Find Academic Supervisors

• Optimal Control
• Partial Differential Equations
• Mean Field Games
• +6 more

Associate Professor at the University of Pavia's Department of Economics and Management, specializing in optimal control, partial differential equations, and mean field games with applications to economics and finance. Holds an Italian qualification (ASN) for associate professorship and a French qualification (maître de conférences). Research focuses on dynamic optimization, stochastic systems, and economic modeling of human capital, environmental quality, and pandemic impacts. Active in teaching mathematics for economics and optimization at undergraduate and graduate levels. Education: PhD in Mathematics from University of Padua (2015), postdocs at University of Graz (Austria) and University of Padua. Extensive visiting collaborations in France, Chile, and Brazil. Research Highlights: Pioneering work on mean field games modeling spatial interactions in human capital dynamics and environmental quality regulation. Contributions to sparse optimization, inverse problems in fracture mechanics, and large deviations in stochastic volatility models. Recent Publications Trends: Focus on infinite-dimensional mean field games (e.g., Hilbert spaces), pandemic economics, and environmental policy modeling. Over 15 peer-reviewed articles spanning optimal control, PDEs, and economic applications. Grants & Leadership: Coordinator of Italian GNAMPA projects on MFGs and environmental models. Organized conferences including AMASES 2022 and special sessions at international optimal control conferences. Referee for top journals like SIAM J. Control Optim. and J. Mathematical Economics. Teaching: Courses include 'Mathematics for Management,' 'General Mathematics,' and PhD-level 'Static and Dynamic Optimization.' Oversees tutoring programs and international collaborations via ERASMUS+.

• Finance
• Probability Theory
• Stochastic Processes
• +5 more

João Amaro de Matos is a Professor and Vice Rector at Nova University of Lisbon, holding dual appointments at the Nova School of Business and Economics. He has served as Associate Dean for International Development (2005–2017) and Director of PhD Programs. His administrative roles include membership on the Executive Board of the Community of European Management Schools (CEMS) and chairing its Research and Doctoral Education Committee. With a dual PhD in Management (INSEAD, 1995) and Physics (São Paulo University, 1988), he has held visiting positions at Heidelberg University, London School of Economics, and MIT. His teaching expertise spans Corporate Finance, Derivatives, and Continuous-Time Finance across multiple institutions. Dr. de Matos' education includes a Bachelor's in Physics (USP, 1981), a Master's in Physics (USP, 1984), a Bachelor's in Business Administration (FGV, 1983), and two PhDs (Physics and Management). He has authored foundational texts like Theoretical Foundations of Corporate Finance (Princeton, 2001) and Sustainability and Organizational Change Management (Routledge, 2015). His research bridges finance, stochastic processes, and social networks, with over 50 publications in journals such as Journal of Econometrics and Mathematical Finance . His publications demonstrate interdisciplinary reach, addressing topics from financial derivatives pricing to the dynamics of international academic cooperation. He initiated the Luso-Brazilian Finance Meetings (2007) and has advised numerous graduate students in Finance and Economics. His work frequently intersects with policy, exploring market liquidity constraints, network effects in corporate governance, and the impact of social networks on organizational change. De Matos' contributions extend beyond academia through leadership in international educational initiatives, including advising Cuban universities' internationalization and shaping European transnational education policies. His research methodologies blend mathematical rigor with applied policy analysis, reflecting his dual background in physics and management sciences.

Gustavo SchwenklerView profile

Associate Professor

• Asset Pricing
• Econometrics
• Risk Management
• +3 more

Gustavo Schwenkler is an Associate Professor of Finance at Santa Clara University's Leavey School of Business. His research focuses on asset pricing, econometrics, risk management, and derivatives, with notable contributions to understanding systemic risk and financial networks. He holds a PhD in Management Science and Engineering from Stanford University (2013) and a diploma in Applied Mathematics and Economics from the University of Cologne. Educations: PhD in Management Science and Engineering, Stanford University, 2013 Master's Diploma in Applied Mathematics and Economics, University of Cologne Research Interests: Dr. Schwenkler explores how news and information shape financial markets, particularly in crypto markets and firm networks. He develops econometric models to analyze risk dynamics, default clustering, and systemic stability. His work bridges theoretical finance with practical applications in policy and risk management. Professional Roles: Associate Editor at Management Science , Journal of Business & Economic Statistics , and Digital Finance Board Member at Cointree and Indicia Labs Articles Trends: Recent studies emphasize crypto market dynamics, news-driven volatility, and systemic risk in financial networks. His 2020s work explores pandemic policy impacts and venture capital mechanisms, reflecting growing interest in real-world economic interventions and emerging financial instruments.

Michael GrabchakView profile

Professor

• Tempered Stable Distributions
• Infinitely Divisible Processes
• Heavy Tails
• +3 more

Michael Grabchak is a Professor in the Department of Mathematics and Statistics at the University of North Carolina at Charlotte (UNC Charlotte). His research focuses on tempered stable distributions, infinitely divisible processes, heavy-tailed phenomena, quantitative finance, and entropy estimation. He has developed software packages such as SubTS , SymTS , and EntropyEstimation for statistical analysis and simulation. His work bridges theoretical probability and applications in finance, statistics, and computational methods. Key contributions include studies on Lévy processes, risk estimation, and biodiversity metrics using entropy-based techniques. He has authored a monograph on tempered stable distributions and edited volumes on extreme value theory. His teaching includes advanced courses on applied probability and statistical methods, and he has presented his research at international conferences across the globe. Grabchak’s research has been published in top journals like Statistics and Computing , Journal of Applied Probability , and Quantitative Finance . He actively contributes to academic service through invited lectures, editorial roles, and software development for statistical education and applications.

Anna FrebelView profile

Professor

• Observational Stellar Astrophysics
• Stellar Archaeology
• Dwarf Galaxy Archaeology
• +3 more

Anna Frebel is a Professor of Physics and Division Head for Astrophysics at MIT. Her research focuses on observational stellar astrophysics, particularly studying the oldest, metal-poor stars and dwarf galaxies to understand the early universe's chemical and physical conditions. She leads the Frebel Research Group and collaborates with the MIT Kavli Institute for Astrophysics and Space Research and the Magellan Telescopes Consortium. Education: PhD in Astrophysics from Australian National University (2007) Postdoctoral positions at University of Texas (2006-2008) and Harvard-Smithsonian Center for Astrophysics (2009-2012) Her work bridges observations with cosmological simulations, using Magellan telescopes for high-resolution spectroscopy. Key interests include r-process nucleosynthesis, first stars/galaxies, and galactic archaeology. Awards Overview: APS Fellow (2022) NSF CAREER Award (2013) AAS Annie Jump Cannon Award (2010) Her advising and grants include MIT's Earll Murman Award (2019) and multiple international fellowships. She leads the Frebel Research Group and contributes to public science communication through lectures, articles, and her book Searching for the Oldest Stars .

João GuerraView profile

Associate Professor

• Stochastic Analysis
• Lévy Processes
• Fractional Stochastic Processes
• +3 more

João Guerra is an Associate Professor at ISEG (University of Lisbon) in the Department of Mathematics, specializing in Financial Analysis and Mathematics. He holds a PhD in Mathematics from the University of Barcelona (2009), as well as degrees in Technological Physics Engineering (IST, 1995) and Applied Mathematics (University of Évora, 1999). His research focuses on stochastic analysis, Lévy processes, and fractional stochastic processes applied to mathematical finance. Education: PhD in Mathematics, University of Barcelona (2009) DEA in Mathematics, Universitat de Barcelona (2003) Master's in Applied Mathematics, University of Évora (2003) Bachelor's in Technological Physics Engineering, Instituto Superior Técnico (1995) Research Interests: Stochastic calculus, Lévy processes, fractional stochastic systems, and their applications in financial modeling. He has published widely in journals like Quantitative Finance , Journal of Computational Finance , and Stochastic Processes and Their Applications . Teaching and Academic Roles: Coordinator of the Master's in Financial Mathematics and PhD in Applied Mathematics for Economics and Management (MAEG) Developed courses on stochastic calculus, differential equations, and financial models Supervised 2 PhD and 19 Master's theses Professional Experience: Assistant Professor at ISEG (2009–2023) Researcher in six international projects, including a current bilateral Portugal-Germany project

João JanelaView profile

Associate Professor

• Operations Research
• Computational Finance
• Fluid Dynamics
• +3 more

João Janela is an Associate Professor at the Instituto Superior de Economia e Gestão (ISEG) within the Universidade de Lisboa. He holds a PhD in Mathematics from Instituto Superior Técnico (2008), following earlier degrees in Mathematics. His academic roles include coordinating the Mestrado em Matemática Financeira and serving on the CEMAPRE executive board. His research spans operations research, computational finance, and fluid dynamics, with notable contributions to waste collection optimization and blood coagulation modeling. He has supervised over 10 master’s theses, focusing on topics like numerical methods in finance and vehicle routing problems. Janela actively contributes to academic leadership through roles in curriculum development and institutional governance. Education: PhD in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (2008) Master of Science in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (2002) Bachelor of Science in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (1997) Research Interests: Janela’s work integrates applied mathematics with real-world challenges. His research in operations research addresses logistics and waste management optimization, while his computational finance studies explore option pricing under stochastic models. In fluid dynamics, he develops numerical methods for blood flow and non-Newtonian fluid behavior. His recent projects emphasize interdisciplinary applications, such as synthetic cell-based models for biomedical systems. Advising & Grants: Supervised 9 master’s students since 2012, with topics ranging from exotic derivatives trading to decentralized exchange analysis. His work benefits from collaborations in CEMAPRE and ECCOMAS, though specific grant details are not provided in the text. Labs/Teams: Active member of CEMAPRE (Center for Mathematics and Applications) and involved in organizing conferences like ICCF2017 and MPF 2005.

Nianqiao JuView profile

Assistant Professor

• Bayesian Statistics
• Monte Carlo Methods
• Differential Privacy
• +3 more

Nianqiao 'Phyllis' Ju is an Assistant Professor of Mathematics at Dartmouth College (starting July 2025) and previously held a tenure-track position at Purdue University's Department of Statistics (2021–2025). She earned her Ph.D. in Statistics from Harvard University (2021) and a B.A. in Mathematics & Physics from Wellesley College (2016). Her research focuses on Bayesian statistics, Monte Carlo methods, differential privacy, and computational biology. She has received awards such as the Showalter Trust Young Investigator Award and NIH-NIAID funding for her work on disease progression modeling. She is also involved in the Online Monte Carlo Seminar and maintains a blog (phylliswithdata.wordpress.com) to communicate statistical ideas broadly. Education: Ph.D., Harvard University; B.A., Wellesley College Affiliations: Dartmouth College (2025–present), Purdue University (2021–2025) Professional Activities: Organizer of the Online Monte Carlo Seminar, YouTube channel for statistical content Her research spans theoretical and applied domains, including privacy-preserving Bayesian inference, MCMC convergence analysis, and computational methods for genetic data. Recent work includes developing the SOMA sampler and SNP-Slice framework for mixed infection analysis. She has authored over 10 peer-reviewed articles and secured multiple grants, including NIH-NIAID funding for systems biology research. Teaching responsibilities at Dartmouth include courses on probability and applied statistics (e.g., MATH 20, MATH 146). Her advising includes overseeing students in computational and privacy-focused projects.

Nuno C. MartinsView profile

Professor

• Control Theory
• Distributed Control Systems
• Evolutionary Dynamics
• +6 more

Nuno C. Martins is a Professor of Electrical Engineering at the University of Maryland, College Park, with a joint appointment in the Institute for Systems Research. He holds a Ph.D. from MIT (2004) and an M.S. from IST, Portugal (1997), and completed a Financial Technology program at MIT's Sloan School. He served as Director of the Maryland Robotics Center (2012–2014) and has held editorial roles for journals like IEEE Transactions on Control of Network Systems and Automatica . His research focuses on distributed control systems, evolutionary dynamics, population games, and networked control. Notable contributions include work on Nash equilibrium learning in large populations, optimal remote estimation, and decentralized control under communication constraints. Martins' team explores applications in robotics, autonomous systems, and infrastructure safety. He has received prestigious awards such as the 2006 O. Hugo Schuck Award, 2007 NSF CAREER Award, and 2008 IEEE Axelby Award. His lab, the CPS and Cooperative Autonomy Lab, validates research on platforms like quadrotors and ground robots. Current projects include the ASTrA initiative for multi-agent cyber-physical systems and strategic traffic management during disasters. Education: M.S., Electrical Engineering, IST, Portugal (1997) Ph.D., Electrical Engineering and Computer Science, MIT (2004) Financial Technology Option, MIT Sloan (2004) Awards: 2023 IEEE TCNS Paper on Pairwise Comparison Dynamics 2022 Population Games with Erlang Clocks (CDC) 2010 George Corcoran Award (UMD) Grants: NSF ASTrA grant for trustworthy autonomous systems AFOSR/ONR-funded distributed tracking and secrecy control His lab hosts interdisciplinary research collaborations and undergraduate/graduate training programs. Recent work includes evolutionary dynamics models for epidemiology and mixed-autonomy systems.

• Stochastic Analysis
• Mathematical Finance
• Energy Markets
• +3 more

Dörte Kreher is a Professor of Applied Stochastic Analysis at Humboldt University of Berlin, leading the Department of Mathematics since August 2017. Her research focuses on stochastic analysis, mathematical finance, and energy markets, with a particular emphasis on stochastic processes, limit order book models, and market microstructure. Education: PhD in Mathematics (2014), Universität Zürich Diplom in Mathematics (2009), Humboldt University of Berlin B.Sc. in Economics (2009), Humboldt University of Berlin Research Interests: Dr. Kreher’s work integrates theoretical stochastic analysis with practical applications in finance and energy markets. Her projects include modeling intraday electricity markets, optimal control strategies for battery storage, and microstructural foundations of rough volatility models. She collaborates with institutions like MATH+, the Berlin-Oxford IRTG 2544, and the SFB/TRR 388. Teaching: She teaches advanced courses in stochastic finance, stochastic analysis, and statistics, including Stochastik I , Stochastische Finanzmathematik , and Methoden der Statistik . Grants & Projects: MATH+ Project AA4-4: Stochastic modeling of intraday electricity markets MATH+ Project AA4-9: Volatile Electricity Markets and Battery Storage Berlin-Oxford IRTG 2544: Stochastic Analysis in Interaction SFB/TRR 388 Project B02: Microstructural Foundations of Rough Volatility Models Labs/Teams: Leads research groups in stochastic finance and mathematical modeling, supervising PhD students such as Cassandra Milbradt, Wilfried Kenmoe Nzali, and Konstantins Starovoitovs.

Theodosiadou OuraniaView profile

Researcher

• Stochastic modeling
• Time series analysis
• Applied probabilities
• +3 more

Ourania Theodosiadou is a Researcher at the Department of Mathematics, Aristotle University of Thessaloniki (since Oct 2024). Previously, she served as a Postdoctoral Researcher at the Institute of Information and Communication Technologies (CERTH) from 2019 to 2024 and held multiple contracted lecturer roles at Aristotle University and the University of Macedonia. She earned a PhD in Mathematics (2019) and a Master's in Statistics and Modeling (2014), both from AUTH, with a focus on stochastic processes and financial applications. Her doctoral thesis explored latent stochastic processes with jumps in finance under Prof. Georgios Tsaklidis. Her research interests span stochastic modeling, time series analysis, computational statistics, and machine learning. Recent work includes real-time threat assessment using Hidden Markov Models (2023), cryptocurrency transaction analysis for illegal activity detection (2023), and centrality-based network node identification (2022). Methodological contributions include state space modeling with constraints (2021) and Kalman filter applications for jump detection in financial markets (2017-2019). Publications reflect interdisciplinary applications in finance, security, and computational methods. Current work extends AI-driven solutions against terrorist financing and explores blockchain forensics through time series analysis.

Andreas WettreView profile

Lecturer

• Systems Oriented Design
• Complexity Management
• Organizational Development
• +3 more

Andreas Wettre is a Lecturer at The Oslo School of Architecture and Design (AHO), where he teaches Systems Oriented Design (SOD) and chairs the Executive Master program—a 3-year part-time course for professionals. His research integrates SOD with organizational development, focusing on complexity management, silo-busting methodologies like 'Jumping Conversations,' and sustainable industrial transitions. With 25 years of prior experience in management consulting and technical leadership, Wettre bridges academia and industry practice. His work explores how SOD enhances organizational capacity to handle complexity, emphasizing practical frameworks for big data utilization, sustainable transitions, and cross-functional collaboration. Recent publications analyze industrial case studies in Norway, workshop-based training methods, and systemic barriers to change. Wettre’s teaching spans bachelor/master levels, including courses on complex systems design and implementation strategies. He has also contributed to student well-being initiatives at AHO.

Christian FlindtView profile

Associate Professor

• Quantum Transport
• Quantum Mechanics
• Condensed Matter Physics
• +4 more

Christian Flindt is an Associate Professor at the Department of Applied Physics , School of Science , Aalto University . His research group focuses on theoretical quantum transport, with expertise in dynamic single-electron emitters, noise and fluctuations in electronic conductors, and charge transport in hybrid microwave cavity architectures. Education : Doctoral degree in Natural Sciences from Danmarks Tekniske Universitet (DTU) Flindt's research spans quantum many-body physics, statistical mechanics, and scattering theory, with applications to quantum devices and nanoelectronics. His recent work explores non-classical correlations, entanglement, and quantum jump trajectories. Key projects include the Finnish Centre of Excellence in Quantum Technology (2023–2025) and the QuLeeYang project on Lee-Yang theory for quantum phase transitions. His publications in 2025 address temperature control using qubits, Lee-Yang phase transition theory, and photon statistics in bosonic networks. Earlier works (2024) analyze Cooper pair splitters and charge tunneling in quantum dots. Flindt frequently collaborates internationally and has led projects funded by the Academy of Finland and EU H2020 MC.

Jinling WangView profile

Associate Professor

• Geospatial Mapping
• Global Navigation Satellite Systems (GNSS)
• Inertial Navigation Systems (INS)
• +5 more

Associate Professor Jinling Wang is an academic at the University of New South Wales (UNSW), affiliated with the School of Civil Engineering and the Department of Civil Engineering. Her research focuses on geospatial mapping and navigation, including Global Navigation Satellite Systems (GNSS), Inertial Navigation Systems (INS), and multi-sensor integration for applications in autonomous vehicles and high-definition mapping. She has supervised 22 PhD and 6 Master’s students, seven of whom won prestigious international awards during their studies. Her awards include being named on the World’s Top 2% Scientists List from 2020 to 2024 and recognized as Australia’s Research Leader in 'Radar, Positioning, and Navigation' multiple times. She leads projects such as the UNSW Engineering Goldstar Award 2024 Research Grant and the UNSW-Tsinghua Collaborative Research Seed Project. Dr. Wang’s research trends emphasize ionospheric analysis, robust positioning algorithms, and sensor fusion for autonomous systems. Her articles span disciplines like atmospheric science, data science, and geomatics engineering, addressing challenges in GNSS reliability and real-time navigation integrity. Her advisory and grants section highlights her role in advancing autonomous vehicle technologies and HD mapping. She is part of research teams utilizing advanced facilities at UNSW’s Mark Wainwright Analytical Centre and collaborates on global navigation challenges.

Renjie LiaoView profile

Assistant Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration