Find Academic Supervisors

Junier OlivaView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +8 more

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

• Human Resource Management
• Online Labor Platforms
• Gig Economy
• +3 more

Professor Anne Keegan is a Full Professor of International Human Resource Management at University College Dublin (UCD), leading the HRM and Employment Relations Group within the School of Business. She previously held roles at the Amsterdam Business School and Erasmus University Rotterdam, and completed her PhD at Trinity College Dublin. Her research focuses on HRM in online labor platforms, gig economies, project-based organizations, and institutional/paradox theories. Education: Bachelor of Arts (BA), Trinity College Dublin Doctor of Philosophy (DPhil), Trinity College Dublin Research Interests: HRM in digital labor platforms and gig economies Institutional complexity and paradox theory applications HR practices in project-based organizations Career dynamics in non-traditional work environments Awards: EGOS Award in Honour of Max Boisot (2014) Van der Schroeff Award for Best Lecturer (2013) Professional Activities: Editorial Board roles for International Journal of Human Resource Management and International Journal of Project Management Committee memberships: UCD Academic Council, Faculty Promotions Committee, Widening Participation initiatives Keynote speaker at NEON Annual Conference (2019) Grants: UCD School Funded Research Account (2024-2027) Labs/Teams: Leading research on algorithmic management, platform ecosystems, and gig economy ethics through collaborative international projects.

Oded RegevView profile

Professor

• Theoretical Computer Science
• Lattice-based Cryptography
• Quantum Computation
• +3 more

Oded Regev is a Silver Professor at the Courant Institute of Mathematical Sciences, New York University. He has previously held positions at Tel Aviv University and École Normale Supérieure, Paris (CNRS). His research spans theoretical computer science, cryptography, quantum computation, and machine learning applications in biological discovery. Education: PhD in Computer Science, Tel Aviv University (2001) Regev is renowned for his work in lattice-based cryptography, including the introduction of the Learning With Errors (LWE) problem and Gaussian measures. He also leads research using interpretable machine learning to decode RNA splicing logic and nuclear speckle dynamics, with implications in disease and therapeutics. His recent articles focus on quantum factoring, RNA localization, and geometric lattice bounds. Scientific Awards: European Research Council (ERC) Starting Grant (2008) 2018 Gödel Prize 2019 Simons Investigator Award Best Paper Awards: STOC 2003, Eurocrypt 2006 Regev mentors students and postdocs in both theoretical computer science and computational biology. His lab has secured funding from NSF, NIH, and Additional Ventures. He co-founded the TCS+ online seminar series and serves as Associate Editor-in-Chief for Theory of Computing .

Arthur BousquetView profile

Associate Professor

• Applied Mathematics
• Numerical Analysis
• Computational Fluid Dynamics
• +3 more

Arthur Bousquet is an Associate Professor of Mathematics at Lake Forest College, affiliated with the Math and Computer Science department. He holds a PhD in Applied Mathematics from Indiana University (Bloomington, IN) and a MS in Engineering in applied mathematics and scientific computing from SuP Galilee Engineering School (Paris, France). His research focuses on numerical methods for partial differential equations, including finite volume and finite element techniques, with applications to geophysical fluid dynamics, climate modeling, and biomedical problems like viral shell mechanics. Notable areas include shallow water equations, phase field modeling, and computational methods for atmospheric dynamics. Bousquet has published extensively on topics such as numerical weather prediction, electrokinetic equations, and virus nanoindentation modeling. His work often combines theoretical analysis with computational simulations to address complex systems in fluid dynamics and materials science. He has received the Rothrock Award for teaching excellence (2014) and held research fellowships including an NSF Graduate Fellowship (2009-2013). His teaching includes courses like Computational Mathematics, Multivariable Calculus, and Real Analysis.

Gauthier GidelView profile

Associate Professor

• Deep Learning
• Adversarial Machine Learning
• Generative Modeling
• +5 more

Gauthier Gidel is an Associate Professor at the Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Science at Université de Montréal, where he also holds the prestigious Canada CIFAR AI Chair position. He is a core faculty member of Mila, Quebec's AI research institute, and maintains active research collaborations with leading institutions. His academic journey includes a PhD in Computer Science under the supervision of Simon Lacoste-Julien, with internships at Sierra, ElementAI, and DeepMind during his doctoral studies. Dr. Gidel's research spans multiple critical areas in machine learning, with particular emphasis on generative modeling , adversarial machine learning , and variational inequalities for machine learning. His work explores the intersection of optimization theory and practical AI systems, focusing on challenges like LLM safety alignment, multi-agent cooperation, and robustness against adversarial attacks. He is particularly known for his contributions to understanding the theoretical foundations of generative adversarial networks through variational inequality frameworks. His recent publications reveal a strong trend toward addressing critical challenges in large language model safety and alignment, with numerous 2024-2025 papers focusing on adversarial robustness, safety evaluation methodologies, and alignment techniques for LLMs. Simultaneously, his foundational work continues in optimization theory, particularly in variational inequalities and performative prediction, demonstrating his dual focus on practical AI safety concerns and theoretical machine learning foundations. Canada CIFAR AI Chair Core member of Mila Organizer of popular NeurIPS workshops on smooth games Co-founder of the ICLR blog post track Dr. Gidel actively supervises an extensive research group with approximately 10 current graduate students and numerous alumni who have secured positions at leading institutions including Inria Lyon, Oxford, and industry research labs. His research is supported by multiple substantial grants from CRSNG, MITACS, and IVADO, including the prestigious CRSNG Discovery Grant program and MITACS Acceleration Québec projects focused on fraud detection in music streaming and conditional generation. His laboratory maintains strong connections with both academic and industry partners, fostering a collaborative environment focused on advancing AI safety and theoretical understanding.

Timothy FraserView profile

Researcher

• Climate Change Adaptation
• Disaster Resilience
• Energy Policy
• +5 more

Dr. Timothy Fraser is a Computational Social Scientist serving as Ezra Systems Research Associate in Cornell University's Systems Engineering Program and Coordinator for the Center for Transportation, Environment, & Community Health (CTECH). Holding a PhD in Political Science from Northeastern University (2022), he focuses on climate change adaptation, disaster resilience, and energy policy using big data analytics, GIS, and AI. His work bridges computational methods with societal challenges, emphasizing community engagement and policy impact. Education: PhD in Political Science, Northeastern University (2022) MA in Political Science, Northeastern University BA in International and Global Studies, Middlebury College Research Interests: Fraser’s research integrates computational social science with environmental policy, exploring how social networks and governance structures influence cities' climate adaptation strategies. Key areas include renewable energy adoption, disaster evacuation dynamics, and pandemic response mechanisms. He employs mixed methods such as network analysis, statistical modeling, and fieldwork. Grants & Awards: Fulbright Fellowship (2016), Kyushu University Japan Foundation Doctoral Fellowship (2020) USDOT Multimillion Grant Administrator (2022-2023) Teaching & Mentorship: Fraser teaches statistical methods and research design at Cornell, advising over 30+ students in projects published in leading journals. He coordinates capstone teams and mentors researchers in data science and policy analysis. Labs & Projects: Leads the Climate Action in Transportation dashboard initiative (Gao Labs), developing tools for emissions visualization. Collaborates on UNDP social capital mapping projects in Mexico and Paraguay, applying spatial analysis techniques for vulnerable communities.

Guido MontúfarView profile

Professor

• Deep Learning Theory
• Mathematical Machine Learning
• Graphical Models
• +4 more

Guido Montúfar is a Professor in the Departments of Mathematics and Statistics & Data Science at the University of California, Los Angeles (UCLA), effective since 2024. He also leads the Mathematical Machine Learning Group at the Max Planck Institute for Mathematics in the Sciences (MPI MIS) in Leipzig, Germany since 2018. His academic journey includes a PhD in Mathematics from Leipzig University (2012), and Diplom degrees in Physics and Mathematics from TU Berlin (2009 and 2007). Montúfar's research focuses on the theoretical foundations of deep learning, mathematical machine learning, and the interplay between geometry and learning. Key areas include neural network architecture theory, optimization landscapes, and information geometry. His work bridges algebraic statistics, graphical models, and topological data analysis. His grants and awards include an ERC Starting Grant (2018-2023), a Sloan Research Fellowship (2022), and an NSF CAREER Award. He has advised numerous PhD students and postdocs, contributing to significant advancements in machine learning theory and applications. Montúfar teaches courses on applied mathematics, optimization, and machine learning at UCLA. His research also explores topics like oversquashing in graph neural networks and the geometry of policy gradients in reinforcement learning.

Loris D'AntoniView profile

Associate Professor

• Programming Languages
• Software Verification
• Program Synthesis
• +4 more

Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.

Kate FarrahiView profile

Associate Professor

• Machine Learning
• Digital Health
• Human Sensing
• +3 more

Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health

• Motivation
• Time Management
• Goal Setting
• +3 more

Jordan Etkin is an Associate Professor of Marketing at Duke University’s Fuqua School of Business, specializing in studies of goal pursuit, motivation, and time management. She explores how goal structures, variety in activities, and personal quantification impact behavior and well-being. Her research bridges consumer behavior, psychology, and decision science, with frequent publications in top-tier journals like the Journal of Consumer Research and Journal of Marketing Research. Education: PhD (Year not specified, but teaches since 2013) Her research interests focus on the interplay between goals and personal resources (e.g., time), including unintended consequences of tracking behaviors like step-counting. Key themes include motivation dynamics, goal conflict resolution, and temporal resource allocation. She frequently engages with popular media, appearing in outlets like the New York Times and BBC. Recent work (2020–2024) highlights topics such as time limits paradoxically increasing consumption, variety’s role in goal conflict, and machine learning’s applications in behavioral research. Her 2019 JCR Award underscores scholarly impact. Awards: 2019 JCR Awards Announcements (Recipient) Teaching responsibilities include the Marketing Core class for Fuqua’s MBA program. While no lab teams are explicitly mentioned, her research themes suggest collaborative work in behavioral science and consumer studies.

Shima NazariView profile

Assistant Professor

• Dynamics and Control
• Powertrain Electrification
• Hybrid Electric Vehicles
• +4 more

Shima Nazari is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Davis. Her research focuses on dynamics and control with applications to transportation systems, energy systems, and electrified/automated vehicles. She leads the CORE Lab, exploring advancements in hybrid powertrains, autonomous vehicle control, and energy-efficient systems. Education: Ph.D., Mechanical Engineering, University of Michigan (2019) M.S., Electrical Engineering, University of Michigan (2016) M.S., Mechanical Engineering, Sharif University of Technology, Iran (2012) B.S., Mechanical Engineering, Sharif University of Technology, Iran (2009) Her research interests span dynamics and control theory applied to hybrid electric vehicles, powertrain optimization, and autonomous systems. She has contributed to advancements in energy-efficient vehicle designs, regenerative braking, and control strategies for electrified powertrains. Her work often bridges mechanical and electrical engineering disciplines to address real-world challenges in transportation and energy. Recent research trends in her articles emphasize data-driven control methods, hybridization strategies for autonomous systems, and thermal management of energy storage solutions. These studies reflect her commitment to interdisciplinary innovation. Although no scientific awards are explicitly mentioned, her contributions to the field are evident through her active research and publications. She advises students in her lab and collaborates on grants focused on sustainable mobility solutions. Dr. Nazari is affiliated with the UC Davis College of Engineering and actively contributes to graduate programs in Dynamics, Controls, Vehicles, and Robotics. The CORE Lab serves as a hub for experimental and computational research in her areas of expertise.

Yen-Chi ChenView profile

Associate Professor

• Nonparametric statistics
• Topological data analysis
• Missing data
• +7 more

Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.

Xiaowen ZhangView profile

Professor

• Cryptography
• Information Security
• Cybersecurity
• +5 more

Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.

Jose Luis SANCHEZ LOPEZView profile

Researcher

• Robotics
• Autonomous Systems
• Computer Vision
• +5 more

Dr. Jose Luis SANCHEZ LOPEZ is a Research Scientist at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) of the University of Luxembourg, leading the Aerial Robotics Lab (AeRoLab) within the Automation & Robotics Research Group (ARG). He joined SnT in 2017 as a Postdoc Research Associate, promoted to Research Scientist in 2021. His research focuses on autonomous robotics, particularly aerial systems, emphasizing situational awareness, SLAM, and trajectory planning. Education: Ph.D. in Robotics (2017), M.Sc. in Automation & Robotics (2012), and Engineering degree in Industrial Engineering (2010), all from the Technical University of Madrid. Visiting Research: Arizona State University (2012), LAAS-CNRS (2014–2016). Research Interests: Multi-agent robotic systems, sensor fusion, localization/mapping, computer vision, machine learning, and trajectory control. He has authored over 56 peer-reviewed publications, with an h-index of 18. Projects: Leads projects like DEUS (PI), NEDA, ÄerdFly (PI), and RoboSAUR. Contributions span European, Luxembourg, and Spain-funded initiatives, focusing on autonomous systems, 5G integration, and construction-site robotics. Teaching: Lectured in MICS, BiCS, BING (Uni.lu) and GITI (UPM). Actively involved in academic service as a reviewer, editor, and competition participant (e.g., IMAV, IARC). Outreach: National Coordinator for Luxembourg’s Robotics European Week, promotes robotics education in schools and public events.

Martin BerggrenView profile

Professor

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .