Find Academic Supervisors

• Household Finance
• Financial Economics
• Behavioral Economics
• +2 more

Tarun Ramadorai is Professor of Financial Economics at Imperial College London, with a distinguished career spanning household finance, financial economics, behavioral economics, real estate, and international finance. He serves as Executive Editor of the Review of Financial Studies and holds prestigious fellowships including Research Fellow of the Centre for Economic Policy Research (CEPR), Senior Academic Fellow of the Asian Bureau of Finance and Economics Research (ABFER), and Nonresident Senior Fellow at the National Council of Applied Economic Research (NCAER). Education BA in Mathematics and Economics from Williams College MPhil in Economics from the University of Cambridge PhD in Business Economics from Harvard University Research Interests Professor Ramadorai's research spans household finance, financial economics, behavioral economics, real estate, and international finance. His work examines how households make financial decisions across different markets and countries, with particular focus on housing markets, investment behavior, and financial inclusion. He has established himself as a leading expert in international comparative household finance, having previously served as Principal Investigator on a transformational initiative financed by the Sloan Foundation to establish this sub-field of finance and economics. His recent research explores the intersection of technology and personal finance, housing market dynamics, and optimal tax policy. He has demonstrated how housing costs impact fertility decisions, how machine learning affects credit markets, and how privacy policies influence consumer data extraction. His work combines rigorous theoretical frameworks with innovative empirical approaches using large-scale datasets from diverse markets. Publication Trends Professor Ramadorai's recent publications reveal a strong focus on household decision-making in financial markets, with increasing attention to the digital transformation of finance. His work bridges theoretical insights with practical policy implications, particularly in emerging economies. There is a clear trend toward interdisciplinary research that combines finance, economics, and data science to address pressing questions about financial inclusion, housing affordability, and the impact of technology on traditional financial services. Scientific Awards Brattle prize for best paper in the Journal of Finance Jensen prize for the best paper in the Journal of Financial Economics Wharton School-WRDS Best Paper Award in Empirical Finance James A Lebenthal Excellence in Municipal Finance Research Prize FMA Napa Conference Best Paper Prize INQUIRE Europe third prize Viz Risk Management Best paper prize Policy Engagement and Advisory Roles Professor Ramadorai has made significant contributions to policy discussions worldwide. He served as Chairman of the Inter-Regulatory Committee on Household Finance constituted by the Reserve Bank of India, which produced the influential "Indian Household Finance" report. He has advised numerous institutions including the Economic Advisory Council to the Prime Minister of India, the European Securities and Markets Authority, and the Norwegian Sovereign Wealth Fund. Currently, he co-chairs the Fintech workstream of the India-UK Financial Partnership, helping to shape the future of financial technology across borders. Research Initiatives Professor Ramadorai previously led the Initiative on International Comparative Household Finance, funded by the Sloan Foundation, which established household finance as a distinct sub-field of research. He is in the process of setting up a new initiative at Imperial College Business School to further advance this area of study. His work has influenced both academic research and practical policy interventions in financial markets around the world.

• Biosecurity
• Synthetic Biology
• Conservation Biology
• +7 more

Stephen Turner is an Associate Professor of Data Science and Assistant Dean for Research at the University of Virginia School of Data Science . His work bridges genomics, data science, and national security , focusing on biosecurity, synthetic biology, conservation, and bioinformatics applications in human health . Previously, he was a faculty member in the UVA School of Medicine’s Department of Public Health Sciences (2011–2019) and directed the UVA Bioinformatics Core . Ph.D., Human Genetics, Vanderbilt University M.S., Applied Statistics, Vanderbilt University B.S., Biology, James Madison University Turner’s research spans computational approaches to biosecurity, biodiversity conservation, and human health . Recent publications highlight tools like the qqman and kgp R packages, PLANES for epidemiological modeling, and biorecap for bioRxiv preprint summarization. His work integrates large-scale sequencing, genome editing, and machine learning in conservation biotechnology and public health forecasting. Scientific contributions include applications in infectious disease forecasting , forensic genomics , and maternal-fetal biology . He has mentored interdisciplinary students and collaborated on NIH-funded research , while advising biotech startups at the intersection of academia, industry, government, and policy .

• Ecological Modeling
• Conservation Biology
• Freshwater Ecology
• +3 more

Hao Yu is an Assistant Professor in the Department of Fisheries and Wildlife at Michigan State University's College of Agriculture & Natural Resources. His research focuses on modeling distributions of stream fish species across the conterminous U.S. to inform conservation decision-making. Research interests include: Species Distribution Modeling Climate Change Impact on Freshwater Ecosystems Habitat Analysis and Conservation Strategies Statistical and Machine Learning Approaches in Ecology Database Evaluation for Ecological Studies Analytical Ecology and Biodiversity Management Recent publications highlight his work with boosted regression trees to enhance predictive accuracy in species distribution models. Contact: Yuhao91@msu.edu

Moe MojtahediView profile

Senior Lecturer

• Construction Management
• Urban and Regional Planning
• Operations Research
• +8 more

Dr Moe Mojtahedi is a Senior Lecturer at the School of Built Environment, University of New South Wales (UNSW). He earned his PhD in 2014 from the School of Civil Engineering at the University of Sydney. As a certified Project Management Professional (PMP) and Professional Engineer (PEng) accredited by Engineers Australia, Dr Mojtahedi bridges academic research with practical application in construction management and disaster risk reduction. PhD (University of Sydney, 2014) MEngSc (University of New South Wales) B.E. (Industrial), Professional Engineer (Australia) His research focuses on the intersection of construction management , urban resilience , and disaster risk reduction , particularly examining: Climate change adaptation in infrastructure Post-disaster recovery frameworks Lean construction methodologies Decision support systems for risk management Evacuation planning optimization Resilient hospital infrastructure Recent publications analyze trends in disaster science using computational modeling, prefabricated construction for industrial buildings, and AI/ML applications in aged care facility evacuation. His 2025 ChemistryOpen article explores sustainable reaction media for chemoselective processes, demonstrating interdisciplinary reach. Scientific recognition includes: Research Excellence Awards (Engineers Australia, 2012 & 2013) Best Conference Paper (ICES, Salford, 2017) Elsevier Outstanding Contribution Award (International Journal of Project Management, 2017) Learning and Teaching Excellence (UNSW, 2017) As a supervisor, he guides 7 PhD candidates and has mentored 4 graduates, including Mahmoud Ershadi (project management office effectiveness) and Kamyar Kabirifar (construction waste management). He contributes to policy discussions on aligning National Construction Code with UN Sendai Framework and advocates for disaster science integration in built environment practices. His media contributions examine hospital flood risks and climate change adaptation in Australia.

Prakash LounganiView profile

Senior Lecturer

• Applied Economics
• Macroeconomics
• Inequality
• +5 more

Prakash Loungani is the Program Director and Senior Lecturer for the MS in Applied Economics program at Johns Hopkins University’s Advanced Academic Programs. He previously served as Assistant Director at the International Monetary Fund and held roles at the Federal Reserve System and the University of Florida. He holds a PhD in Economics from the University of Rochester and is an active researcher and commentator in macroeconomics and inequality. Research Interests: Loungani’s work spans a wide range of topics including macroeconomic policy, labor markets, inequality, energy economics, and climate change. His research often explores the distributional effects of economic shocks and policies, with a focus on emerging markets and developing economies. He has contributed significantly to understanding how fiscal and monetary policies impact inequality and how pandemics and energy crises affect economic outcomes. Scientific Contributions: He is the co-author of the book Confronting Inequality: How Societies Can Choose Inclusive Growth and has published extensively in top-tier journals such as Energy Economics , Journal of Economic Inequality , and The Economic Journal . His work has been featured in major media outlets like The New York Times , The Economist , and Financial Times . Teaching and Outreach: In addition to his role at Johns Hopkins, Loungani has taught at SAIS and Carey Business School. He also blogs as the "Unassuming Economist," engaging with public discourse on economic issues.

Antti H. NiemiView profile

Professor

• Computational Solid Mechanics
• Structural Engineering
• Civil Engineering
• +5 more

Antti H. Niemi is a Professor and Dean at the University of Oulu 's Faculty of Technology , specializing in computational solid and structural mechanics. His research focuses on advanced numerical methods for engineering analysis and design. Research areas include computational mechanics, structural engineering, and metamaterials Develops innovative finite element methods for thin-body problems Current projects address snow structures, timber building envelopes, and machine learning applications in mechanical systems His recent work emphasizes discontinuous Petrov-Galerkin (DPG) methods for plates and shells, with applications in civil and mechanical engineering. Publications cover: Snow and ice vaults (2024) Machine learning for steel beam capacity prediction (2024) Hygrothermal analysis of timber structures (2024) DPG formulation for Reissner-Mindlin plates (2023) Shell element benchmarking (2018-2022)

Kevin J. KircherView profile

Assistant Professor

• Energy Infrastructure Optimization
• Climate Change Mitigation
• Power Grid Interaction
• +4 more

Kevin J. Kircher is an Assistant Professor at Purdue University in the Departments of Mechanical Engineering and (by courtesy) Electrical and Computer Engineering. He works in Herrick Laboratories and the Institute for Control, Optimization and Networks, focusing on optimization and control strategies for distributed energy resources (DERs) to address climate change, pollution, and energy equity issues. Education: PhD in Mechanical Engineering (Cornell University), Postdoctoral Research in Electrical Engineering (MIT) His research spans DERs like electric vehicles, solar PV, and heat pumps, with a focus on grid interaction and energy system modeling for business/government decisions. He co-developed EDGIE, an open-source MATLAB/Python tool for electrification impact simulation. Recent publications analyze methane emissions in energy infrastructure, predictive HVAC control systems, and grid capacity challenges for residential electrification. His team has secured multiple ASHRAE grants and fellowships, with field studies on thermal storage and grid protection protocols. Mentorship priorities include diversity in energy research, with active recruitment of graduate students skilled in optimization, control theory, and thermal sciences. He leads a course on DERs with open-access materials.

Michael FinkView profile

Researcher

• Control Theory
• Automation and Robotics
• Agricultural Engineering
• +4 more

Michael Fink is a researcher at the Chair of Automatic Control Engineering , Technical University of Munich . He holds an M.Sc. in Electrical Engineering and Information Technology (2020) and a B.Eng. in the same field from Technical University Munich and University of Applied Sciences Landshut (2018), respectively. Research Interests : Model Predictive Control (MPC) with focus on stochastic and robust variants Optimal control strategies for autonomous driving and vertical farming Constraint violation probability minimization in dynamic systems Publications span topics in: Time-optimal MPC for linear systems Stochastic and robust MPC frameworks Learning-based control for greenhouse climate systems Vertical farming optimization Contact: michael.fink@tum.de

• Business Intelligence
• Cloud Computing
• Software Engineering
• +7 more

Professor Farookh Hussain is a distinguished academic at the School of Computer Science , University of Technology Sydney , specializing in Artificial Intelligence , Cloud Computing , and Software Engineering . His research spans diverse sectors including agriculture, manufacturing, healthcare, and transportation. Affiliated with the Australian Artificial Intelligence Institute (AAII) , he leads impactful work in business intelligence and carbon credit systems. Key research areas: AI applications, blockchain for provenance, carbon credit analytics Active in Masters/PhD supervision and cloud computing education Research Highlights : Developed KACINO framework for carbon dynamics modeling Created hybrid cybersecurity frameworks for supply chain risk management Advanced chatbot dialogue breakdown solutions through systematic reviews Proposed hypercomplex knowledge graph recommenders Published extensively on carbon credit price prediction and blockchain storage methods Contributions to water demand forecasting and collaborative robotics adoption Grant Activities : Secured funding from Hampton Capital Asset Management , Innovation Connections , and Science and Industry Endowment Fund Projects include LLM-driven text-to-SQL conversion , blockchain for melanoma data , and AI for storm water management

Arnoldo FrigessiView profile

Professor

• Statistics
• Biostatistics
• Machine Learning
• +5 more

Arnoldo Frigessi is Professor of Statistics at the University of Oslo, where he leads the Oslo Center for Biostatistics and Epidemiology and serves as director of BigInsight—a Centre of Excellence for Research-Based Innovation. This consortium unites industry, business, public actors, and academia to develop model-based machine learning methodologies for big data, with strong emphasis on health applications. His research centers on statistical methodology driven by real-world scientific challenges, specializing in stochastic models for complex dependence structures and computationally intensive inference algorithms. Core application domains include: Genomics and personalized cancer therapy (particularly breast and lung cancer) Infectious disease modeling (including pandemic response) eHealth, sensor data analysis, and recommender systems Personalized marketing and viral diffusion dynamics Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in cancer systems biology , where he integrates multi-omics, single-cell transcriptomics, and computational modeling to decode tumor evolution under therapy. Parallel work advances infectious disease epidemiology through time-varying reproduction number estimation and mobility-based transmission modeling, while methodological innovations span synthetic data generation (TVineSynth), causal inference via target trial emulation, and Bayesian ranking models for recommender systems. Scientific Awards: No specific awards mentioned in source materials Frigessi actively supervises graduate students, including a Department of Informatics project on "Utilizing covariate information in recommender systems." His leadership of BigInsight—funded as a Research-Based Innovation Centre by the Research Council of Norway—secures major grants supporting interdisciplinary collaborations with industrial partners (e.g., Telenor, DNB) and public health institutions. Current projects integrate real-world clinical data with mechanistic models for treatment optimization. He directs BigInsight's multidisciplinary team of statisticians, computer scientists, and domain experts, while leading the Oslo Center for Biostatistics and Epidemiology's efforts in developing statistical frameworks for complex health data. These initiatives drive Norway's national strategy for data-driven health innovation.

Nick CheneyView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Machine Learning
• +7 more

Nick Cheney is an Associate Professor in the Department of Computer Science at the University of Vermont, leading the UVM Neurobotics Lab. He also serves as Graduate Program Director and is affiliated with the Vermont Complex Systems Center, an interdisciplinary hub for data-rich complex systems research. PhD in Computational Biology and Biological Statistics from Cornell University Advised by Hod Lipson and Steve Strogatz His research focuses on bio-inspired machine learning algorithms, particularly in evolutionary computation, deep learning, and reinforcement learning. Key applications span robotics, healthcare diagnostics, and environmental science. The lab's interdisciplinary work has been recognized with prestigious awards including the NSF CAREER Award and SIGEVO Impact Award . Recent publications highlight advancements in morphological computation, continual learning, and cross-domain applications of machine learning. His team develops algorithms for soft robots, medical diagnostics using wearable sensors, and sustainable agriculture systems, often publishing in venues like the Nature Scientific Reports , GECCO , and Soft Robotics . Scientific Awards : NSF CAREER Award SIGEVO Impact Award The lab actively mentors graduate students in Complex Systems and Data Science, with alumni securing positions at institutions like Harvard, UC Berkeley, and Medidata. Collaborative grants with biomedical and environmental researchers demonstrate the lab's commitment to societal impact through machine learning applications.

• Artificial Intelligence
• Mobility and Transport
• Traffic (road)
• +13 more

Eric C. van Berkum is a Full Professor at the Digital Society Institute and Transport Engineering and Management. His research spans Artificial Intelligence, Mobility and Transport, and Intelligent Transport Systems, with a focus on optimizing traffic networks, public transport efficiency, and railway maintenance scheduling. He contributes to UN Sustainable Development Goals (SDGs) related to environmental resilience and social safety in transport systems. Expertise in Equilibrium Traffic Models and Stochastic Modelling Active in Data Mining, Machine Learning, and Network Analysis Key themes: Dynamic Traffic Management, Infrastructure Optimization, and Passenger Behavior His recent work addresses railway maintenance scheduling, shared demand-responsive transit, and passenger rerouting in capacitated systems. He has received a Best Paper Award (2017) and served on editorial boards, including Tijdschrift vervoerswetenschap (2014–2017). He organized the E-COST Summer School on Autonomous Road Transport Systems (2014) and contributed to IEEE conferences as a program committee member.

• Economics
• Econometrics
• International Trade
• +6 more

Mustapha Douch is an Associate Professor in Economics at the University of Edinburgh Business School , where he focuses on the intersection of Machine Learning models and economic problems . His research spans international trade , productivity dynamics , non-tariff barriers , and environmental economics . He has held visiting roles at the Bank of Lithuania and Bank of Estonia , and consulted for UNU-WIDER . Education: BS in Economics MSc in Economics MRes in Economics PhD in Economics His research leverages HMRC transaction data and Office of National Statistics databases to analyze firm-level challenges in the UK, including uncertainties , exporting dynamics , and productivity changes . Recent work includes studies on energy intensity , financial fragility in skilled migrants, and Brexit-induced trade impacts . Notable publications include articles in the Journal of Applied Econometrics , Journal of Economic Behavior and Organization , and British Journal of Management . His research has been featured in media outlets discussing UK trade policy and Brexit economics . Consultancy & Collaborations: UNU-WIDER consultant (South Africa’s National Treasury collaboration) Visiting Principal Economist at Bank of Lithuania and Estonia Active in the European Trade Study Group

Chris ReedView profile

Professor

• Electronic Commerce Law
• Artificial Intelligence Regulation
• Blockchain Governance
• +3 more

Chris Reed is a Professor of Electronic Commerce Law at Queen Mary University of London's School of Law, affiliated with the Centre for Commercial Law Studies (CCLS). He holds a BA from Keele University and an LLM from the University of London. His research focuses on AI regulation, blockchain governance, cloud computing law, and cyber law. He has contributed to EU directives on electronic signatures and commerce and advised parliamentary committees. Notable roles include Academic Dean of the Faculty of Law & Social Sciences (2004–2009) and Director of CCLS. His work bridges legal theory and digital innovation, addressing challenges in cross-border regulation, accountability in AI, and data governance. **Research Interests:** Artificial intelligence liability, blockchain applications in sustainability, cloud computing law, cross-border cyber regulation, and electronic commerce frameworks. His interdisciplinary approach addresses legal gaps in emerging technologies, emphasizing ethical and policy dimensions. **Professional Contributions:** Advised UK government on Hague Conference and OECD/G8 initiatives, participated in EU digital signature hearings, and contributed to international conferences. His publications span over decades, focusing on cyberspace jurisprudence, AI governance, and data trusts. He teaches postgraduate courses on e-commerce transactions and regulation. **Labs/Teams:** Active within CCLS, leading projects like the Cloud Legal Project’s Coursera specialization on cloud computing law. Collaborates globally on AI and blockchain governance.

Mihaela GirtanView profile

Associate Professor

• Thin Films
• Solar Cells
• Optoelectronics
• +3 more

Mihaela Girtan is an Associate Professor in the Faculty of Sciences at the University of Angers, heading the Thin Films for Photovoltaic Applications research group. Her work spans thin-film technologies, solar cells, and optoelectronic devices, with expertise in physical/chemical deposition methods and nanomaterials. Education: PhD in Physics, University of Stuttgart (1995) Her research investigates charge transport in oxides, organic/perovskite solar cells, transparent conducting films, plasmonics, and fluid dynamics in CVD reactors. She develops innovative materials for energy conversion, including oxide/metal/oxide electrodes and polymer-based photovoltaics. Recent publications focus on climate-agriculture interactions, including drought risk modeling, irrigation dynamics, and crop yield sustainability. Her work integrates remote sensing, machine learning, and climate modeling to address food security challenges. Scientific Awards: Consistently ranked in top 2% of researchers worldwide since 2020 She leads international collaborations and advises PhD students in materials science. Her group maintains advanced thin-film deposition and characterization facilities at Angers Photonics Laboratory.