Find Academic Supervisors

Naren RamakrishnanView profile

Professor

• Data Science
• Machine Learning
• Urban Analytics
• +2 more

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech, where he directs the Sanghani Center for AI and Data Analytics. He also serves as AI and Machine Learning Lead for the Virginia Tech Innovation Campus. His research spans data science, machine learning, urban analytics, forecasting, and computational epidemiology. Recent publications (2024-2025) focus on language model optimization, AI applications in government and environmental conservation, and spatiotemporal data analysis. Work demonstrates strong emphasis on real-world AI deployments in regulatory compliance, supply chain verification, and network optimization. Methodological innovations include prompt engineering techniques, world models for reinforcement learning, and specialized embedding methods. Dr. Ramakrishnan has received prestigious fellowships from ACM, AAAS, and IEEE. His research has been supported by numerous agencies including DARPA, NSF, NIH, and industry partners like Amazon and Boeing, with 36 PhD students mentored to completion.

Guoquan HuangView profile

Assistant Professor

• Robotics
• Computer Vision
• Robot Learning
• +5 more

Guoquan Huang is an Assistant Professor in the Department of Mechanical Engineering at the University of Delaware. He holds a B.Eng. in Automation from the University of Science and Technology, Beijing (2002), and M.Sc. and Ph.D. degrees in Robotics from the University of Minnesota (2009 and 2012). Prior to his current role, he was a Postdoctoral Associate at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on robotics, computer vision, and autonomous systems, emphasizing probabilistic perception, estimation, and control for ground, aerial, and underwater vehicles. He leads the development of the OpenVINS platform for visual-inertial estimation and has contributed to advancements in SLAM (Simultaneous Localization and Mapping), sensor fusion, and multi-robot coordination. Education: B.Eng in Automation (Electrical Engineering), University of Science and Technology, Beijing, 2002 M.Sc. in Robotics, University of Minnesota, Twin Cities, 2009 Ph.D. in Robotics, University of Minnesota, Twin Cities, 2012 His research interests span robotics, computer vision, and autonomous systems , with a focus on: Visual-inertial navigation and SLAM Sensor fusion (LiDAR, IMU, camera) Autonomous vehicle control and safety Multi-robot cooperative localization His recent publications (2023–2025) emphasize robust algorithms for navigation in GPS-denied environments, real-time sensor calibration, and dataset development for aerial visual localization. He has pioneered techniques like decoupled error-state estimation and consistent parallel frameworks for SLAM. Labs/Teams: Leads the development of the OpenVINS research platform, focusing on visual-inertial state estimation. Collaborates on projects involving human-swarm interactions and resilient ground vehicle navigation.

John ZeleznikowView profile

Professor

• Artificial Intelligence in Law
• Dispute Resolution Systems
• Data Mining in Sports
• +2 more

John Zeleznikow is Professor in both Management and Information Systems at Victoria University, affiliated with the Institute for Sustainable Industries & Liveable Cities. With 48 years of academic experience across Australian, European and US institutions, he has published extensively including 4 monographs and 88 refereed journal articles. His research develops AI applications for decision-making in legal disputes and sports analytics. Notable innovations include negotiation support systems for divorce mediation adopted by Victoria Legal Aid. Professor Zeleznikow has secured over A$8 million in competitive grants and supervised 20 PhD candidates. His educational background includes: PhD from Monash University (1980) Graduate Diploma in Computing from University of Melbourne (1987) BSc (Hons) from Monash University (1973)

Sandy IraniView profile

Professor

• Quantum Computing
• Algorithm Design
• Computational Complexity Theory
• +5 more

Sandy Irani is a Full Professor at the University of California, Irvine (UCI) in the Department of Computer Science within the Donald Bren School of Information and Computer Sciences. She received her Ph.D. from UC Berkeley in 1991 and has been at UCI since 1992. Her research focuses on algorithm design, computational complexity theory, and quantum computing, with notable contributions to online algorithms and quantum complexity theory. She currently serves as Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley, a role she has held since 2022. This position allows her to collaborate with researchers across theoretical computer science and related disciplines. Irani’s teaching excellence is recognized through the UCI Distinguished Faculty Award for Teaching (2021), and she has contributed to education through her zyBook on Discrete Mathematics, used by over 94,000 students globally. Her work bridges foundational computer science with practical applications, including power management strategies and distributed computing algorithms. Notably, she has collaborated with industry leaders like Mike Luby on optimizing distributed systems. Her research in quantum computing explores computational problems inspired by condensed matter physics, aiming to understand quantum advantage over classical systems. She has also authored influential papers on topics like cache hierarchy design, scheduling algorithms, and the theoretical limits of electronic structure calculations. Awards: ACM Fellow (2022), UCI Distinguished Faculty Award for Teaching (2021). Key Roles: Associate Director, Simons Institute; Vice Chair, Computing Division at UCI. Recent Projects: Quantum algorithms for condensed matter systems, maximal independent set algorithms in distributed networks.

Domniki AsimakiView profile

Professor

• Geotechnical Engineering
• Seismology
• Structural Dynamics
• +6 more

Domniki Asimaki is a Professor of Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. Her research focuses on geotechnical engineering, computational mechanics, and structural dynamics, with an emphasis on understanding ground motion effects on natural and engineered systems such as dams, tunnels, and urban infrastructure. She holds a Dipl. from the National Technical University of Athens (1998), an M.S. (2000) and Ph.D. (2004) from MIT, joining Caltech in 2014. Key research interests include soil dynamics, wave propagation, regional ground deformation, and soil-foundation-structure interaction. She has pioneered data-driven approaches to integrate numerical simulations with field observations for resilient infrastructure design. Notable achievements include developing the open-source Seismo-VLAB software for seismic analysis and receiving prestigious awards like the Bodossaki Award of Scientific Excellence and the Geotechnical Earthquake Engineering Award. Her work addresses seismic hazards at urban and regional scales, with recent studies on the 2023 Türkiye earthquake, the 2019 Ridgecrest earthquake, and Kathmandu Basin dynamics. She leads initiatives to enhance ground motion prediction, landslide hazard assessment, and infrastructure resilience through advanced modeling and AI-driven methods. Education: Dipl., National Technical University of Athens, 1998 M.S., Massachusetts Institute of Technology, 2000 Ph.D., Massachusetts Institute of Technology, 2004 Awards: Bodossaki Award of Scientific Excellence Geotechnical Earthquake Engineering Award Labs/Teams: Leads research groups focusing on seismic hazard modeling, open-source software development, and geotechnical data assimilation techniques.

Amritanshu PandeyView profile

Assistant Professor

• Grid Simulation and Optimization
• Grid Cybersecurity
• Modeling of Future Energy Systems
• +3 more

Amritanshu Pandey is an Assistant Professor in Electrical Engineering at the University of Vermont, with a part-time adjunct appointment in Electrical and Computer Engineering at Carnegie Mellon University. His research focuses on enhancing the efficiency, reliability, and security of electric grids through methods in circuit theory, optimization, and machine learning. He pioneered the SUGAR simulation engine for power systems and collaborates globally on grid challenges. Research Interests: Pandey's work spans renewable integration, grid cybersecurity, digital twins, and decarbonization. Key projects include developing algorithms for large-scale grid optimization, anomaly detection, electric vehicle infrastructure modeling, and cyber-resilient energy systems. His research addresses real-world challenges in rapidly evolving grids across Asia and Africa. Awards: Best Paper Award, IEEE PES General Meeting (2017, 2021) Best-of-the-Best Paper Award, IEEE PES General Meeting (2021) Best Student Paper Runner-up, ECML-PKDD (2018) Students & Labs: He advises 7 PhD students and has graduated 11 advisees (PhD/MS/BS). His lab focuses on power systems innovation, including the SUGAR simulation framework and projects on grid cybersecurity and sustainable electrification.

Frank E. CurtisView profile

Professor

• Mathematical Optimization
• Machine Learning
• Numerical Analysis
• +3 more

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been since 2009. He holds a B.S. in Mathematics and Computer Science from the College of William and Mary (2003), an M.S. and Ph.D. in Industrial Engineering from Northwestern University (2004 and 2007), and completed a postdoctoral fellowship at New York University’s Courant Institute (2007–2009). His research focuses on developing numerical methods for large-scale nonlinear optimization, with applications in machine learning, operations research, and energy systems. Key achievements include the 2021 SIAM/MOS Lagrange Prize for Continuous Optimization (with Bottou and Nocedal) and the 2018 INFORMS Computing Society Prize (with Burke, Lewis, and Overton). He has secured significant funding from the NSF, DoE, and ONR, including a TRIPODS grant and ARPA-E awards. His work on the ARPA-E Grid Optimization Competition earned second place in 2020. Curtis’s research interests span mathematical optimization, numerical analysis, and algorithm design. His recent articles emphasize stochastic optimization, fairness in machine learning, and robust algorithm development for constrained systems. He serves as an Area Editor for Mathematics of Operations Research and Associate Editor for multiple top journals, including Mathematical Programming and SIAM Journal on Optimization . Notable grants include DoE ASCR Early Career Awards and NSF TRIPODS funding for collaborative projects with Northwestern, Boston University, and Cornell. His OptML @ Lehigh team develops cutting-edge optimization tools and frameworks for real-world applications.

Luis Nunes VicenteView profile

Professor

• Continuous Optimization
• Computational Science and Engineering
• Machine Learning
• +4 more

Luis Nunes Vicente is the Timothy J. Wilmott '80 Endowed Faculty Professor and Chair of Lehigh University's Department of Industrial and Systems Engineering since August 2018. He previously served as a faculty member at the University of Coimbra's Department of Mathematics. His research focuses on Continuous Optimization, Computational Science and Engineering, and Machine Learning/Data Science. Education: Ph.D. in Applied Mathematics (Rice University, 1996), M.A. in Applied Mathematics (Rice University, 1994), B.S. in Mathematics and Operations Research (University of Coimbra, 1990). Honors include the Lagrange Prize (2015), SIAM Fellowship (2024), and Fulbright Scholarship (1996). He co-authored the influential book Introduction to Derivative-Free Optimization (2009). Research Interests: Development of optimization algorithms for derivative-free and stochastic scenarios, multi-objective optimization, machine learning applications, and computational methods for engineering problems. Key contributions include trust-region methods, bilevel optimization frameworks, and fairness-aware machine learning models. Grants and Leadership: Secured over $1M from the Office of Naval Research (2024) and the Air Force Office of Scientific Research (2023). Served as Editor-in-Chief of Portugaliae Mathematica (2013–2018) and on editorial boards of top journals like SIAM Journal on Optimization . Elected President of the Operations Research Association of Chairs (2024). Visiting Positions: IBM T.J. Watson Research Center (2002/2003), Courant Institute/NYU (2009/2010), and CERFACS/Toulouse (2010–2015). Active in international conferences, delivering plenary lectures at 13th French-German Conference on Optimization, ICCOPT III, and ISMP 2018.

Xujia ZHUView profile

Associate Professor

• Uncertainty Quantification
• Surrogate Modeling
• Stochastic Simulators
• +4 more

Xujia ZHU is an Associate Professor at CentraleSupélec, Paris-Saclay University, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on uncertainty quantification, surrogate modeling, stochastic simulators, and reliability analysis. He holds an engineer’s degree in Mechanics from École Polytechnique (2015), a Master’s in Computational Mechanics from TU Munich (2017), and a Ph.D. from ETH Zurich (2022). He was a postdoctoral researcher at ETH Zurich until 2023. Education: Ph.D., Chair of Risk, Safety, and Uncertainty Quantification, ETH Zurich, 2022 Master’s (high distinction), Computational Mechanics, Technical University of Munich, 2017 Engineer’s Degree, Mechanics, École Polytechnique, 2015 Research Interests: Xujia’s work bridges numerical simulations and statistics, addressing topics like uncertainty propagation, sensitivity analysis, and surrogate modeling for stochastic systems. Key areas include polynomial chaos expansions, Bayesian active learning, and applications in seismic fragility analysis. Publications: His recent work emphasizes emulation techniques for stochastic simulators, multi-fidelity methodologies, and Bayesian active learning strategies in reliability analysis. Key themes include sparse polynomial chaos expansions and latent variable modeling. Labs/Teams: Affiliated with L2S, he collaborates on transversal projects in energy, industry, and health, leveraging interdisciplinary approaches in uncertainty quantification and computational modeling.

Adam AlbrightView profile

Professor

• Phonology
• Morphology
• Language Acquisition
• +4 more

Adam Albright is a Professor of Linguistics at the Massachusetts Institute of Technology (MIT). His research focuses on phonology, morphology, language acquisition, and computational linguistics. Education Ph.D. in Linguistics, UCLA, 2002. Dissertation: "The identification of bases in morphological paradigms" M.A. in Linguistics, UCLA, 1998. Thesis: "Phonological subregularities in productive and unproductive inflectional classes: Evidence from Italian" B.A. in Linguistics and Religious Studies, Cornell University, 1996. Senior Thesis: "The history of stress in Welsh, and the analysis of historical change in OT" Research Interests Albright's main research projects include: Experimental testing and computational modeling of gradient phonotactic well-formedness Investigating the organization of paradigms (base selection and learning) Testing the psychological reality of morphological processes via Wug tests Understanding paradigm gaps Computational modeling of historical change Collaboration with Bruce Hayes on learning algorithms He also has interests in Yiddish and Lakhota phonology and morphology, and the history of stress in Welsh. Teaching He has taught courses at MIT and UC Santa Cruz, including: Advanced Phonology Language Acquisition Sound Change and Phonological Theory Modeling Phonological Learning Language Change Computational Morphology & Phonology Collaborations Albright collaborates with Bruce Hayes (UCLA) on computational modeling of phonological and morphological learning. Contact Email: albright@mit.edu Office: 32-D830, MIT Mailing Address: MIT Linguistics and Philosophy, 77 Massachusetts Avenue, 32-D808, Cambridge, MA 02139, USA

Soufiene DjahelView profile

Professor

• Connected and Autonomous Vehicles (CAVs)
• Unmanned Aerial Vehicles (UAVs)
• Cyber Security
• +7 more

Soufiene Djahel is a Professor at the Centre for Future Transport and Cities (CFTC) at Coventry University, UK. His research focuses on connected and autonomous vehicles (CAVs), unmanned aerial vehicles (UAVs), cyber security, and smart cities. He holds a PhD in Secure Routing and Medium Access Protocols from Université des Sciences et Technologies de Lille (2010), and has held academic positions including Senior Lecturer at the University of Huddersfield and Manchester Metropolitan University. His research interests include CAV coordination protocols, cyber-physical security solutions, and intelligent transportation systems. Djahel leads projects such as the £1.2M AeroPharma Logistics initiative and has secured funding from the Newton Fund and JSPS. He is a recipient of the 2021 JSPS Invitational Fellowship and has published extensively in IEEE journals and conferences. Current projects explore UAVs-as-a-service, digital twins for CAVs, and B5G/6G for smart infrastructure. He advises PhD students on topics like AI-based threat mitigation and transport electrification. Djahel also serves as an external examiner and editorial board member for journals like IEEE Transactions on Intelligent Transportation Systems.

Oliver KennedyView profile

Associate Professor

• Databases
• Programming Languages
• Data Science
• +2 more

Dr. Oliver Kennedy is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Co-Director of Graduate Studies and leads the Online Data Interactions (ODIn) Lab. His research focuses on databases, programming languages, and user interfaces for data science, with particular emphasis on scalable compilers and managing uncertainty in data. Kennedy holds a PhD in Computer Science from Cornell University (2011), MS from Cornell (2008), and dual BS degrees in Computer Science and Computer Engineering from NYU and Stevens Institute of Technology (2005). His work bridges theoretical computer science with practical data management challenges. His recent publications demonstrate a strong focus on improving database query processing, uncertainty management in data systems, and developing practical tools for data integration and exploration. Awarded the NSF CAREER Award in 2018, Kennedy's research has significant implications for efficient data processing in scientific and commercial applications.

Lirong XiaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +9 more

Lirong Xia is a Professor of Computer Science at Rutgers University - New Brunswick and Deputy Director of DIMACS (Center for Discrete Mathematics and Theoretical Computer Science). He holds a Ph.D. in Computer Science from Duke University, an M.A. in Economics from Duke, and a B.E. in Computer Science and Technology from Tsinghua University. His research focuses on the intersection of artificial intelligence, machine learning, and social choice theory, addressing challenges in voting systems, fair division, privacy, and multi-agent systems. Key research areas include algorithmic fairness, computational social choice, and mechanism design. Recent work explores equitable voting rules, privacy-preserving mechanisms, and strategic behavior analysis. Notable publications include advancements in computational social choice and privacy in voting systems. Xia has been recognized with prestigious awards such as the NSF CAREER Award and IEEE’s “AI’s 10 to Watch.” Education: Ph.D. Computer Science, Duke University (2011) M.A. Economics, Duke University (2010) B.E. Computer Science and Technology, Tsinghua University (2004) Awards: NSF CAREER Award Simons-Berkeley Research Fellowship 2018 Rensselaer James M. Tien’66 Early Career Award IEEE Intelligent Systems “AI’s 10 to Watch” Advising: Supervised over 30 students, including PhDs and master’s candidates in AI, algorithms, and social choice theory.

Sandrine DudoitView profile

Professor

• Statistical Methodology
• Computational Biology
• Genomics
• +5 more

Sandrine Dudoit is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She earned her PhD in Statistics from UC Berkeley in 1999 and joined the faculty in 2001. Her research focuses on statistical methodology and computing with applications to genomics, biomedical research, and precision health. She co-founded the Bioconductor Project , an open-source software initiative for biological data analysis, and leads interdisciplinary projects in single-cell transcriptomics and computational biology. Education: PhD in Statistics (UC Berkeley, 1999), M.Sc. in Mathematics (Carleton University, Canada). Research interests include high-dimensional statistical learning, single-cell RNA-Seq analysis, stem cell differentiation in the olfactory system, and statistical computing. She collaborates with biologists like John Ngai to study neuroepithelial regeneration using cutting-edge sequencing technologies. Recent work emphasizes trajectory inference, biomarker discovery, and methodological advances in handling high-dimensional genomic data. Her lab develops tools for normalization, clustering, and differential expression analysis in large-scale biological datasets. She teaches courses on statistical genomics and serves as a leader in UC Berkeley’s Division of Computing, Data Science, and Society (CDSS). Advising: Supervises PhD students in statistical methodology, computational biology, and bioinformatics. Grants: Active in securing funding for interdisciplinary research projects in genomics and data science. Labs/Teams: Core member of the Center for Computational Biology (CCB) and contributes to the Bioconductor community.

• Algorithms
• Data Streams
• Machine Learning
• +3 more

David P. Woodruff is a Professor in the Department of Computer Science at Carnegie Mellon University, part of the Theory Group within the School of Computer Science. He is actively involved in academic leadership roles, including chairing the CATCS (Conference on Theoretical Computer Science) and serving as PC chair for SODA 2024 and ICALP 2022. His research focuses on algorithms, data streams, machine learning, numerical linear algebra, sketching, and sparse recovery. He has been recognized with awards such as the Herbert Simon Award for teaching and the PODS Best Paper Award. Woodruff has advised numerous students and postdocs, including notable scholars like Ainesh Bakshi, Rajesh Jayaram, and Hongyang Zhang. His work often addresses foundational challenges in theoretical computer science, with contributions to distributed computing, streaming algorithms, and privacy-preserving techniques. He has published extensively in top conferences like NeurIPS, ICML, FOCS, and STOC, covering topics ranging from low-rank approximation to adversarial robustness in data streams. His teaching includes courses like Algorithms for Big Data and core algorithms courses, reflecting his commitment to both research and education. Collaborations span academia and industry, with applications in genomics and secure computation. Woodruff is a key contributor to the Foundations of Data Science program at the Simons Institute.