جست‌وجوی استاد و پژوهشگر

Warren Grossمشاهده پروفایل

استاد

• Integrated Circuits and Systems
• Error-Correcting Codes
• Hardware Acceleration
• +۵ مورد دیگر

Warren Gross is a Professor and Chair of the Department of Electrical & Computer Engineering at McGill University's Faculty of Engineering. Holding the prestigious James McGill Professor title, he leads research in integrated hardware systems from his office in Montreal's McConnell Engineering Building. His work bridges theoretical coding theory with practical VLSI implementations for next-generation communication networks. Professor Gross specializes in Integrated Circuits and Systems with deep expertise in error-correcting codes, particularly polar and Reed-Muller codes. His research spans hardware acceleration for machine learning, stochastic computing architectures, and VLSI design for low-latency wireless systems. Current projects focus on 6G air interface technologies, energy-efficient edge AI, and novel decoding algorithms that balance performance with hardware constraints. The ISIP Lab (Integrated Systems and Image Processing) serves as his primary research hub, advancing both theoretical frameworks and physical implementations. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Hardware-optimized decoding algorithms for polar codes targeting sub-1ms latency in 6G systems; (2) Model compression techniques for transformer networks deployed on resource-constrained edge devices; (3) Stochastic computing approaches for combinatorial optimization problems. These themes reflect his dual focus on communication theory and efficient hardware realization, with increasing emphasis on machine learning integration. Scientific recognition includes: James McGill Professor (McGill University's highest academic honor) As Department Chair and research leader, Professor Gross oversees multiple collaborative projects with industry partners in telecommunications and semiconductor sectors. His lab maintains active partnerships with 5G/6G standardization bodies and chip manufacturers, though specific grant details aren't publicly enumerated. The ISIP Lab operates advanced VLSI design facilities and FPGA testbeds supporting both academic research and industry prototyping. Research infrastructure includes specialized labs for stochastic computing implementation, polar code decoder validation, and edge AI acceleration. Current efforts focus on quantum-inspired annealing techniques for MIMO detection and hardware-friendly transformer architectures, with several patents pending in decoding methodology and model compression.

• Deep Generative Models
• Uncertainty Quantification
• Neural Data Compression
• +۳ مورد دیگر

Stephan Mandt is an Associate Professor of Computer Science and Statistics at the University of California, Irvine. He leads research at the intersection of deep generative modeling, uncertainty quantification, neural data compression, and AI-driven scientific discovery. Previously, he directed the machine learning group at Disney Research and held postdoctoral positions at Princeton and Columbia University. His work is supported by NSF, DARPA, IARPA, DOE, Disney, Intel, and Qualcomm. He has received the NSF CAREER Award, UCI Mid-Career Excellence Award, and German Research Foundation's Mercator Fellowship. Education: PhD in Theoretical Physics from the University of Cologne (German National Merit Scholarship recipient). Served as Program Chair for AISTATS 2024, General Chair for AISTATS 2025, and Action Editor for JMLR and Transactions on Machine Learning Research. Active in organizing tutorials at NeurIPS, AAAI, and UAI. Research Interests: Focuses on generative AI foundations, including diffusion models, variational autoencoders, and neural compression techniques. Explores applications in climate science, physics, and chemistry. Current projects include AI for scientific discovery, resource-efficient learning algorithms, and uncertainty-aware neural networks. Grants & Awards: Recognized as a Chan Zuckerberg Investigator and AI Resident. Holds the Kavli Fellowship from the U.S. National Academy of Sciences. His research bridges machine learning theory with interdisciplinary scientific challenges, emphasizing scalable and interpretable algorithms. Labs & Teams: Leads the Mandt Lab at UCI, supervising PhD students and postdocs in generative AI and scientific machine learning. Collaborates with institutions like ETH Zurich (as Guest Professor) and the Chan Zuckerberg Initiative.

Robert Bamlerمشاهده پروفایل

استاد

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +۳ مورد دیگر

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms

• Microelectronics
• Nanotechnology
• VLSI Design
• +۷ مورد دیگر

Sumeet Gupta is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on device-circuit co-design in emerging nanotechnologies, spintronics, and low-power VLSI design. He leads interdisciplinary efforts in ferroelectric devices, neuromorphic computing, and energy-efficient hardware for AI applications. Education: B.Tech from IIT Delhi (2006), M.S. and Ph.D. from Purdue University (2008, 2012). Research Interests: Ferroelectric and spintronic materials for next-gen computing Non-Boolean computing architectures Compute-in-memory accelerators Low-power and variation-tolerant VLSI design Domain wall dynamics in thin films Neuromorphic engineering Key Contributions: Over 50+ peer-reviewed publications on ferroelectric transistors, crossbar array optimizations, and fault-tolerant neural network hardware. His work bridges material science, device physics, and circuit design to enable energy-efficient computing systems.

Mark Siemensمشاهده پروفایل

استاد

• condensed matter physics
• Optics and Photonics
• Semiconductor physics
• +۳ مورد دیگر

Dr. Mark Siemens is a Professor and Associate Chair in the Department of Physics & Astronomy at the University of Denver. He holds a B.S. in Engineering Physics from the Colorado School of Mines (2003) and a Ph.D. in Physics from the University of Colorado Boulder (2009), where he conducted research at JILA. His research focuses on condensed matter physics and optics/photonics, particularly using ultrashort laser pulses to study semiconductor electron transport and exploring topological effects in optical vortices. He has developed advanced microscopy techniques like STED microscopy and pioneered studies on vortex dynamics in light. Dr. Siemens also directs the Undergraduate Research Center and is affiliated with The Optical Society and American Physical Society. Education: B.S., Engineering Physics, Colorado School of Mines (2003) Ph.D., Physics, University of Colorado Boulder (2009) Research Interests: Optical vortex dynamics and interactions Laser-based semiconductor characterization Super-resolution microscopy Quantum fluid phenomena in light Orbital angular momentum applications Advising & Grants: Directed the Undergraduate Research Center since 2018 Recipient of NSF CAREER Award for nanostructure photonics research (2016) Labs & Collaborations: Collaborates with JILA and NIST on advanced optical systems, and leads development of open-source microscopy tools (e.g., OpenSTED).

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +۵ مورد دیگر

Torsten Hoefler is a Professor affiliated with ETH Zurich, leading research in parallel computing, distributed systems, and high-performance computing (HPC). His work bridges theoretical foundations and practical implementations, focusing on optimizing algorithms, network topologies, and hardware-software co-design. Research Interests: His primary areas include parallel algorithms, distributed systems, machine learning infrastructure, and network architectures. He emphasizes scalable solutions for large-scale applications, particularly in data-centric computing and serverless environments. Publications: Recent work highlights include innovations in network topologies (e.g., HammingMesh), serverless benchmarking frameworks (SeBS), and optimizations for large language models (LLMs). His publications often address performance bottlenecks and energy efficiency in HPC and cloud systems. Awards & Grants: While no specific awards are listed here, his prolific publication record and leadership in HPC indicates significant recognition in the field. Active in grant-funded projects related to exascale computing and AI infrastructure. Labs & Teams: Leads the Communication Systems Lab at ETH Zurich, collaborating with industry partners like NVIDIA and IBM on hardware-accelerated computing and cloud-native systems.

Maximilian Starkمشاهده پروفایل

مدرس

• Communication Systems
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Dr. Maximilian Stark is a Lecturer at the Institute of Communications, TU Hamburg. His research focuses on machine learning-driven advancements in communication systems, particularly applying the information bottleneck method to decoding algorithms, signal processing, and quantization techniques. His work bridges theoretical information theory with practical implementation challenges in coding and channel design. Key research areas include LDPC and polar codes optimization, low-bitwidth decoding architectures, and distributed signal processing frameworks. Recent publications emphasize adaptive learning systems for error resilience under quantization constraints and hardware-efficient receiver design. Dr. Stark's academic contributions span over 20 peer-reviewed articles since 2016, with a strong emphasis on integrating machine learning principles into traditional communication engineering problems. Notable themes include neural decoding paradigms, resource-constrained quantization strategies, and distributed information compression methods. No scientific awards explicitly listed. Academic advising details and lab affiliations are not provided in source materials.

• Digital Forensics
• Cyber Security
• Machine Learning
• +۳ مورد دیگر

Arun Kumar is a Lecturer in Digital Forensics and Cyber Security at the University of South Wales, affiliated with the Faculty of Computing, Engineering and Science. His research focuses on interdisciplinary areas such as cyber security frameworks, machine learning applications in healthcare and finance, nanotechnology-based drug delivery systems, and smart city infrastructure protection. His work spans technical innovations like Android-based emergency response systems, intrusion detection in sensor networks, and low-bitwidth neural networks for edge computing. He also explores practical implementations such as solar-powered vehicle control systems and hydrogel synthesis for biomedical applications. Key themes in his publications include predictive analytics for financial markets, ethical hacking practices, and securing industrial cyber-physical systems. His research integrates computational methods with domain-specific challenges in healthcare, materials science, and energy systems.

• Parallel Computing
• Distributed Systems
• Intelligent Storage Systems
• +۵ مورد دیگر

Cheng Li is an Associate Professor at the School of Computer Science, University of Science and Technology of China , and Director of the Information Computing Platform at the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center. His research focuses on parallel and distributed intelligent computing systems, with an emphasis on optimizing computational efficiency through data-centric approaches. Education: PhD in Computer Science from Max Planck Institute for Software Systems (MPI-SWS) and Saarland University (2016) Bachelor's degree in Computer Science from Nankai University (2009) Research interests include distributed systems, machine learning optimization, database systems, and high-performance computing. Notable contributions include innovations in distributed training of deep neural networks, geo-replicated databases, and efficient graph processing. His work has been published in top-tier conferences like NSDI, AAAI, VLDB, and SOSP. Professional Activities : PC member at OSDI 2025, EuroSys 2024/2025, FAST 2024, and SOSP 2021. Teaching includes Systems Seminar and Compiler Principles at USTC since 2017. Grants : Secured $500k/year funding from Chinese government agencies and collaborations with Huawei/Alibaba. Active in cloud-native systems and AI infrastructure research.

Zijie Zhangمشاهده پروفایل

استادیار

• Federated learning
• Graphs
• Trustworthy machine learning
• +۳ مورد دیگر

Zijie Zhang is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on federated learning, graph-based machine learning, and trustworthy AI systems. He holds a Ph.D. in Computer Science from Auburn University and an M.S. from Stevens Institute of Technology. Education: Ph.D. in Computer Science, Auburn University M.S. in Computer Science, Stevens Institute of Technology His work emphasizes secure and robust machine learning techniques, including adversarial defense mechanisms, federated learning optimizations, and graph alignment algorithms. Recent publications explore topics like machine unlearning, resilient graph matching, and cross-lingual knowledge graph alignment. His research bridges theoretical advancements with practical applications in distributed systems and cybersecurity. Dr. Zhang’s contributions address challenges in privacy, model efficiency, and adversarial robustness, reflecting a commitment to advancing trustworthy AI methodologies.

• Computer Vision
• Machine Learning
• Action Recognition
• +۶ مورد دیگر

Prof. Juergen Gall is a Professor at the University of Bonn, affiliated with the TRA Mathematics, Modelling and Simulation of Complex Systems and the Department of Information Systems and Artificial Intelligence. He leads the Computer Vision Group at the Lamarr Institute for Machine Learning and Artificial Intelligence. His research focuses on action recognition, video understanding, anticipation, human pose estimation, and applications in plant science, earth science, and neuroscience. He has contributed to numerous projects and conferences, including organizing workshops on machine learning for Earth systems and holistic video understanding. His work spans over 200 publications in top venues like CVPR, ICCV, and NeurIPS. He oversees software tools like MANTA and STING-BEE, and has developed datasets such as Humans in Kitchens and PoseTrack21. Research interests emphasize advancing computer vision for real-world challenges, with a focus on multi-modal learning and deep learning applications. Recent articles explore parameter-efficient models, diffusion-based anticipation, and multi-view matching for plant analysis. His contributions bridge academia and industry, addressing agricultural and environmental monitoring needs.

• Quantum Condensed Matter Physics
• Topological States of Matter
• Superconductors
• +۷ مورد دیگر

Dr. Derek Lee is an Associate Professor in the Department of Physics at Imperial College London, affiliated with the Faculty of Natural Sciences. His research focuses on quantum condensed matter physics, particularly the emergence of novel phases in quantum mechanical systems such as topological states, superconductors, and ultracold atomic systems. He explores interfaces between condensed matter and quantum information science, addressing fundamental questions about topological phases and exotic ordering phenomena. Key affiliations include the Condensed Matter Theory Group, the Physics Permanent Researchers community, and initiatives in Quantum Engineering and The Matter Community. His work spans theoretical frameworks involving topological band structures, supersolids, and Majorana vortex dynamics in Josephson junctions. Publications highlight investigations into supersolid thermodynamics, quantized conductivity markers, and disorder effects in topological systems. Research themes emphasize collective quantum phenomena, topological protection mechanisms, and experimental signatures of quantum phases. Ongoing projects explore interplay between superfluidity and density wave order in Bose systems, as well as nanoscale topological phenomena in confined geometries. Dr. Lee's contributions bridge advanced theoretical modeling with experimental feasibility, particularly in ultra-cold atom systems and nanoscale superconducting devices. His work underscores the importance of topological concepts in understanding material behavior across scales—from atomic lattices to macroscopic superfluid films.

Yannis Psaromiligkosمشاهده پروفایل

دانشیار

• Statistical Detection and Estimation
• Statistical Signal Processing
• Machine Learning
• +۳ مورد دیگر

Yannis Psaromiligkos is an Associate Professor in the Department of Electrical and Computer Engineering at McGill University. He holds a PhD from the State University of New York at Buffalo and a Diploma in Computer Engineering from the University of Patras. Research Interests: Statistical signal processing and machine learning, with a focus on low-dimensional data scenarios. Applications in wireless communications, biomedical engineering, and distributed signal processing. Recent work includes reconfigurable intelligent surfaces (RIS), CNN architecture optimization, and mMTC systems. Publications: Recent papers address RIS-aided MIMO systems, CNN regularization via subband decomposition, and mMTC load estimation. His work bridges theory and applications in communication and biomedical systems. Students: Supervises PhD and MEng students, including co-supervised projects with Profs. Warren Gross and Benoit Champagne.

Sheng Yangمشاهده پروفایل

استاد

• Information Theory
• Wireless Communications
• MIMO Systems
• +۶ مورد دیگر

Sheng Yang is a full professor at CentraleSupélec, University of Paris-Saclay, and holds an honorary associate professorship at the University of Hong Kong. His research focuses on information theory and its applications to wireless networking, including MIMO systems, non-coherent communication, interference mitigation, and physical layer security. He has authored numerous influential papers and received the 2015 IEEE ComSoc Young Researcher Award. His teaching includes courses on digital communication, information theory, and wireless communications. Educational Background: BE in Electrical Engineering (Jiaotong University, Shanghai, 2001), Engineer Degree and M.Sc. (Telecom ParisTech, 2004), and Ph.D. (Université Pierre et Marie Curie, 2007). Research Interests: Multi-antenna (MIMO) communications, non-coherent systems, hardware impairments, coded caching, federated learning, and the integration of machine learning with communication systems. Notable Contributions: Developed novel rate-splitting strategies for MIMO broadcast channels, analyzed capacity limits of 1-bit quantized MIMO systems, and pioneered CSI compression schemes using context trees. He has supervised over 10 PhD/postdoc students, many now in academia and industry leadership roles. Awards: IEEE ComSoc Young Researcher Award (2015), Associate Editor roles at IEEE Transactions on Information Theory and IEEE Transactions on Wireless Communications. Labs/Teams: Active in the MODESTY, COMEDY, and SYCOMORE research groups at L2S, focusing on system modeling and control. His work intersects energy-efficient communication, distributed computing, and future network architectures.

Gretchen Campbellمشاهده پروفایل

استاد مدعو

• Ultracold Atomic Gases
• Quantum Fluids
• Bose-Einstein Condensates
• +۵ مورد دیگر

Gretchen Campbell is an Adjunct Professor at the University of Maryland and Co-Director of the Joint Quantum Institute (JQI). Her research focuses on ultracold atomic gases, particularly Bose-Einstein condensates (BECs) and their applications in studying quantum fluids and superfluidity. She leads experiments on atom circuits and ultracold strontium systems, exploring analogs of superconducting electronics and cosmological phenomena. Key projects include persistent currents in superfluid rings, Rayleigh-Taylor instabilities in quantum fluids, and grating magneto-optical trapping techniques. Her work bridges quantum simulation, condensed matter physics, and precision measurement. Recent advancements include the creation of sodium BECs in hybrid traps and strontium BECs for quantum simulation. She has pioneered atomtronic devices, such as superfluid SQUID analogs, and used expanding BECs to model cosmic phenomena like Hubble friction. Campbell also holds an appointment as Associate Vice President overseeing UMD’s quantum initiatives, reflecting her leadership in quantum science education and research. Research Groups: JQI, RQS (Quantum Simulation Group) Labs: Sodium Atom Circuits Lab, Ultracold Strontium Experiment Her team includes graduate students and postdocs working on topics like quantum control systems, narrow-line spectroscopy, and experimental instrumentation. Notable contributions include the development of programmable systems for atomic physics and ultra-low noise drivers for precision experiments.