جست‌وجوی استاد و پژوهشگر

Laurence Likforman-Sulemمشاهده پروفایل

دانشیار

• Document Analysis
• Handwriting Recognition
• Historical Document Processing
• +۷ مورد دیگر

Laurence Likforman-Sulem is an Associate Professor at Institut Polytechnique de Paris , affiliated with the Signal, Statistics and Learning (S2A) team in the Image, Data, Signal (IDS) department . She has been at Télécom Paris since 1991, where she teaches Pattern Recognition , Signal Processing , and Document Analysis . PhD from ENST-Paris (1989) HDR from Sorbonne University (2008) Her research integrates Markovian methods (HMMs, Bayesian Networks) and deep learning (BLSTMs, CNNs) for: Handwriting recognition in historical documents Character analysis in Byzantine seals Parkinson’s disease detection through multimodal signals Biometric authentication using hand shape Recent work focuses on Byzantine seal character recognition (BHAi project) and multimodal group cohesion analysis (IEEE ICMI 2021 Best Paper). She has supervised 10 PhD students and numerous Master internships. Scientific Awards Winning system at ICDAR 05 Arabic Hand-Written Word Recognition Competition Fondation Telecom Thesis Award 2014 (2nd prize for Olivier Morillot) Best Paper Award, ICMI 2021 Active in conference leadership, she chaired ICDAR 2015 and ICPR 2022 document analysis tracks. Her 15 most recent publications span Byzantine document analysis, Parkinson’s detection, and low-energy neural architectures.

Sayan Mukherjeeمشاهده پروفایل

استاد

• Geometry and topology in probabilistic modeling
• Stochastic topology
• Statistical and computational biology
• +۷ مورد دیگر

Sayan Mukherjee is a Professor of Statistical Science, Computer Science, and Mathematics at Duke University, with affiliations at the Information Initiative at Duke. His academic work spans multiple departments, reflecting his interdisciplinary research approach. His research focuses on three main areas: Geometry and topology in probabilistic modeling , where he develops methods for modeling geometric and topological properties of objects/data with uncertainty; Modeling massive data , building statistical models and algorithms for dimension reduction, classification, and matrix completion that scale to huge datasets; and Applied Bayesian models in computational biology , collaborating with biologists on projects ranging from functional genomics to population biology. Mukherjee has mentored numerous graduate students and postdoctoral fellows who have gone on to positions at institutions including Harvard, U Chicago, Duke, UNC, and industry positions at companies like Google, Novartis, and SAS. His collaborative network spans across multiple disciplines including geometry, topology, learning theory, cancer biology, computational biology, and evolutionary ecology. He has developed significant software tools including Bayesian Sparse Factor Analysis of Genetic Covariance Matrices (BSFG), Automated 3D Geometric Morphometrics (auto3dgm), and Analysis of Sample Set Enrichment Scores (ASSESS), demonstrating his commitment to creating practical computational methods for complex biological and statistical problems.

Anna Lukinaمشاهده پروفایل

استادیار

• Artificial Intelligence Safety
• Formal Methods
• AI Interpretability
• +۴ مورد دیگر

Anna Lukina is an Assistant Professor in the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. She leads the Sequential Uncertainty Monitoring and Interpretability (SUMI) Lab, focusing on improving safety and interpretability of artificial intelligence through formal methods with applications in engineering, transportation, health, and finance. Her research spans the critical intersection of formal verification and machine learning, particularly in developing techniques for runtime monitoring of neural networks, safety verification of decision-tree policies, and creating verifiable reinforcement learning systems. She has established strong international collaborations with researchers across the US, Europe, Japan, and Australia. Lukina's recent publications (2021-2025) demonstrate consistent output in top AI venues including AAAI, NeurIPS, and IJCAI, with a clear trajectory toward increasingly sophisticated verification techniques for complex AI systems. Her work shows strong emphasis on practical applications while maintaining theoretical rigor, particularly in creating methods that provide formal guarantees for black-box AI systems. As part of her service commitment, she leads initiatives promoting junior computer scientists from underrepresented communities, reflecting her dedication to diversity in the field as highlighted in her DerStandard interview "Warum so wenige Frauen Den Code knacken wollen" and university magazine Delta. She currently supervises multiple PhD researchers including Sterre Lutz, Daniël Vos, Aaron Berger, and Johannes Koch, along with numerous successful MSc graduates who have completed theses on topics ranging from anomaly detection to genetic programming for explainable AI.

• Theoretical Particle Physics
• Cosmology
• Astroparticle Physics
• +۶ مورد دیگر

Dr. Jessica Turner serves as an Associate Professor in the Department of Physics, specializing in theoretical particle physics and cosmology. Her research bridges high-energy phenomena with early universe dynamics, focusing on the interplay between fundamental particle interactions and cosmological evolution. Turner's primary research interests encompass neutrino physics, primordial black holes, leptogenesis mechanisms, gravitational wave signatures from cosmic phase transitions, and Grand Unified Theories. She investigates how primordial black holes influence dark matter production, explores baryogenesis through leptogenesis channels, and analyzes neutrino properties in both terrestrial experiments and astrophysical contexts. Her work frequently combines analytical field theory approaches with numerical simulations of early universe phenomena. Analysis of her recent publications (2021-2025) reveals consistent focus on primordial black hole dynamics and their cosmological implications, with significant contributions to neutrino physics and leptogenesis. She has developed computational tools like the ULYSSES solver for leptogenesis equations and frequently collaborates on multi-institutional projects examining gravitational wave signatures from domain walls and phase transitions. No scientific awards were mentioned in the available information. Turner currently supervises graduate student Joseph Tudor. While specific grant details are absent from the provided text, her extensive publication record in high-impact journals indicates sustained research funding for theoretical investigations in particle cosmology. Her work demonstrates strong collaborative patterns with international research groups focused on early universe physics.

• Dynamical Systems
• Partial Differential Equations
• Pharmacology
• +۴ مورد دیگر

Vivi Rottschäfer is an Associate Professor in the Analysis and Dynamical Systems group at the Mathematical Institute, Leiden University , and holds a Professor position by special appointment in Industrial Mathematics at the Korteweg-de Vries Institute for Mathematics, University of Amsterdam. Her research focuses on Dynamical Systems , Partial Differential Equations , and their applications in Pharmacology , Health , and Ecology . She develops mathematical models for drug distribution in the brain, ecological pattern formation, and industrial problems, with a particular interest in blow-up phenomena , pattern dynamics , and multi-scale modeling . Recent publications (2025-2020) span cellular communication optimization , nanomaterial toxicokinetics , neural feedback dynamics , and ecosystem resilience . Her work often bridges pure mathematical analysis with real-world challenges, including collaborations in receptor theory , drug development , and environmental toxicology . She organizes major conferences like the International Congress on Industrial and Applied Mathematics (ICIAM 2027) and the Study Group Mathematics with Industry , and has taught advanced courses on Bifurcation Theory , Perturbation Methods , and Dynamical Systems since 2010.

Lorenzo Garlappiمشاهده پروفایل

استاد

• Asset Pricing
• Credit Risk
• Real Options
• +۴ مورد دیگر

Lorenzo Garlappi is the TSX Venture Exchange Professor of Finance at the Sauder School of Business, University of British Columbia. He holds a PhD from UBC, a Doctorate from the University of Trieste, and a BS in Economics from Bocconi University, Italy. His research focuses on asset pricing, credit risk, real options, and portfolio choice, with applications to corporate finance and investment strategies. Education: PhD in Finance, University of British Columbia (UBC) Doctorate in Economics, University of Trieste, Italy BS in Economics, Bocconi University, Milan, Italy Research Interests: Garlappi explores theoretical and empirical aspects of financial markets, including: Credit risk dynamics and debt issuance strategies Monetary policy impacts on investment behavior Climate change effects on real estate pricing Optimal portfolio diversification under uncertainty Real options in corporate decision-making Awards & Recognition: Dimensional Fund Advisors Distinguished Paper Award (2021) Best Paper Award at ASU Sonoram Winter Conference (2019) Summer Haven Investment Management Prize (2019) Crowell Memorial Prize (2010) Teaching & Contributions: Teaches Risk Management and Financial Engineering at Sauder. His work bridges academic rigor and practical finance, offering frameworks for addressing market inefficiencies and uncertainty. He collaborates with industry practitioners to refine asset allocation strategies and corporate finance policies. Labs & Teams: Leads research initiatives at Sauder’s Finance Division, focusing on computational finance, corporate governance, and sustainable investing. Engages in interdisciplinary projects with economics and environmental science departments.

Daan Crommelinمشاهده پروفایل

استاد

• Uncertainty Quantification
• Stochastic Modeling
• Data-Driven Modeling
• +۶ مورد دیگر

Daan Crommelin holds a part-time professorship in Numerical Analysis and Dynamical Systems at the KdV Institute for Mathematics, University of Amsterdam, and is a senior researcher at CWI Amsterdam's Scientific Computing group. He serves on CWI's management team and previously led its Scientific Computing group (2013–2021). His research focuses on stochastic modeling of multiscale systems, uncertainty quantification, and rare event analysis, with applications in climate science, renewable energy, and fluid dynamics. Crommelin combines methods from scientific computing, applied probability, and dynamical systems to address challenges in atmosphere-ocean-climate modeling. He has contributed to projects like the EU-funded VECMA initiative for exascale computing and collaborated on superparameterization techniques for climate models. His work also extends to epidemic modeling and computational chemistry. Crommelin earned his PhD in 2003 from Utrecht University, with a thesis co-supervised by KNMI, and holds an MSc in theoretical physics and an MA in philosophy from the University of Amsterdam.

Subhash C. Sarinمشاهده پروفایل

استاد

• Operations Research
• Production Scheduling
• Applied Mathematical Programming
• +۵ مورد دیگر

Subhash C. Sarin is the Paul T. Norton Endowed Professor at Virginia Tech's Grado Department of Industrial and Systems Engineering within the College of Engineering. His expertise spans operations research, production scheduling, applied mathematical programming, and manufacturing systems design. He holds a Ph.D. from North Carolina State University (1978), an M.S. from Kansas State University (1973), and a BSc from Delhi College of Engineering (1970). Research focuses on scheduling optimization, semiconductor manufacturing, and biomass logistics. Notable contributions include stochastic scheduling models, disassembly optimization, and primary pharmaceutical manufacturing scheduling. He has led over 40 funded projects, including USDA/DOE initiatives for biomass logistics. Recipient of numerous awards, including the IIE David F. Baker Distinguished Research Award (2015) and Virginia Tech's Alumni Award for Excellence in Graduate Advising (2013). He directs the Electronics Manufacturing Research Laboratory and serves as associate director of the Center for High Performance Manufacturing. His teaching excellence is recognized through awards like the Sporn Award (1997) and Holzman Educator Award (2000). Professional service includes editorial roles for journals like Computers and Industrial Engineering. Key projects include optimizing nuclear power plant outages and semiconductor wafer fabrication. His work integrates mathematical modeling with real-world applications in manufacturing, logistics, and sustainability.

Yang Caoمشاهده پروفایل

دانشیار

• Computational Biology
• Bioinformatics
• High-Performance Computing (HPC)
• +۳ مورد دیگر

Yang Cao is an Associate Professor in the Department of Computer Science at Virginia Tech, affiliated with the College of Engineering. His research focuses on computational biology, bioinformatics, and high-performance computing, particularly in modeling cell cycle dynamics and stochastic processes in biochemical systems. He holds a Ph.D. from the University of California, Santa Barbara, and degrees from Tsinghua University. Education: Ph.D., Computer Science, University of California at Santa Barbara (2003) M.S., Computational Math, Tsinghua University, Beijing, China (1998) B.S., Applied Math, Tsinghua University (1993) Research Interests: Computational modeling of biological systems, including Caulobacter crescentus and budding yeast cell cycles Stochastic simulation algorithms for biochemical reactions HPC applications in systems biology and computational science Recent Work Trends: Recent articles highlight advancements in cell cycle regulation, parameter estimation for stochastic models, and algorithmic improvements for hybrid simulation methods, emphasizing computational efficiency and biological accuracy.

• Vibrations
• Nonlinear dynamics
• Stochastic systems
• +۳ مورد دیگر

Thomas Breunung is a Researcher and Principal Investigator of the Dynamics, Structures, and Data (DSD) Lab at the University of Wisconsin-Madison's Department of Mechanical Engineering. His work integrates applied mathematics, physics, and data science to study nonlinear structural dynamics, vibrations, and stochastic systems, with applications in aerospace engineering, biological systems, and oceanography. He holds a PhD from ETH Zurich (2021), an MS and BS from Technische Universität Darmstadt (2016, 2013). His research focuses on analytical, computational, and experimental methods to understand complex dynamic systems, including vibration attenuation, rogue wave prediction, and nonlinear oscillator identification. Notable awards include the 2023 ASME Outstanding Reviewer Award and the 2020 USNC/TAM Fellowship. Current courses taught include E M A 545 (Mechanical Vibrations) and M E 440 (Intermediate Vibrations). The DSD Lab emphasizes interdisciplinary collaboration, combining theoretical rigor with practical engineering solutions. Breunung's recent work explores data-driven forecasting of extreme events, stochastic noise utilization in vibration control, and robust system identification techniques. Ongoing projects include improving predictions of freak waves using field measurements and developing computationally efficient models for nonlinear mechanical systems.

• Scientific computing
• Computational mathematics
• Numerical methods
• +۶ مورد دیگر

Christina C. Christara is a Professor in the Department of Computer Science at the University of Toronto, specializing in scientific computing and numerical methods. Her research focuses on numerical solutions of partial differential equations, high performance computing, parallel computation, and financial mathematics applications. She holds a Ph.D. in Computer Science from Purdue University (1988), an M.Sc. from Purdue (1986), and a B.Sc. in Mathematics from Aristotle University (1982). She has taught courses such as Numerical Methods for Optimization Problems (CSC466/2305), High-Performance Scientific Computing (CSC456-2306), and Numerical Algorithms (CSC436), emphasizing both theoretical and computational aspects. Her research interests span numerical weather prediction, computational finance, and advanced numerical techniques like spline collocation and penalty methods. She has supervised over 20 graduate students in topics ranging from GPU-accelerated PDE solvers to XVA pricing models. Her work often addresses challenges in computational efficiency and accuracy, with applications in engineering, finance, and environmental science. She is affiliated with the Numerical Analysis and Scientific Computing Group and actively contributes to high-performance computing methodologies.

Nils Henrik Risebroمشاهده پروفایل

استاد

• Partial Differential Equations
• Conservation Laws
• Numerical Analysis
• +۳ مورد دیگر

Nils Henrik Risebro is a Professor in the Department of Mathematics at the University of Oslo, specializing in Partial Differential Equations and Computational Mathematics. His research focuses on conservation laws, numerical methods for hyperbolic systems, traffic flow models, and stochastic processes. He has contributed extensively to the analysis of nonlinear PDEs, with a particular emphasis on well-posedness, numerical schemes, and applications in fluid dynamics and vehicular traffic. Recent work includes studies on continuum limits of non-local Follow-the-Leader models, non-local traffic flow models, and multilevel Monte Carlo methods for random conservation laws. He collaborates frequently with researchers like Helge Holden, Kenneth Karlsen, and Ulrik Fjordholm. His publications span high-impact journals such as SIAM Journal on Numerical Analysis , ESAIM: Mathematical Modelling and Numerical Analysis , and Zeitschrift für Angewandte Mathematik und Physik . Risebro’s articles often bridge theoretical analysis and computational methods, addressing challenges in fluid dynamics, porous media flow, and stochastic PDEs. His work demonstrates a strong focus on rigorous mathematical frameworks with practical engineering applications.

Basca Jadambaمشاهده پروفایل

استاد

• Inverse Problems
• Continuous Optimization
• Partial Differential Equations
• +۳ مورد دیگر

Basca Jadamba is a Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), part of the College of Science. She serves as Associate Head of the Applied and Computational Mathematics program. Her research focuses on inverse problems, stochastic optimization, partial differential equations, numerical analysis, finite element methods, and mathematical modeling. She has advised undergraduate and graduate students in research and teaches courses at both levels. Jadamba holds a BS from the National University of Mongolia, an MS from the University of Kaiserslautern (Germany), and a Ph.D. from the University of Erlangen-Nuremberg (Germany). Her academic journey includes joining RIT’s School of Mathematics and Statistics in 2008. She is actively involved in academic leadership, including serving as the faculty advisor for RIT’s Student Chapter of the Association for Women in Mathematics. Her research contributions span theoretical and numerical methods for inverse problems, with applications in elasticity imaging and parameter identification in stochastic systems. She has co-authored books and peer-reviewed articles on topics such as uncertainty quantification in variational inequalities, optimization formulations for inverse problems, and numerical methods for partial differential equations. Jadamba’s work emphasizes bridging mathematical theory with practical applications, particularly in engineering and environmental science. Her recent publications highlight advancements in stochastic approximation methods, convex optimization frameworks, and the role of Inf-Sup conditions in inverse problems. She has explored applications ranging from tumor localization in elasticity imaging to congestion network analysis with random data. Her teaching portfolio includes courses like Multivariable Calculus, Mathematical Modeling, and Applied Inverse Problems, reflecting her expertise in both foundational and advanced mathematical topics.

• Stochastic Processes
• Financial Mathematics
• Statistics
• +۳ مورد دیگر

Alexandra Chronopoulou is a Clinical Associate Professor in the Department of Statistics at the University of Illinois. She is affiliated with the Coordinated Science Lab and the Center for Social & Behavioral Science, contributing to interdisciplinary research in both mathematical theory and healthcare applications. Her roles emphasize bridging statistical methodologies with practical challenges in diverse fields. Her research interests span stochastic processes, including fractional Brownian motion and volatility modeling, alongside healthcare systems analysis and medical diagnostics. She applies statistical techniques to study burnout among healthcare professionals, particularly in pandemic testing environments, and explores biomechanical factors in gastrointestinal disorders. Her work also intersects with finance, focusing on risk management and option pricing through advanced stochastic frameworks. In recent years, her articles highlight trends in volatility estimation, fractional processes simulation, and healthcare ergonomics. Earlier contributions include methodological advances in sequential Monte Carlo, community detection algorithms, and multiscale diffusions. No scientific awards were explicitly mentioned in the provided texts. Though no advising or grant information was listed, her research collaborations extend to international partnerships. She contributes to labs and centers that foster interdisciplinary innovation, including work at the Coordinated Science Lab and the Center for Social & Behavioral Science.

Arthur R Schmidtمشاهده پروفایل

دانشیار بالینی

• Surface-water hydraulics
• Hydrology
• Urban stormwater drainage systems
• +۳ مورد دیگر

Arthur R. Schmidt is a Clinical Associate Professor in the Department of Civil Engineering at the University of Illinois, specializing in Water Resources Engineering and Science. He holds a Ph.D. (2002), M.S. (1984), and B.S. (1983) in Civil Engineering-Water Resources from the University of Illinois. His academic journey includes transitioning from Research Assistant Professor (2003-Present) to his current clinical role. Dr. Schmidt's research focuses on surface-water hydraulics , urban stormwater systems , and hydrologic measurement methods . He develops advanced models for urban drainage systems, sustainable infrastructure, and flow quantification techniques. His work integrates sensor technologies (LiDAR, ADCP) for hydrodynamic modeling and environmental applications. His publications emphasize urban hydrology , flood modeling , and infrastructure resilience , with consistent attention to climate impacts and real-time control systems from 2012-2024. Scientific Awards: Universities Council on Water Resources Ph.D. Dissertation Award (2003) Engineering Council Excellence in Advising Awards (2005, 2009, 2011, 2013) He supervises undergraduate research in sustainable drainage and flow measurement, supported by grants including a USDA SBIR grant for instrument development. Dr. Schmidt leads projects at the Hydrosystems Lab and collaborates with agencies like the Metropolitan Water Reclamation District of Greater Chicago.