Find Academic Supervisors

• Operations Research
• Supply Chain Management
• Humanitarian Logistics
• +3 more

Prof. Dick den Hertog is a Full Professor at Tilburg University's Department of Econometrics and Operations Research, part of the Tilburg School of Economics and Management (TiSEM). His research focuses on operations research methodologies with applications in humanitarian logistics, supply chain optimization, and robust decision-making under uncertainty. He collaborates with organizations like the UN World Food Programme to enhance operational efficiency in complex environments. Key research areas include robust optimization techniques, supply chain management in developing regions, and the integration of satellite data with machine learning for infrastructure analysis. His work addresses challenges such as food aid distribution, disaster response logistics, and predictive modeling for transportation systems in data-scarce areas. Notable projects include developing analytical tools for the WFP's supply chain planning and creating algorithms for weather-informed road speed prediction. He is affiliated with the Tilburg Sustainability Center and the Operations Research research group, contributing to both academic advancements and real-world impact through optimization solutions.

• Large-Scale Data Management
• Data Systems
• Big Data Analysis
• +5 more

Nick Koudas is a Professor in the Department of Computer Science at the University of Toronto. His research focuses on large-scale data management, integrating machine learning into data systems, and developing efficient query processing techniques for unstructured and streaming data. He holds a PhD from the University of Toronto, an MSc from the University of Maryland at College Park, and a Bachelor's from the University of Patras in Greece. Research interests include data systems, big data analysis, video query processing, and natural language interfaces for databases. He leads projects like ReDD (Relational Deep Dive), SVQ (Streaming Video Queries), and Reliable Text-to-SQL, aiming to bridge human-readable queries with database execution. His work emphasizes scalability, intelligence, and real-world applicability. Recipient of the University of Toronto's Inventor of the Year Award (2011), he translates research into startups like Sysomos, Aislelabs, and Workorb. His contributions span over 200 publications in top venues such as SIGMOD, VLDB, and ICDE. Courses taught include advanced data systems, database design, and system internals. Current projects explore schema extraction from unstructured data, video query optimization, and cost-effective machine learning pipelines. Collaborations with industry and academic partners drive innovations in both theory and practical applications.

Destenie NockView profile

Assistant Professor

• Energy Justice
• Energy Systems Modeling
• Sustainability Planning
• +7 more

Destenie Nock is an Assistant Professor in Civil and Environmental Engineering (CEE) and Engineering and Public Policy (EPP) at Carnegie Mellon University. Her research focuses on energy justice, sustainable energy transitions, and equitable infrastructure planning. She develops mathematical models to address societal challenges in energy systems, particularly for vulnerable communities. Nock co-founded Peoples Energy Analytics , using algorithms to identify energy poverty and connect households with assistance programs. Education: PhD in Industrial Engineering and Operations Research (UMass Amherst, 2019); BS in Electrical Engineering and Applied Mathematics (North Carolina A&T State University). Research Interests: Energy equity, decarbonization impacts, transportation electrification, and policy analysis. She investigates how energy systems disproportionately affect marginalized groups and designs solutions to mitigate disparities. Awards: 2024 Science Defender (Union of Concerned Scientists), 2025 Outstanding Alumni Award (UMass Amherst). Grants & Funding: Six National Science Foundation grants, Sloan Foundation, and EPA support for projects on energy justice and equity metrics. Labs/Teams: Leads research groups in energy modeling and policy at CMU, collaborating with utilities and policymakers to implement equitable energy solutions.

Samory KpotufeView profile

Associate Professor

• Machine Learning
• Nonparametric Methods
• High-Dimensional Statistics
• +3 more

Samory Kpotufe is an Associate Professor of Statistics at Columbia University's Faculty of Arts and Sciences, affiliated with the Data Science Institute (DSI) as a Foundations of Data Science Co-Chair. He holds additional affiliations in Cybersecurity, Health Analytics, and Smart Cities. His academic journey includes a PhD in Computer Science from UC San Diego (2010), followed by research roles at the Max Planck Institute, Toyota Technological Institute at Chicago, and Princeton University's ORFE department. His research focuses on nonparametric methods and high-dimensional statistics, emphasizing adaptive procedures that self-tune to unknown data structures (e.g., manifolds, sparsity) while addressing modern application constraints like computational efficiency and labeling costs. Key themes include transfer learning, active learning, and online algorithms. Notable contributions span theoretical guarantees for nearest-neighbor methods, covariate shift adaptation, and contextual bandits. His work often bridges statistical theory and practical machine learning challenges, with applications in IoT, cybersecurity, and anomaly detection. He has led collaborative grants, such as the NSF CPS project on data augmentation for IoT systems. As a DSI member and Foundations Co-Chair, he contributes to advancing data science foundations through interdisciplinary collaboration. His lab's research frequently explores the interplay between algorithmic performance and intrinsic data properties.

• Artificial Intelligence
• Machine Learning
• Fairness in AI
• +5 more

Sanmi Koyejo is an Assistant Professor of Computer Science at Stanford University and holds an adjunct position as Associate Professor at the University of Illinois at Urbana-Champaign. He leads the Stanford Trustworthy AI Research (STAIR) group, focusing on fairness, robustness, and healthcare applications in machine learning. His work bridges theoretical foundations with practical systems, emphasizing ethical AI and clinical informatics. Affiliations: SAIL, HAI, CRFM, AIMI, AI Safety, and the Machine Learning Group. Research Interests: His expertise spans trustworthy AI, federated learning, and neuroimaging. He actively addresses challenges in algorithmic fairness, particularly in healthcare, where he collaborates with institutions like OSF Healthcare on projects like federated learning for clinical data. Key Contributions: Co-developed frameworks for unlearning in large language models, evaluated AI systems' societal impacts, and advanced benchmarks for medical applications. His work has been featured in venues like NeurIPS, ICML, and AAAI. Awards: NSF CAREER Award, Alfred P. Sloan Fellowship, and Terman Faculty Fellowship. Grants & Teams: Leads NSF-funded projects on domain adaptation and fairness in breast cancer risk scoring. Collaborates with interdisciplinary teams on NIH's MIDRC and NSF's AIFARMS initiative for agricultural sustainability. Labs/Teams: STAIR lab drives interdisciplinary research in ethical AI, with emphasis on real-world deployment and policy implications.

Abubakar BelloView profile

Senior Lecturer

• Cyber Security
• Cyber Crime
• Cyber Criminology
• +3 more

Dr. Abubakar Bello is a Senior Lecturer in Criminal Justice and Program Leader at Edge Hill University's School of Law, Policing, and Criminal Justice. Previously, he held roles at Western Sydney University, including Academic Program Advisor and Lecturer in Cyber Security and Behaviour. He holds a PhD in Cyber Criminology, an MBA in Business Law and Technology, and degrees in Computer Science. His research focuses on interdisciplinary approaches to cyber security risks, threat intelligence models, and behavioral aspects of cyber crime. Education: PhD (Cyber Criminology, Murdoch University), MBA (Business Law & Tech, Western Sydney University), MSc & BSc (Computer Science, University of Wolverhampton). Research Interests: Combating cyber crime through AI and machine learning, secure systems design, and behavioral cybersecurity. Key areas include ransomware defenses, social engineering, and cybersecurity frameworks for diverse populations. Grants & Projects: Awarded funding for initiatives such as 'Social Engineered Payment Diversion Fraud' (NSW Cyber Security Network), 'Brain-Inspired Algorithm for Network Anomaly Detection' (DST Group), and 'Cyber Security Awareness Framework' (ECR Grant). Awards: 'Award for Teaching and Learning Contributing to Public Good.' Active in professional networks like the International Centre on Racism and Centre for Applied Criminal Justice Research. Labs & Collaboration: Engages in cyber investigations, forensics, and community outreach through initiatives like Western Cyber Aid. Serves as a consultant for corporate espionage cases and a speaker on ransomware and AI in law enforcement.

Roozbeh Razavi-FarView profile

Assistant Professor

• Machine Learning
• Cybersecurity
• Federated Learning
• +5 more

Roozbeh Razavi-Far is an Assistant Professor at the Faculty of Computer Science and the Canadian Institute for Cybersecurity at the University of New Brunswick. His research focuses on machine learning, big data analytics, and cybersecurity of cyber-physical systems and IoT devices. He has authored/co-authored over 150 publications and is listed by Stanford as among the top 2% of most cited researchers (2022). His work spans federated learning, transfer learning, quantum machine learning, and dependable AI systems. He serves as an Associate Editor for Neurocomputing, Machine Learning with Applications, and IEEE Transactions on Industrial Cyber-Physical Systems, among others. As an IEEE Senior Member, he chairs IEEE Computational Intelligence and Systems, Man, and Cybernetics Societies. Previously, he directed the Learning System and Cybernetics Group at the University of Windsor (2016–2022). His research interests emphasize security in non-stationary environments, adversarial machine learning defenses, and real-time analytics for smart grids. Awards include NSERC-DG, NSERC-ECR, and USRG grants. He has mentored students who received NSERC Alexander G. Bell, MITACS, and Ontario Graduate Scholarships. His recent publications highlight advancements in privacy-preserving split learning, blockchain-based federated learning security, and graph-based malware detection. He also explores quantum computing applications in AI and cybersecurity frameworks for cyber-physical systems.

• Analytic Number Theory
• Probabilistic Number Theory
• Random Multiplicative Functions
• +2 more

Professor Adam Harper specializes in analytic and probabilistic number theory at the University of Warwick. His research examines moments of random multiplicative functions, distribution of zeta sums, and extreme values in number theory. Teaches MA4L6 (Analytic Number Theory) and MA257 (Introduction to Number Theory). Recent publications (2019-2024) address Halász's theorem, Fyodorov-Keating conjectures, and character sum distributions. Research bridges combinatorics, probability, and statistical mechanics, with focus on multiplicative chaos and Gaussian processes.

David WanikView profile

Assistant Professor

• Data Science
• Natural Hazards
• Remote Sensing
• +3 more

David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.

Priya NarasimhanView profile

Professor

• Dependable Distributed Systems
• Fault-Tolerance
• Embedded Systems
• +5 more

Priya Narasimhan is a Professor of Electrical & Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. Her research focuses on dependable distributed systems, fault-tolerance, embedded systems, mobile systems, and sports technology. She leads the Intel Science and Technology Center in Embedded Computing (ISTC-EC) and founded YinzCam, a CMU spin-off providing mobile live streaming to sports venues. She holds multiple awards, including the Sloan Fellowship and NSF CAREER Award. Education: Ph.D. and M.S. in Electrical & Computer Engineering from UC Santa Barbara. Notable roles include former CTO of Eternal Systems, Director of Intel Labs Pittsburgh, and Director of CMU's CyLab Mobility Research Center. Research spans failure diagnosis in distributed systems, live upgrades, mobile cloud computing, football technology, assistive tech for the blind (Trinetra), and civic tech (iBurgh). Over 30+ students advised across Ph.D., M.S., and undergraduate programs. Active in entrepreneurship, teaching (courses like 18-349 Embedded Systems), and industry collaborations.

• Earth Systems
• Planetary Geoscience
• Continental Tectonics
• +6 more

Kip Hodges is a Foundation Professor at the School of Earth and Space Exploration, Arizona State University (ASU). He leads the Group 18 Laboratories, focusing on noble gas isotope geochemistry and planetary geology. His work integrates field, laboratory, and theoretical approaches to study mountain formation, planetary processes, and impact events. His research interests span Earth Systems, Planetary Geoscience, Continental Tectonics, Earth Surface Processes, Cosmochemistry, and Planetary Geochemistry. He examines the geodynamic evolution of mountain belts like the Himalaya and Tibetan Plateau, utilizing thermal-kinematic modeling and noble gas isotope data. His planetary research includes lunar and Martian geochronology, impact crater analysis, and strategies for human-robotic collaboration in planetary exploration. Key projects include studies on the thermal evolution of terrestrial/extraterrestrial materials, diffusivity of noble gases, and the dynamics of continental subduction. His work contributes to understanding mountain-building, impact history of the Solar System, and future exploration missions. He directs the Group 18 Laboratories, renowned for innovative noble gas isotope geochemistry techniques to constrain thermal histories of terrestrial and extraterrestrial samples. The lab's work includes studies on noble gas diffusivity in planetary materials.

David SaundersView profile

Associate Professor

• Quantitative Risk Management
• Mathematical Finance
• Stochastic Optimization
• +3 more

David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. His research focuses on quantitative risk management, mathematical finance, and stochastic optimization with applications to credit risk, portfolio optimization, and derivatives pricing. Professor Saunders has held academic positions at the University of Pittsburgh and Cyprus International Institute of Management, and has consulted for institutions like RiskMetrics, TD Bank, and Central Bank of Cyprus. Education: PhD studies at the University of Toronto with industry collaboration at Algorithmics Inc., followed by roles at Cyprus International Institute of Management and University of Cyprus's HERMES Center. His work bridges theoretical stochastic analysis and practical financial risk management challenges. Research interests prominently feature credit risk management, optimal stopping problems, and analytical techniques in finance. Recent work addresses inverse default boundary problems, wrong-way risk in derivatives, and efficient algorithms for portfolio optimization under complex return distributions. Collaborations with financial institutions drive applied research in capital allocation, operational risk modeling, and regulatory compliance. Advising and grants include directing the Professional Science Master's in Mathematical Finance at Pitt, supervising student-industry projects on credit risk and CDO pricing, and securing funding for computational finance initiatives. Active in RiskLab Cyprus and HERMES European Center, his research extends to equity risk management and market risk frameworks.

• Power Systems
• Electrical Engineering
• Renewable Energy Integration
• +6 more

Pavlos S. Georgilakis is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), specializing in modern techniques for power system analysis, optimization, and renewable energy integration. He holds a Diploma (1990) and PhD (2000) in Electrical Engineering from NTUA. His career includes roles as Lecturer (2009) and Associate Professor (2018–2023) at NTUA, and Assistant Professor at the Technical University of Crete (2004–2009). Research focuses on power transmission/distribution systems, transformer design, and applying AI/optimization for grid efficiency. He led 10 research projects, including Horizon 2020 initiatives SHAR-Q, WiseGRID, and NobelGrid. He authored 3 books and over 230 publications (SCOPUS citations: >5,500). Editor of IET Smart Grid, Energies, and Electricity journals; senior IEEE member. He supervised 4 doctoral, 9 master’s, and 76 diploma theses. Awards include the 2013 Best Reviewer Award from Electric Power Systems Research. Active in energy storage, smart grids, and decentralized energy resource integration.

Ferreira Joao CarlosView profile

Professor

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +6 more

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

LJILJANA TRAJKOVICView profile

Professor

• Communication Networks
• Nonlinear Circuits and Systems
• Electronics Engineering
• +3 more

Ljiljana Trajkovic is a Professor in the Department of Engineering Science at Simon Fraser University's Faculty of Applied Sciences. She holds a Ph.D. from the University of California, Los Angeles (1986), M.Sc. from Syracuse University (1979), and Dipl.Ing. from the University of Pristina (1974). Her research focuses on communication networks, nonlinear circuits, and machine learning applications for network security. She actively contributes to IEEE initiatives, including roles as conference committee chair and editorial board member. Education highlights include a strong foundation in electrical engineering and advanced studies in circuit theory and systems science. Her work bridges theoretical analysis with practical applications, such as anomaly detection in communication networks using machine learning. She teaches courses like ENSC 220 D100 Electric Circuits I, integrating research insights into education. Research interests emphasize network security, traffic analysis, and distributed systems. Recent articles explore BGP anomaly classification, ransomware detection, and virtual network embedding. She collaborates on tools like VNE-Sim and Anonym for network analysis. Awards and recognitions are highlighted through her leadership roles in IEEE and academic contributions. Advising and grants involve mentoring graduate students in cybersecurity and networking projects. She leads research teams exploring complex networks and their applications in autonomous systems. Her lab focuses on interdisciplinary projects merging electronics engineering with AI-driven network solutions.