جستجوی استاد راهنمای دانشگاهی

Tao Shuمشاهده پروفایل

دانشیار

• Cybersecurity
• Wireless Communication
• Federated Learning
• +۳ مورد دیگر

Dr. Tao Shu is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University. His research focuses on cybersecurity, wireless communication systems, federated learning, and IoT applications. He holds a Ph.D. in Electrical and Computer Engineering from the University of Arizona, and M.S. and B.S. degrees in Electronic Engineering from South China University of Technology. Dr. Shu's work emphasizes secure communication and distributed learning systems, including projects funded by the NSF such as a novel method to prevent cyberattacks on Low Earth Orbit (LEO) satellites. He has been recognized for academic excellence, including being named to Auburn University’s 2020 promotion and tenure list. His research interests span cybersecurity mechanisms for autonomous vehicles, privacy-preserving federated learning, and resource allocation in metaverse environments. He explores innovative solutions for sensor spoofing detection, adversarial machine learning, and energy-efficient IoT systems. Dr. Shu is affiliated with Auburn’s Center for Artificial Intelligence and Cybersecurity Engineering and actively contributes to interdisciplinary projects. His publications reflect a strong focus on practical applications of theoretical advancements in wireless systems and secure data transmission.

Yue Zhaoمشاهده پروفایل

دانشیار

• Smart Grid
• Renewable Energy Integration
• Electricity Market Design
• +۶ مورد دیگر

Yue Zhao is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University, with an affiliated appointment in Applied Mathematics and Statistics. Prior to this, she held postdoctoral positions at Stanford University and Princeton University. She earned her Ph.D. from UCLA in 2011 and B.E. from Tsinghua University in 2006. Her research focuses on smart grid systems, renewable energy integration, machine learning applications in power systems, and game-theoretic approaches to electricity markets. Key areas include transportation electrification, demand response mechanisms, and cyber-physical security of grid infrastructure. She teaches courses on digital signal processing, convex optimization, and communication systems. Her work spans over 50 publications in top journals and conferences like IEEE Transactions on Power Systems and ACM e-Energy. Notable contributions include dynamic state estimation frameworks for inverter-based resources, incentive-compatible market mechanisms for renewable aggregation, and cyber attack detection methodologies. Dr. Zhao advises a research group focused on interdisciplinary challenges in energy systems. Current openings exist for Ph.D. students with strong analytical backgrounds. Sponsors include NSF, DOE, and industry collaborators.

Juan Alonsoمشاهده پروفایل

استاد

• Aerospace System Design
• Computational Fluid Dynamics
• Multi-Disciplinary Optimization
• +۷ مورد دیگر

Prof. Juan Alonso is the Vance D. and Arlene C. Coffman Professor and James & Anna Marie Spilker Chair in the Department of Aeronautics & Astronautics at Stanford University. He directs the Aerospace Design Laboratory (ADL), focusing on high-fidelity computational methods for aerospace system design. His expertise spans transonic/supersonic/hypersonic aircraft, rotorcraft, and launch vehicles. Alumni include record-holding teams for human-powered watercraft and lightweight unmanned aerial vehicles. Education: PhD (1997) from Princeton University in Mechanical & Aerospace Engineering; M.A. (1993) Princeton; B.S. (1991) MIT Aeronautics/Astronautics. Research emphasizes multi-disciplinary optimization, numerical methods, and parallel computing applied to advanced aircraft design, sustainable aviation, and UAS systems. Notable contributions include computational design frameworks like SU2 and SUAVE, and initiatives in curriculum development for engineering education. Recent work focuses on: GPU-accelerated CFD solvers, multi-fidelity surrogate models (e.g., VortexNet), contrail simulation frameworks, and battery degradation modeling for electric aircraft. Active in urban air mobility and high-fidelity trajectory optimization for hypersonic systems. Labs/Teams: Aerospace Design Laboratory (ADL) leading open-source computational tools development. Involved in NASA-funded projects and industry partnerships for advanced propulsion systems.

Asu Ozdaglarمشاهده پروفایل

استاد

• Optimization
• Machine Learning
• Game Theory
• +۴ مورد دیگر

Asu Ozdaglar is the EECS Department Head and MathWorks Professor at MIT, serving as Deputy Dean of Academics in the MIT Schwarzman College of Computing. Her research bridges optimization theory, machine learning, and network science with societal implications, focusing on AI ethics, data-driven decision systems, and strategic interactions in networked environments. Her technical contributions include foundational work on large-scale optimization algorithms (e.g., distributed methods, first-order methods), game-theoretic models for network systems, and federated learning frameworks. Recent work addresses critical societal challenges like misinformation dynamics, data market inefficiencies, and algorithmic fairness in AI systems. Publications from 2023-2025 highlight advancements in graphon-based network game analysis, privacy-preserving data mechanisms, and multi-agent learning dynamics. She co-leads initiatives in MIT's AI+D program, emphasizing interdisciplinary education and ethical AI development. Notable institutional roles include oversight of MIT's computing education strategy and contributions to pandemic-related research on infection control through testing optimization. Her work integrates technical rigor with policy-relevant insights, influencing both academic and real-world systems.

Henrik von Colerمشاهده پروفایل

استادیار

• Music Technology
• Spatial Audio Systems
• Live Electronics
• +۳ مورد دیگر

Henrik von Coler is an Assistant Professor at Georgia Institute of Technology's School of Music within the College of Design. His work bridges engineering, electronic music, and empirical research, focusing on spatial audio systems, live electronics, and human-computer interaction in musical contexts. He joined Georgia Tech in 2023 after serving as director of the TU Studio for Electronic Music at Technische Universität Berlin from 2015 to 2023, where he founded the Electronic Orchestra Charlottenburg (EOC) to explore live electronic ensembles in multichannel environments. His research emphasizes the integration of sound, space, and HCI to enhance compositional and performative expressiveness. Notable projects include immersive audio installations, virtual instrument design, and AI-assisted composition. Coler has performed globally on immersive audio systems and curated international concerts. His technical contributions span spatial sound synthesis algorithms, networked music systems, and real-time signal processing tools. Key areas of exploration include volumetric music performances in metaverse environments, hybrid spatial interaction via ARCube, and statistical models for spectral synthesis. He has developed open-source systems like Orchestra (for metaverse performances) and SPRAWL (for ensemble interaction). Coler’s work often combines empirical research with artistic practice, aiming to redefine the boundaries of live electronic music through technological innovation. His academic output spans over 30 peer-reviewed articles since 2011, with recent focus on metaverse applications, AI-human collaboration, and immersive audio design. While no formal awards are listed, his leadership roles and project outcomes highlight significant contributions to music technology and spatial audio engineering.

Sijia Gengمشاهده پروفایل

استادیار

• Power Systems
• Control Theory
• Nonlinear Dynamical Systems
• +۴ مورد دیگر

Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University (JHU) and a core researcher at the Ralph O’Connor Sustainable Energy Institute (ROSEI). She directs the Power and Energy Network Systems Analysis (PENSA) Laboratory and co-leads the NSF-funded Electric Power Innovation for a Carbon-free Society (EPICS) Center as co-PI. Her research focuses on integrating control theory, mathematical analysis, and optimization to enhance renewable energy utilization and grid resiliency. Education: Ph.D. and M.S. (ECE & Mathematics) from the University of Michigan-Ann Arbor (2016–2022), B.S. in Automation from Harbin Institute of Technology (2016). Postdoctoral work at MIT (2022) and visiting scholar roles at Purdue University (2015) and Pacific Northwest National Lab (2018). Research Interests: Dynamic analysis of inverter-based power systems, nonlinear control theory, data-driven decision-making, and multi-energy systems. Her work emphasizes achieving autonomous, resilient energy systems through advanced computational tools and theoretical frameworks. Awards: Best Paper Award at MIT/Harvard Applied Energy Symposium (2022), MIT Rising Stars in EECS (2021), Barbour Scholarship (2021), Towner Prize (2018), and Gerald and Esther Forrest Fellowship (2016). She is active in IEEE and INFORMS, organizing sessions at PES General Meeting and CISS conferences. Grants & Collaborations: Funded by NSF, DOE, MIT Energy Initiative, and industry. Leads global initiatives through EPICS, collaborating with UK, Australian, and international stakeholders. Co-leads ROSEI’s Grid pillar to advance fossil-free energy systems. Labs & Teams: Directs PENSA Lab, affiliated with JHU’s Data Science and AI Institute, Applied Mathematics & Statistics, and Computer Science departments.

Zixiang Xiongمشاهده پروفایل

استاد

• Machine Learning
• Image/Video Processing
• Network Information Theory
• +۷ مورد دیگر

Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.

Daisuke Nagaiمشاهده پروفایل

استاد

• Astrophysics
• Cosmology
• Galaxy Formation
• +۴ مورد دیگر

Daisuke Nagai is a Professor of Physics and Astronomy at Yale University and the Director of Graduate Studies in the Yale Physics Department. His research focuses on theoretical and computational cosmology, including dark matter, dark energy, galaxy clusters, and data science. He holds a Ph.D. from the University of Chicago (2005) and a B.S. from the University of Michigan (1999). Before joining Yale in 2008, he was a Sherman Fairchild Postdoctoral Scholar at Caltech. Positions: Professor (2022–present), Director of Graduate Studies (2022–2025), Co-Director Yale Center for Research Computing (2015–2019). Awards: Stephen Murray Lectureship (2018), Cottrell Scholar (2012), IUPAP Young Scientist Prize (2011). His work uses high-resolution cosmological simulations to study galaxy cluster formation, X-ray observations, and the interplay between dark matter and baryonic processes. He also leads efforts in computational astrophysics and data-driven methods for cosmological analysis.

Jialin Dingمشاهده پروفایل

استادیار

• Machine Learning
• Optimization Techniques
• Data Management Systems
• +۱ مورد دیگر

Jialin Ding is an Assistant Professor in the Department of Computer Science at Princeton University, with an appointment starting September 1, 2025. His research focuses on applying machine learning and optimization techniques to enhance data management systems. Prior to this role, he worked as an Applied Scientist at AWS and earned his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022. His work has been honored with the Meta Research PhD Fellowship. Education: Ph.D., Computer Science, Massachusetts Institute of Technology, 2022 Research Interests: Jialin Ding explores the intersection of machine learning and systems, emphasizing practical applications in data management. His work aims to leverage optimization algorithms and AI-driven approaches to improve system efficiency and scalability. Specific areas include database systems, distributed computing, and adaptive resource allocation. Awards: Meta Research PhD Fellowship Professional Background: Before joining Princeton, he contributed to AWS as an Applied Scientist, gaining industry experience in cloud computing and large-scale system design. His academic and industrial experiences inform his research, bridging theoretical advancements with real-world system challenges.

Vladimir Vinogradovمشاهده پروفایل

استاد

• Stochastic Analysis
• Financial Mathematics
• Actuarial Mathematics
• +۱۵ مورد دیگر

Vladimir Vinogradov is a Professor in the Department of Mathematics at the College of Arts and Sciences, Ohio University. He is based in Morton Hall 579 and can be reached at vinograd@ohio.edu. His academic career reflects long-standing engagement in probabilistic and statistical theory with applications in finance, actuarial science, and population dynamics. Research Interests: His work spans a wide spectrum of stochastic processes and statistical methodologies. Key areas include Stochastic Analysis, Lévy and Markov Processes, Branching and Particle Systems, Fluctuation and Extreme Value Theory, Large Deviations, and Asymptotic Expansions. He also contributes to Distribution Theory, Saddlepoint Approximations, Generalized Linear Models, and probabilistic models in Population Genetics. Grants and Awards: Dr. Vinogradov has received sustained research support, including multiple Ohio University Research Challenge Program Grants (1999–2003), NSERC Canada Individual Research Grants (1995–2003), and a Fields Institute Conference Grant (2014). He also received the BC Asia Pacific Scholars' Award and several UNBC Conference Travel Grants. Education: Ph.D., Moscow State University Professional Activities: He co-organized the International Conference on Analysis, Applications and Computations in memory of Lee Lorch in 2014. His funding history indicates active collaboration and academic leadership. There is no mention of advising students or lab affiliations in the provided text.

Madison Loreمشاهده پروفایل

استادیار

• Urban Planning
• Data Analytics
• Sustainable Transitions
• +۱۲ مورد دیگر

Madison Lore is an incoming Assistant Professor in the Department of City and Regional Planning at Cornell University's College of Architecture, Art, and Planning, beginning her tenure in January 2026. Her interdisciplinary research integrates urban planning, data science, and sustainability, focusing on how large-scale data and information environments shape public behaviors and perceptions around sustainable transitions in housing, transportation, and energy systems. She holds a Ph.D. from the School of Community and Regional Planning at the University of British Columbia, a Master's in Applied Mathematics, and a dual Bachelor's in Mathematics and Physics from Rensselaer Polytechnic Institute. Her academic journey reflects a strong technical foundation applied to pressing urban challenges. Madison’s research interests span urban data science, machine learning, infrastructure and land use planning, social policy, and sustainable transportation. She investigates how algorithmic and data-driven methods can be used responsibly to uncover social norms, institutional influences, and individual support for sustainable policies, particularly in contexts of information overload. Her recent publications demonstrate a strong trajectory in applying hybrid deep learning and natural language processing to urban text data, evaluating equity in public mobility, and modeling transportation preferences through digital footprints. These works reflect a consistent theme: leveraging data analytics to promote equitable and sustainable urban futures. Vanier Canada Graduate Scholarship (2023–2026) Bombardier Sustainable Transportation Fellowship (2022) The Bill and Nancy Siegmann Applied Mathematical Modeling Prize (2018) Leonhard Euler Award for Excellence in Mathematical Modeling (2016) Climate Social Science Network Grant on Big Oil’s Climate Disinformation (2024) Madison has presented her work at major conferences including the Association of Collegiate Schools of Planning, the International Conference on Travel Behavior Research, and the American Planning Association National Conference. While no formal advisees are listed, her role as an incoming assistant professor suggests future mentorship of graduate students in urban planning and data analytics. She is affiliated with the PLACE Lab and brings expertise from prior work in nuclear physics and applied mathematics into her current urban sustainability research.

Oskar Hallatschekمشاهده پروفایل

دانشیار

• Biological Physics
• Evolutionary Dynamics
• Population Genetics
• +۳ مورد دیگر

Oskar Hallatschek is an Associate Professor and McAdams Chair in the Department of Physics and Integrative Biology at the University of California, Berkeley. His research focuses on biological physics and evolutionary dynamics, particularly how collective patterns emerge from heterogeneous microbial populations. He leads the Hallatschek Lab, which maintains collaborations with institutions like the University of Leipzig. Research Emphasis: Microbial systems, population genetics, non-equilibrium statistical physics, evolutionary adaptation, and mechanical interactions in cellular populations. Publications span journals like Nature , PNAS , and Science , with recent work on allele surfing, jamming in microbial colonies, and long-range dispersal effects. Grants: Supported by NIH and NSF, as indicated by institutional logos in the lab web presence.

Jeffrey S. Chaseمشاهده پروفایل

استاد

• Distributed Systems
• Operating Systems
• Cloud Computing
• +۷ مورد دیگر

Jeffrey S. Chase is a Professor of Computer Science at Duke University in the Department of Computer Science within Trinity College of Arts & Sciences. He has held this position since 2006 and currently serves as Director of Graduate Studies. His academic career spans decades with significant contributions to distributed systems research. Chase's research focuses on operating systems and distributed systems, with particular emphasis on cloud infrastructure services, trust management and security in cloud environments, energy-aware computing, and network storage. His work has evolved to center on integrity and trust: where it comes from, how we represent it, how we reason about it in software, and how we build software systems worthy of trust. His research group has developed notable systems including ExoGENI, Silver, and ADAMANT. His recent publications demonstrate his continued focus on secure data sharing architectures, networking for multi-tenant data centers, and federated authorization systems. These works collectively address fundamental challenges in distributed computing related to security, privacy, and network performance in cloud environments. Chase has received significant research funding including the CC*Data: ImPACT project (2017-2022), Collaborative Research: CICI: Secure and Resilient Architecture (2016-2020), and TWC: Frontier: Collaborative: Rethinking Security in the Era of Cloud Computing (2013-2019). He has taught advanced courses including CPS 512: Advanced Distributed Systems, CPS 310: Operating Systems, and CPS 510: Advanced Operating Systems. His teaching philosophy emphasizes deep understanding of core distributed systems concepts with practical applications to modern cloud infrastructure.

Matthew J. Grahamمشاهده پروفایل

استاد پژوهش

• Astronomy
• Astrophysics
• Time-domain Astronomy
• +۵ مورد دیگر

Matthew J. Graham is a Research Professor of Astronomy at the California Institute of Technology (Caltech), serving as the Project Scientist for the Zwicky Transient Facility (ZTF). His work bridges astronomy, machine learning, and data science, focusing on time-domain sky surveys that produce hundreds of thousands of public transient alerts per night. Previously, he has worked on the Catalina Real-time Transient Survey (CRTS), NOAO DataLab, Virtual Observatory, and Palomar-Quest Digital Sky Survey. Dr. Graham's primary research interests involve applying machine learning and advanced statistical methodologies to astrophysical problems, particularly the variability of quasars and other stochastic time series. His work addresses the unprecedented data volumes generated by 21st-century astronomy while expanding our ability to work with complex information systems beyond simple correlations. His current projects include real-time low latency inferencing via the NSF-funded A3D3 Institute, reinforcement learning for optimizing astrophysical follow-up campaigns, neural differential models for supermassive black hole variability, and functional analysis of multivariate time series. Analysis of Graham's recent publications reveals a strong focus on time-domain astronomy, particularly leveraging the capabilities of the Zwicky Transient Facility. His work spans multiple areas including gravitational wave counterpart identification, active galactic nuclei variability, supernova characterization, and machine learning applications for transient detection. A notable trend is the integration of artificial intelligence techniques to handle the massive data streams from modern sky surveys, enabling real-time analysis and decision-making that would be impossible with traditional methods. Dr. Graham has been instrumental in developing infrastructure for time-domain astronomy, including the alert distribution system for ZTF and data processing pipelines for handling massive transient datasets. His work on the Catalina Real-time Transient Survey established important methodologies for identifying variable and transient sources that continue to influence the field. As Project Scientist for ZTF, Graham leads a major international collaboration involving Caltech, IPAC, and numerous partner institutions worldwide. The facility represents a significant advancement in time-domain astronomy, providing unprecedented coverage of the dynamic sky and enabling discoveries across multiple areas of astrophysics.

• Microfluidics
• BioMEMS
• Lab-on-a-Chip Technology
• +۱۱ مورد دیگر

Prof. Kwang W. Oh is a tenured Professor at the Department of Electrical Engineering and Department of Biomedical Engineering within the School of Engineering and Applied Sciences at University at Buffalo (SUNY at Buffalo) . He serves as the Director of Graduate Studies in Electrical Engineering and Director of SMALL (Sensors and MicroActuators Learning Lab) . His academic journey includes PhD and MS in Electrical and Computer Engineering from University of Cincinnati (2001, 1997) and BS in Physics from Chonbuk National University (1995). Prof. Oh's research expertise lies at the intersection of microfluidics , BioMEMS , and lab-on-a-chip technologies. His lab has pioneered vacuum-driven microfluidic devices , PDMS-based systems , droplet manipulation , and chemical-free fabrication techniques . His work enables point-of-care diagnostics , single cell analysis , and wearable medical sensors , with significant contributions to sample-to-answer nanosystems and world-to-chip interfacing . The scientific awards section highlights his excellence in teaching and research: SUNY Chancellor's Award for Excellence in Teaching (2020) Meyerson Award for Undergraduate Teaching (2019) Qualcomm Faculty Award (2019) Senior Teacher of the Year (2017) Royal Society of Chemistry's Emerging Investigators (2013) Samsung Electronics' CEO Honor (2003) His lab has produced numerous PhD and MS students including Dr. Anyang Wang (2020), Dr. Nikhila Nyayapathi (2020), Mr. Liam Christie (2021), and Dr. Domin Koh (2019). As a conference chair , he has organized symposia at NanoTech (2012-2026) and served as editorial board member for Sensors , Micromachines , and Biomedical Engineering Letters .