Find Academic Supervisors

Maria PolukarovView profile

Associate Professor

• Artificial Intelligence
• Algorithmic Game Theory
• Multi-Agent Systems
• +4 more

Dr. Maria Polukarov is an Associate Professor in Computer Science at King's College London, serving as Deputy Senior Tutor and Director of the Distributed Artificial Intelligence Research Group. Her affiliations include the Faculty of Natural, Mathematical & Engineering Sciences and the Department of Informatics. She is a Fellow of the Higher Education Academy and an Editorial Board Member of the Electronic Commerce Research and Applications . Her research focuses on algorithmic game theory, multi-agent systems, computational social choice, and strategic voting. She explores applications in financial markets, AI-driven systems, and behavioral models. Recent work includes analyzing carbon allowance auctions, green disclosure policies, and systemic risks in recommendation systems. Key contributions span robust market-making strategies, thermodynamic financial market analysis, and error modeling in voting systems. Her work frequently bridges theoretical foundations with practical applications in economics, finance, and societal systems. Supervised 5 research works Leading the Driving BMW project (EPSRC-funded, 2024–2026) on multi-winner election dynamics Active in hubs like the Trusted Autonomous Systems Hub and Finance Hub

Leonard OrtolanoView profile

Professor

• Environmental Policy
• Water Resources Planning
• Environmental Governance
• +4 more

Leonard Ortolano is the UPS Foundation Professor of Water Resources and Environmental Planning at Stanford University's Department of Civil and Environmental Engineering, part of the School of Engineering. He has held this position since 1970, progressing from Assistant Professor (1970-1973) to Associate Professor (1974-1979) before attaining full Professor status. His research focuses on environmental policy design, water resources management, and sustainable urban development, with emphasis on China, South Asia, and developing nations. Education includes a B.S. in Civil Engineering (1963, Polytechnic Institute of Brooklyn), and M.S./Ph.D. in Engineering from Harvard University (1966/1969), specializing in Water Resources Planning with minors in Economics and Operations Research. Research interests span environmental governance, corporate environmental management, and community-based solutions to resource challenges. Notable projects include studies on China’s environmental policy implementation, Mozambique’s water resale systems, and cleaner production in Pakistan’s industries. His work bridges academia and practice, with consulting roles for agencies like the World Bank and the U.S. Environmental Protection Agency. Recent publications highlight themes such as carbon offset quantification challenges, regulatory enforcement negotiation simulations, and slum dweller mobilization in Tanzania. Awards include the Rhodes Prize (1996), Grant Award (2005), and multiple Fulbright-Hays grants. Leadership roles include Director of Stanford’s Haas Center for Public Service (2003-2006) and Urban Studies Program (1980-2003). Current advising focuses on interdisciplinary PhD research addressing hydropower licensing, chemical policy, and renewable energy integration. His lab collaborations explore topics like acid mine drainage treatment in Appalachia and alternative development scenarios for India’s Sundarbans region.

Ruobin GongView profile

Associate Professor

• Bayesian Methods
• Imprecise Probability
• Differential Privacy
• +3 more

Ruobin Gong is an Associate Professor of Statistics at Rutgers University, with a status-only appointment at the University of Toronto's Department of Statistical Sciences. She holds a PhD from Harvard University and focuses on foundational statistical theory and privacy-aware methodologies. Her research integrates Bayesian and imprecise probability frameworks with differential privacy challenges, emphasizing ethical data science practices. Gong's work includes developing the dapper R package for private posterior estimation and organizing events like the Annual Symposium on Applications of Contextual Integrity and NBER workshops on privacy in applied research. Her research interests span theoretical foundations of uncertainty reasoning (including Bayesian methodology, random sets, and Dempster-Shafer theory) and practical applications in privacy-preserving statistical inference. She serves as an associate editor for Harvard Data Science Review, JASA/TAS Reviews, and Statistics and Public Policy, while also contributing a monthly column, 'Sound the Gong,' to the IMS Bulletin. Gong's recent work addresses challenges in privacy-aware computation, invariant-constrained data sanitization, and the philosophical implications of credence dynamics. Her contributions include pioneering subspace differential privacy frameworks and formal privacy analyses of historical data swapping methods. Gong actively engages in interdisciplinary collaborations, bridging statistical theory with real-world policy applications through workshops and publications on topics like algorithmic fairness and disclosure avoidance systems.

Filiz KONUKView profile

Associate Professor

• Finance
• Accounting
• Business Administration
• +4 more

Associate Professor Dr. Filiz Konuk is a faculty member in the Department of Business Administration at Sakarya University's Faculty of Business Administration. Her primary contact is via email at faygen@sakarya.edu.tr. Her research encompasses multiple finance domains including corporate finance, environmental finance, accounting standards implementation, working capital efficiency, and econometric applications in business contexts. She maintains strong focus on interrelationships between financial policies, firm performance, and sustainability metrics across diverse sectors. Recent publications (2015-2024) demonstrate methodological diversity including panel data analysis, bibliometric reviews, and multi-criteria decision models. Key thematic areas include: working capital optimization strategies, climate finance instruments, financial reporting standards compliance, and sector-specific performance analysis. Research consistently integrates empirical evidence from Turkish markets with international comparative frameworks. No information is currently available regarding scientific awards, student advising, research grants, or laboratory affiliations.

Shuangqing WeiView profile

Professor

• Wireless Communications
• Signal Processing
• Network Security
• +4 more

Shuangqing Wei is the Michel B. Voorhies Distinguished Professor and Division Chairman in the Division of Electrical & Computer Engineering at Louisiana State University (LSU). He holds a Ph.D. (2003) from the University of Massachusetts and is affiliated with the School of Electrical Engineering and Computer Science. His research focuses on wireless communication systems, including OFDMA networks, multiple antenna systems, and privacy-enhanced communication protocols. Key research interests include: Multi-agent reinforcement learning and cooperative strategies Privacy-preserving techniques in federated learning and data analysis Covert communication over AWGN channels Optimization algorithms for distributed systems Cognitive radio networks and spectrum management Recent work emphasizes communication efficiency in distributed optimization (e.g., ADMM with Gaussian process regression) and trade-offs between privacy and utility in machine learning systems. His publications span over two decades, addressing topics from information theory fundamentals to applied security in vehicular networks and social media systems. He maintains an active research group at LSU, exploring interdisciplinary challenges at the intersection of signal processing, machine learning, and network security. Labs/Teams : Active involvement in LSU's wireless communication and machine learning research initiatives, including collaborative projects on federated learning frameworks and secure multi-agent systems.

• Alzheimer's Disease
• Biostatistics
• Clinical Trials
• +3 more

Michael Donohue, PhD is an Associate Professor of Neurology at the University of California, San Diego (UCSD), serving as Associate Director of Biostatistics at the Alzheimer’s Therapeutic Research Institute (ATRI) and Co-Lead of the Biostatistics Unit at the Alzheimer’s Clinical Trial Consortium (ACTC). He holds a PhD in Mathematics (2005) and focuses on applying advanced statistical methods to Alzheimer’s clinical trials and observational studies, particularly leveraging the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. His work emphasizes understanding Alzheimer’s progression through biomarkers and designing innovative clinical trials to slow or prevent disease. Educational background: PhD in Mathematics (year not specified in text). Research interests include: Statistical methodologies for Alzheimer’s trials and observational studies Biomarker analysis (amyloid, tau, neurodegeneration) Clinical trial design, including outcome measure selection Preclinical Alzheimer’s interventions Biomarker validation and harmonization Inclusion of underrepresented populations in research Key articles focus on: His work spans statistical innovation in Alzheimer’s research, biomarker-driven trial design, and safety monitoring in clinical trials. Recent studies address cognitive resilience, imaging standards (e.g., CLARiTI), and plasma biomarker accuracy. Grants and funding include support from the National Institute on Aging, Alzheimer’s Association, Michael J. Fox Foundation, and UCSD’s Clinical and Translational Research Institute. No formal advisees are listed, though his roles in ATRI and ACTC imply collaborative research leadership. His work supports global trials like A4 and AHEAD 3-45, advancing prevention strategies for Alzheimer’s. Labs/teams: Alzheimer’s Therapeutic Research Institute (ATRI), Alzheimer’s Clinical Trial Consortium (ACTC), and the ADNI Biostatistics Core.

Qinglong TianView profile

Assistant Professor

• Statistical Methodology
• Reliability Engineering
• Machine Learning
• +3 more

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.

Kamesh MunagalaView profile

Professor

• Theoretical Computer Science
• Approximation Algorithms
• Online Algorithms
• +5 more

Kamesh Munagala is a Professor in the Computer Science Department at Duke University's Pratt School of Engineering. His academic career spans theoretical computer science with a focus on approximation algorithms, online algorithms, and computational economics. He has made significant contributions to resource allocation, decision making, and provisioning problems across various applications including data networks, facility location, data center scheduling, ad slot allocation, ride-share scheduling, and civic budgeting. Professor Munagala's research interests span several key areas in theoretical computer science: Theoretical foundations of approximation algorithms and online algorithms Computational economics and market design Resource allocation with fairness constraints Algorithmic game theory and mechanism design Persuasion and information revelation in optimization contexts Group fairness based on proportionality and stability His recent publications demonstrate a strong focus on fairness in algorithmic decision-making, particularly in societal contexts like school assignment and participatory budgeting. He has also made significant contributions to the theory of Bayesian persuasion and information disclosure in competitive settings. His work bridges theoretical computer science with practical applications in social choice, economics, and policy-making. Notable scientific achievements include: Best paper award at WINE 2018 for 'A simple mechanism for a budget constrained buyer' Multiple publications in top theoretical computer science conferences including STOC, SODA, and FOCS Significant contributions to the understanding of fairness in resource allocation Innovative work on metric distortion in social choice Professor Munagala has advised numerous students and collaborators, with recent work involving researchers such as Govind S. Sankar, Yiheng Shen, and Kangning Wang. His research has been supported by various grants, though specific grant details aren't provided in the available information. He teaches advanced courses in algorithms, including Algorithm Design, Randomized Algorithms, and Algorithmic Game Theory, shaping the next generation of theoretical computer scientists. His work has implications for real-world systems requiring fair and efficient decision-making, from school assignment algorithms to data exchange markets and civic budgeting platforms. He is actively engaged in both theoretical advancements and practical implementations of his research.

Laura SouthView profile

Visiting Assistant Professor

• Human-Computer Interaction
• Accessibility Technologies
• Data Visualization
• +4 more

Laura South serves as a Visiting Assistant Professor in the Department of Computer Science at Williams College. She joined the institution after completing her PhD at Northeastern University in 2024 under Michelle Borkin's supervision. Her research centers on digital accessibility , with dual specialization in: Photosensitive epilepsy safety in interactive media (VR, data visualizations, social platforms) Assistive technologies for visual impairments (3D-printed braille systems) Notable contributions include seizure-risk mitigation frameworks for dynamic content and empirical evaluations of tactile interfaces. Her 15 most recent publications (2020-2024) reveal three dominant research trajectories: Photosensitivity safety mechanisms across immersive platforms (6 papers) Accessibility extensions for visualization systems (5 papers) Novel evaluation methodologies for inclusive design (4 papers) Key venues include CHI, IEEE VIS, and ACM TOCHI. Laura teaches human-computer interaction and introductory programming courses. Her educational background includes dual B.S. degrees in Computer Science and Statistics from Colorado State University (2018), followed by a Ph.D. in Computer Science from Northeastern University (2024).

Athanasios AndreouView profile

Researcher

• Privacy
• Data Privacy
• Targeted Advertising
• +4 more

Athanasios Andreou is a PhD candidate at EURECOM under the supervision of Patrick Loiseau. His research focuses on enhancing transparency in personalized services, particularly in targeted advertising ecosystems. He investigates privacy risks, develops statistical inference tools, and designs browser plugins to audit and control how user data is leveraged by ad engines. His work combines network measurement, statistical methods, and privacy-preserving techniques to address gaps in user control and awareness over digital targeting. Research interests include privacy in online advertising, statistical inference for transparency, cybersecurity, and social media analysis. His contributions aim to provide users with actionable insights into why they are targeted with specific ads and how to mitigate privacy risks through noise addition and transparency mechanisms. Collaborations include work with Prof. Krishna Gummadi and Dr. Oana Goga from the Max-Planck Institute for Software Systems, strengthening interdisciplinary research in systems and privacy.

Arthur KraftView profile

Associate Professor

• Financial Reporting
• Capital Markets
• Corporate Governance
• +3 more

Arthur Kraft is an Associate Professor at Bayes Business School, City St George's, University of London. Previously, he held faculty positions at the University of Rochester and London Business School. His research focuses on financial reporting's impact on capital markets, corporate governance, and voluntary disclosure mechanisms. He holds a BBA and MAcc from the University of Michigan and a PhD from the University of Chicago. Key research areas include the relationship between accounting information and security prices, the credibility of voluntary disclosures, and managerial myopia induced by frequent reporting. Recent work explores how financial reporting frequency affects corporate innovation and equity costs. Professional Activities: Served as referee for major journals including Journal of Accounting Research, Journal of Accounting and Economics, and The Accounting Review. Media Engagement: Featured in Accountant Middle East and The Sunday Times discussing tax strategies and corporate governance. Editorial Roles: Former Editorial Board member of European Accounting Review and active referee for multiple top-tier journals. Supervises research students Yun Sun and Richard Blades. His work has been presented at international venues like INSEAD, ESADE, and WHU-Otto Beisheim School of Management.

Syam MenonView profile

Professor

• Privacy
• Data Mining
• Software Design Optimization
• +3 more

Syam Menon is a Professor in the Information Systems department at the School of Management , University of Texas at Dallas. He teaches courses such as MIS 6316 Data Communications, MIS 6324 Business Intelligence Software and Techniques, and MIS 6334 Advanced Business Intelligence. PhD , University of Chicago, 1997 MBA , University of Chicago, 1996 BTech , University of Kerala, India, 1989 His research focuses on Privacy , Data Mining , and Software Design Optimization . His work addresses network congestion in peer-to-peer systems, optimization of cellular networks, and privacy-preserving techniques in shared databases. He has published extensively in journals like Information Systems Research , IEEE Transactions on Communications , and Management Science . His recent publications highlight trends in Telecommunications and Computer Science , particularly in Network Optimization , Data Privacy , and Algorithm Design . He is affiliated with professional organizations such as INFORMS , IEEE , ACM , and Association for Information Systems . Contact Syam Menon via syam@utdallas.edu or at (972) 883-4779.

Haibing LuView profile

Professor

• AI Fairness and Governance
• Data Security & Privacy
• Big Data Analytics
• +4 more

Haibing Lu is a Professor and Department Co-Chair of Information Systems & Analytics at Santa Clara University's Leavey School of Business. He holds a Ph.D. in Information Technology from Rutgers University (2011), and B.S./M.S. in Mathematics from Xi'an Jiaotong University (2002/2005). His research focuses on AI fairness, data privacy, cybersecurity, and big data analytics, addressing real-world challenges in healthcare, transportation, and cloud computing. Education: Ph.D. in Information Technology, Rutgers University, 2011 M.S. in Mathematics, Xi'an Jiaotong University, 2005 B.S. in Mathematics, Xi'an Jiaotong University, 2002 Research interests include: AI governance and ethical frameworks Privacy-preserving technologies (e.g., differential privacy) Cybersecurity for IoT and smart grids Applications of machine learning in healthcare and transportation His work has been featured in prestigious journals like IEEE Transactions on Dependable and Secure Computing, ACM Transactions on MIS, and M&SOM, alongside media outlets such as The New York Times and WIRED. Key publications include studies on AI fairness in LLMs, privacy in cloud computing, and secure authentication protocols. Awards include: AACSB Innovations That Inspire Award (2021) M&SOM Best Paper Award Repeated SCU Leavey School Excellence Awards for Teaching/Research/Service Grants and funding sources include NSF, IBM, PARC, and industry partnerships like AKTANA and Ultimate Software. He advises on interdisciplinary projects involving data science, cybersecurity, and sustainable energy systems. Labs/Initiatives: Leads research in Information Systems & Analytics at SCU, with collaborations on metaverse-driven data management and AI governance frameworks.

Christopher StapenhurstView profile

Research Fellow

• Economics
• Game Theory
• Environmental Policy
• +4 more

Christopher Stapenhurst is a Research Fellow with expertise in Economics and Game Theory. He holds a PhD in Economics, an MSc in Economics, and an MSc in Applicable Mathematics. His professional experience includes serving as an Assistant Economist at the Office for National Statistics. Stapenhurst's research focuses on applying game-theoretic mechanisms to policy design, inequality analysis, and environmental economics. Education PhD Economics MSc Economics MSc Applicable Mathematics Research Interests Game Theory applications in industrial policy and environmental governance Inequality measurement through ordinal data analysis Anti-corruption mechanisms and principal-agent frameworks International cooperation dynamics for pollution reduction Mathematical modeling of economic stability Statistical inference for economic indices Article Trends Stapenhurst's publications demonstrate an interdisciplinary focus combining economics, mathematics, and environmental policy. His recent work (2024-2025) centers on international cooperation mechanisms for marine pollution reduction and industrial policy optimization using social risk preferences. Earlier studies (2021-2023) explore corruption deterrence through strategic information control and inequality analysis via median-preserving spreads. The research spans theoretical game theory, empirical economic analysis, and environmental economics applications.

• Multivariate Analysis
• Mathematical Statistics
• Distribution Theory
• +4 more

Carlos A. Coelho is a Full Professor of Statistics at the Mathematics Department of NOVA School of Science and Technology, NOVA University of Lisbon. He holds a Ph.D. in Biostatistics from the University of Michigan, Ann Arbor, USA, supported by a Fulbright scholarship. His research focuses on Multivariate Analysis, including likelihood ratio tests for covariance structures and MANOVA models, near-exact distributions, and applications in Statistical Disclosure Control. He also explores High-dimensionality testing and Linear Models. Carlos serves on several editorial boards, including the Journal of Statistical Theory and Practice , American Journal of Mathematical and Management Sciences , and Springer's Emerging Topics in Statistics and Biostatistics book series. He is currently President of Fulbrighters Portugal, the Portuguese Fulbright Alumni Association, and a member of the International Council of the Tsenov Academy of Economics' library. His work emphasizes bridging theoretical advancements in statistics with practical applications, particularly in biostatistics and interdisciplinary research.