جستجوی استاد راهنمای دانشگاهی

Jennifer Widomمشاهده پروفایل

استاد

• Nontraditional Data Management
• Data Streams
• Crowdsourcing Quality Management
• +۳ مورد دیگر

Jennifer Widom is the Frederick Emmons Terman Dean of Stanford University's School of Engineering and holds the Fletcher Jones Professorship in Computer Science and Electrical Engineering. She previously served as Chair of the Computer Science Department (2009–2014) and Senior Associate Dean (2014–2016). Widom earned her Ph.D. in Computer Science from Cornell University (1987) and completed her undergraduate degree in Music at Indiana University (1982). She joined Stanford in 1993 after research at IBM Almaden. Education: Ph.D., Computer Science, Cornell University, 1987 MS, Computer Science, Cornell University, 1985 MS, Computer Science, Indiana University, 1983 BS, Music, Indiana University Jacobs School of Music, 1982 Research Interests: Widom's work focuses on nontraditional data management, including data streams, uncertain databases, crowdsourcing, and query processing systems like STREAM and Deco . She has pioneered methods for managing and querying uncertain data, optimizing graph algorithms, and integrating human computation into data systems. Key Contributions: Developed the STREAM system for real-time data stream management Advanced techniques for crowdsourcing quality management Contributed to foundational work in uncertain databases and provenance tracking Awards & Recognition: ACM Fellow (2005) Member, National Academy of Engineering (2005) Edgar F. Codd Innovations Award (2007) ACM-W Athena Lecturer (2015) EPFL-WISH Erna Hamburger Prize (2018) Teaching & Leadership: Widom teaches courses on data analytics and database systems, advising students like Arnav Joshi. She has led major initiatives in computational education and institutional leadership at Stanford.

• Theoretical Computer Science
• Algorithms
• Convex Optimization
• +۴ مورد دیگر

Rasmus Kyng is an Assistant Professor in the Department of Computer Science at ETH Zurich, where he has been since 2019. His research focuses on fast algorithms for graph problems, convex optimization, and their applications in machine learning. He has received grants from the Swiss National Science Foundation, including project grants and a starting grant. Education: B.A. in Computer Science from the University of Cambridge (2011), PhD in Computer Science from Yale University (2017), advised by Daniel A. Spielman. Postdoctoral positions included Harvard University (2018–2019) and a research fellowship at the Simons Institute, UC Berkeley (2017). Research Interests: Development of nearly linear-time algorithms for fundamental graph problems (e.g., maximum flow, minimum-cost flow), dynamic graph algorithms, discrepancy theory, and fine-grained complexity. His work bridges numerical linear algebra and combinatorial optimization, emphasizing practical implementations such as the Laplacians.jl package. Awards: FOCS Best Paper Award (2022), Inaugural ICBS Frontiers of Science Award (2022), Machtey Award (Best Student Paper, FOCS 2017). Teaching: Advanced Graph Algorithms and Optimization (ETH Zurich, 2020–2023), Algorithms, Probability, and Computing (ETH Zurich, 2020–2022). Supervised numerous PhD students and mentored postdocs in theoretical computer science. Labs/Teams: Co-leads a research group with Maximilian Probst Gutenberg, focusing on dynamic graph algorithms and optimization. Collaborations include work on sparsification, spectral graph theory, and machine learning applications.

Ryan Engstromمشاهده پروفایل

استاد

• Remote Sensing
• GIS (Geographic Information Systems)
• Climate Change
• +۳ مورد دیگر

Ryan Engstrom is a Professor and Director of Data Science in the Department of Geography at George Washington University (GW). He is affiliated with the Columbian College of Arts and Sciences and holds a Ph.D. from the joint program at San Diego State University and UC Santa Barbara. His research focuses on Remote Sensing, GIS applications, Climate Change impacts, Arctic environments, and Population Estimation, with an emphasis on poverty mapping and urban deprivation analysis. Engstrom has led major initiatives such as YouthMappers and IDEAMAPS, leveraging geospatial data and satellite imagery to address global development challenges. His work includes developing methodologies for georeferencing historical imagery, estimating non-monetary poverty, and mapping population density in regions like Sri Lanka and Ghana. He has published extensively in Remote Sensing , World Bank Economic Review , and Global Change Biology , among others. Key research trends in his publications involve integrating satellite-derived features with machine learning to model urban poverty, climate-driven land-use changes in Arctic regions, and applications of open-source geospatial tools for international development. Engstrom collaborates globally, contributing to projects like the World Bank’s welfare tracking in disaster-affected regions.

Immanuel Trummerمشاهده پروفایل

استاد

• Database Systems
• Query Optimization
• Large Language Models
• +۳ مورد دیگر

Immanuel Trummer is a Professor of Computer Science at Cornell University, specializing in database systems, query optimization, and applications of large language models (LLMs) and quantum computing. He leads research projects such as DB-BERT, UDO, and SkinnerDB, focusing on automated database tuning, adaptive query processing, and leveraging LLMs for code synthesis and system optimization. His research interests span quantum computing for database optimization, cost-efficient LLM utilization, and voice-based data exploration. Key contributions include developing systems like CEDAR for claim verification, CodexDB for LLM-driven code generation, and ThalamusDB for multimodal data querying. Trummer has received prestigious awards, including the NSF CAREER Award (2023-2028) and the Best Demonstration Award at BDA 2020. His work has been funded by NSF, Google, Huawei, and others, supporting projects like quantum-index selection and misinformation detection. He advises graduate students in database systems and teaches advanced courses such as CS 6320 (Advanced Database Systems) and CS 7390 (Seminar in Database Systems). His research lab hosts open-source tools like JoinGym and maintains extensive collaborations in industry and academia.

• Structural Bioinformatics
• Protein Structure Prediction
• Immunoinformatics
• +۳ مورد دیگر

Professor Charlotte Deane is a leading academic in structural bioinformatics, holding the position of Professor at the University of Oxford's Department of Statistics and Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC). She leads the Oxford Protein Informatics Group (OPIG), focusing on protein structure prediction, immunoinformatics, and AI-driven drug discovery. Her research integrates computational methods with biological insights, developing tools widely used in academia and industry. Prior roles include Head of the Department of Statistics, Deputy Head of the Mathematical, Physical and Life Sciences (MPLS) Division at Oxford, and Chief Scientist of Biologics AI at Exscientia. During the COVID-19 pandemic, she served on SAGE and as UKRI's COVID-19 Response Director. In 2022, she was awarded an MBE for her contributions to pandemic research. Her research group's work spans antibody design, T-cell receptor analysis, and small molecule discovery, with a focus on open-source software development. Current projects include advancing AI methods for protein structure prediction and therapeutic antibody engineering. Recent publications highlight innovations in computational drug design, antibody developability, and machine learning applications in structural biology.

• District Heating and Cooling
• Energy Systems
• Renewable Energy
• +۳ مورد دیگر

Sven Werner is a Senior Professor at Halmstad University's Academy of Entrepreneurship, Innovation and Sustainability. His expertise lies in district heating and cooling systems, with a focus on decarbonization, energy efficiency, and fourth/fifth generation technologies. He has authored foundational textbooks, such as Energiförsörjning: En introduktion till vårt energisystem , and contributed to international platforms like DHC+ and the IEA's Low-Temperature District Heating Annex. His research includes pioneering the concept of 'Fourth Generation District Heating' and analyzing global heating/cooling demands. Key achievements include over 50 short courses for industry professionals and presentations across 20 countries. Current projects include a book on the world history of district heating. Research Highlights: Low-temperature systems, renewable integration, and thermal storage. Impact: Most-cited article on district heating (Annex X Final Report, 2014), cited every six hours in 2021. International Contributions: Co-developed the Pan-European Thermal Atlas (PETA) and advised the EU Energy Council on energy efficiency. Werner's work bridges academia and industry, emphasizing practical solutions for sustainable energy systems. His publications span journals, conference papers, and policy guides, addressing topics like heat distribution costs, industrial heat recovery, and smart grid technologies.

• Legged locomotion
• Humanoid and quadrupedal robots
• Wearable robotics
• +۵ مورد دیگر

Yan Gu is an Associate Professor of Mechanical Engineering at Purdue University, located in West Lafayette, Indiana. He is affiliated with the School of Mechanical Engineering within the College of Engineering. His research focuses on legged locomotion, humanoid and quadrupedal robots, wearable robotics, hybrid dynamical systems, control systems, state estimation, and dynamics. He leads the TRACE Lab and has been recognized with prestigious awards including the NSF CAREER Award (2021) and multiple teaching accolades. Gu holds a Ph.D. from Purdue University (2017) and a B.S. from Zhejiang University, China (2011). His work emphasizes robust control strategies for legged robots in dynamic environments, including adaptive ankle torque control, time-varying foot-placement algorithms, and state estimation techniques for non-inertial surfaces. His recent publications explore multimodal datasets in animal-robot interaction and the stabilization of quadrupedal locomotion on accelerating platforms. His research has been supported through grants such as the NSF CAREER Award, and his contributions span both theoretical advancements in hybrid control systems and practical applications in wearable robotics and exoskeleton design. Gu’s TRACE Lab serves as a hub for innovative robotics research, addressing challenges in robot-environment interaction and dynamic stability.

Nina Balcanمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +۵ مورد دیگر

Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.

AI Xinمشاهده پروفایل

مدرس

• Artificial Intelligence
• Multi-Agent Systems & Algorithmic Game Theory
• Machine Learning and Deep Learning
• +۵ مورد دیگر

AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.

Roberto Tamassiaمشاهده پروفایل

استاد

• Cryptography
• Information Security
• Algorithm Design
• +۴ مورد دیگر

Roberto Tamassia is the James A. and Julie N. Brown Professor of Computer Science and Chair of the Computer Science Department at Brown University. He is also Director of Brown's Center for Geometric Computing. His research focuses on information security, cryptography, algorithms, graph drawing, and computational geometry. He has authored six textbooks and over 250 publications, and his work has been funded by ARO, DARPA, NATO, NSF, and industry sponsors. Education: PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. Research Interests: Cryptography and Secure Systems Algorithm Design and Optimization Graph Drawing and Geometric Computing Encrypted Database Security Awards and Honors: IEEE Fellow Technical Achievement Award from IEEE Computer Society Listed among the 360 most cited computer science authors by ISI Grants and Funding: His research has been supported by major agencies and organizations including ARO, DARPA, and NSF, as well as industry partnerships. Labs and Affiliations: Directs Brown's Center for Geometric Computing, a hub for interdisciplinary research in computational geometry and graph algorithms.

Ioannis Panageasمشاهده پروفایل

استادیار

• Algorithmic Game Theory
• Multi-agent Reinforcement Learning
• Optimization
• +۲ مورد دیگر

Ioannis Panageas is an Assistant Professor in Computer Science at UC Irvine's Donald Bren School, directing the GOALLab. His research develops theory for learning in multi-agent systems, game dynamics, and optimization. Funded by NSF and NRF, he focuses on last-iterate convergence guarantees in games, efficient equilibrium computation, and multi-agent reinforcement learning. Recent Work: Provides first exponential lower bounds for fictitious play in potential games (NeurIPS 2023), efficient Nash equilibrium computation methods (ICLR 2023), and semi-bandit learning dynamics with no-regret guarantees (ICML 2023). Teaching: Offers courses in Algorithmic Game Theory and Optimization for Machine Learning. Currently advising 3 PhD students and 2 MS students.

YingLi Tianمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.

• Computer Science
• Algorithmic Transparency
• AI Ethics
• +۷ مورد دیگر

Jeanna Matthews is a Full Professor of Computer Science at Clarkson University and an affiliate at Data and Society. She holds a PhD from UC Berkeley (1999) and teaches courses ranging from operating systems to cybersecurity. Her research focuses on algorithmic transparency, AI ethics, and societal impacts of automated systems. Matthews is a prominent ACM leader, serving on multiple committees including the Technology Policy Subcommittee on AI Accountability. She has pioneered work on forensic software analysis in criminal justice systems and delivered DEF CON presentations on virtualization security and adversarial testing. Awards include ACM Distinguished Speaker and Fulbright Specialist roles. Her work emphasizes open-source tools and critical thinking in education, extending to global service learning programs in the Dominican Republic and Brazil. Education: PhD in Computer Science (UC Berkeley, 1999), B.S. in Math/Computer Science (Ohio State, 1994), B.A. in Spanish (SUNY Potsdam, 2016). Research Interests: Cybersecurity vulnerabilities, algorithmic accountability frameworks, automated decision systems in justice contexts, and ethical AI design. Recent projects include investigating bias in DNA forensic software through a Brown Institute grant and analyzing political polarization on social platforms. Awards & Recognition: ACM Distinguished Speaker (2018-present) Fulbright Specialist (2018-present) 2018-2019 Brown Institute Magic Grant ACM SIGOPS Chair (2011-2015) ACM Special Interest Group Governing Board Chair (2016-2018) Teaching & Outreach: Designed courses integrating open-source tools and critical inquiry, including abroad programs in Mexico, Brazil, and the Dominican Republic. Advocates for lifelong learning strategies and questioning underlying assumptions in computing systems. Key Projects: Forensic Software Accountability: Examining discrepancies in DNA analysis tools Algorithmic Transparency: Frameworks for auditing automated systems Cybersecurity Education: Adversarial testing methodologies for justice software

Minyi Huangمشاهده پروفایل

استاد

• Mean Field Stochastic Control
• Stochastic Algorithms in Multi-Agent Systems
• Wireless Networks
• +۴ مورد دیگر

Minyi Huang is a Professor in the School of Mathematics and Statistics at Carleton University. His research focuses on Mean Field Stochastic Control, Stochastic Algorithms in Multi-Agent Systems, and Wireless Networks. He holds a Ph.D. from McGill University (2003) and has held postdoctoral positions at the University of Melbourne and the Australian National University. Dr. Huang is a Fellow of IEEE and a Member of SIAM. Education: Ph.D. in Electrical and Computer Engineering, McGill University (2003) M.Sc. in Systems and Control, Chinese Academy of Sciences (Beijing) B.Sc. in Mathematics, Shandong University (Jinan, China) Research Interests: Huang's work centers on stochastic control, mean field games, and multi-agent systems. His contributions include theoretical advancements in mean field social optimization, graphon-based control frameworks, and applications in wireless networks and economic models. He has organized workshops on Mathematical Cybernetics and Stochastic Processes, fostering interdisciplinary collaboration. Scientific Awards: Fellow of the IEEE Advising & Grants: Huang has advised numerous graduate students on topics in stochastic control and mean field theory. His grants include funding for international PhD students through Carleton's initiatives. He collaborates on projects involving mean field models for production output and social dynamics. Labs/Teams: Associated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), contributing to collaborative research in control theory and applied mathematics.

Kaiyang Liuمشاهده پروفایل

استادیار

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +۳ مورد دیگر

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.