Chun Liu is Chair and Professor of Applied Mathematics at the Department of Applied Mathematics, Illinois Institute of Technology (IIT), within the College of Computing. His research focuses on Nonlinear Partial Differential Equations , Complex Fluids , and Multiscale Modeling , with applications in electrophysiology and materials science. He earned a Ph.D. from New York University’s Courant Institute, an M.S. from Duke University, and a B.S. from Fudan University. Prof. Liu leads projects on General Diffusion Systems , Ion Channel Dynamics , and Viscoelastic Fluids . He has secured grants from NSF, BSF, and DAAD for research in energetic variational approaches, multiscale materials modeling, and biomolecular systems. Key contributions include the development of Poisson-Boltzmann models , coarse-grained dynamics , and energetically stable numerical methods . He serves on editorial boards for Communications in Mathematical Sciences , SIAM Journal on Mathematical Analysis , and others. His work bridges applied mathematics with engineering and biophysics, addressing challenges in fluid mechanics, ion transport, and nonlinear systems.
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.
Michael Feig serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, leading the Feig Lab within the BioMolecular Science Gateway initiative. His research bridges computational modeling and molecular biology to investigate protein behavior in cellular contexts, with particular emphasis on molecular dynamics simulations and machine learning applications. His academic background includes: Ph.D. (1999) from the University of Houston M.S. (1994) from Technical University of Berlin Feig's research program focuses on computational biophysics of protein systems, specializing in molecular dynamics simulations of crowded cellular environments, bacterial microcompartments, and intrinsically disordered proteins. His lab develops advanced modeling techniques including coarse-grained approaches (COCOMO2) and machine learning frameworks to predict protein properties and conformational landscapes. Current work explores temperature-dependent structural ensembles, enzyme cargo loading mechanisms in engineered microcompartments, and biomolecular condensate physics under shear flow. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) integration of deep learning with molecular dynamics for protein structure prediction, (2) engineering of bacterial microcompartments for synthetic biology applications, and (3) fundamental studies of macromolecular crowding effects on diffusion and phase separation. His work consistently emphasizes methodological innovation with biological relevance, notably through enhancements to the CHARMM simulation platform. His scientific recognition includes: Alfred P. Sloan Fellowship (2005) As principal investigator of the Feig Lab, he directs research teams in computational biophysics projects supported by active funding mechanisms. While specific grant details aren't provided, his continuous publication pipeline and lab infrastructure indicate sustained research support. His mentorship spans graduate students in the Cell & Molecular Biology Program, with recent work involving multi-institutional collaborations on bacterial microcompartment engineering and protein phase separation. The Feig Lab operates at the intersection of high-performance computing and molecular biology, maintaining strong connections with experimental groups for method validation. Current initiatives include developing generative models for temperature-dependent protein conformations and investigating cytoplasmic protein capture mechanisms in microcompartments, with potential applications in metabolic engineering and nanobiotechnology.
Ji Hwan Park is an Assistant Professor in the School of Interactive Games and Media at RIT's Golisano College of Computing and Information Sciences (GCCIS). He holds a PhD from Stony Brook University under Prof. Arie Kaufman. His research focuses on accessible data visualization, digital twins, human-AI collaboration, and VR/AR applications. Notable contributions include developing tools for ADHD-friendly visualizations and interactive protein motif identification. He has received funding from the Department of Defense for biomedical research and earned an Honorable Mention at CHI 2024. Current teaching includes courses on game design and advanced algorithms. Research activities span medical imaging analytics (e.g., CMed framework for crowd-sourced diagnostics), climate modeling through Bayesian deep learning, and creative visualization techniques like Graphoto. His work bridges technical innovation with human-centered design principles, particularly in healthcare and neurodivergent accessibility contexts.
Gamze Z. Dane is a tenured Assistant Professor at the Department of Built Environment of Eindhoven University of Technology (TU/e), affiliated with EAISI Mobility and EAISI Health. She leads the Digital City Program (2020-2024) and specializes in decision-support systems, GIS, urban informatics, and data analytics for sustainable urban development. Her research integrates citizens into urban decision-making using digital tools like VR twins and data-driven approaches. Education: PhD in Urban Planning, MSc in Geographical Information Systems (GIS) and Decision Making. Research Interests: Focuses on human-environment interaction, transdisciplinary urban projects, and the impact of digitalization on cities. She develops tools for public participation and uses big data to analyze citizen behavior and urban experiences. Projects: Principal Investigator for EU/national projects involving cities like Eindhoven, Bologna, and Lisbon. Notable projects include UBeX Urban Behavior eXtended reality lab (2024-2026) and ROCK (2017-2020). Awards: Cuperusprijs 2020 (2nd place for student thesis) Drivers of Change Exhibition 2021 ISPRS International Journal Cover Story (2020) Teaching & Innovation: Coordinates courses like Smart Cities and Urban Redevelopment. Developed online teaching materials using VR, drones, and mobile apps. Guest lectures at Istanbul Technical University and visiting scholar at National University of Singapore. Labs & Networks: Leads the UBeX lab exploring immersive technologies for urban analysis. Active in academic networks including Urban Planning journals and international conferences.
Robert Braun is an Associate Professor of Sociology and Political Science at the University of California, Berkeley. He holds a PhD from Cornell University (2017) and previously taught at Northwestern University. His research focuses on civil society dynamics during social upheaval, intergroup relationships in conflict settings, and the historical dimensions of political fear and persecution. His work has been published in prestigious journals like the American Journal of Sociology and American Political Science Review. Key contributions include his book Protectors of Pluralism (Cambridge UP, 2019), analyzing community responses to mass persecution, and his forthcoming project Bogeymen , tracing political fear through Central European folklore. Braun also examines healthcare economics, studying private equity impacts on medical practices and hospice care quality. His interdisciplinary approach bridges historical sociology, political theory, and quantitative analysis. Education: PhD in Sociology, Cornell University, 2017 Research Interests: Genocide Studies, Healthcare Policy, Ethnopolitical Conflict, Moral Mobilization Awards: Not explicitly mentioned in available texts Braun’s work on Holocaust resistance patterns and bystander dynamics has influenced debates on collective action and moral responsibility. His recent studies on healthcare consolidation highlight financialization effects in medical sectors, contributing to policy discussions on equity and access.
Panayiotis Kolios is an Assistant Professor at the Department of Computer Science, University of Cyprus (UCY). Previously, he served as a Research Assistant Professor at the KIOS Research and Innovation Centre of Excellence (2013–2024) and a Visiting Lecturer at UCY. He holds a BEng and PhD in Telecommunications Engineering from King’s College London (2008 and 2012, respectively). His research focuses on networked intelligent systems, emergency management using AI and UAV technologies, and cyber-physical systems. Education: BEng in Telecommunications Engineering, King’s College London, 2008 PhD in Telecommunications Engineering, King’s College London, 2012 Research Interests: His work centers on autonomous systems, intelligent transportation, and emergency management. Key areas include AI-driven disaster response, UAV-based surveillance, and algorithmic optimization for critical infrastructure. He develops solutions for real-time situational awareness and decision-support in emergencies. Recent work trends show a focus on multi-UAV coordination, disaster management platforms (like AIDERS), and AI applications in emergency response. His team’s 2023 win in the Cooperative Aerial Robots Inspection Challenge highlights advancements in UAV inspection algorithms. Scientific Awards: First Prize in Cooperative Aerial Robots Inspection Challenge (CDC 2023) Grants and Advising: He has secured over €40 million in EU and industrial grants, leading projects like PREDICATE, SWIFTERS, and AIDERS. His team advises on emergency response strategies and has trained first responders through EU-funded programs such as the Exchange of Experts training. Labs and Teams: He leads the Security and Emergency Response Group at KIOS CoE and established the Cyprus Civil Defence Aerial Observation Unit. His team collaborates with institutions like the Cyprus Police and Fire Service to operationalize UAV technologies in disaster scenarios.
Arpit Gupta is an Associate Professor of Finance at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 2016. His research lies at the intersection of real estate, household finance, and urban economics, with a strong empirical focus on using large datasets to analyze financial behavior and market dynamics. Ph.D. in Finance and Economics, Columbia Business School B.S. in Mathematics and Economics, University of Chicago His research interests include household finance, real estate markets, mortgage default, bankruptcies, urban economics, and the financial impacts of health shocks and pandemics. He explores how financial constraints affect minority borrowers, how remote work reshapes urban real estate, and how public infrastructure influences property values. His recent publications span top journals such as the Journal of Finance , American Economic Review , Journal of Financial Economics , and PNAS . Themes across his work include the economic consequences of the COVID-19 pandemic, the valuation of private equity, and the spillover effects of foreclosures. His research often combines innovative data sources with rigorous econometric methods. Notable scientific awards include the Brattle Prize First Place (2019) and the 2016 Top Finance Graduate Award from Copenhagen Business School. He has advised on policy-relevant topics such as office-to-residential conversions, housing equity, and bail reform, and has received media attention for his insights on urban development and real estate trends. Professor Gupta collaborates with a wide network of researchers and maintains active engagement through a Substack newsletter and public data repositories. He advises students and contributes to academic discourse through conference presentations and policy discussions. He is affiliated with research labs and teams focused on urban economics and financial innovation, often collaborating with scholars at Columbia, NYU, and other institutions. His ongoing projects continue to explore the evolving dynamics of housing, finance, and urban resilience in the post-pandemic era.
Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.
Prof. Dr. Janick Edinger is a Professor of Distributed Operating Systems at the Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. He leads a research group focused on distributed, context-aware, and adaptive computing systems, with a strong emphasis on edge computing, computation offloading, and assistive technologies. Education: PhD in Computer Science, University of Mannheim Studies at National Taiwan University Studies at University of Alberta, Canada Research stays at University of British Columbia, Hong Kong Polytechnic University, and Georgia State University, USA His research explores how edge computing and computation offloading can enable efficient, privacy-preserving processing of sensor and video data close to their sources, particularly in dynamic environments. He investigates the integration of autonomous and heterogeneous systems—such as drone fleets and mobile devices—into scalable middleware platforms for real-time monitoring and decision-making in logistics and industrial operations. His work also emphasizes societal impact, contributing to accessible routing, adaptive interfaces, and crowd-sourced mapping. The recent publications reflect a strong trend in edge computing, federated learning, privacy-preserving analytics, and assistive technologies. Topics include WebAssembly-based offloading, emotion prediction via eye tracking, real-time traffic detection, and predictive maintenance in Industry 4.0, showcasing a blend of foundational systems research and applied human-centered computing. Scientific Awards: PerCom 2021 Mark Weiser Best Paper Award Best Paper Award at IEEE PerCom 2021 for 'Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning' Prof. Edinger actively advises students and leads research projects involving grants and collaborations. His team includes PhD candidates and researchers working on middleware, edge systems, and context-aware applications. He has served on conference program committees, such as shadow PC member for EuroSys 2021, and publishes in top venues including IPDPS, PerCom, CHIIR, and COMPSAC. Labs and Teams: He leads the Distributed Operating Systems research group at the University of Hamburg, where he mentors students and collaborates on projects involving edge computing, IoT, and adaptive systems.
Asuman Ozdaglar is the MathWorks Professor of Electrical Engineering and Computer Science and Department Head of EECS at MIT. She also serves as Deputy Dean of Academics for the MIT Stephen A. Schwarzman College of Computing. Her research focuses on large-scale networked systems, including optimization, game theory, social networks, and distributed algorithms. Education: BS in Electrical and Electronics Engineering from Middle East Technical University (1996), SM (1998) and PhD (2003) in Electrical Engineering and Computer Science from MIT. Research emphasizes nonlinear optimization, machine learning, and network economics. She leads work on robust algorithms, misinformation dynamics, and networked systems. Affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). Her contributions span theoretical and applied domains, including distributed optimization methods, social network analysis, and privacy-preserving data mechanisms. Active in shaping academic policy through her roles in the College of Computing.
Christoforos Mavrogiannis is an Assistant Professor of Robotics at the University of Michigan, leading the Fluent Robotics Lab within the Department of Robotics. He holds a Ph.D. and M.S. from Cornell University and a Diploma in Mechanical Engineering from the National Technical University of Athens. His research focuses on enabling robots to seamlessly integrate into dynamic, unstructured environments through advancements in human-robot interaction, shared autonomy, multiagent systems, and navigation algorithms. Education: Ph.D. and M.S., Cornell University Diploma in Mechanical Engineering, National Technical University of Athens Research Interests: His work spans human-robot interaction , shared autonomy , and multiagent systems . He develops algorithms for navigating dynamic environments , decentralized control , and behavior prediction , with applications in robotic manipulation and collaborative transport . Recent projects include the HOUND off-road robot and pixel-art generation with mobile robots. Awards: Best paper award at the RSS Social Navigation Workshop (2024) Grants & Advising: He advises the Fluent Robotics Lab and contributes to organizing conferences like ICRA and HRI. His lab focuses on human-centered robotics and socially competent navigation . Labs & Teams: Fluent Robotics Lab at the University of Michigan, collaborating on projects like the HOUND platform and electrostatic brake systems for manipulation.
Professor Lydia Bourouiba is a faculty member at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Civil and Environmental Engineering and the Institute for Medical Engineering and Science (IMES). Her research focuses on the intersection of fluid dynamics and disease transmission, with a strong emphasis on biophysics, interfacial flows, turbulence, and multiphase systems. Ph.D. in Applied Mathematics from McGill University (2008) Research on fluid fragmentation, droplet/bubble dynamics, and pathogen transport Co-Founder and Associate Director of the NSF Center for Pandemic Prevention (APPEX) Her work spans fundamental and applied domains, including: Biophysics of pathogen transport in air and water Turbulent multiphase flows in respiratory events Fluid-plant interactions in agricultural disease Nonlinear dynamics in public health contexts Key article trends highlight: Biomedical fluid mechanics Climate and environment-driven disease modeling High-risk/high-reward public health innovations Interdisciplinary approaches combining physics, biology, and engineering Scientific awards include: Fellow of the American Physical Society (2021) American Institute for Medical and Biological Engineering Fellow (2022) Smith Family Foundation Odyssey Award for biomedical research Ole Madsen Mentoring Award She serves as Associate Editor for the journal FLOW and has founded conferences like the Gordon Research Conference on Fluids in Disease Transmission. Her lab (Fluids and Health Network) develops experimental and theoretical frameworks to address contamination, pandemic control, and fluid-health-environment interactions.
Joseph Salmon is a Senior Researcher at Inria (Team Iroko) in Montpellier, working with the Pl@nNet team. He previously served as a Full Professor at Université de Montpellier from 2018 to 2024 and was a Junior member of the Institut Universitaire de France (IUF) from 2021 to 2024. His research focuses on machine learning, optimization, and data science, with applications in citizen science and crowd-sourcing. He leads the doctoral program 'Statistics and Data Science' at Université de Montpellier. Education: Ph.D. in Statistics and Image Processing (2010) from Université Paris Diderot, under supervision of Dominique Picard and Erwan Le Pennec. Earlier roles include Assistant Professor at Telecom Paris (2012-2018), postdoctoral work at Duke University (2011-2012), and visiting positions at UW Statistics (2018) and the Simons Institute (2022). Research interests span isotonic regression, convex optimization algorithms (e.g., PAVA), statistical learning theory, and applications in environmental AI (Pl@ntNet). His work bridges theory and practice, emphasizing scalable algorithms for high-dimensional problems. Key contributions include advancements in PAVA convergence analysis, Slope penalty optimization, and peer-reviewed frameworks for crowdsourced data. Grants include ANR VITE (variable importance/explainability) and CaMeLOt (Cooperative Machine Learning Optimization). Labs/Teams: Active in Inria's Iroko team and collaborates with Pl@ntNet's AI development. Maintains the STATLEARN conference and ML-MTP initiative in Montpellier.
