جست‌وجوی استاد و پژوهشگر

• Macroeconomics
• Monetary Policy
• Macro-prudential Policy
• +۴ مورد دیگر

Professor Tatiana Damjanovic is a Professor of Economics at the University of Durham's Business School since 2015. She holds a PhD from the Stockholm School of Economics (supervised by Lars Ljungqvist) and prior roles include Associate Professor at the University of Exeter and positions at the University of St. Andrews. Her research focuses on Macroeconomics, Monetary Policy, Banking, and Public Economics. Notable contributions include studies on fiscal policy design, financial intermediation, and gender equality's interplay with economic growth. Affiliations: Business School, Department of Economics, University of Durham. Education: BSc in Functional Analysis (Moscow State University), MSc from New Economic School, PhD (Stockholm School of Economics). Research Interests: Her work bridges macroeconomic policy analysis with institutional frameworks, emphasizing monetary policy's welfare impacts, banking sector dynamics, and fiscal frameworks. Recent studies explore heterogeneous firms, loan-to-collateral ratios, and gender equality's macroeconomic dimensions. Esteem Indicators: 2019: MMF Committee Member (Money Macro and Finance Research Group). 2019: FCT Evaluation Panel Member (Portuguese Science Funding Agency). 2017: Academy of Finland Review Panel Member (Economics). Publications Trends: Over 30 peer-reviewed articles since 2003, with a focus on monetary policy design, banking stability, and fiscal frameworks. Recent work highlights include 2024's analysis of heterogeneous firms in monetary policy, 2021's Ramsey policy contributions, and 2020 studies on financial bailouts and gender equality. Advising & Grants: While specific student names are not listed, her work implies significant academic mentorship. Grant details are not explicitly mentioned in the text. Labs/Teams: No dedicated lab affiliations noted, but collaborative work with institutions like the MMF Group and FCT suggests active network engagement.

Vladimir Itskovمشاهده پروفایل

دانشیار

• theoretical neuroscience
• applied algebraic topology
• neural networks
• +۳ مورد دیگر

Vladimir Itskov is an Associate Professor in the Department of Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on theoretical neuroscience, applied algebraic topology, and neural networks. He holds a Ph.D. in Mathematics from the University of Minnesota (2002) and a B.S. from Moscow Institute of Electronics and Mathematics (1995). His career includes roles at the University of Nebraska-Lincoln (2009–2014), Columbia University’s Center for Theoretical Neuroscience (2006–2009), and Rutgers University (2004–2006). Research interests include understanding neural coding, network dynamics, and topological methods in neuroscience. Notable work involves applying algebraic topology to analyze neural correlations and developing models for neural network behavior. He has received grants from NIH, NSF, and DARPA, focusing on projects like olfactory coding and neural network dynamics. His lab, the Mathematical Neuroscience Laboratory, develops computational tools and collaborates on interdisciplinary projects. Software packages are hosted on GitHub (nebneuron repository). Publications span journals such as PNAS, SIAM, and Neural Computation, addressing topics from clique topology to competitive network dynamics. His theoretical contributions emphasize bridging data-driven neuroscience with mathematical rigor.

Vincent Y. F. Tanمشاهده پروفایل

استاد

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +۳ مورد دیگر

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

Mark Hertzbergمشاهده پروفایل

دانشیار

• Theoretical Physics
• Cosmology
• Particle Physics
• +۵ مورد دیگر

Mark Hertzberg is an Associate Professor in the Department of Physics and Astronomy at Tufts University, located within the School of Arts and Sciences. He holds a PhD from MIT (2010), following degrees from the University of Sydney. His research focuses on theoretical physics at the intersection of cosmology, particle physics, and astrophysics, with a particular emphasis on dark matter (e.g., axions), cosmological inflation, gravitation theory, and quantum phenomena. He has been Director of the Institute of Cosmology at Tufts since 2023. Education: PhD Physics, MIT, 2010 MSc Physics, University of Sydney, 2004 BSc Physics & Mathematics, University of Sydney, 2002 Research Interests: Dark matter structure and axion physics Cosmological inflation and post-inflationary dynamics Gravitational theory and quantum gravity constraints Large-scale structure and cosmic microwave background analysis Grants: Multiple NSF awards including 'Cosmology and Fundamental Physics' (2024-2026) and 'Constraining Physics Beyond the Standard Model with Cosmological Observations' (2023-2026). Teaching: Courses include General Relativity, Cosmology, Quantum Field Theory, and graduate research supervision.

Banu Lokmanمشاهده پروفایل

استاد

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +۳ مورد دیگر

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Helmut H. Streyمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Biophysics
• Microfluidics
• +۶ مورد دیگر

Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.

Ioannis Emirisمشاهده پروفایل

استاد

• Computational Geometry
• Algebraic Algorithms
• Robotics
• +۴ مورد دیگر

Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.

Mike Tehranchiمشاهده پروفایل

مدرس

• Mathematical Finance
• Stochastic Analysis
• Probability Theory
• +۳ مورد دیگر

Dr. Mike Tehranchi is a faculty member at the University of Cambridge, affiliated with the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) . His research focuses on mathematical finance, stochastic processes, and probability theory. He holds a Lecturer position and is actively involved in academic research, with notable contributions to financial models, term structure analysis, and stochastic calculus. His work bridges theoretical probability and applied finance, addressing topics such as interest rate modeling, implied volatility, and optimal investment strategies. Tehranchi’s research often intersects with optimization, statistical methods, and interdisciplinary applications in astrophysics and fluid dynamics. He maintains an active publication record and contributes to the academic community through his role in the Statistical Laboratory. Key research trends in his articles include the analysis of financial derivatives, stochastic processes in market dynamics, and the application of advanced mathematical techniques to real-world financial problems. His work emphasizes rigorous theoretical foundations while addressing practical challenges in quantitative finance. Dr. Tehranchi has no listed students or academic awards in the provided texts. He can be reached via email and is based in Room D1.04 at the Statistical Laboratory.

Harish Krishnanمشاهده پروفایل

استاد

• Supply Chain Management
• Operations Research
• Business Strategy
• +۳ مورد دیگر

Harish Krishnan is a Professor in the Operations and Logistics Division at the UBC Sauder School of Business. He holds degrees from Delhi, Alabama, Michigan (MA and PhD). His research focuses on incentive distortions in supply chains, contracts for supply chain coordination, and broader supply chain management strategies. He teaches courses such as Process Fundamentals and Supply Chain Management in the 2024-2025 academic year. Education: BEng from Delhi MS from University of Alabama MA from University of Michigan PhD from University of Michigan Research Interests: Incentive distortions in supply chains Contracts and supply chain coordination Supply chain management with a focus on sustainability and operational risk Recent Work Trends: Blockchain applications in sustainable global value chains Climate policy analysis, particularly international carbon mitigation frameworks Strategic collaboration between competitors in operational contexts Affiliations: Member of the Entrepreneurship and Innovation Group Part of the Operations and Logistics Division leadership

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +۳ مورد دیگر

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

Dmitry Turaevمشاهده پروفایل

استاد

• Applied Mathematics
• Pure Mathematics
• Mathematical Physics
• +۵ مورد دیگر

Professor Dmitry Turaev is a Professor in Dynamical Systems at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His primary role includes teaching courses such as Dynamical Systems and Bifurcation Theory. He is affiliated with the Applied Mathematics and Mathematical Physics groups and the Mathematics research and teaching staff. His research focuses on dynamical systems, chaos theory, bifurcation theory, and their applications in physics and engineering. Education: Ph.D. in Mathematics (details inferred from academic position). Research interests span applied and pure mathematics, with a strong emphasis on dynamical systems, including Hamiltonian systems, homoclinic tangencies, and chaotic behavior in reversible systems. Turaev's work explores complex phenomena such as the emergence of Lorenz-like attractors, Fermi acceleration, and the breakdown of symmetry in dynamical systems. His recent publications highlight studies on pseudohyperbolic attractors, chaotic dynamics in symmetric networks, and nonuniformly expanding random systems. Turaev advises numerous PhD students, reflecting his active role in nurturing the next generation of researchers in dynamical systems. He maintains a lab/working group within the Dynamical Systems group at Imperial College, collaborating with colleagues like Jeroen Lamb and Martin Rasmussen. His research often intersects with interdisciplinary topics like quantum physics and nonlinear optics.

Tim Coloniusمشاهده پروفایل

استاد

• Fluid Dynamics
• Cavitation and Bubble Dynamics
• Aerodynamic Sound
• +۵ مورد دیگر

Tim Colonius is the Frank and Ora Lee Marble Professor of Mechanical Engineering and Medical Engineering and holds the Cecil and Sally Drinkward Leadership Chair at the California Institute of Technology. He has been affiliated with Caltech since 1994 and currently serves as Executive Officer for Mechanical and Civil Engineering . Colonius earned his B.S. from the University of Michigan (Ann Arbor), and both his M.S. and Ph.D. from Stanford University. Research Interests: His work focuses on fluid dynamics (global instabilities, cavitation, aerodynamic sound), flow control (closed-loop control, reduced-order modeling), and biomedical applications (shock waves, lithotripsy, ultrasound). He also develops advanced numerical methods for interface capturing, immersed-boundary techniques, and high-order accuracy. Scientific Contributions: Recent publications highlight his research in multiphase flows, vortex ring collisions, turbulent jet analysis, GPU-accelerated simulations, and biomedical applications. His group uses computational and data-driven approaches to study turbulence, instabilities, and flow optimization. Scientific Awards: AIAA Aeroacoustics Award Fellow of the Acoustical Society of America Fellow of the American Physical Society (APS) NSF and DoD research grants

• Neural Dynamics
• Computational Neuroscience
• Spatiotemporal Patterns
• +۳ مورد دیگر

Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.

• Machine Learning
• Medical Imaging
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Cheng Ouyang is a Departmental Lecturer at the University of Oxford's Institute of Biomedical Engineering, part of the Department of Engineering Science. Affiliated with St. Peter's College, his research focuses on developing data-efficient, robust machine learning approaches for medical imaging and signal analysis. Key interests include domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal learning applied to medical data such as ultrasound and MRI. Prior to Oxford, he conducted postdoctoral research in cardiac imaging at Imperial College London, where he also earned his PhD in Computing. His work emphasizes practical medical applications, such as accelerating MRI reconstruction and enhancing ECG classification through multimodal techniques. Recent contributions include the CMRxRecon2024 dataset for cardiac MRI and federated learning approaches for low-dose CT denoising. His methods address challenges in generalizability, stability, and user interaction in clinical AI systems. Awards and recognitions are pending explicit mentions in the text. Dr. Ouyang's research spans foundational machine learning theory and applied biomedical engineering, with a focus on bridging gaps between algorithmic innovation and clinical utility. His lab collaborates across disciplines to advance medical imaging analysis and decision support systems.

Jason Schweinsbergمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Mathematical Biology
• +۵ مورد دیگر

Jason Schweinsberg is a Professor in the Department of Mathematics at the University of California, San Diego (UCSD), where he has been a faculty member since Fall 2004. His academic journey began with a Ph.D. in Statistics from the University of California, Berkeley in 2001, followed by a three-year NSF Postdoctoral Fellowship at Cornell University. His research focuses on probability theory with applications to evolutionary biology and population genetics. Schweinsberg's work centers on stochastic processes involving coalescence, branching Brownian motion, and their connections to biological phenomena. He has made significant contributions to understanding population models undergoing selection, cancer evolution, and spatial mutation processes. Recent publications reveal a strong emphasis on coalescent theory (particularly Λ-coalescents and nested coalescents), branching processes with absorption, and spatial evolutionary models. His work often bridges rigorous mathematical analysis with biological applications, especially in population genetics and cancer modeling. The 15 most recent articles demonstrate consistent focus on asymptotic analysis of stochastic processes, genealogical structures, and mutation dynamics in evolving populations. Scientific Awards: Fellow of the Institute of Mathematical Statistics NSF Postdoctoral Research Fellowship While specific student names aren't listed in the source material, Schweinsberg has delivered numerous lecture series at international institutions including the Indian Institute of Science (Bangalore), Centre de Recherches Mathématiques (Montreal), and the Isaac Newton Institute (Cambridge), indicating active mentorship and academic leadership. His collaborations span multiple institutions, with frequent co-authorship with researchers like Julien Berestycki, Nathanaël Berestycki, and Rick Durrett. Though no formal lab structure is mentioned, Schweinsberg participates in interdisciplinary research communities through workshops at institutions like BIRS (Banff International Research Station), where he presented on mutation patterns in spatially structured populations in May 2025. His work connects probability theory with biological applications through sustained collaborations across mathematics, statistics, and computational biology fields.