جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computer Vision
• Data Mining
• +۶ مورد دیگر

Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.

Yingyan Linمشاهده پروفایل

دانشیار

• Efficient Machine Learning
• Cross-Layer Innovations
• Green AI
• +۷ مورد دیگر

Yingyan (Celine) Lin is an Associate Professor in the School of Computer Science at Georgia Institute of Technology, leading the Efficient and Intelligent Computing (EIC) Lab. Her work focuses on cross-layer innovations in machine learning systems, from algorithms to chip design, aiming to advance green AI and ubiquitous machine learning. She holds a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2017). Research interests include efficient machine learning, neural rendering (e.g., NeRF), hardware-software co-design for AI acceleration, and graph neural networks. Her lab has pioneered projects like RTML and 3DML, funded by NSF, NIH, DARPA, and industry partners (Qualcomm, Intel, Meta). Awards: NSF CAREER Award (2021), ACM SIGDA Outstanding Young Faculty (2022), Meta Faculty Research Award (2022) Grants: Multi-university projects funded by NSF, NIH, DARPA, SRC, ONR, and industry Recognition: First-place wins at DAC 2022 and TinyML Design Contest 2022, IEEE Micro Top Pick 2023 Her research bridges algorithmic innovation with hardware implementation, emphasizing energy efficiency and real-time performance for applications in AR/VR, computer vision, and neuro-symbolic AI systems.

Sewoong Ohمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Security & Privacy
• +۴ مورد دیگر

Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.

• Computer Vision
• Robotics
• 3D Data Understanding
• +۳ مورد دیگر

Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .

Furong Huangمشاهده پروفایل

دانشیار

• Machine Learning
• Trustworthy Machine Learning
• Generative AI
• +۷ مورد دیگر

Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.

Brent Doironمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Networks
• Theoretical Neuroscience
• +۲ مورد دیگر

Brent Doiron is a Professor at the University of Chicago, holding appointments in the Departments of Neurobiology and Statistics, and serving on the Committee on Computational and Applied Mathematics (CCAM). His research integrates nonlinear dynamics and statistical mechanics to study neural circuit variability, focusing on mechanisms underlying neural coding and network learning through collaborations with experimentalists in sensory systems. Education: PhD in Physics (University of Ottawa, 2004) Postdoc: Center for Neural Science at New York University (2017) Previous Roles: Mathematics Professor at University of Pittsburgh (2007-2020), Co-Director of Neural Computation Program at Carnegie Mellon Neuroscience Institute Research interests center on neuronal population dynamics, recurrent circuit mechanisms, and computational neuroscience. Current work investigates correlated variability in cortical networks, inter-areal communication, and stochastic spiking models. Recent publications emphasize cortical stability/gain modulation, asynchronous/synchronous activity balance, and Bayesian inference frameworks. Key themes include sensory processing, network plasticity, and dimensionality reduction in neural coding. Scientific Awards Alfred P. Sloan Research Fellowship in Neuroscience Vannevar Bush Faculty Fellowship Chancellor’s Distinguished Research Award (University of Pittsburgh) Active grants include NIH R01 and R90/T90 awards for neuronal dynamics research and computational neuroscience training programs.

Alan Ritterمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

Shweta Shindeمشاهده پروفایل

استادیار

• Trusted Computing
• System Security
• Hardware Security
• +۴ مورد دیگر

Shweta Shinde is an Assistant Professor in the Department of Computer Science at ETH Zurich where she leads the Secure & Trustworthy Systems (SECTRS) research group. She is also a member of the Institute of Information Security and the ZISC Center at ETH Zurich. Her work bridges theoretical foundations with practical implementations in hardware and system security. Dr. Shinde's research focuses on the intersection of trusted computing, system security, and program analysis. Her group investigates both foundational aspects of security to protect phones, servers, and accelerators, as well as practical aspects of realizing these solutions through large-scale system building. Her work spans hardware security primitives, trusted execution environments, confidential computing, and secure system design across multiple platforms including mobile devices, cloud infrastructure, and blockchain systems. Her publication record demonstrates significant impact in the field, with numerous papers in top security venues including IEEE S&P, USENIX Security, CCS, and NDSS. Her research has uncovered critical vulnerabilities in major hardware security technologies like Intel SGX and AMD SEV-SNP while also proposing novel frameworks for building more secure systems. The work spans multiple domains including hardware security, cloud security, mobile security, and blockchain security, with a consistent thread of identifying and addressing fundamental security challenges in trusted computing. Among her notable achievements are multiple best paper awards including at SysTEX 2025 and nominations at DATE 2025, a Distinguished Paper Award at IEEE S&P 2024, and recognition as a Most Innovative Research Pwnie Award Nominee in 2024. Her work on WeSee received particular attention for breaking AMD SEV-SNP through novel interrupt-based attacks. Dr. Shinde actively mentors PhD students including Mark Kuhne, Supraja Sridhara, Mélisande Zonta-Roudes, Benedict Schlüter, Andrin Bertschi, and others. She teaches courses on Systems Security, Information Security Lab, Seminar on Systems Security, and Current Topics in Information Security at ETH Zurich. Her service to the community includes extensive program committee work for major security conferences including Oakland (S&P), NDSS, USENIX Security, and CCS, where she has served as track co-chair and program co-chair for several workshops.

Lutz Schubertمشاهده پروفایل

پژوهشگر

• Operating Systems
• Distributed Computing
• Parallel Processing
• +۳ مورد دیگر

Lutz Schubert is a researcher at the Institute of Computer Science , University of Cologne. He focuses on efficient distributed and parallel execution environments for heterogeneous systems, notably the MyThOS operating system in collaboration with Brandenburg University of Technology, Cottbus-Senftenberg. His interdisciplinary work bridges computer science and digital archaeology , addressing modeling of non-deterministic events from sparse excavation data and exploring human behavior constraints in archaeological contexts. Research Interests Design of modular, scalable operating systems for distributed systems Optimization of execution environments for heterogeneous hardware Application of complex systems modeling to digital archaeology Statistical and probabilistic methods for archaeological interpretation Autonomic resource distribution and adaptation in computing Publication Trends : His work spans operating systems , parallel computing , and digital humanities , with recent emphasis on probabilistic reasoning in archaeology and adaptive OS design for multicore architectures. Labs & Collaborations : He collaborates with Brandenburg University of Technology on MyThOS and leads research in computational archaeology as chair of Computer Applications and Quantitative Methods in Archaeology (CAA) , Germany.

Gianluca Iaccarinoمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +۶ مورد دیگر

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

Richard M. Murrayمشاهده پروفایل

استاد

• Control Theory
• Autonomous Vehicles
• Biological Circuit Design
• +۳ مورد دیگر

Richard M. Murray is the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at the California Institute of Technology (Caltech). He holds a B.S. from Caltech (1985), M.S. from UC Berkeley (1988), and Ph.D. from UC Berkeley (1990). He has served in academic roles from Assistant Professor (1991–1997) to his current endowed professorship. He chaired the Engineering and Applied Science division (2000–2005) and Biology and Biological Engineering (2020–2024). His research focuses on feedback control in biological and autonomous systems, synthetic cells, and networked control systems. Collaborators include experts in robotics, synthetic biology, and systems biology. Key awards include the IEEE Control Systems Award and election to the National Academy of Engineering. His educational contributions span courses on control systems, robotics, and bioengineering. Current research projects include the Developer Cell initiative (Sloan Foundation), layered testing for autonomous systems (AFOSR), and microbiome-based environmental solutions (CHARMME, ARO). He advises numerous graduate students and postdocs, with notable alumni in academia and industry. Labs include facilities in Keck and Steele laboratories at Caltech. His work bridges control theory, synthetic biology, and autonomous systems to address societal challenges like environmental monitoring and safe autonomy.

• Semiconductor Physics
• Gallium Nitride (GaN) Electronics
• Two-Dimensional Materials
• +۶ مورد دیگر

Tomas Palacios is a Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT) , where he directs the Center for Graphene Devices and 2D Systems and leads the Microsystems Technology Laboratories (MTL). His research focuses on pushing the boundaries of microelectronics through novel semiconductor materials and device architectures, including Gallium Nitride (GaN) and 2D materials like graphene and molybdenum disulfide (MoS2). Professor, MIT Electrical Engineering and Computer Science Director, MIT Center for Graphene Devices and 2D Systems Clarence J. LeBel Professor, MIT Faculty Director, Northeast Microelectronics Internship Program (NMIP) Research Interests span multiple cutting-edge domains: High-frequency electronics (>300 GHz) for 6G and quantum applications High-voltage power devices (600V–10kV) for energy conversion Post-silicon logic devices using 2D materials High-temperature electronics (e.g., Venus rover applications) Distributed neural networks on large-area 2D materials Graphene-based biosensors and chemical detection systems Scientific Contributions include: Double recipient of the IEEE George Smith Award for groundbreaking GaN transistor work Co-invented first MoS2 electronic circuits Developed world’s first Wi-Fi-to-electricity conversion antenna Led MIT’s Microsystems Technology Laboratories since 2021 Advising Philosophy emphasizes cross-layer expertise, with students gaining experience from materials synthesis to system-level prototyping. His lab has incubated startups like Vertical Horizons , focused on GaN power devices for AI and EVs.

Hyesoon Kimمشاهده پروفایل

استاد

• Computer Architecture
• GPU
• Compilers and Runtime Systems
• +۳ مورد دیگر

Hyesoon Kim is a Professor at the Georgia Institute of Technology , affiliated with the College of Computing and leading the HPArch research group . She co-directs the Center for Research into Novel Computing Hierarchies (CRNCH) . Her research focuses on Computer Architecture , GPU , Compilers and Runtime Systems , and Hardware Security , particularly for heterogeneous systems. Contact : hyesoon@cc.gatech.edu Location : 266 Ferst Drive, KACB 2344, Atlanta, GA Research Trends Her recent work spans RISC-V extensions for security, CUDA optimization on softcore GPUs, memory safety techniques, and energy-efficient deep learning architectures. Articles emphasize heterogeneous computing , GPU performance scaling, and IoT -oriented neural network methods. Open Source Projects She leads development of Macsim (heterogeneous architecture simulator) and Vortex (open-source GPU platform).

Daniel W. Blissمشاهده پروفایل

استاد

• Radar
• Wireless Communications
• Precision Positioning
• +۷ مورد دیگر

Daniel W. Bliss is a Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University and Director of ASU's Center for Wireless Information Systems and Computational Architectures (WISCA). With over $50 million in research funding as principal investigator from organizations including DARPA, ONR, Google, and Airbus, his work bridges theoretical foundations with practical implementations across multiple domains of wireless systems. Dr. Bliss received his educational foundation with a B.S.E.E. from Arizona State University (1989), followed by M.S. and Ph.D. degrees in Physics from the University of California-San Diego (1995, 1997). His academic journey includes significant industry experience at General Dynamics (1989-1993) and MIT Lincoln Laboratory (1997-2012) before joining ASU. His research program focuses on advanced wireless systems spanning radar, communications, precision positioning, computational architectures, and medical monitoring applications. Bliss employs information theory, estimation theory, and signal processing to develop novel system concepts with disruptive capabilities. Current research emphasizes RF convergence, integrated sensing and communications, and anticipatory medical analytics using wireless technologies, with particular focus on extracting physiological data from radar signals. Analysis of recent publications reveals a strong trend toward integrated sensing and communications systems, particularly utilizing mmWave and radar technologies for medical monitoring applications. His work increasingly bridges traditional communications and radar domains while expanding into physiological monitoring, demonstrating a clear trajectory toward convergence of wireless technologies for healthcare applications and remote vital sign detection. Dr. Bliss has received significant recognition for his contributions: Fellow of the IEEE (2015) 2021 IEEE Warren D. White Award for Excellence in Radar Engineering 2016-2017 Top 5% Teaching Award at ASU 2017 ASU Fulton Engineering Exemplar Faculty As a dedicated mentor, Dr. Bliss has supervised numerous graduate students through successful dissertation and thesis defenses across both PhD and Master's programs. His research portfolio includes substantial funding from diverse sources with over $50 million secured as principal investigator. Current projects include the $17M DARPA DASH project focused on advanced software-reconfigurable heterogeneous SoCs for next-generation RF systems, and multiple initiatives in contactless vital sign monitoring using radar technologies. Dr. Bliss leads the BLISS Lab and serves as director of WISCA, fostering interdisciplinary research in wireless systems. His team includes researchers working on distributed coherent systems, MIMO radar, RF convergence, and medical monitoring applications, with recent successes including the Making Waves team that tied for first place in the Air Force Spark Tank challenge. He has founded two startup companies: DASH Tech Integrated Circuits Company and the Big Little Sensor Company, focusing on high-performance embedded processing and small-scale radar physiological monitoring, respectively.

John Oakeyمشاهده پروفایل

استاد

• Microfluidics
• Microscale transport phenomena
• Tissue engineering and regenerative medicine
• +۴ مورد دیگر

John Oakey is a Professor and Graduate Coordinator in the Department of Chemical and Biomedical Engineering at the University of Wyoming, with additional affiliations to the INBRE Program, Molecular and Cellular Life Sciences Program, and Materials Science and Engineering Program. Education Postdoctoral Fellow, Center for Engineering in Medicine, Massachusetts General Hospital & Harvard Medical School (2007–2010) Ph.D. Chemical Engineering, Colorado School of Mines (2003) M.S. Chemical Engineering, Colorado School of Mines (1999) B.S. Chemical Engineering, Penn State University (1997) Research Interests Oakey’s laboratory integrates fluid dynamics, colloidal science and materials science to understand how biological systems behave under flow, on surfaces and within complex 3-D geometries. A unifying theme is the use of microfabrication and microfluidics to create new diagnostic, prognostic and therapeutic platforms. Current thrusts include: Heterogeneous biomaterials: self-assembled particulate tissue scaffolds whose mechanical and transport properties can be temporally programmed. Inertial microfluidics: exploiting lift forces for membrane-free particle sorting, enrichment and diagnostics. Multi-temporal analysis by flow cytometry: development of closed-loop, high-throughput microfluidic cytometers for longitudinal single-cell studies. Publication Trends From 2025 back to 2010, Oakey’s articles reveal a consistent trajectory that marries fundamental physics (microtubule mechanics, inertial focusing) with translational applications (cell encapsulation, tissue scaffolds, drug delivery). Recent work (2023-2025) increasingly targets injectable granular hydrogels, single-cell therapeutic delivery and sustainable carbon-sequestering living materials, demonstrating an evolution from microscale transport phenomena to macroscopic biomedical and environmental impact. Scientific Awards No named awards are listed in the supplied text. Advising & Coordination Roles As Graduate Coordinator for the Department of Chemical and Biomedical Engineering, Professor Oakey oversees graduate program development and student mentoring. While no individual students are named, his role implies active supervision of M.S. and Ph.D. advisees in chemical and biomedical engineering. Laboratory & Teams The Oakey Research Group operates from the Energy and Environmental Research Building (EERB 435A) at the University of Wyoming. The lab enjoys R1-level research infrastructure and collaborates broadly with the Wyoming INBRE network, the Molecular and Cellular Life Sciences Program, and the Materials Science and Engineering Program.