Dr. Alvin J. K. Chua is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship. He holds a joint appointment in the Department of Mathematics and a courtesy appointment in the Department of Statistics and Data Science. His research focuses on gravitational-wave astronomy , particularly extreme mass ratio inspirals (EMRIs) as key sources for the LISA mission. He develops computational and statistical methods for modeling GW sources and analyzing detector data, with recent work on machine learning and Bayesian inference techniques. His selected publications highlight advancements in modeling beyond-vacuum-GR effects in EMRIs non-local parameter degeneracies rapid relativistic waveform generation neural networks for GW inference statistical sampling on manifolds These works span gravitational-wave astrophysics , computational relativity , and applied statistics . Scientific recognition includes the NUS Presidential Young Professorship. He collaborates with the LISA Consortium and the North American Nanohertz Observatory for Gravitational Waves (NANOGrav).
Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.
Simon Hanslmayr is a Professor in the School of Psychology & Neuroscience at the University of Glasgow. His research investigates neural oscillations' role in attention and memory processes, employing EEG, fMRI, and transcranial stimulation techniques. He focuses on healthy populations and clinical conditions like Schizophrenia and PTSD. His lab develops tools like the Brain Time Toolbox for electrophysiological data analysis. Education: Ph.D. in Cognitive Neuroscience (not explicitly detailed in text) Research interests include understanding how precise neural timing via oscillations underpins cognitive functions. Key areas: hippocampal memory coding, theta phase synchronization in associative memory, and causal effects of rhythmic stimulation on memory plasticity. Recent articles highlight mechanisms linking theta oscillations to memory formation, thalamocortical interactions in perception, and hippocampal-neocortical coupling. His work bridges experimental and computational approaches to model memory dynamics. Grants: Sensory stimulation for memory impairment (BIAL Foundation, 2025-2026) EU-funded studies on neural oscillations and memory (2020-2021) Awards: None explicitly listed, but active grant recipient. Supervised students include Kiera Capstick, Eleonora Marcantoni, and others. Collaborates with researchers worldwide through lab affiliates and visiting scholars. Current work emphasizes scalable neurotechnologies for cognitive enhancement and memory rehabilitation. Labs/Teams: Leads the Memory & Oscillations Lab at the University of Glasgow, collaborating with institutions like the University of Zurich and Maastricht University on neuroimaging and clinical studies.
Eshed Ohn-Bar is an Assistant Professor in the Department of Electrical & Computer Engineering at Boston University. He leads the Human-to-Everything (H2X) Lab, focused on developing intelligent systems for assistive and autonomous technologies. His research bridges machine perception, learning, and human-computer interaction, with applications in autonomous driving and accessibility for visually impaired individuals. Educated at UCLA (BS in Mathematics, 2010; MEd, 2011) and UCSD (PhD in Electrical Engineering, 2017), he holds a Humboldt Fellowship and has received the IEEE ITS Society Best PhD Dissertation Award (2017) and the 2025 BU Early Career Excellence in Research Award. His work emphasizes robust autonomy, real-time assistance, and inclusive design, collaborating with industry partners like Motional and receiving NSF grants (e.g., IIS-2152077). Research interests include autonomous systems, computer vision, and assistive technologies. Recent trends in publications highlight advancements in decision-making frameworks, neural volumetric models, and scalable learning for navigation. His lab’s projects address challenges in accessibility, such as blind motion generation and inclusive autonomous vehicle design. Awards: Humboldt Fellowship, IEEE ITS Best Dissertation, BU Early Career Award Grants: NSF IIS-2152077 Labs/Teams: H2X Lab, collaborating on projects with industry and academic partners
Michael J. Shelley is the Lilian and George Lyttle Professor of Applied Mathematics and holds joint appointments in Mathematics, Neural Science, and Mechanical Engineering at New York University's Courant Institute of Mathematical Sciences. He also serves as Co-Director of the Applied Mathematics Laboratory and Director of the Center for Computational Biology at the Flatiron Institute. Education: PhD (Applied Mathematics) from the University of Arizona (1985), MS (Applied Mathematics) from the University of Arizona (1984), BA (Mathematics) from the University of Colorado (1981). Research: Focuses on complex phenomena in active matter, biophysics, and complex fluids. Key areas include fluid-structure interactions (e.g., swimming/flying mechanics), cytoskeletal dynamics, and collective behavior in biological systems. Collaborates closely with experimentalists through the Applied Math Lab and Flatiron Institute. Labs & Affiliations: Co-Director, Applied Mathematics Laboratory; Director, Center for Computational Biology (Simons Foundation); affiliated with NYU’s Courant Institute and Department of Mathematics. Notable Work: Models for microtubule-motor assemblies, active suspensions, and fluid-structure interactions. Pioneered computational frameworks for Stokes suspensions and fiber dynamics in viscous fluids.
Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.
Jiri Srba is a Professor at Aalborg University's Department of Computer Science, part of the Technical Faculty of IT and Design. He leads research in the Distributed, Embedded and Intelligent Systems group and contributes to projects like "ControLing wAter In an uRban Environment" and "Collective Adaptive System SynThesIs using Non-zero-sum Games". His office is located at Selma Lagerløfs Vej 300, 9220 Aalborg Øst, Denmark. Contact him at +4599409851 or srba@cs.aau.dk. His core research focuses on formal methods and applied computer science: Model checking and verification of concurrent systems Petri nets and their applications Network protocol verification and synthesis Distributed system correctness Automated reasoning for industrial systems His publication record shows strong emphasis on network verification, model checking optimization, and applying formal methods to environmental systems. Recent work integrates computer science with sustainable engineering, particularly in water management systems and energy control.
Dr. Jeff Schoenau is a Professor of Soil Fertility and Saskatchewan Ministry of Agriculture Strategic Research Chair in Soil Nutrient Management at the University of Saskatchewan's Department of Soil Science within the College of Agriculture and Bioresources. His research focuses on soil management strategies to enhance fertility, productivity, and health in prairie agricultural soils, including nutrient cycling, organic amendments, and salinity management. Education: Ph.D. (Soil Science, 1988) and B.S.A. (Agronomy, 1984) from the University of Saskatchewan. Affiliations: Fellow of the Agricultural Institute of Canada, member of the Saskatchewan Agriculture Hall of Fame (2022). Research Interests His work emphasizes 4R fertilization practices, cropping systems for soil organic matter improvement, novel nutrient/water management strategies, problem soils (e.g., saline/sodic lands), and greenhouse gas emissions mitigation. Awards Recipient of multiple accolades, including the 2024 Teaching Excellence Award, 2023 Professor of the Year, and the 2004 NSERC Synergy Award for innovation in plant root simulator technology. Teaching & Outreach Teaches courses on soil fertility, field studies, and soil characterization. Actively involved in farming with his wife, balancing academic and agricultural pursuits.
Affiliations & Roles Professor of Computer and Information Science at University of Pennsylvania Faculty in Graduate Groups: Bioengineering (School of Engineering) Genomics & Computational Biology (School of Medicine) Operations, Information & Decisions (Wharton School) Psychology (School of Arts & Sciences) Research Affiliations: Annenberg Public Policy Center (Distinguished Fellow) Center for Cognitive Neuroscience Institute for Translational Medicine Research Interests Focuses on explainable AI, natural language processing (NLP), and machine learning applications in psychology and medicine. Key areas include: Language analysis for well-being and mental health Spectral methods for NLP (e.g., Eigenwords) Forecasting and decision-making models Bioinformatics and genomics Teaching Teaches advanced courses in Machine Learning, Deep Learning, and AI ethics, including: CIS 5200: Machine Learning CIS 5220: Deep Learning CIS 6200: Advanced Topics in Deep Learning Key Collaborations Works with interdisciplinary teams on projects like the Good Judgment Project (forecasting) and WWBP (Well-Being and Language). Collaborators include Martin Seligman (positive psychology), Dean Foster (statistics), and Michael Collins (NLP).
Brian K. Arbic is a Professor in the Department of Earth and Environmental Sciences at the University of Michigan. He holds a PhD in Physical Oceanography from MIT/Woods Hole Oceanographic Institution (2000) and a BS in Mathematics and Physics from the University of Michigan (1988). His research focuses on global ocean dynamics, including internal tides, gravity waves, mesoscale eddies, and tsunamis. He collaborates with institutions like NASA's Jet Propulsion Lab, NOAA, and international partners to advance ocean modeling and satellite missions (e.g., SWOT and S-MODE). Arbic leads capacity-building initiatives such as the Coastal Ocean Environment Summer School in Ghana and co-founded EquiSea, promoting equitable access to ocean science. He is a key contributor to UNESCO's Ocean Decade Challenge 9, addressing global disparities in ocean science capacity. Education: PhD, Physical Oceanography, MIT/Woods Hole, 2000 BS, Mathematics and Physics, University of Michigan, 1988 Research Interests: Internal tides and gravity wave dynamics Air-sea interactions and surface tides Mesoscale eddy energetics Tsunami modeling and paleotsunamis Global climate modeling Key Collaborations: NASA SWOT/S-MODE missions US Naval Research Laboratory, NOAA GFDL International institutions (Mercator Modeling Center, LANL) Initiatives: Coastal Ocean Environment Summer School (COESS) Global Ocean Corps and EquiSea Fund UNESCO Ocean Decade Challenge 9 Arbic's work bridges advanced modeling with global capacity development, emphasizing equitable access to ocean science resources and education.
Associate Professor Naseem Ahmadpour is a leading researcher in Human-Computer Interaction (HCI) and Design at the University of Sydney 's School of Architecture, Design and Planning. She directs the Affective Interactions Lab and serves as Associate Dean (Research) and Chair of the EDI Committee . PhD, University of Montreal MSc, Chalmers University of Technology BSc, Bahonar University Her research critically examines the ethical implications of sociotechnical systems in care and work futures, focusing on participatory design with vulnerable populations. She explores immersive technologies like VR for mental health, pain management, and clinical training, while advocating for equity in design practices. Recent work includes 15+ publications (2023-2026) on VR applications, ethical design tools, and sociotechnical critiques. She has received the SOAR Prize (2022-2023) and led grants on VR in clinical education, digital volunteering, and aging technologies. Dr. Ahmadpour supervises PhD students in projects like Collaborative Reflection in Mental Health and Cultural Bias in Digital Design . She contributes to the Design. Think. Make. Break. Repeat methodology handbook and co-leads the VirtX Community of Practice for immersive realities.
Rahul Kapoor is a Professor at the Wharton School of the University of Pennsylvania, where he also serves as the Chair of the Management Department. His research focuses on innovation management, business ecosystems, and technology strategy, with a particular emphasis on how firms navigate technological and organizational challenges in dynamic industries. University: University of Pennsylvania School: Wharton School Department: Management Department Academic Rank: Professor His research explores the interplay between organizational design, external collaboration, and innovation outcomes. Recent work includes studies on forecasting strategies, ecosystem interdependencies, and the role of setbacks in technology development. He has contributed to leading journals such as Strategic Management Journal and Research Policy , with a recurring focus on technology ecosystems and modular innovation. Kapoor’s research spans both theoretical and applied dimensions. He has examined how firms can optimize value creation in business ecosystems, the impact of organizational design on invention sourcing, and the dynamics of technology emergence. His studies often integrate historical case analyses and simulation models to uncover patterns in innovation management. Scientific Awards: Inaugural Academy of Management Emerging Scholar Award Strategic Management Journal Best Paper Prize Wharton Teaching Excellence Award (multiple years) Editorial Roles: Associate Editor, Strategic Management Journal Contributing Editor, Strategy Science Teaching: Undergraduate, MBA, Executive MBA, and PhD courses on technology and innovation strategy Leadership in Wharton’s Executive Education programs He has advised firms on innovation initiatives and draws on over seven years of industry experience in high-tech, including roles at Texas Instruments and co-founding a startup, to inform his academic work.
Prof. Dr. Bernd Skiera is a leading Marketing Professor at Goethe University Frankfurt since 1999 and a member of the managing board of the efl - The Data Science Institute. His work bridges information systems and marketing, with a focus on data-driven decision making and digital transformation.
Cem Say is a Professor in the Department of Computer Engineering at Boğaziçi University's Faculty of Engineering, where he has established himself as a leading researcher in theoretical computer science and artificial intelligence. His academic journey began with the completion of his doctoral dissertation titled Qualitative System Identification in 1992, which was the first thesis of Boğaziçi University's Computer Engineering PhD program. Professor Say's research interests span multiple domains of computer science, with significant contributions to quantum computing, artificial intelligence, and theoretical computer science. His early work focused on qualitative reasoning and simulation, particularly through the QSIM algorithm, where he made significant improvements to filtering techniques and addressed challenges in representing physical systems. Over time, his research evolved toward quantum computation, where he has made substantial contributions to quantum finite automata theory, space-bounded quantum computation, and quantum complexity classes. His recent work explores the energy complexity of computation, bridging theoretical computer science with thermodynamics. His publication record shows a clear evolution from classical AI and qualitative reasoning toward quantum computation. The most recent articles demonstrate his focus on space-bounded quantum computation, energy complexity of regular languages, and interactive proof systems with minimal resources. His work consistently addresses fundamental questions about computational limits, particularly in quantum and sublogarithmic-space models. Professor Say has also made significant contributions to science communication through several books written for general audiences, including 50 Soruda Yapay Zekâ (2018), Yeni Dünya, Yeni Ağ (2020), and En Hakiki Mürşit (2021), which explain complex concepts in artificial intelligence and scientific methodology in accessible terms. Throughout his career, Professor Say has been actively involved in the Turkish academic community, editing proceedings for multiple Turkish symposia on artificial intelligence and neural networks. His doctoral dissertation established foundational work in qualitative system identification, and his subsequent research has consistently pushed boundaries in theoretical computer science, particularly in quantum computation where he has collaborated extensively with Abuzer Yakaryılmaz and other researchers.
Dr. Alastair Key serves as Director of Studies in Archaeology and Official Fellow in Archaeology at Queens' College, University of Cambridge. His research bridges Paleolithic archaeology, stone tool technology, and hominin behavioral evolution through experimental and computational approaches. Director of Studies and Official Fellow at Queens' College, Cambridge Specializes in Paleolithic stone tool analysis, Acheulean technology, and hominin adaptation Conducts experimental archaeology and computational modelling to assess tool functionality Key's research focuses on Acheulean handaxe production , lithic microwear patterns , and ergonomic constraints in prehistoric tool use . He has extensively published on topics including glacial-stage hominin occupations , Oldowan toolmakers , and machine learning applications to archaeological analysis . His recent publications (2025-2023) span diverse subfields: Acheulean chronology , hominin tool use biomechanics , experimental projectile testing , and computational morphometric methods . The work often integrates multidisciplinary datasets and open-source analytical tools to address fundamental questions about human technological evolution. Current research directions include stone tool sharpness quantification , handaxe social signaling potential , and cross-species tool use comparisons through primate studies.
