focus : Eye-tracking, Machine Learning Analysis, Cross-Cultural Comparisons, Field Experiments, Neuroimaging Recent Contributions : Investigated urban art's role in stress reduction, developed network models of aesthetic experiences, explored non-visual color navigation for the blind, and applied machine learning to art evaluation. His work bridges psychology with cultural studies, examining how aesthetic experiences shape well-being and cognitive processes. Current projects emphasize the neural and behavioral distinctions between real and imagined art encounters, while challenging traditional gender-based perception models.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Professor Khin Than Win is a leading academic in health informatics and digital health at the University of Wollongong (UOW), holding appointments as Professor in the School of Computing and Information Technology, Head of Postgraduate Studies, and Deputy Head (Research). She also serves as Academic Program Director for UOW's Master of Health Informatics and Graduate Certificate in Health Analytics programs. Her research focuses on applying information technology to healthcare, particularly in behavior change support systems, persuasive technology, and ethical AI applications. She has supervised over 20 PhD students and holds leadership roles including Deputy Chair of UOW's Health and Medical Research Ethics Committee, and membership in international committees like the Persuasive Technology Steering Committee. Education: MBBS from Rangoon University, Master's and PhD in IT from Assumption University (Bangkok) and UOW (Australia) Research Interests: Health data analytics, AI in healthcare, privacy/security of health systems Leadership: Program/General Chair roles at ACIS and Persuasive Technology conferences Awards: Best Paper Awards (2023, 2018) Her extensive funding portfolio includes ARC grants and NHMRC projects, totaling over 24 funded initiatives. Current research explores blockchain in medical passports, AI ethics, and culturally tailored health interventions.
Kevin Corlette is a Professor of Mathematics at the University of Chicago and serves as Director of the Institute for Mathematical and Statistical Innovation. His primary affiliation is within the Department of Mathematics. He holds a Ph.D. in Mathematics from the University of Chicago, though specific details of his education are not provided here. His research focuses on differential and algebraic geometry, including Kahler geometry, locally symmetric spaces, and geometric partial differential equations such as harmonic map and Yang-Mills equations. He explores the interplay between geometric structures and analytical systems, contributing to foundational theories in these areas. Corlette was honored as a Mathematically Gifted & Black Honoree in 2020, recognizing his scholarly contributions and leadership in mathematics. His work bridges pure mathematics with applications in geometric analysis, influencing both theoretical and applied fields. As Director of IMSI, he oversees interdisciplinary initiatives at the intersection of mathematics, statistics, and computational science. His role involves fostering collaborations among researchers across disciplines to address complex scientific challenges.
Motahhare Eslami is an Assistant Professor at Carnegie Mellon University’s School of Computer Science, Human-Computer Interaction Institute. Her research bridges human-computer interaction, social computing, and AI ethics. Education : PhD in Computer Science from University of Illinois at Urbana-Champaign, advised by Karrie Karahalios Research Focus : Dr. Eslami investigates algorithmic opacity and user behavior in socio-technical systems, developing frameworks to enhance transparency and stakeholder participation in AI governance. Her work addresses: Algorithmic bias mitigation through participatory audits Ethical implications of generative AI and smart assistants Inclusion of marginalized communities in AI design Transparency mechanisms for opaque algorithms Civic technology and public sector AI Recent Article Trends : Her publications analyze algorithmic harms through lenses of: Medical imaging and data generation Labor market equity and low-wage employment Youth perspectives on AI ethics Content creator experiences with demonetization Explainability in black-box AI systems Scientific Recognition : Best Paper at AAAI HCOMP (2025) Google Academic Research Award (2024) Microsoft AI & Society Fellowship (2024) 100 Brilliant Women in AI Ethics (2023) Teaching Innovation Award at CMU (2023) Advising & Collaborations : Mentors PhD students Shixian Xie, Wesley Deng, Seyun Kim, and former post-doc Jaemarie Solyst. Collaborates with NSF AI Institute for Collaborative Assistance (2022–2027), Amazon, Google, and Microsoft on responsible AI initiatives.
Prof. Liam Murphy is a Full Professor of Computer Science & Informatics at University College Dublin (UCD) and Director of the Performance Engineering Laboratory. He holds a B.E. from UCD, M.Sc. and Ph.D. from UC Berkeley. His research focuses on performance engineering of networks, software systems, and multimedia transmissions. He has published over 150 peer-reviewed papers and is an IEEE member and Fellow of the Irish Computer Society. Education: B.E. in Electrical Engineering, UCD (1985) M.Sc. & Ph.D. in Electrical Engineering & Computer Sciences, UC Berkeley (1988, 1992) Research Interests: Dynamic resource allocation in networks Cloud computing efficiency Software performance engineering Wireless multimedia systems Quality of Service (QoS) optimization Recent work emphasizes energy-efficient cloud workflows, multi-objective data center optimization, and decentralized traffic simulation. Grants & Awards: Fellow of the Irish Computer Society (2007) Conference Paper Awards (2004, 2002, 2001) Principal Investigator in multiple funded projects (e.g., EU-funded traffic simulation, cloud resource allocation) Advising & Labs: Directed 24 Ph.D. and 8 M.Sc. students. Leads the Performance Engineering Laboratory (PEL), focusing on distributed systems, cloud efficiency, and network performance. Collaborates on industry-relevant projects like crovan (UCD/DCU campus company). Teaching: Coordinates courses on computer science fundamentals, distributed systems performance, and software engineering at UCD.
Simon Langlois-Bertrand serves as a Part Time Lecturer in the Department of Political Science at Concordia University, teaching core courses including Introduction to International Relations (POLI205), Sustainability and Governance (POLI208), and Global Energy Politics and Policy (POLI486). His interdisciplinary academic foundation combines engineering and political science, reflected in his educational trajectory: PhD International Affairs, Carleton University M.Sc. Political Science, Université de Montréal M.Ing. Industrial Engineering, École Polytechnique de Montréal B.Ing. Computer Engineering, École Polytechnique de Montréal Langlois-Bertrand's research critically examines energy politics and policy , global environmental governance , and sustainability transitions , with particular emphasis on social-technical dimensions of development and U.S. political dynamics. His work bridges engineering perspectives with political analysis to explore how technological systems interact with institutional frameworks. Analysis of his 15 most recent publications reveals concentrated expertise in North American energy transitions, featuring empirical studies on electricity rate structures, Quebec's carbon policy, and theoretical investigations of uncertainty in energy governance. Key thematic threads include decarbonization pathways, circular economy implementation, and life-cycle policy approaches, predominantly focused on Canadian and Quebec contexts. Scientific awards: No awards documented in source material. Regarding academic mentorship, the provided text contains no information about graduate students supervised or research grants secured. His current research projects indicate ongoing work on the geopolitics of ecological transition and environmental state theory through life-cycle analysis frameworks. No laboratory affiliations or research team memberships are specified in the available documentation.
