Find Academic Supervisors

Keith CuthbertsonView profile

Lecturer

• Fund Management
• Hedge Funds
• Fund Performance
• +3 more

Keith Cuthbertson is a Special Lecturer at Bayes Business School, City, University of London. He holds a BSc in Physics and Mathematics from the University of Sussex and an MA in Economics from the University of Manchester. His professional experience includes roles at H.M. Treasury, Bank of England, National Institute of Economic and Social Research (NIESR), Imperial College London, and visiting positions at the Federal Reserve Bank (Washington), Freie University (Berlin), and University of Bordeaux. His research focuses on investment fund performance, behavioral finance, and risk management. Key areas include: (1) Measurement and persistence of mutual fund performance, (2) Statistical methods to distinguish skill from luck in fund management, (3) Factor models for portfolio optimization, and (4) Market timing strategies in equity and bond funds. His work emphasizes practical applications for investors and regulators. Recent publications (2016–2023) demonstrate a consistent focus on fund performance analytics, with evolving emphasis on factor models, portfolio size effects, nonparametric methods, and sustainability-linked finance. His research frequently combines academic rigor with practitioner-oriented insights. PhD Supervision: Elisa Pazaj (2015–present): Topics in Financial Markets Niall O'Sullivan (2006–2009): Performance of Mutual Funds Consultancy: Extensive work with Centrica, Towers Perrin, RBS, Department of Energy and Climate Change (DECC), and other financial/government institutions. Focuses on applied finance, derivatives, and risk management solutions.

Rakesh V. VohraView profile

Professor

• Economics
• Computer Science
• Game Theory
• +5 more

Rakesh V. Vohra is the George A. Weiss and Lydia Bravo Weiss University Professor at the University of Pennsylvania, holding primary appointments in the departments of Computer and Information Science, Economics, and Electrical and Systems Engineering. He co-directs the Warren Center for Network and Data Sciences. His research spans game theory, market design, auction theory, and computational economics, with a focus on mechanism design, spectrum markets, and systemic risk in financial networks. He explores interdisciplinary topics such as NFTs, blockchain applications, and algorithmic fairness in healthcare equity. His work integrates economic theory with computational methods, addressing challenges in resource allocation, strategic behavior, and network dynamics. Recent studies analyze optimal bailouts in financial systems, sequential information acquisition, and the implications of unlicensed spectrum policies on wireless competition. He also investigates behavioral aspects of decision-making under uncertainty and the design of robust economic systems. Notable contributions include foundational work on combinatorial auctions, stable matching mechanisms under constraints, and the role of network effects in market stability. Though no formal awards are listed, his prolific publication record reflects significant academic influence across multiple disciplines.

• Computational Finance
• Time Series Econometrics
• Dynamic Programming
• +3 more

Dr. François-Michel Boire is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. His research focuses on computational finance, time series econometrics, and optimal stochastic control. He teaches courses such as MAT 4372 (Financial Mathematics), MAT 4376 (Mathematical Foundations of Risk Management), and MAT 3779 (Introduction to Time Series Analysis). His work emphasizes algorithmic improvements for financial modeling, particularly in Monte Carlo methods and option pricing. Notable contributions include bias correction techniques for the Least-Squares Monte Carlo algorithm, variance reduction strategies for American options, and analysis of policy impacts on GDP growth distributions. Dr. Boire's research spans computational finance applications, stochastic control theory, and econometric methodologies. His recent articles (2021–2024) highlight advancements in derivative pricing techniques and financial risk quantification. He maintains an active presence on SSRN, ResearchGate, and Google Scholar.

Agathoklis GiaralisView profile

Senior Lecturer

• Structural Dynamics
• Earthquake Engineering
• Structural Health Monitoring
• +11 more

Dr. Agathoklis Giaralis is a Senior Lecturer in Structural Engineering within the Department of Civil Engineering at the School of Engineering and Mathematical Sciences, City, University of London. He earned his PhD in Civil & Environmental Engineering from Rice University, USA, in 2008, following an MSc and a 5-year Diploma (Ptychion) in Structural Engineering from Aristotle University of Thessaloniki, Greece. He is a Fellow of the Higher Education Academy, UK. PhD, Civil & Environmental Engineering, Rice University, USA (2008) MSc, Seismic Design of Structures, Aristotle University of Thessaloniki, Greece (2004) Ptychion (5 yr Diploma), Civil/Structural Engineering, Aristotle University of Thessaloniki, Greece (2003) Dr. Giaralis's research is centered on nonlinear stochastic dynamics and time-frequency signal analysis , applied to critical areas such as earthquake engineering , seismic structural design and assessment , structural health monitoring (SHM) , and passive vibration control . His work is pioneering in the development and application of inerter-based vibration control systems, such as the Tuned Mass-Damper-Inerter (TMDI), for enhancing the resilience of structures. He also explores compressive sensing and low-power wireless sensors for sustainable SHM, as well as digital twinning and machine learning in wind engineering. His recent research has significant implications for offshore wind turbines and urban wind comfort. An analysis of his 15 most recent publications reveals a consistent and cutting-edge research trajectory focused on the optimization and application of inerter-based devices for structural control. The work spans from developing analytical tuning formulas for TMDIs to conducting experimental validation on shaking tables and exploring novel applications in offshore wind turbines and composite floors. A strong emphasis is placed on performance-based design , uncertainty quantification , and practical implementation for real-world infrastructure. His scientific contributions have been recognized by prestigious awards, including the Fulbright Exchange Student Program scholarship for his PhD studies and a Fellowship from the Higher Education Academy . He is also a member of several leading professional organizations such as the American Society of Civil Engineers (ASCE) and the Society for Earthquake and Civil Engineering Dynamics (SECED). Dr. Giaralis is actively involved in research funding and academic mentorship. He has secured significant grants from Innovate UK and the EPSRC to support his work on machine learning-based design and optimal inerter configurations. He supervises a cohort of PhD students whose theses focus on advanced topics like adaptive control of bridge joints, inerter-based energy harvesting, and seismic assessment using digital twins. He also leads the Smart Structures and Structural Health Monitoring Research Unit , driving collaborative research in smart infrastructure technologies.

Robert NobleView profile

Senior Lecturer

• Cancer
• Evolutionary Biology
• Mathematical Biology
• +7 more

Dr. Robert Noble is a Senior Lecturer in Mathematics at City St George's, University of London, specializing in the evolution and ecology of cancer . His research integrates mathematical modeling with computational biology to address four key themes: systematic understanding of somatic evolution, patient-specific tumor forecasting, treatment strategies leveraging evolutionary dynamics, and disentangling ecological contributions to cancer risk. Education: DPhil in Zoology (University of Oxford, 2014) Previous Appointments: Postdoctoral roles at ETH Zurich (2017-2020) and University of Zurich (2018-2020) His methodological toolkit spans agent-based models , stochastic processes , and Bayesian data analysis , with software contributions like the ggmuller and demon packages. Recent work focuses on universal tree balance indices for biological data comparison and adaptive therapy frameworks for clinical translation. Collaborations with experimental biologists and clinicians inform his research, which has produced key insights into spatial tumor evolution , drug resistance dynamics , and clonal diversity-survival relationships . He maintains active peer-review roles for journals like PLOS Computational Biology and Nature Ecology & Evolution.

• Construction Engineering
• Simulation Science
• Information Technology in Construction
• +3 more

Simaan Abourizk, PhD, PEng, is the Dean of the Faculty of Engineering at the University of Alberta and holds the rank of Professor in Construction Engineering and Management. He also served as the NSERC/Alberta Construction Industry Research Chair (1997–2011) and Canada Research Chair in Operations Simulation (2001–2008). His roles include Executive Board membership at the Construction Research Institute of Canada. Education: Doctor of Philosophy in Construction Engineering and Management from Purdue University (1990), Master of Science and Bachelor of Civil Engineering (Honors) from Georgia Institute of Technology (1985 and 1984). Research focuses on advancing simulation technologies for construction and natural resource industries, including: Development of the Simphony simulation environment and COSYE framework Integration of AI, visualization, and scheduling tools (e.g., SmartEst, MTRACK) Optimization of tunneling, modular assembly, and industrial fabrication processes Key awards include the E. Whitman Wright Award (2002), E.W.R. Steacie Memorial Fellowship (2001), and Walter Shanly Award (2001). Current projects emphasize synthetic environments, safety analytics, and data-driven decision support systems in construction. He leads initiatives on risk simulation, resource allocation, and industrial automation.

• Logistics Optimization
• Transportation Planning
• Operations Research
• +3 more

Prof. Michael Schneider is Universitätsprofessor and Chair of Computational Logistics at RWTH Aachen University since 2016. Previously, he held positions at TU Darmstadt (2013-2016) and earned his doctoral degree from TU Kaiserslautern (2012). His research focuses on logistics optimization, including transportation routing, warehouse management, and metaheuristic methods for solving complex operational challenges. He leads the Global Challenges Lab and serves as communication chair of VeRoLog (EURO's vehicle routing group). Research interests include: quantitative modeling for supply chains, heuristic/exact optimization methods, electric vehicle routing, territory design, and production planning. Notable achievements include the INFORMS Journal on Computing Meritorious Paper Award (2021) and editorial roles in journals like Applied Mathematical Modelling. Publications span topics like vehicle routing with time windows, electric logistics networks, and warehouse automation strategies. His work integrates advanced algorithms with real-world industry applications (e.g., DHL, Picnic). The Computational Logistics group collaborates on projects addressing sustainability, autonomous systems, and modern warehousing challenges. Education: Business Admin & Computer Science (University of Mannheim), PhD (TU Kaiserslautern) Professional Roles: VeRoLog Communication Chair, EURO Working Group Key Projects: Electric vehicle routing optimization, warehouse automation, and sustainable logistics networks

Linda K. NozickView profile

Professor

• Transportation Systems Engineering
• Network Science
• Critical Infrastructure Resilience
• +5 more

Dr. Linda K. Nozick is a Professor and Director of Civil and Environmental Engineering at Cornell University, leading research in infrastructure resilience and disaster risk management. She co-founded the College Program in Systems Engineering and previously served as a Visiting Associate Professor at the Naval Postgraduate School. Her work focuses on mathematical modeling for complex systems, including transportation networks, natural hazard mitigation, and critical infrastructure protection. Education: B.S. in Systems Analysis and Engineering, George Washington University (1989) M.S./Ph.D. in Systems Engineering, University of Pennsylvania (1990-1992) Research Interests: She pioneers models for transportation systems, network science, and disaster risk management. Key areas include: Optimization of infrastructure resilience Hurricane evacuation modeling Insurance-market dynamics for catastrophic risks Seismic retrofitting strategies Data-driven decision support systems Recent Work Highlights: Recent studies address hurricane evacuation behavior prediction using mobility data, equity in hazmat routing, and multi-hazard risk assessment for power grids. Awards: 2011 Presidential Early Career Award for Scientists and Engineers NSF CAREER Award (1997) Sandia National Labs Recognition Award (2009) Member of Nuclear Waste Technical Review Board Academic Service: Leads Cornell's Master of Engineering strategic planning and coordinates the Engineering Systems & Management mission area. She advises federal agencies on infrastructure renewal through National Academy Committees. Lab/Team Affiliations: Directs research initiatives in disaster resilience engineering and collaborates with Sandia National Labs on infrastructure optimization tools.

Joshua P. KilbornView profile

Academic

• Marine Biology
• Life Sciences
• Fisheries Management
• +3 more

Joshua P. Kilborn is a Research Assistant Professor at the University of South Florida’s College of Marine Science. His expertise spans marine biology, ecosystem dynamics, and fisheries management. He holds a Ph.D. (2017) and B.S. (2002) from the University of South Florida and Jacksonville University, respectively. His research focuses on understanding ecological impacts of environmental disasters (e.g., Deepwater Horizon oil spill), climate-driven changes in fisheries, and ecosystem resilience. Kilborn leads projects funded by NOAA and the Gulf of Mexico Fishery Management Council, including studies on Gulf of Mexico large marine ecosystems and stock-recruitment relationships in amberjack fisheries. He teaches Applied Multivariate Statistics and Biometry , and his awards include the Eagle Scout Award (1995) and recognition as Outstanding Mathematics Student (2000). His work emphasizes data-driven solutions for marine resource management and ecological recovery.

Jo BirdView profile

Associate Professor

• Digital Technologies in Early Childhood Education
• Play and Play-Based Learning
• STEM in Early Childhood
• +7 more

Jo Bird is an Associate Professor in the School of Education at the University of New England (UNE), Faculty of Humanities, Arts, Social Sciences and Education. She serves as the Higher Degree Research Coordinator, supporting postgraduate research students in early childhood education. Her work bridges academia and practice, with a strong focus on digital technologies in early childhood settings. Her research interests include: Digital technologies in early childhood play Integration of STEM and nature-based learning Development of 21st-century skills in young children Supporting educators in using digital tools for planning and documentation Early childhood leadership and professional development Jo Bird's recent publications reflect a consistent trajectory in digital play, nature education, and military family support programs. Her work emphasizes children’s agency, innovative pedagogies, and the role of technology in enhancing learning outcomes. She has contributed significantly to frameworks like the Digital Play Framework and digital storytelling applications. Her scientific achievements include: Council for the Humanities, Arts and Social Sciences (CHASS) Award for Distinctive Work (2022) Australian Postgraduate Award (APA) Scholarship (2013–2015) She actively supervises doctoral and master’s students in early childhood technology, play, and primary education transitions. Her research has been supported by grants from The Ian Potter Foundation and UNE. She has also led digital application development, such as 'Waiting for Daddy: Rose’s story', to amplify children's voices. Jo Bird is involved in several research teams and initiatives, including projects on military family education and digital portfolio systems (ARTefacts). She collaborates widely across institutions and contributes to national discourse through professional learning sessions, conference presentations, and practitioner publications.

• Statistics
• Machine Learning
• Financial Econometrics
• +5 more

Jennifer Chan is a Professor in the Statistics Department at the University of Sydney's Faculty of Science. She earned her PhD from the University of New South Wales in 1997 and previously lectured at the University of Hong Kong before joining her current institution in 2006. Her research integrates statistical and machine learning models with applications in finance and insurance, including volatility modeling, Bayesian methods, and neural network applications. Her interdisciplinary research focuses on: Generalized linear mixed models and multivariate volatility measures Machine learning techniques for financial risk assessment Bayesian robustness and portfolio optimization Time-series analysis of cryptocurrencies and equity markets Recent publications demonstrate strong focus on Bayesian models in finance (42% of last 15 papers), machine learning applications (33%), and actuarial science (25%), with emerging emphasis on neural networks for financial forecasting. Awards & Honors: Second prize, Natural Science Award of China's Ministry of Education (2008) National Drug Strategy Research Scholarship (1994-1996) She supervises doctoral candidates working on machine learning applications in finance and insurance. Her international collaborations include institutions in Israel, Japan, Malaysia, and the United States.

• Statistical Problems in Marketing
• Functional Data Analysis
• Statistical Methodology
• +2 more

Gareth James is the John H. Harland Dean and Professor of Information Systems & Operations Management at Emory University's Goizueta Business School, a position he assumed in July 2022. Prior to this, he held significant leadership roles at USC Marshall School of Business, including interim dean (2019-2020), vice dean of faculty, and deputy dean (2020-2022). James received his PhD in Statistics from Stanford University, complemented by a BComm in Finance and BS in Statistics from The University of Auckland. His academic journey reflects a strong foundation in both statistical methodology and business applications. James specializes in statistical problems in marketing, functional data analysis, statistical methodology, and high-dimensional regression. His research bridges theoretical statistics with practical business applications, particularly in the areas of machine learning, advertising optimization, and technology adoption. His work demonstrates a consistent focus on developing methodological innovations that address real-world business challenges. His research contributions have been widely recognized, with over 30,000 citations. James has led multiple National Science Foundation research grants and served as associate editor for five top research journals. His most notable scholarly contribution is co-authoring the highly successful textbook "An Introduction to Statistical Learning," which has become a standard reference in the field. Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Dean's Research Awards (two times) Evan C. Thompson Faculty Teaching and Learning Innovation Award Three-time winner of the Marshall School of Business' Golden Apple Award Dean's Ph.D. Advising Award USC Mellon Award Provost's Mentoring Award James is equally distinguished as a mentor and educator. His teaching excellence is evidenced by multiple Golden Apple Awards, while his mentoring impact is reflected in the Dean's Ph.D. Advising, USC Mellon, Evan C. Thompson, and Provost's Mentoring awards. As Dean, he leverages his expertise in data-informed decision making to strengthen Goizueta's mission of preparing principled leaders to have a positive influence on business and society.

Marco RealdonView profile

Senior Lecturer

• Credit Risk Pricing
• Term Structure Models
• Bond Pricing
• +3 more

Dr. Marco Realdon serves as an Honorary Senior Lecturer at Brunel Business School, which is part of the College of Business, Arts and Social Sciences at Brunel University. His office is located in Marie Jahoda 234a, and he can be contacted at marco.realdon@brunel.ac.uk or by phone at +44 (0)1895 265102. Dr. Realdon's research focuses on several key areas in quantitative finance: Credit risk pricing models Models of the term structure of interest rates Bond pricing and swap pricing His scholarly work demonstrates expertise in developing sophisticated mathematical models for financial instruments, particularly in fixed income markets. Dr. Realdon has published extensively in leading finance journals, with research spanning from theoretical model development to practical applications in credit derivatives, interest rate modeling, and bond valuation. His recent work has addressed contemporary challenges such as negative interest rates in the Eurozone and the efficiency of European swap markets. Analysis of his publication record shows a consistent focus on quantitative finance methodology, with particular emphasis on affine and quadratic term structure models, credit risk modeling, and bond valuation techniques. His research has evolved to address emerging market conditions, including the unique challenges presented by negative interest rates in European markets. Dr. Realdon maintains an active research profile as evidenced by his 2024 publication on Estr overnight index swap market efficiency, indicating ongoing scholarly contributions to the field of quantitative finance. His scholarly network includes collaborations with researchers such as Dr. Aris Kartsaklas, Dr. Evgeniya Nolde, Professor Yiannis Karavias, Dr. Elena Boguslavskaya, and Professor Alper Kara, suggesting active engagement with the academic finance community.

Jonathan Yu-Meng LiView profile

Associate Professor

• Business Analytics
• Financial Engineering
• Machine Learning
• +12 more

Jonathan Yu-Meng Li is an Associate Professor and holder of the RBC Financial Group Professorship in Financial Risk Analytics at the Telfer School of Management , University of Ottawa. He serves as Principal Coordinator of the Center for Responsible Wealth Transition and leads the Risk Intelligence and Resilient Solutions cluster. B.Sc. in Electrical Engineering from National Sun Yat-Sen University M.A.Sc. in Electrical and Computer Engineering from McMaster University Ph.D. in Operations Research (Financial Engineering specialization) from the University of Toronto Specializing in quantitative methodologies , his research focuses on: Optimization and machine learning tools for data-driven decisions Financial risk analytics (portfolio management, asset pricing, fraud detection) Operations management (inventory control, revenue management) Healthcare analytics (clinical risk assessment, patient triage) Climate risk modeling and technology disruption management His recent publications demonstrate an evolving research trajectory : 2024: Distributional robustness under distorted expectations 2023: Deep reinforcement learning for financial derivatives 2022: Equal risk pricing with convex measures 2018-2021: Inverse optimization and robust modeling Key scientific awards include: Emerging Researcher Award at Telfer First place in CORS Paper Competition Finalist in INFORMS Financial Services Section Major funded research projects: 2023-2029: NSERC-funded framework for robust machine learning 2023-2026: Telfer School grant on decentralized finance security 2022-2024: SSHRC-funded research on DeFi criminal activity detection 2014-2019: NSERC grant on risk measure modeling

Michael R. GibbonsView profile

Professor

• Asset Pricing
• Econometrics
• Portfolio Theory
• +3 more

Michael R. Gibbons is the I.W. Burnham II Professor of Investment Banking at the Wharton School of the University of Pennsylvania. He has been affiliated with Wharton since 1989, serving in multiple leadership roles including Deputy Dean (2007-2021) and Chairperson of the Finance Department (1994-2006). Previous appointments include Stanford University and the University of Chicago, with visiting roles at the latter institution. Education PhD in Finance, University of Chicago (1980) MS in Finance, University of Chicago (1979) BS in Mathematics, Butler University (1975) Dr. Gibbons' research focuses on asset pricing, econometric validation of financial models, and portfolio theory. His work includes testing the Cox-Ingersoll-Ross term structure model and analyzing consumption-oriented capital asset pricing frameworks. Recent publications examine derivatives pricing applications and risk management strategies. His publications (1989-1993) demonstrate methodological innovations in econometric testing and practical applications to Treasury-bill returns. Key research trends include non-arbitrage pricing principles, dynamic portfolio modeling, and empirical validation of theoretical financial frameworks. Scientific Awards Batterymarch Fellow (1983-84) Graduate Division Excellence in Teaching Award (1996, 2001) Dr. Gibbons teaches advanced finance courses including Financial Derivatives (FNCE7170) , which covers pricing models (binomial trees, Black-Scholes-Merton), hedging strategies, and applications in corporate valuation. He also supervises Independent Study (FNCE8990) projects for finance students.