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Shuang ZhaoView profile

Associate Professor

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
• +7 more

Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

Stephanie DenisonView profile

Associate Professor

• Developmental Psychology
• Cognitive Development
• Probabilistic Reasoning
• +4 more

Stephanie Denison is an Associate Professor and Associate Chair of Undergraduate Affairs at the University of Waterloo. Her research focuses on developmental psychology, particularly how children develop reasoning abilities related to probability, counterfactual thinking, and social cognition. Her work explores topics such as causal attribution, sunk cost fallacy, social network inference, and decision-making strategies in children. She has published extensively on probabilistic reasoning in infants and preschoolers, demonstrating how young children use statistical information to form beliefs about the world. Her recent studies investigate children's understanding of counterfactual scenarios (e.g., 'What if I had done something differently?'), their ability to infer emotions based on probabilistic outcomes, and their reasoning about social relationships through mutual connections. Denison also examines how children integrate physical constraints and emotional factors into their decision-making processes. Her contributions span cognitive development, philosophy of mind, and educational psychology, with a strong emphasis on bridging theoretical models with empirical findings from developmental research.

Andrew BoutrosView profile

Assistant Professor

• Reconfigurable Computing
• Field-Programmable Gate Array (FPGA) Architecture
• Computer-Aided Design (CAD) Tools
• +4 more

Andrew Boutros is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on reconfigurable computing architectures, FPGA design, and domain-specific acceleration for deep learning and datacenter workloads. He holds a PhD from the University of Toronto (2024) and has worked at Intel Labs and MangoBoost. Education: PhD, Electrical and Computer Engineering, University of Toronto, Canada (2024) MASc, Electrical and Computer Engineering, University of Toronto, Canada (2018) BSc, Electronics Engineering, German University in Cairo, Egypt (2016) Research Interests: Developing efficient FPGA architectures and CAD tools for reconfigurable hardware, domain-specific acceleration, and application/hardware co-design. Specific areas include 3D-stacked acceleration devices, graph neural network acceleration, and FPGA-based smart NICs for AI training. Publications: Over 30 papers in top venues like IEEE FPL, FCCM, and FPGA conferences, with 4 best paper awards. Recent work includes 3D-stacked architectures, graph neural network inference, and FPGA optimization for deep learning. Awards: Best Paper Award (FPT 2023) Best Paper Award (ICM 2021) Stamatis Vassiliadis Best Paper Award (FPL 2018) Advising/Grants: Currently accepting graduate students. Collaborations include 84 co-authors and industry partnerships with Intel and MangoBoost.

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +3 more

George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.

David WildView profile

Professor

• Statistical Bioinformatics
• Bayesian Computation
• Systems Biology
• +5 more

Prof. David Wild is a Professor in the Department of Statistics and Zeeman Institute at the University of Warwick. His research focuses on statistical bioinformatics, Bayesian computation, and systems biology, with applications in functional genomics, proteomics, and protein energetics. He has been funded by organizations including the UK BBSRC, EPSRC, EU, and the US NSF and NIH. His work includes developing state-space models for gene regulatory networks, nonparametric Bayesian methods for data fusion, and computational approaches to protein folding and energetics. Collaborations include Lorantis Ltd., Warwick School of Life Sciences, and the University of Birmingham. Recent projects involve inferring dynamic biological networks with time-varying topologies, improving computational efficiency via GPU methods, and advancing knowledge of protein energetics using Bayesian techniques. Future research aims to explore synthetic biology applications in protein design. Key funding sources include the BBSRC (SABR/SYSMO initiatives), EPSRC, EU, Leverhulme Trust, and NIH/NSF grants. His lab's work has led to advancements in understanding plant responses to pathogens and environmental stress, as well as T-cell activation dynamics.

• Neuroscience
• Motor Control
• Sleep Physiology
• +4 more

Prof. Dr. Sarah Meissner is an Assistant Professor at the Department of Health Sciences and Technology, ETH Zürich, affiliated with the Institute of Human Movement Sciences and Sport. Her research focuses on neural mechanisms underlying movement control, sleep physiology, and neuromodulation. She investigates how arousal levels, measured through pupil dynamics and EEG, influence cortical excitability and sleep architecture. Her work bridges clinical neuroscience with neuroimaging techniques, exploring applications like biofeedback for arousal regulation. Research Interests: Neural basis of motor control Sleep-wake neurophysiology Pupilometry and arousal dynamics fMRI and EEG applications Neuromodulation therapies Clinical neuroscience of movement disorders Her recent studies highlight novel methods to modulate arousal states and reveal how sleep oscillations impact cognitive and motor performance. No scientific awards or grants are explicitly listed in the provided information. Prof. Meissner’s work is conducted within multidisciplinary teams at ETH Zürich, though specific lab affiliations are not detailed. No advising relationships or student collaborations are noted in the data provided.

Nori JacobyView profile

Assistant Professor

• Cognitive Science
• Computational Neuroscience
• Music Cognition
• +3 more

Nori Jacoby is an Assistant Professor in the Department of Psychology at Cornell University and a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt. His research examines how internal representations shape sensory and cognitive abilities through computational approaches, cross-cultural studies, and large-scale behavioral experiments. Ph.D. from Hebrew University of Jerusalem (Edmond and Lily Safra Center for Brain Sciences) Postdoctoral training at MIT, UC Berkeley, and Columbia University Research areas include: Computational modeling of human perception Cross-cultural analysis of musical cognition Collective behavior in social networks Recent publications focus on: Global rhythm perception patterns Machine learning alignment with human cognition Oral transmission mechanisms in music evolution Scientific awards: NSF grant for collaborative creativity research Presidential Scholar in Society and Neuroscience Lab: Cornell Computational Cognition (CoCoCo) Lab

Alexander L. WolfView profile

Professor

• distributed systems
• networking
• software engineering
• +3 more

Alexander L. Wolf is the Dean of the Baskin School of Engineering and a Distinguished Professor of Computer Science and Engineering at the University of California, Santa Cruz. He holds a B.A. in Geology and Computer Science from Queens College (CUNY), and M.S./Ph.D. degrees from the University of Massachusetts Amherst. His career includes foundational roles at AT&T Bell Laboratories, the University of Colorado (where he held the Charles Victor Schelke Chair), the University of Lugano (founding the Faculty of Informatics), and Imperial College London. Wolf's research bridges distributed systems , networking , and software engineering , with recent projects in cloud computing, data-center networking, and mobile ad hoc networks (MANETs). His work emphasizes scalable architectures, fault tolerance, and adaptive service deployment. Key projects include HARNESS (hardware-enhanced cloud systems) and SERECA (secure cloud enclaves). His publications focus on optimizing networked systems, fault localization, and adaptive resource management. Trends include rigorous simulation-based validation, cross-layer optimization (e.g., integrating GPUs/networking), and practical applications in geosciences (Reverse-Time Migration) and machine learning (Bing's AdPredictor). Awards: ACM/IEEE/AAAS Fellow; Royal Society-Wolfson Research Merit Award; Two-time ACM SIGSOFT Research Impact Award; ACM SIGSOFT Outstanding Research & Distinguished Service Awards. Wolf leads major grants (e.g., EU H2020 SERECA, EPSRC NaaS) and has chaired ACM's Executive Committee. He founded ACM's Europe Council and contributed to seminal tools like the Siena publish/subscribe system. His team develops experimental platforms (e.g., Weevil for distributed testbeds) and collaborates with industry (AT&T, Microsoft Bing).

Guimin GaoView profile

Researcher

• Statistical Genetics
• Bioinformatics
• Genomics
• +3 more

Guimin Gao is a researcher with expertise in statistical genetics and bioinformatics. His research focuses on applying statistical methods to analyze genetic and genomic data, including collaborations in clinical and translational science. He has developed methods for haplotyping, gene mapping in admixed populations, and analyses of GWAS, methylation, and DNA sequencing data. He holds a Ph.D. in Biostatistics from Sun Yat-sen University of Medical Sciences and has completed postdoctoral training at Virginia Tech and Creighton University. His work spans diverse areas such as epigenetic biomarker identification in schizophrenia and statistical methodologies for genomic association studies. Education: Ph.D. (Biostatistics, 2000), Postdoc in Statistical Genetics (2001-2005) Research interests include statistical genetics, bioinformatics, and the application of computational methods to biomedical data. His publications demonstrate contributions to haplotype analysis, admixture mapping, and epigenetic studies. Gao has collaborated on projects involving family-based studies, meta-analyses, and the development of tools like MethylPCA for confounder control in methylome-wide studies.

David SpielerView profile

Professor

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +3 more

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

Mohammad T. IrfanView profile

Associate Professor

• Computational Game Theory
• Artificial Intelligence
• Social Network Analysis
• +3 more

Mohammad T. Irfan is the John F. and Dorothy H. Magee Associate Professor of Digital and Computational Studies (DCS) and Computer Science (CS) at Bowdoin College, with a joint appointment between the two departments. He also serves as the Director of the DCS Program. Irfan holds a PhD in Computer Science from Stony Brook University (2013), and an MS and BS in Computer Science and Engineering from Bangladesh University of Engineering and Technology (2006 and 2004, respectively). His research focuses on computational game theory, AI, social network analysis, economics, and image analysis of art, supported by an NSF grant. He teaches courses such as DCS 1500 and CSCI 2320, emphasizing interdisciplinary approaches to computational methods. Research interests include strategic interactions in networks, microfinance markets, and the application of game theory to social influence and art authentication (e.g., Jackson Pollock paintings). Irfan’s work bridges AI and social sciences, with notable achievements like a Best Paper Award with an undergraduate student. His teaching philosophy emphasizes student engagement and the liberal arts tradition, fostering a supportive environment for learning and research. Scientific contributions span algorithmic game theory, equilibrium computation, and network influence models. Grants and awards highlight his interdisciplinary impact, including NSF funding for research on influence games. Irfan’s leadership in creating Bowdoin’s DCS program reflects his commitment to fostering interdisciplinary education and innovation.

• Machine Learning
• Causal Inference
• Signal Processing
• +4 more

Yongqiang Chen is a Professor at the Chinese Academy of Sciences in the School of Engineering, Department of Computer Science and Engineering. With over 15 years of continuous research contributions spanning from 2005 to 2025, Dr. Chen has established himself as a versatile scholar working at the intersection of theoretical mathematics, machine learning, and practical engineering applications. Dr. Chen's research expertise encompasses: Machine Learning and Causal Inference, with recent focus on large language models and out-of-distribution generalization Signal and Image Processing, particularly in keyword spotting and watermarking applications Optimization Algorithms for engineering and management problems Theoretical Mathematics, including partition functions and combinatorial identities Engineering Management and Project Governance in construction contexts His recent publication trajectory (2023-2025) reveals a strategic evolution toward integrating causal reasoning with modern neural architectures, while maintaining strong connections to practical applications. Dr. Chen's work demonstrates exceptional interdisciplinary range, bridging pure mathematics with cutting-edge AI development and engineering solutions. His publications in venues like AAAI, NeurIPS, and IEEE Transactions reflect both theoretical rigor and practical relevance across multiple domains including renewable energy, neuroscience, and construction management. Dr. Chen maintains active collaborations with researchers across institutions, particularly with Bo Han on causal language models, James Cheng and Yiping Ke on brain network analysis, and various colleagues in engineering management. His research group appears to focus on developing theoretically sound yet practically applicable computational frameworks that address real-world challenges while advancing fundamental knowledge in machine learning and optimization.

• Ambient Intelligence
• Intelligent Environments
• Pervasive Computing
• +7 more

Victor Callaghan is a Professor at the University of Essex in the Department of Computer Science, School of Computer Science and Electronic Engineering. With an extensive publication record spanning four decades from 1982 to 2022, he has established himself as a leading researcher in the fields of Ambient Intelligence, Intelligent Environments, and Pervasive Computing. His work bridges the gap between theoretical computer science and practical applications in smart environments. Callaghan's research interests focus on Ambient Intelligence, Intelligent Environments, Pervasive Computing, Human-Computer Interaction, Educational Technology, Virtual and Augmented Reality, and Fuzzy Logic Systems. His work demonstrates a consistent trajectory from foundational computer science topics toward increasingly sophisticated intelligent environments that adapt to human needs. He has pioneered approaches that integrate multiple technologies to create seamless intelligent spaces that enhance human experience and learning. His publication pattern reveals a strong emphasis on educational applications of intelligent environments, with numerous papers exploring how virtual and augmented reality can transform learning experiences. His recent work shows a maturation of the field from theoretical foundations to practical implementations in real-world settings, particularly in education and healthcare contexts. The interdisciplinary nature of his work is evident in collaborations spanning computer science, education, psychology, and engineering disciplines. Callaghan has played significant editorial roles, including serving as guest editor for special issues and contributing prefaces to journal editions focused on future intelligent educational environments. His leadership in the academic community is further demonstrated through organizing and reporting on major international conferences in the field of Intelligent Environments. His research methodology often combines theoretical frameworks with practical implementations, creating systems that demonstrate the real-world applicability of intelligent environment concepts. The evolution of his work shows a progression from basic computational models to sophisticated multi-agent systems that can adapt to user behavior and environmental conditions.

Víctor López DomínguezView profile

Assistant Professor

• Spintronics
• Magnetic materials
• Electrical transport
• +5 more

Víctor López Domínguez is an Assistant Professor and Principal Investigator at the Institute of Advanced Materials (INAM) of Jaume I University in Castelló, Spain, where he leads the Spintronics for Advanced Devices Lab (SPINAD Lab). His research focuses on developing next-generation spintronic devices for computing and sensing applications. His academic background includes: PhD in Nanoscience and Nanotechnology (2014) from University of Barcelona Physics Degree (2009) from University of Barcelona López Domínguez's research centers on electrical transport in magnetic materials, magnetization dynamics, and voltage-controlled spintronic devices. His work bridges fundamental physics with practical applications in neuromorphic computing, probabilistic systems, and nanoscale sensors. Key focus areas include antiferromagnetic spintronics, skyrmion manipulation, and energy-efficient computing paradigms that leverage spin-orbit torque and voltage control mechanisms. His 15 most recent publications reveal a strong emphasis on antiferromagnetic memory devices, voltage-controlled magnetism, and neuromorphic applications. The research spans from fundamental material studies to device integration, with significant contributions to silicon-compatible spintronic memory, probabilistic computing hardware, and magnetomechanical sensors. His scientific recognition includes: CIDEGENT grant (2022) from Generalitat of Valencia López Domínguez secured the CIDEGENT grant to establish his research group at INAM, supported by Jaume I University and INAM. His lab trains undergraduate, master's, and PhD students in experimental spintronics while collaborating with international clean rooms and industry partners. Current projects focus on implementing novel computing paradigms using spintronic devices. The SPINAD Lab maintains comprehensive facilities for material fabrication, device characterization, and nanofabrication through Spanish clean room networks. The group collaborates extensively with Northwestern University (where López Domínguez previously worked) and participates in international research consortia focused on next-generation computing technologies.

Emmanuel VAZQUEZView profile

Professor

• Bayesian Statistics
• Computer Experiments
• Uncertainty Quantification
• +3 more

Emmanuel Vazquez is a Professor and researcher at CentraleSupélec, specializing in Bayesian design and analysis of computer experiments. He coordinates Data Science projects and leads research in Gaussian processes, uncertainty quantification, and optimization. He holds a PhD (2005) and HDR (2015) from Université Paris-Saclay, and has taught Bayesian statistics at CentraleSupélec. His work focuses on sequential design strategies, multi-fidelity simulations, and risk assessment in engineering and microbiology. He collaborates with institutions like the GdR MascotNum and PGMO, advancing methodologies for efficient simulation-based decision-making. His research includes applications in TBM excavation modeling, renewable energy integration, and food safety through quantitative microbial risk analysis. Education: École Normale Supérieure de Cachan (1997–2001), DEA/M2 in Mathematics (2001), PhD in Applied Mathematics (2005), Habilitation (2015). Research interests span Bayesian optimization, Gaussian process metamodeling, and stepwise uncertainty reduction (SUR) strategies. He develops algorithms for efficient sampling in expensive-to-evaluate systems, with applications to engineering, environmental modeling, and public health. His recent articles emphasize quantile set inversion, risk assessment in food safety, and scalable Gaussian process methods. Collaborations include projects on railway infrastructure digital twins (MINERVE) and multi-year renewable energy planning. He has contributed to open-source tools like the STK toolbox for kriging.