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.

Jennifer Rossمشاهده پروفایل

استاد

• Biophysics
• Soft Matter Physics
• Biological Condensed Matter Physics
• +۵ مورد دیگر

Jennifer Ross is a Professor of Physics and Associate Dean for Creativity, Scholarship, and Research at the College of Arts & Sciences of Syracuse University . As a biophysicist, she investigates how cells organize their interiors through self-assembly and active matter principles, focusing on the microtubule cytoskeleton and enzyme-driven systems using single-molecule imaging . Her research bridges fundamental physics with biological organization . Education: Ph.D. in Physics, University of California, Santa Barbara (2004) B.A. in Physics and Mathematics, Wellesley College (2000) Research Focus: Self-organization of cytoskeletal networks Active matter dynamics in biological systems Motor protein interactions and cargo transport Programming circadian materials via biomolecular systems Microtubule severing mechanisms Recent Article Trends: 2025 studies explore kinesin-driven cytoskeletal composites, urease-DNA origami engineering, and crosslinker-regulated network mechanics 2024-2023 work examines ionic strength effects on microtubules, programmable circadian materials, and motor-cargo dynamics Earlier studies analyze actin-microtubule composites, liquid crystal phase control, and severing enzyme mechanisms Scientific Awards: Fellow of the American Physical Society (APS) and American Association for the Advancement of Science (AAAS) Cottrell Scholar (2025) and STAR Award Margaret Oakley Dayhoff Award (Biophysical Society) Grants: Leads multiple NSF, Sloan Foundation, and Research Corporation grants for projects like "Energy and Entropy Sculpting" and "Explorations: SUPER-Tech SHIP" . Teaching: Offers courses in experimental physics, microscopy, and biophysics, including a globally adopted hands-on microscope-building curriculum. Lab: Heads the Bio-Active Matter Lab , studying how cells harness noisy systems for autonomous organization.

Tianqi Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +۶ مورد دیگر

Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

• Machine Learning
• Probability
• Mathematical Physics
• +۶ مورد دیگر

Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .

Hedan Baiمشاهده پروفایل

استادیار

• Robotics Materials
• Bioelectronics
• Sensors Technology
• +۳ مورد دیگر

Prof. Hedan Bai is an Assistant Professor at the Department of Materials, ETH Zürich, specializing in robotics materials, bio-inspired systems, and soft robotics. Their research focuses on developing advanced materials for sensing, energy-efficient systems, and biomedical applications. Notable projects include the SmartSuit architecture for space exploration and self-healing optical sensors for soft robots. Research interests span bioelectronics, stretchable sensors, haptic interfaces, and biomimetic materials. Bai's work integrates material science with robotics to create adaptive, sustainable, and intelligent systems. Key areas include wireless implants for neuromodulation, environmental-responsive textiles, and energy-harvesting devices. Publications highlight innovations in optical waveguides, self-healing materials, and wearable technologies. Their interdisciplinary approach bridges robotics, biomedical engineering, and aerospace applications. No formal awards are listed, but contributions to next-gen materials for robotics are prominent. Advising and grants are not detailed in the text, but Bai's lab focuses on projects like SmartSuit for extravehicular activities and synthetic afferent neural networks. Collaborations likely involve aerospace and biomedical sectors.

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.

• Telecommunication Networks
• Beyond 5G/6G Mobile Networks
• Edge/Cloud Computing
• +۷ مورد دیگر

Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.

• Electrochemical Engineering
• Catalysis Engineering
• Separations Engineering
• +۳ مورد دیگر

Raj Ganesh S. Pala serves as a Professor in the Department of Chemical Engineering at the Indian Institute of Technology Kanpur. His research focuses on electrochemical engineering with particular expertise in energy storage systems, catalysis, and materials science. Dr. Pala maintains an active research program investigating fundamental electrochemical processes and their applications in energy conversion and storage technologies. Dr. Pala earned his PhD in Physical Chemistry from the University of Utah (1999-2005), followed by a Master's in Science (Biophysical Chemistry) from the Indian Institute of Science and National Center for Biological Science (1997-1998), and a Bachelor's in Chemical and Electrochemical Technology from the Central Electro-Chemical Research Institute (1992-1996). His research interests span electrochemical engineering, catalysis, and separation processes with applications in energy storage, water treatment, and sustainable chemical production. Dr. Pala's work bridges fundamental electrochemistry with practical engineering applications, particularly in battery technology and electrocatalysis. His recent publications demonstrate a strong focus on energy storage materials, particularly lithium-ion battery cathodes, and electrocatalytic processes for sustainable chemical production. The research spans from fundamental materials characterization to practical device implementation, with particular emphasis on improving efficiency, selectivity, and longevity of electrochemical systems. Dr. Pala is based in Office NL-II 304 at IIT Kanpur, where he conducts his research and teaching activities in the Department of Chemical Engineering. His work contributes significantly to IIT Kanpur's research initiatives in sustainable energy and advanced materials.

Kyros Kutulakosمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• 3D Sensing
• +۹ مورد دیگر

Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.

Brandon M. Luciaمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.

Sumanta Acharyaمشاهده پروفایل

استاد

• Thermal and Fluid Sciences
• Computational Fluid Dynamics
• Turbulence Modeling
• +۵ مورد دیگر

Sumanta Acharya is a Professor in the Department of Mechanical Engineering at Illinois Tech's Armour College of Engineering. His career spans computational methods, experimental fluid mechanics, and combustion, with affiliations including ASME, AIAA, and ASTFE. Ph.D. in Mechanical Engineering, University of Minnesota (1982) M.S. in Mechanical Engineering, University of Minnesota (1980) B.S. in Mechanical Engineering, Indian Institute of Technology (1978) A leading expert in thermal and fluid sciences, Acharya focuses on gas turbine heat transfer, turbulence modeling, and advanced cooling systems. His work integrates Computational Fluid Dynamics (CFD) with experimental validation for applications in biofuels , hydrogen combustion , and phase change materials . Recent publications highlight innovations in Brayton cycle integration, impingement cooling, and aerothermal performance optimization. Awarded by ASME, AIAA, and LSU, his honors include the ASME Heat Transfer Memorial Award (2011) and ASME Fellow (1999). He has contributed to key committees, including the ASME Heat Transfer Division Executive Committee and the Department of Energy's University Turbine Systems Research program. Researcher to Know, Illinois Science & Technology Coalition (2022) ASME Dedicated Service Award (2019) AIAA Thermophysics Award (2015) Contact: sacharya1@illinoistech.edu | Phone: 312.567.3701

Hassan Qudrat-Ullahمشاهده پروفایل

استاد

• Sustainability
• Global/Climate Change
• System Dynamics
• +۳ مورد دیگر

Dr. Hassan Qudrat-Ullah is a Professor at the School of Administrative Studies, York University, and Coordinator of the Certificate in Logistics Management. He holds a PhD in Decision Sciences from NUS Business School and completed a post-doctoral fellowship at Carnegie Mellon University. His research focuses on dynamic decision making, system dynamics modeling, energy planning, and interactive learning environments. He teaches courses on quantitative methods, logistics, and decision analysis, informed by global industry experience across 20+ countries. Research interests include sustainability, climate change, systems thinking, and educational applications of decision sciences. He serves as Editor-in-Chief of the International Journal of Complexity in Applied Science and Technology and is a member of IEEE, DSI, and the International System Dynamics Society. His work has been published in Energy , Decision Support Systems , and others. Key projects include studies on 'structured-debriefing in dynamic decision making' and renewable energy policies in Africa. His recent articles (2023–2025) address AI integration in energy governance, system dynamics for supply chain resilience, and education for sustainability. Hassan advocates for systems thinking in K-12 education and enjoys traveling (visited 129 countries) and bird-watching.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۸ مورد دیگر

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Kai S. Exnerمشاهده پروفایل

استاد

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +۷ مورد دیگر

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.