جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Neuroscience
• Applied Mathematics
• +۳ مورد دیگر

Anastasia Borovykh is an Assistant Professor in the Department of Mathematics at Imperial College London, affiliated with the Faculty of Natural Sciences and the Imperial-X initiative. Her research focuses on developing computational models to enhance artificial intelligence systems, integrating insights from neuroscience, applied probability, signal processing, and mathematical modeling. Key application areas include healthcare (e.g., Parkinson’s disease biomarkers), neuroscience (brain connectivity analysis), and privacy preservation in neural networks. Her affiliations include Academic Women in Mathematics, the Applied Mathematics and Mathematical Physics group, and the Artificial Intelligence Network. She leads a research group emphasizing interdisciplinary approaches to AI, leveraging tools from statistical mechanics and advanced signal processing techniques. Recent publications highlight her work on diffusion models, privacy in machine learning, and reinforcement learning applications in chaotic systems control. Her research often bridges theoretical foundations with practical implementations in healthcare and cybersecurity domains. No scientific awards are explicitly listed in the provided information. Her advising and grants section remains unspecified, though her active research group suggests ongoing collaborative projects. She is part of Imperial-X, a platform for cross-disciplinary innovation.

Thomas Courtadeمشاهده پروفایل

دانشیار

• Information Theory
• Data Science
• Network Communication
• +۵ مورد دیگر

Thomas Courtade is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined Berkeley in 2014 after a postdoctoral fellowship at Stanford University, supported by the NSF Center for Science of Information. His research focuses on information theory, data science, and their intersections with machine learning and privacy-preserving algorithms. Education: Ph.D. in Electrical Engineering, University of California, Los Angeles (2012) M.Sc. in Electrical Engineering, University of California, Los Angeles (2008) B.Sc. in Electrical Engineering, Michigan Technological University (2007, summa cum laude) Research Interests: Information Theory and its applications to network communication Privacy-preserving data analysis and differential privacy Statistical estimation under heterogeneous privacy constraints Optimization in distributed systems and market design Functional inequalities (Brascamp-Lieb, Poincaré-Korn) Machine learning with emphasis on model robustness and efficiency Awards and Fellowships: Electrical Engineering Award for Outstanding Teaching (2020) Hellman Fellow (2016) Advising and Grants: Supervised no listed students (student names not provided in text) Recipient of NSF CAREER Award (2018) Labs and Collaborations: Berkeley Laboratory for Information and System Sciences (BLISS) Center for Theoretical Foundations of Learning, Inference, and Mathematics (CLIMB)

Pierluigi Nuzzoمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Artificial Intelligence
• Control Systems
• +۵ مورد دیگر

Pierluigi Nuzzo is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on high-assurance design of cyber-physical systems, including methodologies for AI, autonomous systems, secure hardware, and electronic design automation. He holds faculty positions at Berkeley and previously at the University of Southern California, where he co-directed the Center for Autonomy and Artificial Intelligence. Education: Ph.D. (UC Berkeley, 2015), M.S./B.S. (University of Pisa and Sant'Anna School) Affiliations: Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Wireless Research Center (BWRC), Industrial Cyber-Physical Systems Center (iCyPhy) Research interests span compositional methods, system design, and certification frameworks. His work integrates formal verification, optimization, and machine learning to ensure safety and trustworthiness in complex systems. Recent projects include DeFacto (cyber-physical production systems) and secure logic locking for ICs. Awards include the NSF CAREER Award, DARPA Young Faculty Award, and multiple best paper awards. His teaching includes EECS C249B on cyber-physical system design principles.

Ferdian Jovanمشاهده پروفایل

استادیار

• Mobile Robotics
• Multimodal Learning
• Time Series Analysis
• +۱۱ مورد دیگر

Ferdian Jovan is an Assistant Professor (Lecturer) at the University of Aberdeen, affiliated with the Aberdeen - South China Normal University Joint Institute within the School of Natural and Computing Sciences. He is also a visiting research fellow at the Faculty of Engineering, University of Bristol. PhD in Computer Science, University of Birmingham (2019) MSc in Computational Logic, Technische Universitaet Dresden (2014) BSc in Computer Science, Universitas Indonesia (2010) His research focuses on mobile robotics, multimodal learning, and time series analysis , with applications in service robotics, energy, healthcare, and extreme environments. He is particularly interested in AI systems that function efficiently in dynamic, resource-constrained settings. His expertise bridges statistical machine learning and real-world robotic deployment. His recent publications show a strong trend in applied AI for robotics and health , including work on digital biomarkers for Parkinson’s disease, battery health prediction, multirobot windfarm maintenance, and adaptive planning. These works have been presented at top venues like KDD, IAAI, and published in journals such as Autonomous Robots and Journal of Field Robotics. DAAD AInet Fellow for Human Centered AI (2023) Ferdian advises PhD students and is actively involved in research collaborations across the UK and internationally. He has held research positions at the University of Bristol, Royal Holloway University of London, and the University of Oxford. His work often involves interdisciplinary teams and real-world data, emphasizing robust and deployable AI solutions. He is associated with the Department of Computer Science at the Meston Building, University of Aberdeen, and leads research in intelligent systems for extreme and dynamic environments.

Peter Laknerمشاهده پروفایل

دانشیار

• Mathematical Finance
• Stochastic Processes
• Stochastic Optimization and Control
• +۶ مورد دیگر

Peter Lakner is an Associate Professor in the Department of Information, Operations, and Management Sciences at the Leonard N. Stern School of Business, New York University. He has been a faculty member at Stern since 1989, teaching courses in statistics, stochastic processes, and financial modeling. Education: Ph.D. in Statistics, Columbia University, 1989 M.A. in Mathematics, Eötvös Loránd University, Budapest, 1980 B.A. in Mathematics, Eötvös Loránd University, Budapest, 1978 His research centers on mathematical finance and stochastic modeling, with a focus on stochastic processes, optimization, and their applications in financial markets. Key areas include option pricing, portfolio optimization, and control problems under partial information. His work integrates deep probabilistic analysis with practical financial engineering challenges. The recent publications reflect a strong trend in stochastic control, reflected diffusions, and high-frequency financial modeling. Topics include limit order books, optimal cash management, and the behavior of reflected Brownian motion in constrained domains. These works demonstrate consistent collaboration with leading researchers in operations research and probability. Scientific Awards: No awards listed in the provided text. Professor Lakner has advised several students, though specific names are not provided in the text. His research has been supported by academic grants implied through publication in top journals, though specific grant details are not mentioned. He is a core member of the IOMS group at Stern, contributing to both theoretical and applied research in operations and statistics. Labs and Research Teams: While no formal lab is mentioned, he is associated with research in stochastic modeling and financial systems, likely collaborating within the IOMS division and with external researchers in probability and finance.

Bozenna Pasik-Duncanمشاهده پروفایل

استاد

• Stochastic Adaptive Control
• System Identification
• Stochastic Systems
• +۵ مورد دیگر

Bozenna Pasik-Duncan is a Professor of Mathematics at the University of Kansas, with courtesy appointments in Electrical Engineering & Computer Science (EECS) and Aerospace Engineering (AE). She holds affiliations with the Information and Telecommunication Technology Center (ITTC) and is a Chancellor’s Club Teaching Professor. Her research focuses on stochastic adaptive control, stochastic systems, and STEM education. Pasik-Duncan has held prestigious roles in IEEE, IFAC, and SIAM, including Chair of the Technical Committee on Control Education and Deputy Chair of IEEE CSS Technical Committee on Control Education. She is an IFAC Fellow, IEEE Fellow, and recipient of numerous awards, including the Kemper Award and KU Chancellor’s Club Teaching Professorship. Education: M.Sc. in Mathematics from Warsaw University (1970), Ph.D. and Habilitation from Warsaw School of Economics (1978, 1986). Active in global academic leadership, she has organized international conferences and workshops, promoting control education and outreach. Her work bridges research and teaching, emphasizing interdisciplinary STEM approaches. Awards and Recognitions: Includes the HOPE Award, L. Hay Award, Steeples Service to Kansas Award, and recognition in the KU Women’s Hall of Fame. She is an advocate for women in STEM, founding the KU AWM Student Chapter and leading initiatives like Mathematics Awareness Month. Professional Contributions: Editor-in-Chief of Springer’s 'Women in Control Around the Globe,' and founder of the Women in Control (WIC) group. Her involvement spans IEEE WIE, IFAC Technical Board, and leadership roles in control education at global venues.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.

Raimundo Saonaمشاهده پروفایل

پژوهشگر ارشد

• Game Theory
• Probability Theory
• Algorithmic Game Theory
• +۳ مورد دیگر

Raimundo Saona is a Research Fellow in the Department of Mathematics at the London School of Economics and Political Science (LSE), where he joined in 2025. His work bridges theoretical mathematics with practical applications in computer science and decision theory. His research focuses on Game Theory, Probability Theory, Algorithmic Game Theory, Stochastic Analysis, Optimal Control, and Imprecise Probability Theory. He develops mathematical tools to provide new insights on situations involving uncertainty, working with both continuous and discrete models, proving theoretical existence theorems, coding efficient algorithms, and analyzing both dynamic online and static offline settings. His publication record shows a strong focus on stochastic games, Markov decision processes, and prophet inequalities. His recent work (2022-2024) has centered on ergodic unobservable MDPs, marginal values in stochastic games, and zero-sum random games on directed graphs, demonstrating his continued contribution to advancing theoretical frameworks in game theory and decision processes. His earlier work established foundational results in POMDPs and prophet inequalities.

Christoph Mathejaمشاهده پروفایل

دانشیار

• Formal Verification
• Probabilistic Programming
• Software Systems Engineering
• +۸ مورد دیگر

Christoph Matheja is an Associate Professor at the Technical University of Denmark , affiliated with the Department of Applied Mathematics and Computer Science . His work focuses on formal verification, probabilistic programming, and software systems engineering. Research Highlights : Operational semantics for probabilistic programs, fixed-point characterizations of rewards in MDPs, and data-driven Petri net modeling for process mining. Publications span formal verification techniques, probabilistic programming paradigms, and tools for process mining, emphasizing interdisciplinary applications in computer science and software engineering. Scientific Awards : POPL 2021 Distinguished Paper Advising & Grants : Supervises PhD students in automated reasoning about randomized algorithms, with project funding active from 2023 to 2026. Professional Activities : Serves on committees for ACM SIGPLAN Symposium on Principles of Programming Languages (2022–present), International Conference on Computer Aided Verification (2023–present), and Formal Methods Symposium (2022–present). Organizer of the 2022 Workshop on Verification of Probabilistic Programs.

Brian Dentonمشاهده پروفایل

استاد

• Operations Research
• Data Science
• Healthcare Systems
• +۳ مورد دیگر

Brian Denton is the Stephen M. Pollock Collegiate Professor of Industrial and Operations Engineering at the University of Michigan College of Engineering, with a courtesy appointment as Professor of Urology. His research centers on data-driven sequential decision making and optimization under uncertainty, applied to healthcare delivery, public health, and chronic disease management. His educational background includes: Ph.D. in Management Science from McMaster University (2001) M.Sc. in Physics from York University (1996) B.Sc. in Chemistry and Physics from McMaster University (1994) Denton develops mathematical models for sequential decision making under uncertainty, including stochastic programming and Markov decision processes. These are applied to medical decision making for cancer, cardiovascular disease, and diabetes, as well as optimization of scheduled systems in manufacturing. His work bridges operations research, data science, and clinical medicine to improve patient outcomes and system efficiency. Recent publications demonstrate integration of machine learning with robust optimization for healthcare applications, particularly in cancer detection and chronic disease management. His group develops novel methods for multi-model decision processes and dynamic prediction models addressing clinical data uncertainty. His accolades include: NSF CAREER Award (2009) INFORMS Daniel H. Wagner Prize (2005) IISE Outstanding Publication Award (2005, 2020) INFORMS Service Section Best Paper Prize (2010) Distinguished Mentor Award (2018) Distinguished Educator Award (2017) INFORMS Fellow IISE Fellow Dr. Denton secured NSF grants CMMI 1462060 for chronic disease treatment optimization and CMMI 1536444 for cancer screening strategies. He mentors PhD students including Weiyu Li and collaborates with clinical departments across the University of Michigan and Mayo Clinic. He leads a research group in healthcare decision analytics affiliated with the University of Michigan Cancer Center and Institute for Healthcare Policy and Innovation (IHPI), working with urology, internal medicine, and pathology departments on translational research projects.

• Machine Learning
• Contact/Symplectic Geometry
• Computational Geometry
• +۶ مورد دیگر

Maia Fraser is an Associate Professor at the University of Ottawa , cross-appointed to the School of Electrical Engineering and Computer Science and affiliated with the Brain and Mind Research Institute . Her research bridges Machine Learning , Symplectic Geometry , and Computational Geometry , with interdisciplinary collaborations in Neuroscience and Mathematical Biology . PhD in Computer Science (University of Chicago, 2013) PhD in Mathematics (Stanford University, 1994) Postdoctoral work: University of Toronto Industry experience: Supercomputing Systems AG (Zurich) Her recent work explores AI safety through the lens of living systems, combining mathematical principles with societal implications. She examines temporal abstraction in continual learning, reinforcement learning limits, and geometric constraints in AI. Publications span topology , biological navigation , and neural time scales . Scientific contributions include: Co-organizing Fields Medal Symposium (2022) Co-moderating New Paradigms session at Fields Institute (2024) Leading INTER-MATH-AI (IMA) NSERC-CREATE program (2022) She investigates AI's impact on mathematical research and is writing a book on mathematical frameworks for AI safety .

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

Erik Lindstromمشاهده پروفایل

استاد

• Financial Mathematics
• Statistical Inference
• Stochastic Processes
• +۲ مورد دیگر

Erik Lindstrom is a Professor of Mathematical Statistics and Head of Division at Lund University's Centre for Mathematical Sciences within the Faculty of Science. He additionally serves as assistant Director for the Industrial Engineering and Management program and directs the statistical consulting service for the Science faculty. His educational background includes: M.Sc. in Engineering Physics (2000) from Lund University B.Sc. in Economics and Business (2000) from Lund University M.Sc. in Economics and Business (2001) from Lund University Ph.D. in Mathematical Statistics (2004) from Lund University Lindstrom's research bridges theoretical statistics with real-world applications in finance and energy systems. His work focuses on parameter estimation algorithms for discretely observed diffusion processes, financial mathematics including option valuation under uncertainty, energy finance, power system stability, and statistical testing methodologies. The 2015 book "Statistics for Finance" synthesizes key aspects of his financial statistics research. Recent publications (2023-2025) reveal a concentrated research trajectory applying statistical jump models to cryptocurrency markets and financial options, alongside advanced time series techniques for building energy load forecasting. This demonstrates his consistent interdisciplinary approach connecting stochastic processes with practical financial and energy sector challenges. His scientific recognition includes: Excellent Teaching Practitioner (ETP) award (2013) Lindstrom actively mentors doctoral researchers including current PhD students Carl Åkerlindh and Peter Nystrup, and postdoctoral fellows Josef Höök and Sidi Mohamed Aly. His teaching portfolio spans Financial Statistics (MSc), Non-linear Time Series (MSc/PhD), and Statistical Inference for Partially Observed Stochastic Processes (PhD), supported by research grants in financial mathematics and energy applications. As head of division and director of the statistical consulting service, he leads initiatives providing statistical methodology support across the Science faculty, fostering cross-disciplinary research collaboration through structured consultation frameworks.

• Mathematical statistics
• Analysis
• Probability theory
• +۳ مورد دیگر

Shohei Nakajima serves as an Assistant Professor (without tenure) at Waseda University with dual appointments in the School of International Liberal Studies under the Faculty of International Research and Education, and the School of Fundamental Science and Engineering under the Faculty of Science and Engineering. He teaches undergraduate courses including Introductory Calculus, First Year Seminar, and Stochastic Processes across these institutions. Education: Ph.D. in Fundamental Science and Engineering, Waseda University (2017.04 - 2023.03) Master's degree in Fundamental Science and Engineering, Waseda University (2015.04 - 2017.03) Dr. Nakajima's research program centers on mathematical statistics, analysis, and probability theory with specialized focus on statistical inference for stochastic processes driven by fractional Brownian motion. His theoretical work addresses parameter estimation challenges under various noise conditions, developing asymptotic properties of estimators for both continuous and discrete observation settings. He has made significant contributions to understanding maximum likelihood estimation techniques and least squares methods for complex stochastic systems with non-standard noise structures. His publication pattern reveals a concentrated research trajectory examining statistical properties of estimators for stochastic differential equations with fractional components. The work consistently bridges theoretical mathematical analysis with practical statistical methodology, particularly addressing cases involving small noise asymptotics, multiplicative fractional noise, and non-Lipschitz coefficients that present unique analytical challenges. Dr. Nakajima currently leads a Japan Society for the Promotion of Science (JSPS) funded research project (2024.04-2026.03) titled "Statistical inference theory for stochastic differential equations driven by fractional Brownian motion," which extends his internal special research projects conducted from 2021-2023 on related theoretical frameworks.

• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +۵ مورد دیگر

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.