Find Academic Supervisors

Moïse BlanchardView profile

Assistant Professor

• Machine Learning Theory
• Online Algorithms
• Optimization
• +4 more

Moïse Blanchard is an Assistant Professor and Tennenbaum Early Career Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, having joined in August 2025. Previously, he was a Postdoctoral Fellow at Columbia University Data Science Institute. His academic journey includes a Ph.D. in Operations Research from MIT (2024), and M.Sc. and B.Sc. degrees in Applied Mathematics from École Polytechnique. Blanchard's research focuses on the intersection of machine learning theory, statistics, and optimization. His work addresses fundamental questions in universal learning, online algorithms, and convex optimization under memory constraints. His research program explores learnability under minimal assumptions, query complexity/memory tradeoffs, and decision-making in adversarial environments. This work has significant implications for theoretical computer science, operations research, and statistical learning theory. His publications reveal a strong emphasis on foundational aspects of learning theory and optimization. Recent work demonstrates expertise in universal learning frameworks, memory-constrained optimization, and probabilistic analysis of combinatorial problems. His research often bridges theoretical computer science with practical optimization challenges, particularly in contexts where traditional i.i.d. assumptions don't hold. Columbia DSI postdoctoral fellowship, 2024 INFORMS Transportation Science & Logistics (TSL) best student paper award, 2023 Air Force Office of Scientific Research Grant (AFOSR), with Prof. Patrick Jaillet, 2023 COLT 2022 Best student paper runner-up Bronze medal, Alibaba Global Mathematics Competition, 2022 Blanchard has received significant research funding including an Air Force Office of Scientific Research Grant. His work has been recognized with multiple prestigious awards, including the INFORMS TSL best student paper award for his research on the k-Traveling Salesman Problem. His extensive publication record in top venues demonstrates a strong research trajectory with impactful contributions to theoretical machine learning and optimization.

• computer security
• formal methods
• program logics
• +3 more

Mads Dam is a Professor in Teleinformatics at the School of Computer Science and Communication at Kungliga Tekniska Högskolan (KTH), where he heads the Department of Theoretical Computer Science. His research focuses on computer security, formal methods, and program logics, with particular emphasis on the formal modeling and verification of low-level hardware and software execution platforms for security and application isolation. His educational background includes: PhD in Computer Science from the University of Edinburgh (1990) MSc in Computer Engineering from Aalborg University, Denmark BSc in Information Technology from Aalborg University, Denmark Mads Dam's research interests center on computer security, formal methods, and program logics. His current work focuses on the formal modeling and verification of low-level hardware and software execution platforms such as hypervisors and OS kernels and their underlying hardware. He has made significant contributions to information flow security, verification of microarchitectural systems, and network programming language security. His research bridges theoretical foundations with practical security applications. His recent publications show a strong trend toward verifying low-level systems, with a focus on information flow security for processors, network programming languages (particularly P4), and microarchitectural vulnerabilities. His work combines formal methods with practical security concerns, developing verification techniques that address real-world security challenges in hardware and software systems. The research spans theoretical foundations in temporal and epistemic logics to practical applications in network security and processor verification. His scientific awards and recognition include: Two framework grants from the Swedish Foundation for Strategic Research A junior individual grant from the Swedish Foundation for Strategic Research Project grants and a five-year research fellowship from the Swedish Research Council (VR) Project grants from Ericsson, Microsoft Research, US Air Force, and Vinnova (the Swedish Innovation Agency) Mads Dam has been a principal investigator on numerous research projects and has supervised many graduate students. He has been a partner in several European projects including HATS, S3MS, VerifiCard, LOMAPS, and UaESMC. His research has been supported by substantial grants from major funding bodies, reflecting the significance and impact of his work in computer security and formal methods. He is a founding member of several research centers at KTH, including Access, the CASTOR software research center, and the CDIS center for cyber defense and information security. These centers bring together researchers from multiple disciplines to address complex challenges in cybersecurity and software engineering.

Samir ElhedhliView profile

Professor

• Large-scale Optimization
• Logistics
• Supply Chain Management
• +11 more

Samir Elhedhli is a Professor in the Department of Management Sciences at the University of Waterloo, within the Faculty of Engineering. His research focuses on Large-scale Optimization, Logistics, Supply Chain Design, Healthcare Operations, Airline Scheduling, and Data Analytics. He has held grants from NSERC, CFI, OCE, and MITACS, collaborating with industries in aircraft manufacturing, airline scheduling, and warehouse management. Education: PhD in Management Science, McGill University (2001) Master's in Industrial Engineering, Bilkent University (1996) Bachelor's in Industrial Engineering, Bilkent University (1994) Research Interests: Data Analytics & Data Science Large-scale Optimization (Interior-point methods, decomposition, column generation) Supply-chain Analytics (Logistics, warehousing, routing, scheduling) Environmental Sustainability in Supply Chains Key Awards: CORS Service Award (2013) University of Waterloo Distinguished and Outstanding Performance Awards (2005–2019) Grants & Advising: Active grants from NSERC, CFI, OCE, and MITACS Currently accepting graduate student applications Administration & Service: Chair, Department of Management Sciences (2014–2018) President, Canadian Operational Research Society (2011–2012) Co-Editor-in-Chief, INFOR Journal (2014–present) Labs & Teams: Leads the WanOpt research group focused on optimization methodologies and applications.

John D. KubiatowiczView profile

Professor

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +7 more

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.

Benjamin RechtView profile

Professor

• Machine Learning
• Optimization
• Statistics
• +5 more

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

Daniele CavigliaView profile

Professor

• Ultra-Low Power Electronics
• Biomedical Circuit Design
• Ultrasound Imaging Systems
• +3 more

Daniele Caviglia serves as Full Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering at the University of Genoa, Italy. He holds the position of Coordinator for the Master's Degree in Electronic Engineering and teaches advanced courses including Radio Frequency Electronics, Electronic Devices, and Electronic Systems for Telecommunication across both Bachelor's and Master's programs. His research program focuses on ultra-low-power electronics for biomedical and environmental applications, with three primary thrusts: (1) nW-scale circuit design for bio-signal processing and neural interfaces, (2) advanced beamforming techniques in medical ultrasound imaging, and (3) energy harvesting systems for autonomous environmental monitoring. His group has pioneered inverter-based OTAs achieving sub-10nW operation and developed novel genetic algorithm-optimized apodization methods for plane-wave ultrasound imaging. Recent publications (2024-2025) reveal strong thematic continuity with increasing emphasis on practical implementations - particularly sea wave energy harvesters for environmental buoys and satellite microwave link systems for rainfall monitoring in urban settings. The work consistently bridges fundamental circuit innovation with real-world medical and environmental applications, maintaining high impact in IEEE and Elsevier journals.

• Dark Matter
• Particle Astrophysics
• Cosmology
• +2 more

Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.

• Advanced Semiconductor Packaging
• Thermal Management
• Co-Design and Multiphysics Modelling
• +3 more

Christopher Bailey is a Professor of Advanced Semiconductor Packaging and Director of the Centre for Advanced Semiconductor Packaging at Arizona State University (ASU). He previously served as Professor of Computational Mechanics & Reliability and Associate Dean for Research at the University of Greenwich, UK. At ASU, he leads research on advanced semiconductor packaging, including roles as Principal Investigator (PI) and Co-Investigator (Co-I) on major projects such as the SRC-funded Thermo-Mechanical Modelling and US Chips Act initiatives (e.g., SWAP-Hub, SHIELD, ITSI). His research focuses on semiconductor packaging reliability, thermal management, co-design methodologies, and multiphysics modeling. Education: MBA (Technology Management), Open University, UK PhD, Thames Polytechnic, UK Research Interests: Advanced Semiconductor Packaging Thermal Management Solutions Co-Design and Multiphysics Modeling Reliability of Electronic Components His work integrates computational mechanics, materials science, and engineering to address challenges in high-reliability electronics. Recent projects emphasize predictive modeling for semiconductor packaging failures under thermal-mechanical stress. Awards: IEEE Region 8 Europe Award (2024) IEEE David Feldman Award (2022) Visiting Professorships at IIT Kharagpur (2018/2022) and Hong Kong (2018) Service & Leadership: Former President of IEEE Electronics Packaging Society (2020–2021) Associate Editor for IEEE Transactions on Components, Packaging, and Manufacturing Technology Conference Leadership (e.g., Program Chair for IEEE PAINE 2024) He has secured over $40M in research funding and authored 400+ archival papers, with expertise spanning industry collaborations (e.g., BAe Systems, Rolls Royce) and government advisory roles (EPSRC Peer Review College, UK Research Excellence Framework).

Jiaxuan LiView profile

Assistant Professor

• Seismology
• Distributed Acoustic Sensing
• Earthquake Rupture Dynamics
• +7 more

Jiaxuan Li is an Assistant Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. His research focuses on developing fiber-optic sensing technologies for seismic monitoring across diverse geological environments including volcanic, crustal, and glacial settings. Dr. Li's educational background includes a Ph.D. in Geophysics from the University of Houston (2015-2020) and a B.S. in Geophysics from Peking University (2011-2015). He previously held a postdoctoral position at Caltech Seismolab under Prof. Zhongwen Zhan. His research program centers on distributed acoustic sensing (DAS) applications, with major contributions in volcanic eruption forecasting through minute-scale magma migration imaging, earthquake rupture dynamics via high-frequency fault asperity analysis, and subsurface characterization for carbon sequestration and geothermal energy. Recent work demonstrates DAS capabilities as dense geodetic arrays for real-time volcanic monitoring systems deployed in Iceland through collaborations with the Icelandic Met Office and Reykjavik University. Analysis of Dr. Li's publication record reveals a strong emphasis on operationalizing fiber-optic networks for geophysical monitoring, with significant advancements in eruption early warning systems, earthquake source characterization, and subsurface imaging techniques. His work bridges fundamental seismological research with practical hazard mitigation applications. Dr. Li actively mentors graduate students and recently welcomed postdoc Dr. Tianfan Yan to his research team. His lab operates real-time DAS streaming systems for volcanic eruption monitoring in Iceland, developed through international collaborations involving the University of Houston, Caltech, Ljósleiðarann, and Reykjavik University. Current research directions include expanding DAS applications for carbon sequestration verification and deep geothermal reservoir characterization.

Srinivas NarayanaView profile

Assistant Professor

• Computer Networks
• Formal Verification
• Compilers
• +4 more

Srinivas Narayana is an Assistant Professor in the Department of Computer Science at Rutgers University, specializing in programmable networking, formal verification, and systems research. He holds a PhD from Princeton University and a B.Tech from IIT Madras, with postdoctoral work at MIT. His research focuses on building safe, high-performance networks through optimizing compilers, verified programming, and distributed system monitoring. He has received NSF grants, the CGO 2022 Distinguished Paper Award, and the 2017 SIGCOMM Best Paper Award. Education: PhD and MA in Computer Science, Princeton University (2016) B.Tech in Computer Science, IIT Madras (2010) Postdoctoral Research, MIT (2018) Research Interests: His work bridges networking and systems with a focus on compilers, formal methods, and programmable hardware. Notable projects include K2 compiler for eBPF, the eBPF verifier soundness work, and congestion control mechanisms like CCP. He explores parallel packet processing, privacy-preserving analytics, and load balancing strategies. Grants & Awards: NSF Awards #2422076, #1910796, #2019302 eBPF Foundation Grant Facebook Networking Research Award Network Programming Initiative (NPI) Funding Lab & Teams: Leads the NetSys group at Rutgers, collaborating with teams on projects like the eBPF verifier, verified packet processing, and network monitoring tools like Marple. His lab emphasizes open-source contributions and industry collaboration.

Amr RizkView profile

Professor

• Networking
• Communication Systems
• Network Calculus
• +5 more

Professor Amr Rizk is the Director of the Networks and Communication Systems (NCS) Lab at the University of Duisburg-Essen, where he has been serving as Professor since April 2021. Previously, he was Assistant Professor at Ulm University (2019-2021) and completed his habilitation at TU Darmstadt in 2019. His academic journey includes research positions at prestigious institutions including University of Massachusetts Amherst, University of Warwick, and TU Darmstadt where he was an Athene Young Investigator. Professor Rizk's research spans multiple aspects of networking and communication systems with a particular focus on network performance analysis, stochastic modeling, and practical implementations. His work bridges theoretical foundations with real-world applications, especially in content delivery, video streaming, and network protocols. He has made significant contributions to network calculus, quality of experience optimization, and novel approaches to congestion control and caching mechanisms. His publication record demonstrates consistent high-impact contributions across top networking conferences and journals. Recent work shows a growing emphasis on programmable data planes, AI/ML applications in networking, and advanced techniques for network measurement and performance prediction. His research group at Duisburg-Essen maintains strong connections with both academic and industrial partners in the networking ecosystem. Best Paper Award at ACM MMSys Conference (2023) Distinguished TPC Member for IEEE INFOCOM (2020, 2022) Best Paper Award at ACM/USENIX Middleware Conference (2017) Athene Young Investigator Award, TU Darmstadt (2017) Professor Rizk serves as Associate Editor for Elsevier Computer Communications and has extensive experience with research funding bodies as a reviewer. His leadership extends to conference organization, including roles as PC Co-Chair for IEEE MIPR (2023) and Steering Committee member for Workshop on Network Calculus (2022). He maintains active participation in numerous top networking conferences as Technical Program Committee member, reflecting his standing within the international networking research community.

• Biosecurity
• Synthetic Biology
• Conservation Biology
• +7 more

Stephen Turner is an Associate Professor of Data Science and Assistant Dean for Research at the University of Virginia School of Data Science . His work bridges genomics, data science, and national security , focusing on biosecurity, synthetic biology, conservation, and bioinformatics applications in human health . Previously, he was a faculty member in the UVA School of Medicine’s Department of Public Health Sciences (2011–2019) and directed the UVA Bioinformatics Core . Ph.D., Human Genetics, Vanderbilt University M.S., Applied Statistics, Vanderbilt University B.S., Biology, James Madison University Turner’s research spans computational approaches to biosecurity, biodiversity conservation, and human health . Recent publications highlight tools like the qqman and kgp R packages, PLANES for epidemiological modeling, and biorecap for bioRxiv preprint summarization. His work integrates large-scale sequencing, genome editing, and machine learning in conservation biotechnology and public health forecasting. Scientific contributions include applications in infectious disease forecasting , forensic genomics , and maternal-fetal biology . He has mentored interdisciplinary students and collaborated on NIH-funded research , while advising biotech startups at the intersection of academia, industry, government, and policy .

Fei HeView profile

Associate Professor

• formal methods
• program analysis
• automated reasoning
• +2 more

Fei He is an Associate Professor at Tsinghua University's School of Software, where he leads the THUFV research lab focused on formal verification and program analysis. His research spans formal methods, automated reasoning, and program verification, with applications in concurrent systems, networking (P4 programs), and probabilistic systems. Education & Employment: PhD from Tsinghua University (2008) Visiting Scholar at Carnegie Mellon University (2010-2011) and Politecnico di Milano (2006-2007) Faculty positions at Tsinghua since 2008 (Assistant Professor 2008-2011, Associate Professor 2011-present) Research: He's developed innovative techniques in SMT solving for concurrency verification, termination analysis, and regression verification. His tools like Deagle have won gold medals at SV-COMP. Current work focuses on probabilistic program verification and network program analysis. Publications: His 80+ publications demonstrate consistent contributions across formal methods (PLDI, OOPSLA, ICSE), networking (NSDI, INFOCOM), and software engineering (TSE, TOSEM), with recent emphasis on data-driven verification and automated invariant inference. Awards: Gold Medals in SV-COMP ConcurrencySafety (2022, 2023, 2025) Best Paper Awards at PPoPP 2022 and SETTA 2022 Advising: Mentors 13 PhD/Master's students in THUFV lab, with graduates joining Huawei, MPI-SP, and research institutions. Secured multiple NSF China grants for trustworthy software research. Service: Associate Editor for Theory of Computing Systems, program committees for PLDI/ICSE/OOPSLA, and former Local Chair for ISSTA 2019.

Ahmed KarmouchView profile

Professor

• Network Slicing
• Software Defined Networking
• Named Data Networking
• +5 more

Professor Ahmed Karmouch is a faculty member at the University of Ottawa's School of Electrical Engineering and Computer Science. He holds a Ph.D. and specializes in advanced networking research, including Network Slicing, Software Defined Networks (SDN), Named Data Networking (NDN), and Cloud Computing. His IMAGINE Lab focuses on developing innovative solutions for autonomic and cognitive networks, emphasizing programmable data planes and in-network computing. Research Interests: Network Slicing Software Defined Networking Named Data Networking Programmable Data Plane Intelligence In-Network Computing Ambient Intelligence & IoT Publications reflect a focus on SDN, NDN, and cloud infrastructure optimization. His work often bridges theory and practical implementation, addressing challenges in network efficiency, reliability, and scalability. Supervised over 30 graduate students, contributing to advancements in edge computing, virtual networks, and autonomic systems. Labs/Teams: Leads the IMAGINE Lab, dedicated to research in mobile autonomic networks, context-aware systems, and future broadband infrastructure. Projects include WiMAX security, policy-based overlay networks, and semantic resource discovery.

Charles GillanView profile

Senior Lecturer

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +4 more

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.