جست‌وجوی استاد و پژوهشگر

Cédric Foucaultمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Cognitive Modeling
• Decision-Making
• +۴ مورد دیگر

Cédric Foucault is a Junior Research Fellow at Christ Church College, University of Oxford, affiliated with the Department of Experimental Psychology. His research focuses on computational models of human cognition and decision-making in dynamic environments, leveraging tools from computer science, neuroscience, and machine learning. Prior to academia, he worked as a software engineer at Apple in Silicon Valley, contributing to iOS development. Education: PhD in Cognitive Computational Neuroscience (2023), University of Paris-Saclay/Sorbonne University MSc in Cognitive Neuroscience (2020), École Normale Supérieure de Paris MSc in Computer Science (2014), École Normale Supérieure Paris-Saclay His research integrates fMRI , M/EEG , and Bayesian modeling to study learning under uncertainty, probabilistic inference, and decision-making processes. Collaborations include work with Laurence Hunt’s research group at Oxford. He maintains an active GitHub profile with repositories on neural networks and computational cognitive science. Recent work emphasizes expanding research into broader decision-making domains while continuing to develop computational tools for neuroscience applications.

Ece Erdogmusمشاهده پروفایل

استاد مدعو

• Masonry Design and Analysis
• Sustainable and Resilient Construction
• Nondestructive Testing
• +۴ مورد دیگر

Ece Erdogmus is an Adjunct Professor at the School of Building Construction at Georgia Institute of Technology. She holds a PhD and MS in Architectural Engineering from Penn State University and a bachelor’s in Architecture from Middle East Technical University, Turkey. A licensed civil/structural engineer in Virginia, her interdisciplinary work focuses on improving the built environment through sustainable design, masonry innovation, and XR applications in education. She has authored over 100 peer-reviewed articles and led projects funded by NSF, NCMA Foundation, and others. Education: PhD in Architectural Engineering, Pennsylvania State University MS in Architectural Engineering, Pennsylvania State University Bachelor’s in Architecture, Middle East Technical University Research Interests: Erdogmus leads research in masonry design, nondestructive testing of historical structures, and AR/VR in STEM education. Her work bridges engineering and education, emphasizing sustainability, resilience, and inclusive design. Recent studies include sleep health in aging populations and gender equity in construction management. Key Contributions: Founder of the Women in Masonry Fund Chair of TMS 402/602 Structural Members Subcommittee Journal of Architectural Engineering Chief Editor (ASCE/AEI) Grants & Awards: NSF, NCMA Foundation, Nebraska DOT, NPS/NCPTT grants 2022 Leading Women@Tech Cohort AEI Outstanding Educator Award (2019) Labs & Teams: Collaborates with interdisciplinary teams on projects like the ARCHES group for historical structure assessment and VADERs (VR-based discipline exploration rotations).

Yuval Klugerمشاهده پروفایل

استاد

• Immunology
• Bioinformatics
• Single-cell Omics Analysis
• +۴ مورد دیگر

Yuval Kluger is a Professor of Pathology at the Yale School of Medicine, Yale University. His research focuses on integrating computational methods with biomedical data to address challenges in immunology, cancer biology, and virology. He specializes in single-cell omics analysis, machine learning applications in healthcare, and the development of bioinformatics tools for genomic and proteomic data interpretation. Key research interests include HIV pathogenesis mechanisms in the central nervous system, tumor immune microenvironment dynamics, and the development of algorithms for analyzing spatial transcriptomics and proteomics. He leads collaborative efforts such as the SCORCH consortium to study opioid responses in HIV contexts. His work spans methodological advancements in optimal transport for single-cell trajectory inference and noise reduction techniques in omics datasets. Publications emphasize cutting-edge tools like AMULETY for adaptive immune receptor analysis and SIMVI for spatial omics disentanglement. Kluger’s team also explores therapeutic implications of tumor-reactive T-cell suppression in melanoma and cytokine signaling pathways in inflammation regulation. No scientific awards are explicitly listed in the provided texts. His work is supported by grants enabling humanized mouse models for studying immune-oncology and viral persistence. Collaborations include multi-institutional projects on HIV reservoirs and cancer immunotherapy mechanisms.

Arlei Silvaمشاهده پروفایل

استادیار

• Data Science
• Network Science
• Machine Learning
• +۵ مورد دیگر

Arlei Silva is an Assistant Professor of Computer Science at Rice University. He holds a Ph.D. from the University of California, Santa Barbara (UCSB), and M.Sc. and B.Sc. degrees from Universidade Federal de Minas Gerais, Brazil. His research focuses on data science, network science, machine learning, and graph algorithms, with applications in flood prediction, graph anomaly detection, and temporal graph analysis. He has held visiting roles at Rensselaer Polytechnic Institute and has contributed to projects like trajectory compression and network process discovery. Education: Ph.D. in Computer Science, University of California, Santa Barbara (2019) M.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2011) B.Sc. in Computer Science, Universidade Federal de Minas Gerais, Brazil (2008) Research Interests: His work integrates machine learning and graph theory to address challenges in network dynamics, spatiotemporal modeling, and anomaly detection. Recent projects include FloodGNN-GRU for flood prediction, DOPPLER for dataflow optimization, and Pole for signed network embeddings. Awards: SNAKDD Best Paper Runner-up (2013) Best M.Sc. Thesis (Brazilian Computer Society, 2011) Best Undergraduate Research (top 6, Brazilian Computer Society, 2009) Labs/Teams: Engages in interdisciplinary projects at Rice, focusing on environmental informatics and network science. His GitHub repositories include work on trajectory compression, anomaly detection, and spectral algorithms for graph cuts.

• Category Theory
• Compositional Game Theory
• Categorical Cybernetics
• +۷ مورد دیگر

Jules Hedges is a mathematics and computer science researcher affiliated with the Institute for Categorical Cybernetics and the Mathematically Structured Programming group at the University of Strathclyde. As of September 2024, he is working 90% for the CyberCat Institute and 10% for the University of Strathclyde, focusing on applying category theory across various interdisciplinary domains. Hedges completed his PhD at Queen Mary University of London in 2016 with a thesis titled "Towards compositional game theory," which established foundational work for his ongoing research program. His academic journey spans from early work on game theory to current developments in categorical cybernetics. His primary research involves applying category theory in economics, machine learning, and control theory, with a view towards what was historically called cybernetics. Hedges is particularly known for developing compositional game theory, which provides a categorical framework for understanding strategic interactions through concepts like "open games" and "selection functions." His other research interests include graphical calculi for visual representation of categorical structures, functional programming paradigms, proof theory, and the semantics of natural language. He has made significant contributions to understanding how complex systems can be built from simpler components while preserving mathematical properties. Hedges' publication record reveals a consistent trajectory toward developing compositional frameworks for complex systems, with recent work increasingly focused on categorical cybernetics—the application of category theory to systems with feedback loops and goal-directed behavior. His research often bridges abstract mathematical concepts with practical applications in economics and artificial intelligence, particularly in reinforcement learning where he has demonstrated connections between value iteration algorithms and categorical optic composition. Hedges is a signatory of the "No Free View? No Review!" pledge, indicating his strong commitment to open access publishing. He has publicly stated he will not peer review for closed access or hybrid venues, reflecting his principled stance on making academic research freely available. While specific details about his advising activities are not documented in the provided text, his extensive publication record with numerous co-authors suggests active research collaboration and likely mentorship of junior researchers. His work spans multiple institutions and involves collaborators across mathematics, computer science, and economics. Hedges is a founding member of the Institute for Categorical Cybernetics, formally incorporated in October 2023, which represents the primary focus of his current work (90% of his professional time as of September 2024). This institute serves as a hub for researchers working at the intersection of category theory, cybernetics, and their applications to complex adaptive systems, potentially supporting collaborative projects, workshops, and publications in this emerging field.

Edmond Chowمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +۷ مورد دیگر

Edmond Chow is a Professor and Associate Chair in the School of Computational Science and Engineering at Georgia Institute of Technology. His research focuses on numerical methods, high-performance computing, and scientific computing applications in quantum chemistry, molecular dynamics, and machine learning. He holds awards including the 2009 ACM Gordon Bell Prize and 2002 PECASE. He leads the Intel Parallel Computing Center, advancing computational chemistry algorithms for Intel architectures. Education: Ph.D., Computer Science (minor in Aerospace Engineering), University of Minnesota, 1997 Honors B.A.Sc., Systems Design Engineering, University of Waterloo, 1993 Research Interests: Parallel algorithms for sparse linear systems and preconditioning High-performance quantum chemistry simulations Asynchronous iterative methods for extreme-scale computing Kernel-based methods and hierarchical matrices for Gaussian processes Applications in biological macromolecule dynamics and machine learning Awards & Recognition: ACM Gordon Bell Prize (2009) PECASE (2002) SIAM Fellow (2021) SC23 Test of Time Award Advising & Grants: Advised 11+ PhD/MSc students in numerical methods and HPC Funded by NSF, DOE, DARPA, and Intel Developed software tools like H2Pack (hierarchical matrices), GTFock (quantum chemistry), and Simint (electron integrals) Labs & Teams: Leads the Intel Parallel Computing Center and collaborates with institutions like LLNL, Sandia, and Temple University on asynchronous computing and quantum chemistry projects.

• Cognitive Neuroscience
• Decision Making
• Attention & Cognitive Control
• +۴ مورد دیگر

Professor Nick Yeung is a Professor of Cognitive Neuroscience at the University of Oxford and Tutorial Fellow at University College, Oxford. His primary affiliation is with the Department of Experimental Psychology. His research focuses on the cognitive and neural mechanisms underlying human learning, memory, attention, and decision-making. He explores how subjective confidence in choices influences information-seeking behavior and error detection, with applications in mental health (e.g., depression) and human-machine collaboration. Yeung holds a BA (Hons) and PhD in Psychology. His work integrates computational modeling, neuroimaging (EEG/fMRI), and experimental psychology to study metacognition, task switching, and attentional control. Recent studies investigate domain-general error signals, advice utilization dynamics, and neural correlates of confidence. Research highlights include EEG-based attention monitoring for safety-critical tasks (e.g., driving) and exploring how confidence biases affect decision-making in healthcare and collaborative systems. His lab also develops practical applications of cognitive neuroscience findings, such as attention-stabilizing neurostimulation devices. Key contributions span over 80 publications, including high-impact work on decision confidence, error monitoring, and cognitive control. His 2024 perspective paper outlines foundational advancements in cognitive control research, while 2025 studies reveal shared neural error signals across sensory modalities.

Michael Boehnkeمشاهده پروفایل

استاد

• Biostatistics
• Genetics
• Genomics
• +۳ مورد دیگر

Michael Boehnke is the Richard G. Cornell Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He has been on the faculty since 1984 and directs the University of Michigan Center for Statistical Genetics and the Genome Science Training Program. His research focuses on statistical methods for human genetics, particularly in type 2 diabetes and genome-wide association studies (GWAS). He leads major consortia such as DIAGRAM and MAGIC, and has published over 400 peer-reviewed articles. He has trained 36 doctoral students and post-doctoral fellows, many of whom hold academic positions at major research institutions. Education PhD in Biomathematics, University of California, Los Angeles (1983) BA in Mathematics, University of Oregon (1977) Research Boehnke develops statistical tools for analyzing genetic data and investigates genetic contributors to type 2 diabetes, lipid traits, and anthropometric measures. His work emphasizes collaborative consortia to aggregate large-scale genetic datasets. Recent projects include the Accelerating Medicines Partnership (AMP) to integrate genomic data for metabolic disease research. Publications His most recent work addresses pleiotropy in genetic variants, sex-specific gene expression in skeletal muscle, and causal mechanisms of complex traits using multi-omics approaches. These studies highlight the utility of integrative genomics and population-scale data. Awards Member, National Academy of Medicine Fellow, American Statistical Association Fellow, American Association for the Advancement of Science Grants & Advising He oversees NIH-funded projects (e.g., R01 HG009976, U01 DK062370) and has trained 24 doctoral students and 12 postdocs. His lab collaborates globally through initiatives like the Finland-United States Investigation of NIDDM (FUSION) study. Labs & Teams He leads the Center for Statistical Genetics, a hub for interdisciplinary research in computational genetics and statistical methodology.

Giulio Chiribellaمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Networks
• Information-Theoretic Foundations of Quantum Mechanics

Giulio Chiribella is a Professor and Associate Director (Research & Graduate Studies) at the School of Computing and Data Science, University of Hong Kong. He holds a PhD from the University of Pavia, Italy. His research focuses on quantum information theory, quantum networks, and the foundational aspects of quantum mechanics. Chiribella is a CIFAR-Azrieli Global Scholar and a Fellow of the National Virgilian Academy of Sciences. He also serves on the editorial boards of the Journal of Physics A and the Standing Committee of the International Colloquia on Group Theoretical Methods in Physics. His research explores quantum protocols, causal structures, and the interplay between quantum mechanics and information theory. Notable contributions include work on quantum superposition of causal orders and the replication of quantum operations at the Heisenberg limit. His publications span high-impact journals such as Physical Review Letters and Nature Communications . Grants & Awards: Croucher Senior Research Fellowship (2018–2019) Principal Investigator for multiple grants, including the CIFAR-Azrieli Fund (2016–2018) and Hong Kong RGC grants (2016–2019). Research Leadership: Leads the Quantum Information and Foundations Theory (QIFT) research group ( http://qift.weebly.com/ ). Coordinates international projects on quantum foundations and information dynamics.

Giovanni Negliaمشاهده پروفایل

پژوهشگر

• Performance Evaluation of Distributed Systems
• Cache Networks
• Distributed Machine Learning
• +۷ مورد دیگر

Giovanni Neglia is a Research Director (DR2) at Inria, affiliated with the NEO team and the 3IA Côte d'Azur Chair, focusing on performance evaluation of distributed systems, cache networks, and large-scale machine learning. He is based in Sophia Antipolis, France. Inria, Sophia Antipolis NEO Team 3IA Côte d'Azur Chair His research interests center on distributed systems, leveraging mathematical tools such as Markov processes, control theory, fluid models, and game theory. Key areas include caching policies, distributed optimization for machine learning, and performance modeling of networks. He has previously worked on P2P networks, delay tolerant networks, smart grids, and complex networks. Recent publications demonstrate a strong focus on similarity caching, federated learning, and distributed optimization. Trends include developing online learning algorithms for caching, improving privacy and efficiency in federated systems, and optimizing content delivery in 5G and small cell networks. Best paper award at ITC-33 IEEE Infocom Distinguished TPC member (2017, 2018, 2022) Best paper award at ITC28 2016 Best paper award at IEEE Online Greencomm 2014 Best paper award at IEEE Infocom NetSciCom 2014 Best paper award at IEEE VTC2013-Spring Best student paper award at VALUETOOLS 2012 Best paper award at BIONETICS 2007 Giovanni Neglia leads the Fed-Malin Inria challenge on federated machine learning and has secured grants including a 3IA Côte d'Azur chair for PERUSALS (Pervasive Sustainable Learning Systems). He advises PhD students such as Othmane Marfoq and Chuan Xu, and collaborates with Accenture Labs, SAP, and Nokia Bell Labs. He has organized winter schools and tutorials on complex networks and similarity caching. He leads the NEO team and previously led the Maestro team. He is involved in the joint Brazilian-French research team Thanes on network science and has served on numerous technical program committees for major conferences including IEEE Infocom, INFOCOM, and SIGMETRICS.

• Biomedical Signal Processing
• Embedded Systems
• FPGA Hardware Implementation
• +۷ مورد دیگر

Jose Vicente Frances Villora is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is an active researcher in digital systems and biomedical signal processing, with a strong focus on real-time embedded systems and hardware acceleration. His research interests include: Biomedical Signal Processing, particularly ECG and microelectrode recordings FPGA-based hardware implementation of neural networks and real-time algorithms Machine learning applications in healthcare, including arrhythmia detection and Parkinson’s disease treatment Neuromorphic computing and spiking neural networks Digital signal processing in embedded and real-time systems The analysis of his recent publications (2017–2015) reveals a consistent focus on leveraging machine learning—especially KNN and neural networks—for clinical applications such as ventricular fibrillation detection and deep brain stimulation. A significant portion of his work emphasizes hardware implementation on FPGAs, aiming to achieve real-time performance and low resource consumption in medical and robotic systems. He has contributed to educational materials, including textbooks and OCW resources, and supervises academic theses. While no specific scientific awards are mentioned in the provided text, his sustained publication output and integration of theory with hardware systems underscore his technical expertise. He advises students in thesis work and has supervised doctoral research, though specific student names are not listed. His research is supported by practical applications in medical diagnostics and robotics, and he is affiliated with the GPDD (Group for Digital Design and Processing), which focuses on digital system design and processing. He is actively involved in laboratory development, including a remote laboratory for industrial robots, and his team works on co-design environments for neural network hardware, FPGA acceleration, and real-time biomedical systems.

• Structural Dynamics
• Railway Structural Dynamics
• Structural Health Monitoring
• +۷ مورد دیگر

Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU’s Faculty of Engineering Science. His research focuses on structural dynamics, railway systems, and bridge engineering. He leads projects on structural health monitoring, wind engineering, and architectural collaboration in structural design. Key collaborations include work with KTH Stockholm, Vilnius University, and the University of Porto. He teaches courses such as Structural Dynamics, Steel Structures II, and Railway Catenary Systems. His work integrates advanced sensor technologies, machine learning, and probabilistic methods to address challenges in infrastructure longevity and safety. Research interests span structural monitoring, overhead contact line dynamics, and reliability assessments. Notable contributions include the development of Kalman filter-based systems for crosswind load identification and shape grammar approaches for architectural-structural design integration. His team has pioneered non-contact measurement techniques using digital image correlation and advanced neural networks for bridge rivet inspection. Publications highlight innovations in railway catenary optimization, timber structure dynamics, and probabilistic fatigue analysis. His work emphasizes interdisciplinary approaches, bridging engineering and architectural disciplines to enhance sustainable infrastructure solutions.

Mingshuai Chenمشاهده پروفایل

استادیار

• Formal Verification
• Programming Theory
• Logical Aspects of Computer Science
• +۵ مورد دیگر

Mingshuai Chen is an Assistant Professor at Zhejiang University, leading the Formal Verification Group. He previously held a postdoctoral position at RWTH Aachen University. Education: Ph.D. in Computer Science from Institute of Software, Chinese Academy of Sciences (2019) B.Sc. in Computer Science from Jilin University (2013) His research focuses on formal verification, synthesis, programming theory, and probabilistic/quantum systems. Notable contributions include Exact Bayesian Inference and Lower Bounds for Probabilistic Programs . Scientific awards include: NSFC Excellent Young Scientists Fund Program (Overseas) Distinguished Paper Award at ATVA 2018 Best Paper Award at FMAC 2019 CAS-President Special Award (2019) 2nd Prize@ChinaSoft'24 He serves on program committees for OOPSLA 2026, TACAS 2026, and multiple other conferences.

Johannes Schmidt-Hieberمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Johannes Schmidt-Hieber is a Professor of Statistics at the University of Twente, Department of Applied Mathematics. He has held positions at Leiden University (2014-2018) and conducted postdoctoral research at Vrije Universiteit Amsterdam and ENSAE Paris. His work bridges deep learning theory with nonparametric statistics, focusing on convergence rates, regularization techniques, and Bayesian inference. Education: PhD from Universität Göttingen and Bern (2010), studies at Freiburg and Göttingen Research: Specializes in statistical learning theory, neural network analysis, and high-dimensional inference His 15 most recent publications address topics like GCN convolutions in regression, implicit regularization in deep networks, and posterior contraction in Gaussian processes. Key contributions include theoretical guarantees for optimization algorithms and bias-variance trade-off analysis. Scientific recognition includes: ERC Consolidator Grant (2024-2029) IMS Fellow (2024) Van Dantzig Award (2025) Frontiers of Science Award (2025) NWO Vidi grant (2019-2024) He served as associate editor for leading journals and organized multiple international workshops. Current projects include mathematical foundations of machine learning and statistical uncertainty quantification.

Qiang Liuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +۸ مورد دیگر

Qiang Liu is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. He leads the Statistical Learning & AI Group, focusing on probabilistic graphical models, variational and Monte Carlo inference, deep reinforcement learning, and kernel methods with applications in crowdsourcing, computer vision, and bioinformatics. NSF CAREER Award recipient Co-organized workshops at ICML 2019 and NIPS 2013 Specializes in rectified flow for generative modeling and optimal transport His recent work includes fast inference via straight trajectories in ODE models and certifiable robustness in neural architectures. Publications span conferences like NeurIPS, ICML, ICLR, and JMLR.