Prof. Jan Theeuwes is a Full Professor of Cognitive Psychology at the Vrije Universiteit Amsterdam's Faculty of Behavioural and Movement Sciences, leading the ERC-funded research group on statistical learning and attention. He holds additional roles as Distinguished Visiting Research Professor at the William James Center for Research (Lisbon) and Visiting Professor at Zhejiang University (China). With over 474 publications and two ERC Advanced Grants (2013 & 2019), his work spans visual attention, statistical learning, and applied human factors in road design. He pioneered the 'Self-Explaining Roads' concept, revolutionizing global road safety design, and co-developed Open Sesame, a widely used open-source experiment builder. His research integrates EEG, fMRI, and computational modeling, addressing topics like distractor suppression, emotion, and leadership influence. Over 25 PhD students have graduated under his mentorship, many securing academic roles across Europe. Education: Cum laude degrees in Experimental Psychology (Tilburg University) and a PhD (VU Amsterdam, 1992). Key awards include the Bertelson Award (2001) and KNAW membership (2010). He advises the Dutch Department of Transportation and founded the Institute for Brain and Behavior Amsterdam (IBBA) in 2016. Ancillary roles include leadership in European psychological societies and consultancy for Attention Architects, applying eye-tracking research in corporate settings.
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Prof Aaron Thean is the Deputy President (Academic Affairs) and Provost at the National University of Singapore (NUS). Formerly, he served as Dean of the College of Design and Engineering at NUS and held senior roles at IMEC (Belgium) as Vice President of Logic Technologies and Director of Logic Devices Research. His expertise spans advanced semiconductor device technologies, including FinFETs, nanowire FETs, III-V/Ge channels, and emerging beyond-CMOS architectures. He holds degrees from the University of Illinois Urbana-Champaign (B.Sc., M.Sc., Ph.D. in Electrical Engineering) and has published over 300 papers with 50+ patents. His awards include the Gregory Stillman Award (2001) and Compound Semiconductor Innovation Award (2014). Research interests focus on semiconductor innovation, device-process co-optimization (DTCO), and monolithic 3D integration. Notable contributions include industry-first Gate-First HKMG technologies, advanced strained silicon platforms, and neuromorphic computing hardware. His work bridges academia and industry through collaborations with Qualcomm, IBM, and foundry partners. Current initiatives emphasize energy-efficient computing and wearable sensor systems. Prof Thean’s leadership spans NUS-wide academic strategy and global research partnerships. His technical legacy includes foundational advancements in transistor scaling, low-power CMOS design, and AI-driven failure analysis methodologies.
Gautham Narayan is an Associate Professor in the Department of Astronomy at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Physics and the National Center for Supercomputing Applications (NCSA). He holds roles as Deputy Director for Astrophysics Research at the NSF-Simons SkAI Institute and Deputy Director of the Center for AstroPhysical Surveys. His research focuses on multi-messenger and time-domain astrophysics, cosmology, and machine learning applications in astronomy. Education: PhD in Physics from Harvard University (2013) and BS (Hons) in Physics from Illinois Wesleyan University (2005). His work includes pioneering AI methods for transient detection, leading collaborations like the Young Supernova Experiment (YSE), and developing standards for LSST and WFIRST. He is a Simonyi NSF-CAREER Fellow and Analysis Coordinator for the LSST Dark Energy Science Collaboration. Research interests span cosmology, supernovae, and survey science. Key projects include establishing spectrophotometric standards via HST observations and advancing real-time analysis pipelines like ANTARES. Recent work emphasizes Bayesian models for supernova cosmology and multi-messenger astrophysics. Awards: Simonyi NSF-CAREER Fellowship. Collaborations include DESC, SCiMMA, and the KEGS team. Teaching includes courses on astrophysics and data science, with mentorship of students across undergraduate and graduate levels. Public outreach efforts include Astronomy on Tap events and science communication initiatives.
Henry Hoffmann is a Professor and Liew Family Chair in the Department of Computer Science at the University of Chicago. His research focuses on self-aware computing systems that adapt to meet goals like power efficiency, performance, and security. He leads the SEEC project and has contributed to advancements in computer architecture, embedded systems, and quantum computing. Hoffmann received the PECASE (2019), DOE Early Career Award (2015), and was inducted into the Samsung Hall of Fame for discovering vulnerabilities in SmartTVs. He holds a PhD from MIT (2013) and has co-founded Config Dynamics (2019). His work bridges control theory, machine learning, and traditional computer systems to create adaptive solutions for modern computing challenges. Education: PhD in Electrical Engineering and Computer Science from MIT (2013), SM (2003), and B.S. (1999) with highest honors from UNC Chapel Hill. Professional experience includes roles at Tilera Corporation and MIT Lincoln Laboratory. Research Interests: Self-aware systems, adaptive resource management, quantum computing optimization, and cybersecurity. His SEEC framework enables systems to autonomously adapt to constraints like energy and performance. Recent work explores applying adaptive techniques to AI/ML models for energy-efficient inference and security. Awards: Over $19M in research funding, 100+ publications, and leadership roles in NSF Expedition EPiQC (quantum computing). Named Chair of UChicago CS Department (2023-2024). Labs/Teams: Systems Group, EPiQC (quantum computing), and CERES (unstoppable computing systems). Current students include Jerry Ding and Ryien Hosseini. Notable alumni include Yi Ding (now faculty at Purdue) and Nikita Mishra.
Ali Mani is an Associate Professor of Mechanical Engineering at Stanford University and a faculty affiliate at the Institute for Computational and Mathematical Engineering. He earned his PhD in Mechanical Engineering from Stanford in 2009, following an M.S. (2004) and B.S. (2002) from Stanford and Sharif University of Technology, respectively. His research focuses on fluid mechanics, turbulence, and numerical simulations, with applications in multiphase flows, electrokinetic systems, and applied mathematics. His group develops high-fidelity simulation tools and reduced-order models to understand transport processes in turbulent and chaotic systems. Research interests include turbulence modeling, two-phase flow dynamics, and electrochemical transport. Recent work explores eddy viscosity operators, nonlocal transport phenomena, and computational methods for multiphase systems. The group's studies often bridge experimental validation and numerical analysis to improve predictive engineering models. Key contributions span electrokinetic transport in porous media, superhydrophobic surface slip effects, and phase field modeling. His lab’s work is supported by grants focusing on fluid dynamics, renewable energy systems, and advanced simulation frameworks.
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.
Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).
Hima Lakkaraju is an Assistant Professor at Harvard University with dual appointments in the Harvard Business School and the Department of Computer Science. Her research focuses on trustworthy AI, including machine learning interpretability, fairness, privacy, and safety. She holds a PhD from Stanford University and has received accolades such as the Alfred P. Sloan Fellowship and NSF CAREER Award. Her work bridges algorithmic foundations and societal implications of AI, with applications in healthcare, policy, and business. Education: PhD in Computer Science from Stanford University (2013-2017). Academic background includes roles at IBM Research, Microsoft Research, and Adobe. Research Interests: Algorithmic Foundations of AI Interpretability and Explainable AI Fairness and Bias Mitigation Privacy-Preserving ML Generative Models and LLMs Ethical AI Policy and Regulation Key Achievements: Over 100 publications in top venues like NeurIPS and ICML; co-founder of the Trustworthy ML Initiative; featured in MIT Tech Review, Forbes, and Harvard Business Review. Current projects include the AI4LIFE research group and work on regulatory frameworks for AI. Advising and Grants: Supervises over 30 students across PhD, master's, and postdoc levels. Research supported by NSF, Sloan Foundation, Schmidt Sciences, Google, Amazon, and others. Initiatives include the Regulatable ML workshop and NeurIPS ethics co-chair roles. Labs and Collaborations: Leads Harvard's AI4LIFE group and collaborates with industry partners like Fiddler AI. Active in policy discussions on AI regulation and societal impact.
Prof. Massimo Fornasier holds the Chair of Applied Numerical Analysis at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His research focuses on mathematical modeling, numerical analysis, and data-driven methods, particularly in areas like compression, sparse recovery, and optimization. He has made significant contributions to consensus-based optimization, control of multiagent systems, and applications in image/signal processing. Education: PhD in Computational Mathematics, University of Padua (2003) Postdoctoral fellowships at University of Vienna, Sapienza University of Rome, and Princeton University Awards: ERC Starting Grant (2012) START Prize (2011) Prix de Boelpaepe (2009) His work bridges theoretical analysis and computational methods, with applications ranging from compressive sensing to machine learning. Recent research emphasizes consensus-based optimization frameworks and their global convergence properties. Editorial roles include journals like Networks and Heterogeneous Media and Calcolo . He leads research groups in areas such as Data Science and Numerical Analysis at TUM.
K. Rajibul Islam is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC) and the Department of Physics and Astronomy. He holds a joint appointment with the Perimeter Institute for Theoretical Physics and co-founded Open Quantum Design and Lightflow Optics Inc. His research focuses on quantum information processing, quantum simulation, and trapped ion systems, with applications in quantum computing and entanglement studies. Education: Ph.D. in Physics (2012, University of Maryland), M.Sc. in Physics (2007, Tata Institute of Fundamental Research), B.Sc. in Physics (2005, Jadavpur University). Postdoctoral research at Harvard University (2012–2015) and MIT (2015–2016). Research Interests : Quantum simulation of spin models, quantum computing with trapped ions, entanglement measurement, frustrated spin systems, and quantum materials. His lab, QITI (Quantum Information with Trapped Ions), develops scalable quantum simulators and open-access quantum computers like 'QuantumIon.' Awards : Fellow of the American Physical Society (2024), VAIBHAV Fellowship (2024), Excellence in Teaching Award (2024), Early Researcher Award (2019), and Distinguished PhD Dissertation Award (2012–13). Teaching : Courses include PHYS 701 (Graduate Quantum Physics), PHYS 234 (Quantum Physics I), PHYS 393 (Physical Optics), and PHYS 256 (Geometrical and Physical Optics). He emphasizes outreach via initiatives like Bigyan.org.in , a Bengali-language science platform. Lab and Collaborations : Active in developing trapped-ion quantum hardware, including ion trap designs, optical addressing systems, and holographic control methods. Collaborates on quantum algorithms, machine learning for quantum systems, and experimental quantum thermodynamics.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Hanbyul Joo is an Assistant Professor in the Department of Computer Science and Engineering at Seoul National University (SNU). Prior to joining SNU, he was a Research Scientist at Facebook AI Research (FAIR) in Menlo Park. He completed his Ph.D. in the Robotics Institute at Carnegie Mellon University, working with Yaser Sheikh, and received his M.S. in Electrical Engineering and B.S. in Computer Science from KAIST, Korea. Dr. Joo's educational journey began at KAIST, where he earned both his Bachelor's and Master's degrees. He then pursued his Ph.D. at Carnegie Mellon University's Robotics Institute, completing his dissertation titled "Sensing, Measuring, and Modeling Social Signals in Nonverbal Communication." His doctoral work focused on developing the Panoptic Studio, a unique sensing system with over 500 synchronized cameras for capturing social interactions. Dr. Joo's research primarily focuses on endowing machines and robots with the ability to perceive and understand human behaviors in 3D . His goal is to build "social Artificial Intelligence" that can interact with humans using social signals (body languages). He pursues this direction using data-driven methods where data is collected by measuring the wide spectrum of social signals transmitted during interpersonal social interaction. His research spans computer vision, machine learning, computer graphics, and robotics , with particular emphasis on 3D human pose estimation, human-object interaction, and social signal processing. His recent publications demonstrate a clear trend toward leveraging diffusion models for 3D reconstruction and generation tasks, with a focus on human-centric applications. His work bridges the gap between 2D image understanding and 3D scene reconstruction, often utilizing pre-trained models to overcome data limitations. The research consistently addresses fundamental challenges in understanding human behavior, interaction with objects, and social dynamics in 3D space. Dr. Joo is a recipient of several prestigious awards including the Samsung Scholarship and the CVPR Best Student Paper Award in 2018 . His paper "Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies" received this honor at CVPR 2018. His research has been widely recognized in top computer vision and AI conferences, with multiple oral presentations at venues like CVPR, ICCV, and ECCV. Dr. Joo actively mentors a large group of students, with approximately 15 current students working toward MS/PhD degrees under his supervision. His lab, the SNU VCLab, focuses on cutting-edge research in computer vision and graphics. He has secured significant research funding through his work, though specific grant details aren't provided on his website. Dr. Joo frequently serves as an area chair for major conferences including CVPR, ICCV, and NeurIPS, demonstrating his standing in the academic community. Dr. Joo leads the SNU VCLab, which has developed several notable datasets and tools including SNU ParaHome, FrankMocap, and the CMU Panoptic Studio Dataset. His lab maintains strong industry connections, with students interning at leading companies like Meta. The lab's research focuses on building the infrastructure and algorithms needed for social AI, with an emphasis on practical applications that can be deployed in real-world settings.
Akshay R. Rao is a Professor and General Mills Chair in Marketing at the University of Minnesota's Carlson School of Management . He holds a PhD in Marketing from Virginia Tech and has served as founding Director of the Institute for Research in Marketing (2005-2010) and Chairman of the Marketing & Logistics Management Department (2003-2006). Rao has also held visiting positions at MIT and Hong Kong University of Science & Technology. Bachelor of Arts in Economics (Honors), Madras University (1978) Master of Business Administration in Marketing, XLRI - Xavier School of Management (1980) Doctor of Philosophy in Marketing, Virginia Polytechnic Institute and State University (1986) Rao's research spans consumer behavior, pricing strategy, brand management , and political marketplace segmentation , with recent work examining vaccine hesitancy , political ideology effects on consumer behavior , and fake news dynamics . His work appears in top journals including Journal of Consumer Research , Journal of Marketing , and Harvard Business Review , covering topics from neuromarketing to information economics . Scientific recognition includes: Robert Ferber Award (1987) Harold Maynard Award (2000) Distinguished Alumnus, XLRI (2011) He has testified before the Federal Trade Commission and served on editorial boards of Journal of Consumer Research and Journal of Marketing . Beyond academia, Rao consults for global firms like 3M and Medtronic, and has conducted the Minnesota Orchestra twice in 2024-2025.
