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Daniel KiousView profile

Professor

• Probability Theory
• Stochastic Processes
• Random Walks
• +5 more

Daniel Kious is a Reader at the University of Bath, where he serves as Head of the Statistics and Probability Group and is affiliated with the Prob-L@B research center. His work focuses on advanced probability theory, including random walks, branching processes, and reinforcement models. Research Interests: Random walks with self-interaction Random walks in dynamic random environments Branching processes and tree structures Reinforcement learning applications in probability Article Trends: His recent publications emphasize trapping phenomena, reinforcement mechanisms, and phase transitions in random processes. Key themes include spatial non-local branching, once-reinforced walks, and connections to statistical physics. Advising: Co-supervised PhD students: Wilfred Armfield, Pawel Rudnicki, Carlo Scali Postdoc supervision: Guillaume Conchon-Kerjan (EPSRC-funded), Umberto De Ambroggio (co-supervised with Matt Roberts) Organizational Contributions: Co-organized conferences like CUWB IV: Frontiers in Statistics and Probability, CUWB II: Probability-on-sea, and the Random Walks in Bath conference. Active in the Prob-L@B research center. Personal Interests: Brazilian Jiu Jitsu practitioner (blue belt at Gracie Barra Frome); contributed to mathematics popularization through a 2016 article for the French Committee for the Popularization of Mathematics (CIJM).

• Applied Mathematics
• Financial Mathematics
• Numerical and Computational Mathematics
• +3 more

Xiaoping Lu is an Associate Professor at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. She has served as Academic Program Director for the Bachelor of Mathematics (Advanced) program since 2008 and holds an ORCID identifier (0000-0003-1090-8437). Her research focuses on applied mathematics and financial mathematics, particularly in option pricing, stochastic volatility models, and computational finance. Research Themes: Transaction cost modeling, regime-switching financial markets, numerical methods for PDEs, utility-indifference valuation, and stochastic optimization algorithms. Awards: 2024 AustMS-WIMSIG Anne Penfold Street Award 2024 Cheryl E. Praeger Travel Award Leadership: President of the Asia Pacific Consortium of Mathematics for Industry (APCMfI) since 2024; leadership roles in ANZIAM and WIMSIG committees. Teaching: Coordinated courses like MATH142, MATH141, and MATH283; currently available for PhD supervision in topics including financial derivatives and stochastic liquidity risk. Funding: Contributed to grants like 'The AI Tutor' (2024) and industry partnerships for advanced mathematics education.

Zhijian HuangView profile

Associate Professor

• Corporate Finance
• Financial Markets
• Risk Management
• +3 more

Zhijian Huang is an Associate Professor in the Department of Finance and Accounting at Saunders College of Business, Rochester Institute of Technology, with expertise in corporate finance, behavioral finance, and risk management. Education: B.Eng., Shanghai Jiaotong University (China) M.S., Michigan State University M.Eng., Cornell University Ph.D., Pennsylvania State University His research focuses on financial markets, cognitive dissonance in investor behavior, cryptocurrency volatility, and climate policy impacts on stock prices. Recent publications explore asymmetric responses to earnings news, social media sentiment effects, and credit risk modeling. Huang teaches courses in equity analysis, options/futures, and risk management, with a strong emphasis on derivative instruments and portfolio optimization strategies.

• Stochastic Processes
• Modeling
• Simulation
• +3 more

H.A.P. Blom is a Professor in Aerospace Engineering, specializing in Operations & Environment. His research spans stochastic processes , unmanned aircraft systems (UAS) , and safety risk analysis , with a focus on modeling, simulation, and safety assurance for future air transportation systems. Recent work addresses 2050 sustainable aviation goals through engineering challenges. Investigates UAS-human collision risks and impact mitigation strategies. Develops advanced regime-switching jump diffusion models for stochastic systems. Blom's publications highlight interdisciplinary approaches combining aircraft design , risk modeling , and human safety in aviation contexts. Key collaborations include studies on parachute systems for UAS and interdisciplinary risk analysis frameworks.

• Stochastic Processes
• Queueing Theory
• Probability Theory
• +7 more

Michel Mandjes is a Professor at the University of Amsterdam's Faculty of Science and holds a Visiting Professor position at the Faculty of Economics and Business (FEB). His research focuses on stochastic processes, queueing theory, and probability theory, with applications in risk modeling, network analysis, and operations research. Recent publications highlight his contributions to multivariate Hawkes processes , Lévy-driven systems , and dynamic random graphs , emphasizing large deviations, rare event simulation, and statistical inference. His work bridges theoretical probability with practical challenges in traffic flow, financial risk, and social network modeling. The trends in his research include the development of stochastic models for network stability, appointment scheduling optimization, and inference techniques for non-stationary processes. His methodological innovations often leverage advanced probability theory and queueing frameworks to address real-world problems in transportation, healthcare, and finance.

Caterina Ida ZeppieriView profile

Professor

• Calculus of Variations
• Elliptic PDEs
• Gamma-convergence
• +5 more

Professor Caterina Ida Zeppieri is a distinguished mathematician at the Westfälische Wilhelms-University Münster (University of Münster) in Germany, where she leads the Research Group 'Analysis and Modelling' within the Institute for Analysis and Numerics. She has maintained a continuous academic presence since at least the Winter semester 2012/13 through to upcoming semesters in 2025/26, consistently teaching advanced mathematics courses and supervising research activities. Her research focuses on fundamental aspects of mathematical analysis with significant applications to materials science. She specializes in Calculus of Variations, Elliptic PDEs, Gamma-convergence, Homogenization theory, Free-discontinuity problems, Nonlinear elasticity, and Plasticity. Her work bridges theoretical mathematics with practical applications in understanding material behavior, particularly fracture mechanics and composite materials. Professor Zeppieri's publication record demonstrates a consistent and impactful research trajectory from 2007 through forthcoming publications in 2025. Her recent work shows a strong emphasis on stochastic homogenization techniques applied to free-discontinuity problems and singularly-perturbed functionals, revealing sophisticated mathematical approaches to modeling complex material behaviors across multiple scales. She regularly collaborates with leading researchers including Filippo Cagnetti, Gianni Dal Maso, and Lucia Scardia, contributing to significant advances in the mathematical understanding of material science phenomena. Her research has been published in top-tier mathematics journals including Calculus of Variations and Partial Differential Equations, Archive for Rational Mechanics and Analysis, and SIAM Journal on Mathematical Analysis. Within the department, Professor Zeppieri plays an active role in teaching advanced courses such as Partial Differential Equations, Calculus of Variations, and Advanced Topics in the Calculus of Variation, while participating in the department's Advanced Seminar in Applied Mathematics and Colloquium on Applied Mathematics.

Nikolaus HautschView profile

Professor

• Econometrics
• Finance
• Market Microstructure
• +3 more

Nikolaus Hautsch is a full Professor at the Faculty of Economics, Institute of Statistics and Operations Research. His work focuses on econometrics, finance, and high-frequency data analysis. Research Interests : Market microstructure, volatility modeling, transaction costs, systemic risk, and machine learning applications in finance. Publication Trends (2025–2018): 2025: High-dimensional portfolio optimization, dynamic systemic risk 2024: Blockchain asset arbitrage, DeFi, polarization metrics, jump detection 2023–2022: Microstructural noise, volatility forecasting, neural networks Scientific Awards : Fellow of the Society for Financial Econometrics (2014) Projects : Artificial Intelligence in Rowing (2022–2025) Vienna Graduate School of Finance (2018–2022) Risk management of CCPs

Christoph LeitnerView profile

Research Fellow

• Convergent Technologies
• Ultrasonics
• Machine Learning
• +3 more

Dr. Christoph Leitner is a Research Fellow at ETH Zurich's Integrated Systems Laboratory under Prof. Luca Benini, focusing on biomedical and IoT applications. His work integrates printed piezoelectric transducers and flexible electronics with energy-efficient systems. He holds a PhD in Biomedical Engineering from TU Graz (2022) and has collaborated with institutions like Sant'Anna School of Advanced Studies and KTH Stockholm. Notable achievements include the Josef Krainer Young Researcher Award and Motorik Scholarship. Leitner's research spans ultrasonics, machine learning, and wearable devices, with contributions to muscle-tendon dynamics and real-time biofeedback systems. Education: PhD in Biomedical Engineering, TU Graz (2022), Advisor: Prof. Christian Baumgartner Previous roles: University Assistant (2006–2011) and R&D Engineer at Virtual Vehicle GmbH Research Interests: Convergent technologies merging biomedical engineering and IoT Ultrasound-based monitoring for musculoskeletal systems Energy-efficient embedded systems for wearable applications Collaborations & Awards: Recipient of Josef Krainer Young Researcher Award (2023) and Motorik Scholarship (2018) Active collaborations with University of Zurich, Veterinary University of Vienna, and Queensland University of Technology Labs & Projects: Integrated Systems Laboratory at ETH Zurich Developed a patented ultrasound-transparent tattoo-based SEMG system with Prof. Francesco Greco

Paul TjossemView profile

Professor

• Optics
• Atomic Physics
• Nonlinear Dynamics
• +4 more

Paul Tjossem is a Professor in the Physics Department at Grinnell College , specializing in experimental physics with a focus on electromagnetic phenomena, laser spectroscopy, and combustion diagnostics. His work bridges classical electromagnetism with modern optical measurement techniques. Current Research: Non-linear laser spectrometry, trace atom detection, and electromagnetic puzzles His research has produced significant publications spanning 1983-2019, with recent work on the candle seesaw resonance phenomenon (2019) and parametric feedback in coupled oscillators (2017). Earlier work explores photodissociation dynamics (1992), combustion diagnostics (1990-1991), and Rydberg state spectroscopy (1988-1989). Educational Background Postdoc (Molecular Physics), National Institute of Standards and Technology (1987-89) Ph.D. (Applied Physics), Cornell University (1987) M.S. (Applied Physics), Cornell University (1983) B.A. (Physics), Swarthmore College (1981) Student Collaborations V. Cornejo '98: Thomson's Jumping Ring K. Ni '09: Computed Tomography and the Radon Transform F. Friesen '10: FPGA applications E. C. Brost '10: Optimizing Thomson's Jumping Ring

Lutz WarnkeView profile

Professor

• Combinatorics
• Probability Theory
• Random Graphs
• +5 more

Lutz Warnke is a Professor of Mathematics at the University of California, San Diego, with prior affiliations at Georgia Institute of Technology (where he received tenure in 2021) and Peterhouse, Cambridge University (Junior Research Fellow until 2016). His research focuses on probabilistic combinatorics, random graphs, phase transitions, and combinatorial probability, with applications to extremal combinatorics and Ramsey theory. Education : Ph.D. in Mathematics from the University of Oxford (2012), supervised by Oliver Riordan. Dr. Warnke's research explores the structure and evolution of random graphs and processes, including Achlioptas processes, Ramsey numbers, and extremal problems. His work often bridges probabilistic methods with algorithmic applications and theoretical computer science. His publications from 2022–2025 reveal trends in random graph isomorphisms, clique coloring thresholds, extremal subgraph counts, and hardness of online algorithms. Key subfields include percolation, phase transitions, and probabilistic methods applied to combinatorial structures. Scientific Awards : Dénes König Prize (2016), Alfred P. Sloan Research Fellowship (2018), NSF CAREER Award (2020), Richard Rado Prize (2014). Dr. Warnke actively supervises PhD students and postdocs, including Matthew Cho (PhD ongoing), Erlang Surya (PhD 2025), Emily Zhu (PhD 2025), and He Guo (PhD 2021). He has received teaching accolades at Georgia Tech and contributes to graduate courses in probabilistic combinatorics, random graph theory, and stochastic processes.

Andrea CollevecchioView profile

Professor

• Probability
• Mathematical Physics
• Statistical Mechanics
• +3 more

Andrea Collevecchio is a Professor in the School of Mathematics at Monash University, Australia, where he has been a faculty member since 2012. His research focuses on the intersection of Probability, Mathematical Physics, and Statistical Mechanics, with particular expertise in stochastic processes and theoretical modeling. He earned his PhD in Statistics from Purdue University in 2004, followed by postdoctoral positions in Italy and Germany. In 2006, he became Assistant Professor at Ca’Foscari University in Venice before joining Monash University. Collevecchio specializes in Reinforced Processes and Large Deviations, investigating complex systems through random walk models. His work bridges abstract probability theory with applications in statistical mechanics, examining phenomena like memory effects in stochastic processes and phase transitions in lattice systems. Recent research emphasizes hypercube structures, non-reversible dynamics, and reinforcement mechanisms. His 2021-2025 publications reveal a concentrated focus on hypercube random walks, with increasing exploration of non-reversible processes, vertex-reinforced dynamics, and bootstrap methods. These works consistently apply probabilistic frameworks to problems in mathematical physics, demonstrating strong connections between theoretical probability and physical modeling. Collevecchio has secured multiple research grants including ARC-funded projects on self-interacting random walks (2023-2026) and random walks with long memory (2018-2022). He contributes to interdisciplinary initiatives like the Smart Vehicles project for dementia support (2025-2027) and actively organizes academic events including the AIM Day series connecting mathematics, AI, and industry applications.

Hyejin KuView profile

Professor

• Mathematical Finance
• Machine Learning
• Risk Management
• +4 more

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +7 more

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

Christian HellandView profile

Assistant Professor

• Physical Training
• Strength Training
• Athlete Testing
• +4 more

Christian Helland serves as an Assistant Professor in the Department of Sport and Social Sciences at the Norwegian School of Sport Sciences (NIH). Additionally, he works as a consultant in the strength department at Olympiatoppen (the Norwegian Olympic Training Center), where he is responsible for physical training programs for beach volleyball, para table tennis, and rowing athletes. Christian holds a master's degree from the Institute of Physical Performance and has been engaged in both practical and theoretical lecturing since 2012. His professional background includes competing as a beach volleyball player at the World Tour level since 2014, which provides him with valuable practical experience to complement his academic work. Dr. Helland's research focuses on optimizing athletic performance through scientific approaches to physical training. His work examines strength development, jump and sprint performance metrics, tendon mechanics, and recovery processes in elite athletes. His research has significant practical applications for coaches and athletes seeking to improve performance while minimizing injury risk. His publication record demonstrates a strong focus on force-velocity profiling, strength and power testing methodologies, and the physiological responses to different training modalities. A recurring theme in his work is the comparison of different training approaches and assessment methods, particularly examining how strength-oriented versus power-oriented training affects recovery needs and performance outcomes. His research often involves collaborations with other leading sports scientists across Norway and internationally. Christian teaches courses in volleyball, coaching methodologies, sports psychology, and sports practice for children and youth. His practical experience as an elite athlete informs his teaching approach, bridging theoretical knowledge with real-world application.

Jonathan BoreykoView profile

Associate Professor

• Interfacial fluid mechanics
• Biomimetic engineering
• Phase-change heat transfer
• +5 more

Jonathan Boreyko serves as an Associate Professor and John R. Jones III Faculty Fellow in Virginia Tech's College of Engineering, Department of Mechanical Engineering. His research integrates fluid dynamics, heat transfer, and biomimetic engineering to develop sustainable solutions for water and energy harvesting through innovations like Fog Harps and synthetic trees. Dr. Boreyko earned his Ph.D. in Mechanical Engineering from Duke University (2012), following an M.S. and B.S. in Mechanical Engineering and Physics from Trinity College (2007). His academic journey includes postdoctoral research at Oak Ridge National Laboratory and faculty appointments in Biomedical Engineering and Mechanics at Virginia Tech before transitioning to Mechanical Engineering. His research program centers on interfacial phenomena in micro/nano-structured materials, with core expertise in droplet dynamics, phase-change heat transfer (condensation, evaporation, boiling), and biomimetic water harvesting systems. The Nature-Inspired Fluids and Interfaces Lab examines how natural designs—from plant transpiration to insect surfaces—can inform engineered solutions for atmospheric water collection, anti-icing, and thermal management. Analysis of his 15 most recent publications reveals three dominant research thrusts: (1) biomimetic water harvesting systems (Fog Harps for atmospheric water collection), (2) thermal diodes leveraging droplet bridging for directional heat transfer, and (3) anti-clogging/anti-tangling mechanisms in fog harvesting meshes. These works consistently bridge fundamental fluid mechanics with practical sustainability applications. His scientific achievements include: John R. Jones III Faculty Fellow (2020) NSF CAREER Award for Thermal Transport Processes (2017) AFOSR Young Investigator Program Award (2016) 3M Non-Tenured Faculty Award (2016) Multiple best poster awards at APS, Gordon Research Conferences, and MRS meetings Dr. Boreyko has advised graduate students including Weiwei (Ph.D., 2020) and Viverjita (M.S., 2020), with research funded by NSF, AFOSR, and 3M. His lab maintains active collaborations with industry partners and national laboratories for technology translation. The Nature-Inspired Fluids and Interfaces Lab combines experimental fluid dynamics, materials characterization, and computational modeling to develop deployable systems. Current projects include scaling Fog Harps for real-world water harvesting, optimizing synthetic trees for passive desalination, and exploring jumping-droplet phenomena for thermal management in electronics.