جستجوی استاد راهنمای دانشگاهی

Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.

• Human-Computer Interaction
• Ubiquitous Computing
• Sustainability
• +۷ مورد دیگر

Andrea Arpaci-Dusseau is the Susan Beth Horwitz WARF Professor of Computer Sciences and Catherine A. Erickson Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. She has been a professor at UW-Madison since January 2000 and currently serves as Graduate Advising Chair for the Computer Sciences Department. Her research focuses on computer systems with primary emphasis on file and storage systems, but also making significant contributions in distributed systems, virtualization, and scheduling. According to csrankings.org, she has published the fourth-most papers in premier systems conferences (SOSP and OSDI) and the most at the top file and storage conference (FAST). Professor Arpaci-Dusseau's recent publications demonstrate continued leadership in storage systems research, with increasing focus on cloud architectures, persistent memory technologies, and system reliability. Her work bridges theoretical foundations with practical implementations, consistently addressing real-world challenges in modern computing environments. ACM SIGOPS Mark Weiser Award 2018 (highest honor in systems field) UW-Madison Van Hise Outreach Teaching Award 2017 Multiple Best Paper awards at FAST, SOSP, OSDI, and EuroSys conferences Carolyn Rosner Award for Excellence in Teaching (2010, 2017) Co-chaired major conferences including USENIX ATC '04, FAST '07, OSDI'18, and SOSP'24 Professor Arpaci-Dusseau has co-advised 28 Ph.D. students and is deeply committed to educational outreach through the CaTaPuLT project, which connects UW-Madison students with K-12 schools to teach computational thinking using Scratch. This initiative has reached hundreds of students across Madison and earned her significant recognition for teaching excellence. She leads the Arpaci-Dusseau Systems Lab (ADSL) and is involved with the Wisconsin Institute on Software-defined Datacenters in Madison (WISDoM), where her research group continues to advance state-of-the-art solutions in computer systems.

• File and Storage systems
• Operating systems
• Distributed systems
• +۱ مورد دیگر

Dr. Chengmo Yang is a Professor in the Department of Electrical and Computer Engineering at the University of Delaware, with a joint appointment. She received her PhD and MSc in Computer Science from the University of California, San Diego, and a BSc in Microelectronics from Peking University, China. PhD, University of California, San Diego MSc, University of California, San Diego BSc, Peking University, China Her research focuses on enhancing security, reliability, non-volatility, and energy efficiency in embedded systems, cyber-physical systems (CPS), and Internet-of-Things (IoT). She leads the Computer Architecture, Reliability, and Security (CARES) group and has authored over 70 peer-reviewed publications. Recent publications emphasize neural network accelerators, non-volatile memory (NVM) optimization, and security frameworks for embedded systems. Her work explores fault tolerance, wear leveling, and side-channel attack mitigation in NVM-based FPGAs, STT-RAM, and SSDs. National Science Foundation Career Award (2013) University of Delaware Mid-Career Faculty Excellence in Scholarship Award (2020) Best paper award at ICESS (2019) Three best paper nominations at DAC, DATE, and GLSVLSI (2019) Dr. Yang serves as Associate Editor for IEEE TCAD and ACM TODAES. She is the Diversity & Inclusion Representative for IEEE CEDA and has organized numerous conferences including Technical Program Chair for CODES-ISSS 2022. Her lab has hosted current and former students like Fanruo Meng (PhD candidate), Fateme Hosseini (Qualcomm), and Patrick Cronin (Intel), focusing on graduate-level research in hardware security and reliability.

• Embedded Systems
• Design Automation
• Computer Architecture
• +۶ مورد دیگر

Mark Y. Liberman is the Christopher H. Browne Distinguished Professor of Linguistics and Trustee Professor at the University of Pennsylvania. He holds a joint appointment in the Department of Linguistics and the Department of Computer and Information Science. His roles include Director of the Linguistic Data Consortium (LDC), Faculty Director of Ware College House, and former Director of the Institute for Research in Cognitive Science. Education: A.B. in Linguistics and Applied Mathematics from Harvard University (1965–1969), M.S. (1972) and Ph.D. (1975) in Linguistics from MIT. Research focuses on corpus-based phonetics, clinical linguistics applications, tonal phonology, formal models for linguistic annotation, and computational linguistics. He explores speech production, prosody, and interdisciplinary topics like language evolution and neurobiology of speech. Recent articles highlight advancements in speech biomarkers for neurodegenerative diseases, autism analysis, and computational linguistics. Awards include Fellowships from the AAAS and Linguistic Society of America. He advises graduate students and leads large-scale language resource initiatives like LDC, contributing to open-access linguistic datasets. Labs/Teams: Linguistic Data Consortium (LDC), Institute for Research in Cognitive Science (IRCS), and collaborations in computational linguistics and neuroscience.

• Corpus Linguistics
• Speech and Language Technology
• Clinical Linguistics
• +۴ مورد دیگر

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۷ مورد دیگر

Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Stefano Ermon is an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Laboratory and a Senior Fellow at the Woods Institute for the Environment. His research focuses on advancing machine learning and generative AI techniques to address societal and environmental challenges, including computational sustainability, geospatial analysis, and climate science. He holds a Ph.D. from Cornell University (2015). Education: Ph.D. in Computer Science, Cornell University (2015). Research Interests: Ermon’s work bridges foundational machine learning (e.g., diffusion models, generative AI, and optimization) with applications in sustainability, geospatial analysis (via satellite imagery), and earth observation systems. Notable contributions include predicting poverty using satellite data and developing scalable methods for molecule generation. Articles Trends: His recent work emphasizes diffusion models for generative tasks (e.g., text-to-image, molecule design), geospatial AI (e.g., environmental monitoring), and ethical AI (e.g., bias mitigation in LLMs). He also explores applications in robotics and scientific computing. Awards: He has received prestigious awards, including the ICML 2024 Best Paper Award, Sloan Research Fellowship, Microsoft Research Faculty Fellowship, and the IJCAI Computers and Thought Award. Advising and Grants: Ermon teaches courses like Probabilistic Graphical Models (CS228) and has secured grants from NSF, ONR, AFOSR, and private foundations. His lab develops tools for climate science and sustainable development. Labs/Teams: Leads the Stanford AI Lab group focused on computational sustainability and generative AI, collaborating with institutions like the Woods Institute for environmental applications.

• Machine Learning
• Generative AI
• Computational Sustainability
• +۷ مورد دیگر

Joseph A. November is an Associate Professor in the Department of History at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. His research focuses on the history of biomedical computing, distributed computing, and the intersection of technology and medicine. He holds a Ph.D. from Princeton University (2006), an M.A. from the University of Chicago (2002), and a B.A. from Hamilton College (1997). His work includes the award-winning book Biomedical Computing: Digitizing Life in the United States (2012), which explores the co-development of biomedicine and computing technologies. Current projects include Revolutions@home , examining distributed computing in protein folding research, and a biography of computing pioneer Robert S. Ledley. He has received grants from the NSF, NIH, and the Charles Babbage Institute. Teaching interests span the history of science and technology, including courses on the history of medicine, digital humanities, and the role of games in historical education. He actively contributes to professional organizations like SHOT and the History of Science Society. Awards include the Computer History Museum Prize (2013) and the National Institutes of Health DeWitt Stetten Fellowship (2007-2008). His research bridges historical analysis with contemporary issues in technology and biomedical ethics.

• History of Science and Technology
• Biomedical Computing
• History of Medicine
• +۳ مورد دیگر

David Silver is a Professor of Computer Science at University College London and Principal Research Scientist at DeepMind, leading the Reinforcement Learning Research Group. His pioneering work in deep reinforcement learning has revolutionized artificial intelligence through breakthroughs in computer game-playing algorithms. His educational background includes: Bachelor's and Master's degrees from Cambridge University (1997, 2000) PhD in Computer Science from the University of Alberta (2009) Silver specializes in deep reinforcement learning where algorithms learn through trial-and-error in interactive environments. His research combines deep neural networks with reinforcement learning strategies to solve complex decision-making problems. He is renowned for developing AlphaGo (defeating Go world champion Lee Sedol in 2016), AlphaZero (mastering Chess, Shogi and Go through self-play), and AlphaStar (conquering Starcraft II). His work demonstrates unprecedented generality in game-playing AI and has catalyzed industry-wide adoption of reinforcement learning techniques. Analysis of his publications reveals a consistent trajectory toward self-supervised learning systems. His 2015-2016 Nature papers established foundational architectures combining neural networks with Monte Carlo tree search, shifting the field from human-data dependency toward pure self-play methodologies. This evolution enabled superhuman performance across diverse game domains while minimizing domain-specific knowledge. His scientific awards include: ACM Prize in Computing (2019) for breakthrough advances in computer game-playing Marvin Minsky Medal (2018) for outstanding achievements in AI Royal Academy of Engineering Silver Medal (2017) for UK engineering contributions Mensa Foundation Prize (2017) for best AI scientific discovery Silver's research has generated significant real-world impact beyond gaming, including optimizing the UK power grid, reducing Google data center energy consumption, and planning European Space Agency probe trajectories. While not explicitly documented in advising roles, his leadership at DeepMind fosters collaborative research environments that train next-generation AI scientists through high-impact projects. As lead of DeepMind's Reinforcement Learning Research Group, Silver directs cutting-edge work extending deep reinforcement learning to robotics, complex real-world systems, and multi-agent environments. Current initiatives focus on transferring game-playing breakthroughs to industrial automation and scientific discovery applications.

• Deep Reinforcement Learning
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Ruben Juanes is a Professor of Civil and Environmental Engineering and Earth, Atmospheric, and Planetary Sciences at MIT. His research focuses on multiphase flow in porous media, energy resources, and CO₂ sequestration. He holds appointments in both departments and has a strong interdisciplinary focus on geosciences and environmental engineering. His work bridges theory, simulation, and experimentation to address energy and environmental challenges. Education: Ingeniero de Caminos (Civil Engineering), University of La Coruña, Spain (1997) MS in Civil Engineering, UC Berkeley (1999) PhD in Civil Engineering, UC Berkeley (2003) Research interests emphasize fluid dynamics in geologic media, especially CO₂ storage, methane hydrates, and ecohydrology. His group develops computational models to predict large-scale Earth processes, with applications to carbon capture and storage, energy resource management, and subsurface engineering. Notable contributions include advancing understanding of fluid displacement mechanisms, capillary trapping in aquifers, and induced seismicity risks during CO₂ injection. His work has been recognized through awards like the APS Fellowship (2024), DOE Early Career Award (2010), and ARCO Energy Professorship (2008). Advising and Grants: Juanes advises graduate students in CEE and EAPS, focusing on thesis research in multiphase flow and geomechanics. His grants include NSF and DOE funding for projects on subsurface energy systems and induced seismicity. His lab, the Juanes Research Group, collaborates on experimental facilities like the FluidFlower CO₂ storage simulator. Labs/Teams: Active in MIT's Carbon Capture, Utilization, and Storage (CCUS) initiatives and the MIT Energy Initiative (MITEI). Collaborates with industry partners on field-scale CO₂ storage validation and subsurface monitoring technologies.

• Environmental Engineering
• Geophysics
• Energy Resources
• +۳ مورد دیگر

Elizabeth M. Belding is a Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). She holds leadership roles as an Associate Dean and Faculty Equity Advisor in the UCSB College of Engineering, and serves as Associate Director of the Center for Information Technology and Society (CITS). Her research focuses on mobile/wireless networking, network measurement, and ICTD, with a focus on improving Internet accessibility in underserved regions globally. She has authored over 180 technical papers and led the development of the AODV routing protocol, foundational to standards like 802.11s and Zigbee. Education: Ph.D./M.S. in Electrical and Computer Engineering from UCSB (2000/1997). Awards include ACM and IEEE Fellowships, the 2018 ACM SIGMOBILE Test-of-Time Award, and mentoring accolades. She directs the MOMENT Lab, exploring broadband measurement, rural connectivity, and satellite networks. Recent projects include the NSF-funded PuebloConnect initiative and analysis of US broadband inequities via the Connect America Fund. Research emphasizes socially impactful solutions, including tribal community internet access, disaster communication (e.g., refugee camps), and combating gender-based violence via social media analysis. She has held leadership roles in the UCSB CS department and campus-wide initiatives, including the Chancellor's Advisory Committee on the Status of Women. Key grants include NSF, Bill & Melinda Gates Foundation, and industry partnerships (e.g., ViaSat). Notable students include Udit Paul (2024 SIGCOMM Dissertation Award) and Jiamo Liu. The MOMENT Lab's work spans technical innovations in mesh networks, cellular coverage, and policy-driven broadband analysis.

• Mobile Wireless Networking
• Information and Communication Technologies for Development (ICTD)
• Network Measurement
• +۵ 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 .

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

Justin Johnson is an Assistant Professor at the University of Michigan's College of Engineering, Department of Electrical Engineering and Computer Science, and a Research Scientist at Facebook AI Research (FAIR). His work bridges computer vision, machine learning, and deep learning, focusing on visual reasoning, vision-language tasks, image generation, and 3D reasoning using neural networks. PhD, Stanford University (advised by Fei-Fei Li) His research interests span visual reasoning , vision and language , 3D vision , and image generation , with a focus on innovative applications of deep neural networks. Recent publications highlight work on 3D consistency, self-supervised learning, and multimodal integration of vision and text. Notable contributions include PyTorch3D for 3D data processing, and foundational work in visual question answering , neural style transfer , and scene graph-based image generation . Publications span top conferences like ICCV, CVPR, and NeurIPS. He teaches courses including EECS 498/598: Deep Learning for Computer Vision and EECS 442: Computer Vision at University of Michigan, with prior involvement in Stanford's CS 231N in co-teaching roles. Software projects include open-source frameworks like fast-neural-style for real-time artistic style transfer, and PyTorch3D for efficient 3D deep learning. These tools demonstrate his commitment to practical implementations and community-driven research.

• Computer Vision
• Machine Learning
• Visual Reasoning
• +۷ مورد دیگر

Kai-Wei Chang is an Associate Professor at the University of California, Los Angeles (UCLA) in the Department of Computer Science, part of the Henry Samueli School of Engineering. He is also an Amazon Scholar at Alexa AI, focusing on advancing trustworthy AI and multimodal foundation models. His research bridges NLP, machine learning, and ethical AI, with a focus on fairness, robustness, and bias mitigation in language and vision-language systems. Education: Ph.D. in Computer Science (UIUC, 2015), M.S. and B.S. in Computer Science and Electrical Engineering from National Taiwan University. Research Interests: Trustworthy NLP (fairness, robustness), Multimodal Foundation Models (e.g., VisualBERT, GLIP), Reasoning in LLMs, and mitigating societal biases in AI systems. Notable contributions include pioneering work on aligning NLP models with human values and developing SOTA multimodal models like DesCo and GLIP. Awards: Sloan Research Fellowship (2021), Okawa Grant (2018), EMNLP Best Paper (2017), KDD Best Paper (2010). His work is funded by NSF, IARPA, ONR, and industry partners like Amazon, Google, and Facebook. Service: VP-Elect of SIGDAT, Ethics Committee Chair (NAACL 2022), Organizer of Trustworthy NLP Workshops, and Senior Area Chair for top conferences (ACL, NeurIPS, AAAI). Labs/Teams: Leads the UCLA Natural Language Processing Group, fostering interdisciplinary research on ethical AI and multimodal systems.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Musa Jouaneh is a Professor in the Mechanical, Industrial and Systems Engineering department at the University of Rhode Island 's College of Engineering . His research spans Robotics, Automation, Mechatronics , and Motion Control systems, with recent work focusing on robotic rehabilitation platforms, fastener extraction, and neural network applications in disassembly processes. Education: Ph.D., Mechanical Engineering, University of California at Berkeley (1989) M.Eng., Mechanical Engineering, University of California at Berkeley (1986) B.S., Mechanical Engineering, University of Louisiana, Lafayette (1984) Research Trends in Jouaneh's recent publications emphasize robotic rehabilitation using magnetic actuation, automated fastener detection via neural networks, and trajectory optimization for servo motor systems. His work bridges mechatronic design with industrial automation , particularly in disassembly and assembly applications. Grants include projects like "Cobots for Outfitting of Hangers" (ONR, 2023) and "Device for Proprioception Training" (RI Commerce, 2024). He leads the Mechatronics Lab and Intelligent Control and Robotics Laboratory , focusing on practical automation solutions.

• Robotics
• Automation
• Mechatronics
• +۳ مورد دیگر