Find Academic Supervisors

• Computer Science
• Numerical Analysis
• Algebraic Algorithms
• +4 more

Dr. Victor Y. Pan is a Distinguished Professor of Mathematics and Computer Science at Lehman College, The City University of New York (CUNY). He has been affiliated with Lehman College since 1988 and holds one of the highest academic ranks reserved for influential scholars. His research focuses on numerical and algebraic algorithms, with a particular emphasis on polynomial computations, matrix structures, and root-finding methods. Dr. Pan's work bridges numerical and symbolic computing, aiming to optimize computational efficiency while ensuring accuracy. Educational Background: Ph.D. in Mathematics from Moscow University Research experience at the Soviet Academy of Science Research Interests: His key areas include polynomial root-finding, matrix eigenproblems, structured matrices (e.g., Toeplitz, Hankel, and Cauchy), and low-rank approximation. He has pioneered methods combining numerical and algebraic techniques to enhance computational speed and precision. Recent work emphasizes algorithms for sparse polynomials, superfast root-finders, and efficient matrix computations. Publications & Impact: With over 200 peer-reviewed papers and three books, Dr. Pan’s research has influenced global computational mathematics. His articles address topics like fast root-finding, matrix eigenvalue problems, and low-rank approximation at sub-linear cost. His work is widely cited in computer science and applied mathematics. Awards & Recognition: Appointment as Distinguished Professor (CUNY, 2000) Global recognition as a leader in theoretical computer science and numerical analysis Advising & Grants: Recipient of continuous NSF funding for over 20 years. He has mentored 17 Ph.D. students through his seminar program, focusing on algebraic and numerical computing. His seminar fosters collaborative research in topics like polynomial equations, coding theory, and eigen-solving techniques. Labs & Teams: Leads the Algebraic Numerical Computing Seminar at CUNY’s Graduate Center, integrating Computer Science and Mathematics students. The seminar explores cutting-edge topics such as displacement-structured matrices, polynomial root-finding, and eigen-solving algorithms.

• Dependable Computing Systems
• Network-on-Chip (NoC) Design
• Fault Tolerance
• +5 more

Gert Jervan is a Tenured Full Professor in the Department of Computer Systems at the School of Information Technologies , Tallinn University of Technology. He also serves as the Dean of the School of Information Technologies . His research focuses on dependable computing systems, network-on-chip (NoC) architectures, fault tolerance mechanisms, and embedded systems security. He has contributed to advancements in optical communication networks, wearable health monitoring systems, and hardware verification methodologies. Key areas of expertise include: Dependable system design Fault-tolerant routing in NoC-based systems Optical spectrum service characterization Real-time wearable sensor systems Hardware-software co-verification Recent research emphasizes: Edge computing and UAV-based object detection for rescue operations Optimization of optical spectrum allocation in long-haul networks Development of automated assertion mining techniques Biometric fatigue assessment using wearable sensors He leads the Centre for Dependable Computing Systems , fostering interdisciplinary research in secure embedded systems and network reliability. His work integrates systems engineering principles with cutting-edge technologies to address challenges in both academic and industrial domains.

Neill CampbellView profile

Professor

• Visual Computing
• Machine Learning
• Computer Vision
• +6 more

Neill D. F. Campbell is a Professor of Visual Computing and Machine Learning at the University of Bath, Department of Computer Science. He holds an honorary Associate Professor position at University College London. His research focuses on shape modeling, machine learning, and their applications in computer vision and graphics. Campbell directs the CAMERA research centre and co-leads the Centre for Mathematics and Algorithms for Data (MAD). He leads the MyWorld project, a £46M creative hub in the Bath-Bristol region. Education: PhD in Engineering (Automatic 3D Model Acquisition from Uncalibrated Images) from the University of Cambridge (2011), MEng in Engineering from the University of Cambridge (2006). Research Interests: Modeling shape and appearance using machine learning, Bayesian nonparametric models, generative models, uncertainty quantification, and applications in entertainment, health, and sports science. Active in developing structured uncertainty prediction networks (SUPN) and exploring generative models for inverse problems. Key Projects: MyWorld (Strength in Places Fund), CAMERA, EPSRC CDT in Statistical Applied Mathematics, and collaborations with industry partners. Recent work includes anomaly detection, depth estimation, and conditional sampling techniques. Awards: Royal Society Industry Fellow (2017–present). Over 54 research outputs spanning CVPR, ECCV, SIGGRAPH, and NeurIPS, focusing on structured uncertainty, generative models, and vision applications. Labs/Teams: Director of CAMERA, co-director of MAD, and collaborator in UKRI CDT in Responsible AI. Active in organizing workshops on uncertainty quantification in computer vision (UNCV).

Alan RuddockView profile

Associate Professor

• Sport Physiology
• Strength and Conditioning
• High-Intensity Interval Training
• +4 more

Dr Alan Ruddock is an Associate Professor of Sport Physiology and Performance at Sheffield Hallam University's School of Sport and Physical Activity. He holds positions as Course Leader for the BSc Sport and Exercise Science and Taught Research Ethics Lead. A Fellow of the British Association of Sport and Exercise Sciences (FBASES) and Senior Fellow of the Higher Education Academy (SFHEA), Ruddock specializes in high-intensity interval training (HIIT), thermoregulation, and elite sport performance. His research extends to boxing physiology, endurance sports, and strength and conditioning practices. Educationally, Ruddock earned a PhD and has authored/co-authored over 40 scientific manuscripts. His work includes collaborations with institutions globally and scientific support for Olympians, Paralympians, and professional athletes. He has advised elite boxing teams in 15 world-title fights and contributed to Premier League Football and the English Institute of Sport. Ruddock's research interests focus on optimizing athletic performance through HIIT, thermal physiology, and ergogenic aids. He has pioneered studies on hand cooling for thermoregulation and developed practical guidelines for combat sports conditioning. His recent work includes investigations into hydration practices in squash and load-velocity profiling in strength training. Key awards include FBASES and SFHEA distinctions, underscoring his academic and professional contributions. Ruddock actively supervises PhD candidates in areas like thermoregulation, velocity-based training, and overtraining in strength sports. He is affiliated with the Sport and Physical Activity Research Centre and Health Research Institute, driving interdisciplinary research in sport science.

John BabrajView profile

Senior Lecturer

• High-Intensity Training
• Exercise Metabolism
• Omega-3 Supplementation
• +2 more

Dr. John Babraj is a Senior Lecturer in Exercise Physiology at Abertay University with research focusing on health and performance adaptations to high-intensity training and omega-3 supplementation. His work explores minimal effective exercise doses for health benefits and nutritional strategies for exercise recovery and tendon function. Key Research Areas: Supramaximal sprint training (30-sec bouts) for cardio-metabolic health Omega-3 fatty acids for inflammation reduction and recovery enhancement Whole-body vibration training for aging populations Physiological monitoring in combat sports He serves as Chief Scientific Adviser to Edinburgh Biotec Ltd and provides performance support to Dundee FC, Dundee United FC, and combat sports athletes. His research has demonstrated that 15-minute high-intensity sessions (1-3 min actual exercise) significantly improve metabolic health markers.

Stephen StanleyView profile

Professor

• Film Production
• Screenwriting
• Film Distribution
• +3 more

Stephen Stanley is a Professor of Film and Television at Savannah College of Art and Design (SCAD), holding this position since 2018. He previously served as Assistant Professor of Film, Theatre, and Creative Writing at the University of Central Arkansas (2016–2018) and taught at UCLA Extension in 2016. His academic background includes an M.F.A. in Film and Television from SCAD and a B.A. in French from Rhodes College. Stanley's professional work spans film production and distribution, including roles as producer for major films like They Live in the Grey (AMC/Shudder) and What Lies Below (Netflix), which debuted as Netflix's top film in North America. His production credits also include Conway Pride , an award-winning documentary, and Avenge the Crows , an official SXSW title. He maintains industry connections through the Producers Guild of America (PGA), where he serves on the LGBTQIA+ Working Group as a College Ambassador Mentor. His career combines academic teaching with hands-on production experience in both independent and studio films, emphasizing cross-platform distribution strategies and genre innovation.

Charles AudetView profile

Professor

• Optimization
• Operational Research
• Blackbox Optimization
• +4 more

Charles Audet is a Full Professor in the Department of Mathematical and Industrial Engineering at Polytechnique Montréal , with affiliations to the Institute for Data Valorization (IVADO) and Research Group in Decision Analysis (GERAD) . Specializing in optimization and derivative-free methods, his work focuses on algorithm development for complex engineering problems characterized by black-box functions, nonconvexity, and nonsmooth constraints. B.Sc. (Ottawa), M.Sc./Ph.D. (Polytechnique Montréal) Joint supervisor with multiple institutions and researchers His research spans algorithmic improvements in pattern search methods (GPS/MADS), global optimization for structured nonconvex problems, and blackbox optimization applications. Current projects involve multidisciplinary design and categorical variable handling in optimization frameworks. Recent publications emphasize mixed-variable domains and robust algorithm design . The NOMAD software ecosystem, which he co-developed, remains central to derivative-free optimization implementations. Key scientific contributions include: Advances in mesh adaptive direct search algorithms Structural exploitation in bilevel/quadratic programming Parallel decomposition techniques Robustness in noisy function optimization Students under his supervision have explored applications in hydroelectric resource modeling , compressor blade redesign , and telecommunication frequency assignment .

Andrew Ellis JohnsonView profile

Associate Professor

• Social Practice Art
• Political Art
• Contemporary Art
• +3 more

Andrew Ellis Johnson is an Associate Professor of Art at Carnegie Mellon University's School of Art. Born in Cortland, NY, he has a multidisciplinary artistic practice spanning film, sculpture, and socially engaged performance. His work addresses social and political issues, challenging boundaries between aesthetics, politics, and ethics. Education: BFA from the School of the Art Institute of Chicago, MFA from Carnegie Mellon University. Notable roles include artist-in-residence at the Pennsylvania Department of Corrections and co-founder of PED, a collective active globally since 2004. Residencies include institutions in Seoul, Cairo, London, and Jerusalem. Research interests focus on awakening civic engagement through art, using diverse media including public collaborations and exhibitions. His work has been featured in museums, galleries, and festivals across the Americas, Europe, Asia, and the Middle East. Recent projects include solo exhibitions at Binghamton University and group shows at UC Irvine and Northern Illinois University. Professional activities: Advised MFA alum Lena Chen, exhibited at White Box NYC, and participated in international residency programs. Active in fostering cross-cultural dialogues through art.

Sarah GiesyView profile

Researcher

• Metabolism
• Endocrinology
• Animal Science
• +3 more

Sarah Giesy serves as a Researcher in the Department of Animal Science at Cornell University's College of Agriculture and Life Sciences, conducting pivotal work in Yves Boisclair's laboratory on hormonal regulation of metabolism using dairy cows, sheep, and mice models. Her research integrates in vivo and in vitro methodologies to investigate metabolic pathways critical to agricultural animal physiology. Her academic credentials include: Doctorate in Animal Science, Cornell University (2009) Master of Science in Animal Science, Cornell University (2004) Bachelor of Science in Animal Science, University of Idaho (2000) Dr. Giesy's research program centers on endocrine control of metabolism, with specific expertise in feed intake regulation, insulin action, and inflammatory responses during physiological transitions. She examines how factors like fibroblast growth factor-21, melanocortin signaling, and birth weight parameters influence metabolic outcomes in ruminants, bridging fundamental endocrine mechanisms with practical agricultural applications. Analysis of her 2018-2023 publications reveals consistent investigation into metabolic adaptation in dairy cows during early lactation and neonatal development in lambs. Key thematic threads include FGF21's role in improving insulin sensitivity, inflammatory biomarkers in transition cows, and neuroendocrine regulation of appetite—demonstrating methodological rigor through combined molecular, physiological, and whole-animal approaches. Within the Boisclair laboratory, Dr. Giesy provides comprehensive research leadership including protocol development, experimental execution, and data analysis while mentoring graduate and undergraduate students. Her work operates within Cornell's animal science infrastructure, utilizing specialized facilities for metabolic studies in agricultural species.

Johannes BetzView profile

Professor

• Autonomous Systems
• Vehicle Control
• Machine Learning
• +3 more

Prof. Dr. Johannes Betz is a Rudolf Mößbauer Professor at the Technical University of Munich (TUM), leading the Chair of Autonomous Vehicle Systems within the Department of Mobility Systems. His research focuses on software development for autonomous vehicles, emphasizing algorithms for environmental perception, path planning, and control to enable safe, efficient autonomous operation. He holds a B.Eng. and M.Sc. in Automotive Engineering from Coburg University and the University of Bayreuth, followed by a Ph.D. in Engineering from TUM (2019). Notable achievements include founding the TUM Autonomous Motorsport Team, which won the Indy Autonomous Challenge ($1M prize), and extensive postdoc work at TUM and the University of Pennsylvania. Education: B.Eng., Automotive Engineering, Coburg University of Applied Sciences (2013) M.Sc., Automotive Engineering, University of Bayreuth (2013) Ph.D. in Engineering, Technical University of Munich (2019) Research Interests: Autonomous vehicle software architecture Real-time trajectory and behavior planning Adaptive control for high-performance systems Simulation and validation frameworks Ethics of autonomous decision-making Awards: IEEE ITS Young Professional Travel Fellowship (2022) 1st Place, Indy Autonomous Challenge (2021) New Generation Star Project Award (2024) Outstanding Reviewer Award (2024) Labs & Teams: TUM Autonomous Motorsport Team (race competition leader) AVS Lab (Autonomous Vehicle Systems)

• Structural Engineering
• Resilience Engineering
• Multihazard Risk Analysis
• +7 more

Eyitayo Ademola Opabola is an Assistant Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley. His research combines Structural Engineering, Reliability Theory, Statistics, and Hazard Science to solve complex engineering problems affecting civil infrastructure systems. He has extensive experience in large-scale field and laboratory testing of structural components and systems, nonlinear analysis of structures, multihazard risk analysis, and building-level and community-level resilience quantification. Education Ph.D., Civil Engineering - University of Auckland, New Zealand, 2020 M.S., Structural Engineering - Voronezh State University of Architecture and Civil Engineering, Russia, 2015 B.E., Civil and Industrial Engineering - Voronezh State University of Architecture and Civil Engineering, Russia, 2013 Dr. Opabola's research focuses on both high- and low-income communities across North America, Europe, Oceania, Asia, and Africa. His work centers on three major themes: (a) how future building codes can address resilience, sustainability, and durability under extreme loading events and changing climate; (b) how natural hazards interact and impact civil infrastructure systems and communities, especially marginalized communities; (c) innovative ideas on rehabilitation and adaptive reuse of buildings to meet net-zero goals. His group has developed a repairability-based design approach for structural systems that is being adopted by design guidelines in the US and New Zealand, a probabilistic framework for simulating multi-hazard scenarios, and resilience quantification models for various infrastructure systems. His publication record demonstrates strong focus on practical applications of resilience engineering, with recent papers examining post-disaster recovery trajectories, multihazard risk modeling, and seismic design innovations. The research spans theoretical development, experimental validation, and real-world implementation across diverse geographical contexts. Scientific Awards 2023 Shah Family Innovation Prize 2022 Marie Curie Individual Post-Doctoral Research Fellowship 2021 University of Auckland Vice-Chancellor Award for top PhD theses 2020 University of Auckland Doctor of Philosophy Dean's list of excellence 2019 New Zealand Society of Earthquake Engineering Research award Dr. Opabola has secured funding from multiple prestigious sources including United Kingdom Research and Innovation (UKRI), European Commission, National Research Council Canada, Federal Emergency Management Agency (FEMA), and the World Bank. His research group actively collaborates with stakeholders to translate technical findings into practical disaster mitigation policies. Current projects investigate repairability-based design approaches, life-cycle carbon emission optimization, and multi-hazard interaction frameworks that account for climate change impacts. His laboratory work spans large-scale structural testing, nonlinear analysis, and field investigations across multiple continents, with a particular focus on developing methodologies applicable to both developed and developing contexts. The group's work on multihazard scenario generation and resilience quantification has direct applications for infrastructure owners, policymakers, and emergency management agencies worldwide.

Niao HeView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +4 more

Prof. Niao He is an Associate Professor in the Department of Computer Science at ETH Zürich. His research focuses on optimization theory, reinforcement learning, stochastic systems, and their applications in machine learning and multi-agent systems. He holds an academic position at one of the world's leading technical universities, contributing to both theoretical advancements and practical algorithmic solutions. His research interests span optimization theory (e.g., convex/non-convex optimization, stochastic optimization), reinforcement learning (policy gradient methods, multi-agent systems), and statistical learning (risk-averse methods, entropy regularization). He has also explored applications in data science, network revenue management, and deep reinforcement learning for complex systems. Recent work emphasizes algorithmic reproducibility, robustness under model uncertainty, and efficient methods for large-scale problems. Notable contributions include novel approaches to minimax optimization, mean-field games, and adaptive learning frameworks. His articles consistently address theoretical guarantees while maintaining practical relevance for real-world computational challenges.

• Gambling Psychology
• Behavioral Addictions
• Public Health
• +4 more

Professor Sally Gainsbury is a leading academic in gambling psychology and behavioral addictions, holding the rank of Professor in the School of Psychology at the University of Sydney. She serves as Director of the Gambling Treatment and Research Clinic (GTRC), the only university-based gambling treatment center in Australia, focusing on reducing gambling harms through research and policy. Additionally, she leads the Technology Addiction Team at the Brain and Mind Centre, investigating technology's role in addictive behaviors. Her work bridges clinical practice and research, collaborating with governments, industries, and communities to inform policies. Professor Gainsbury is internationally recognized, serving as Co-Editor-in-Chief of International Gambling Studies , and has authored over 135 journal articles and two books. Her research interests include gambling disorder, decision-making in emerging technologies, and harm reduction strategies. Awards include the Winston Churchill Fellowship (2024) and NSW Tall Poppy Scientist of the Year (2019). She has supervised numerous postgraduate and undergraduate students, focusing on topics like gambling and technology interactions. Her global collaborations span institutions in Germany, Switzerland, and the US. Current projects emphasize cashless gambling systems, online self-exclusion tools, and interdisciplinary frameworks to address gambling-related mental health issues. Education: PhD, Doctor.ClinPsych, BPsych(Hons) Affiliations: Gambling Treatment and Research Clinic (GTRC), Brain and Mind Centre, University of Sydney Grants: Over $6.2 million in research funding Labs/Teams: Technology Addiction Team, GTRC Media Presence: Featured in CNN International and the Jim Jefferies Show Her research addresses gambling policy, consumer behavior, and mental health, with a focus on translating findings into actionable interventions. Ongoing projects include evaluating digital payment systems' impact on gambling harm and developing evidence-based guidelines for emerging technologies.

Francine LafontaineView profile

Professor

• Industrial Organization
• Vertical Relationships
• Contracting
• +4 more

Francine Lafontaine is a Professor of Economics (Courtesy) at the University of Michigan, affiliated with the Ross School of Business. She holds the William Davidson Professorship in Business Economics and Public Policy. Her research focuses on Industrial Organization, Vertical Relationships, and Franchising, with emphasis on contract theory applications in franchising arrangements and trucking industry dynamics. She has a Ph.D. from the University of British Columbia (1988). Her work examines topics like no-poaching clauses in franchises, financial constraints in franchising, and the impact of organizational forms on firm performance. Lafontaine also investigates modern retail market evolution, including online/offline dynamics and labor market trends in trucking. Notable contributions include analyses of franchise vs corporate ownership effects and the role of regulatory stability in international investments. Key research areas are organized into three pillars: Franchise Contracting Mechanisms Vertical Mergers & Antitrust Policy Retail Industry Transformation Recent work highlights the misuse of price regression in merger reviews and the microeconomic implications of labor market rigidities. Despite prolific research output, no scientific awards are explicitly mentioned in the provided texts. She advises no listed students and has no noted labs/teams, focusing instead on individual and collaborative academic projects.

Xiaowei GuView profile

Senior Lecturer

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +5 more

Dr. Xiaowei Gu is a Senior Lecturer at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering and the Computer Science Research Centre. He holds a PhD in Computer Science from Lancaster University (2018). Previously, he served as a Lecturer at the University of Kent, Aberystwyth University, and as a Senior Research Associate at Lancaster University. His research focuses on transparent machine learning models that balance interpretability and performance, particularly in semi-supervised and streaming data contexts. Key applications include disaster response via satellite imagery analysis and environmental monitoring. Notable projects include developing a human-trustable machine learning framework for rapid disaster response using satellite data. Teaching responsibilities include courses like Programming Fundamentals (COM1027) and AI & AI Programming (EEEM005). He actively supervises exceptional PhD candidates with strong research backgrounds in machine learning and data analytics. No scientific awards are explicitly listed, but his work emphasizes impactful, application-driven research. Publications span explainable AI, fuzzy systems, and deep learning applications in environmental and cybersecurity domains. His work often bridges theoretical advancements with real-world challenges, such as wildlife tracking and ocean temperature modeling.