جستجوی استاد راهنمای دانشگاهی

Urs Langمشاهده پروفایل

استاد

• Differential Geometry
• Metric Geometry
• Geometric Measure Theory
• +۳ مورد دیگر

Urs Lang is a Full Professor at the Department of Mathematics, ETH Zurich, where he has held a professorship since 2001. His academic journey began with mathematics studies at the University of Berne and the University of Freiburg i.Br., culminating in a doctoral degree focused on hyperbolic geometry and minimal surfaces. Education: University of Berne (undergraduate) University of Freiburg i.Br. (doctoral degree) Lang's research lies at the intersection of differential geometry , metric geometry , and geometric group theory . His work explores non-positive curvature spaces, geometric measure theory, and large-scale Lipschitz analysis. Recent publications examine combinatorial hyperbolicity, isoperimetric inequalities, and rank-rigidity phenomena. The publications overview reveals a consistent focus on geometric structures, including: Higher-rank hyperbolicity in singular spaces Convex geodesic bicombings Injective hulls in geometric group theory Nonlinear potential theory on hyperbolic metric spaces Lipschitz extension problems Curvature comparison theorems Lang actively contributes to academic discourse through editorial roles at journals like Geometry and Topology and Analysis and Geometry in Metric Spaces , as well as organizing major conferences including the 2025 Metric Analysis Trimester Program at Bonn's Hausdorff Institute.

Albert S. Berahasمشاهده پروفایل

استادیار

• Nonlinear Optimization
• Machine Learning Optimization
• Stochastic Optimization
• +۵ مورد دیگر

Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan's College of Engineering. He joined the university in 2020 after completing postdoctoral positions at Lehigh University (2018-2020) and Northwestern University (2018). He holds a PhD in Engineering Sciences and Applied Mathematics from Northwestern University (2018), an MS in Applied Mathematics from Northwestern (2012), and a BSE in Operations Research and Industrial Engineering from Cornell University (2009). His research focuses on designing, developing, analyzing, and implementing algorithms for solving large-scale nonlinear optimization problems. His work spans multiple sub-fields including constrained optimization, optimization for machine learning, stochastic optimization, derivative-free optimization, and decentralized optimization. He is affiliated with the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). Berahas has received numerous honors including the Charles Broyden Prize (2025), the Air Force Office of Scientific Research Young Investigator Program award (2025), the IISE Operations Research Division Teaching Award (2024), and the North Campus Dean's MLK Spirit Award for Community Building & Impact (2024). His recent publications demonstrate strong activity in developing novel optimization frameworks with theoretical guarantees for challenging problem settings. His research has been supported by significant grants including from the Office of Naval Research (ONR) and the Air Force Office of Scientific Research. He actively mentors PhD students and has successfully advised Jiahao Shi, who defended his dissertation in March 2025 and joined Amazon. Berahas is also engaged in community outreach, particularly through initiatives like Engage Detroit that aim to empower Detroit's next generation of engineers.

• Computational Biology
• Structural Biology
• Bayesian Inference
• +۱۰ مورد دیگر

Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.

• Geometric Measure Theory
• Optimal Transport
• Calculus of Variations
• +۳ مورد دیگر

Luigi Ambrosio is a Full Professor at the Scuola Normale Superiore di Pisa (SNS), specializing in geometric measure theory, optimal transport, and partial differential equations. His research focuses on the interplay between geometric analysis, functional analysis, and calculus of variations, with applications to metric measure spaces and stochastic processes. He has organized numerous conferences and schools on optimal transport and geometric analysis, including the 2025 'XXXV Convegno Nazionale di Calcolo delle Variazioni.' Key research interests include the theory of currents, regularity of flows, and the application of optimal transport to problems in probability and geometry. Notable contributions include foundational work on metric Sobolev spaces, RCD spaces, and the analysis of geometric flows. Ambrosio frequently collaborates with leading institutions and has supervised numerous seminars on topics ranging from gradient flows to non-smooth geometric structures. His publications span over 150 papers, addressing topics such as the regularity of vector fields, entropy flows in Carnot groups, and the stability of action functionals. Recent works (2021–2025) explore superposition principles for currents, sharp PDE estimates for random matching, and embedding theorems for metric spaces. Ambrosio is also active in academic leadership, contributing to editorial boards and international research networks.

Anthony Blochمشاهده پروفایل

استاد

• Geometric Mechanics
• Nonlinear Dynamics
• Hamiltonian Mechanics
• +۱۰ مورد دیگر

Anthony Bloch is the Alexander Ziwet Collegiate Professor of Mathematics and Professor of Mathematics at the University of Michigan, serving as Chair of the Department of Mathematics. He is affiliated with the Center for the Study of Complex Systems (CSCS) in Weiser Hall. His research focuses on: Geometric Mechanics : Hamiltonian/Lagrangian mechanics, symplectic geometry, and integrable systems including Toda lattices and rigid body dynamics Nonlinear Dynamics : Nonholonomic systems with nonintegrable constraints, stability analysis, and continuous-discrete flow relationships Control Theory : Nonlinear and optimal control applications extending to quantum dynamics and astrophysical systems Recent publications demonstrate geometric methods applied to nonholonomic control, virtual constraints, and stabilization, with growing interdisciplinary work in network dynamics, quantum control, and cosmological models like cosmic reheating. Through the Center for the Study of Complex Systems, Professor Bloch collaborates across disciplines to investigate complex phenomena in natural and engineered systems, leveraging geometric frameworks to address fundamental questions in mechanics and dynamics.

Ozgur Yilmazمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Signal Processing
• Compressed Sensing
• +۵ مورد دیگر

Ozgur Yilmaz is a Professor in the Department of Mathematics at the University of British Columbia (UBC). He is the Director of the Pacific Institute for the Mathematical Sciences (PIMS) and has held roles such as Interim Deputy Director at PIMS and Deputy Director at the Banff International Research Station (BIRS). His research focuses on applied harmonic analysis, signal processing, compressed sensing, and seismic signal processing. Education: PhD in Applied and Computational Mathematics from Princeton University (2001), B.Sc. in Mathematics and Electrical Engineering from Boğaziçi University (1997). Research Interests: Mathematical problems in analog-to-digital conversion, blind source separation, sparse approximations, compressed sensing, and their applications in seismic exploration. He has contributed to advancements in sigma-delta quantization, low-rank matrix recovery, and compressed sensing algorithms. Funding: Recipient of NSERC Discovery Grants, UBC Data Science Institute grants, and leadership in collaborative research groups (CRGs) on high-dimensional data analysis and applied harmonic analysis. His work bridges theoretical mathematics with practical applications in signal processing and AI-driven medical imaging. Students and Postdocs: Supervised numerous PhD and MSc students in areas like compressed sensing, seismic data reconstruction, and machine learning. Current advisees include Aaron Berk and Xiaowei Li. Former students hold positions at academic institutions and tech companies. Labs and Collaborations: Affiliated with UBC’s Data Science Institute (DSI), Centre for Artificial Intelligence Decision-making and Action (CAIDA), and the Institute of Applied Mathematics (IAM). Collaborates on projects integrating AI with scientific discovery, such as retinal biomarker identification using deep learning.

Ilyas Khanمشاهده پروفایل

پژوهشگر ارشد

• Geometric Analysis
• Geometric Flows
• Differential Geometry
• +۳ مورد دیگر

Ilyas Khan is an NSF Postdoctoral Research Fellow at Duke University's Department of Mathematics, supervised by Robert Bryant. His research focuses on geometric analysis and geometric flows, particularly studying the structure of geometric PDEs and their solutions in higher codimension settings. He holds a position within the Department of Mathematics without part-time status. Research Interests: Ilyas' work centers on geometric flows (e.g., mean curvature flow, Laplacian solitons) and geometric analysis, with an emphasis on self-shrinkers, translating surfaces, and solutions with finite total curvature. His research combines differential geometry with partial differential equations to explore uniqueness properties of geometric structures and their asymptotic behaviors. Publications Overview: His work includes studies on G 2 Laplacian solitons, ancient solutions to curve shortening flows, and applications of dyadic decomposition to convex domains. Recent contributions address uniqueness theorems for asymptotically conical self-shrinkers and self-expanders in higher codimensions, demonstrating rigorous analysis of geometric PDEs. Advising & Grants: No advising roles or specific grants listed in the provided information. Labs/Teams: Affiliated with Duke University's Mathematics Department but no specific labs or collaborative teams explicitly mentioned.

• Mixed-Integer Optimization
• Combinatorial Optimization
• Discrete Mathematics
• +۳ مورد دیگر

Timm Oertel is a Professor in the Department of Data Science at Friedrich Alexander University Erlangen-Nuremberg (FAU), holding the Chair of Analytics & Mixed-Integer Optimization. His office is located in Room 03.344 at Cauerstraße 11, Erlangen, and he can be contacted via email at timm.oertel@fau.de or phone at +49 9131 85-67313. His research focuses on mixed-integer optimization, combinatorial optimization, and discrete mathematics, with significant contributions to sparse solutions in lattices and semigroups, integer Carathéodory rank, knapsack polyhedra, and parametric integer optimization. His work bridges theoretical computer science, operations research, and discrete geometry, emphasizing structural properties of integer solutions and algorithmic efficiency. Professor Oertel's publication record (2013-2025) reveals consistent trends in theoretical integer programming, with recent work (2020-2025) concentrating on sparsity patterns, approximation in algebraic structures, and complexity bounds. He frequently collaborates with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat, publishing in top venues like Mathematical Programming and SIAM Journal on Optimization. His research demonstrates deep connections between combinatorial geometry and optimization theory.

• Mathematical Analysis
• Partial Differential Equations
• Sub-Riemannian Geometry
• +۳ مورد دیگر

Annamaria Montanari is a Full Professor at the Department of Mathematics, University of Bologna, where she has worked since 2016. Her research focuses on Mathematical Analysis, particularly degenerate elliptic partial differential equations and Sub-Riemannian geometric structures. University of Bologna (1998-present) Ph.D. in Mathematics (University of Bologna, 1998) Visiting positions at Temple University, MSRI Berkeley, Pittsburgh University, ETH Zurich Her research explores: Regularity theory for Levi curvature equations Viscosity solutions in complex geometry Sub-Riemannian metrics and cut loci Optimal transport with non-quadratic costs Recent publications demonstrate expertise in: Geometric PDEs Convexity on Carnot groups Harnack inequalities in quasi-metric spaces Transport equations and conservation laws She supervises doctoral students and collaborates on international projects, serving as Editor-in-Chief of the Bruno Pini Mathematical Analysis Seminar journal.

• Operations Management
• Inventory Management
• Stochastic Models
• +۹ مورد دیگر

Marcus ANG Teck Meng is an Associate Professor of Operations Management (Education) at the Lee Kong Chian School of Business, Singapore Management University. He is also the Academic Director and Track Coordinator of the International Trading Institute (ITI). His academic journey began at the National University of Singapore, where he earned dual B.Sc. degrees in Mathematics, followed by an S.M. in High Performance Computing and a Ph.D. in 2008. Ph.D., National University of Singapore, 2008 S.M., High Performance Computing for Engineering Systems, Singapore-MIT Alliance under NUS, 2003 B.Sc. (Hon), Mathematics, National University of Singapore, 2002 B.Sc., Mathematics, National University of Singapore, 2001 His research focuses on the theoretical and applied aspects of operations management, particularly in inventory management , stochastic models , and robust optimization . His work bridges mathematical rigor with real-world applications in supply chains, healthcare, and e-commerce. He has made significant contributions to warehouse storage optimization, hospital resource planning, and online retail fulfillment under uncertainty. The recent articles reflect a strong trend in applying robust optimization and stochastic modeling to logistics and service operations. His research spans domains such as healthcare analytics, e-commerce fulfillment, and inventory systems. Keywords like operations research , supply chain management , and data analytics dominate, with sub-fields including appointment scheduling, risk measures, and warehouse layout optimization. His teaching excellence has been widely recognized: Winner, SMU Undergraduate Excellent Teacher Award (2020) Winner, SMU Undergraduate Innovative Teacher Award (2018) Nominee, SMU Undergraduate Innovative Teacher Award (2021, 2022) Multiple appearances on SMU Dean’s Teaching Honour List (Undergraduate and Postgraduate) MPA Research Fellow (AY2019–2023) Dean’s List, NUS Department of Mathematics (AY1998–2001) He leads the International Trading Institute (ITI) and has been involved in pedagogical innovation through TEL grant-funded projects such as Inn or Out and Pricing Boss . While no formal list of advisees is provided, his role as Academic Director and course coordinator suggests active mentorship and supervision of students in operations and analytics. He has no known lab or research team explicitly mentioned, but his case studies and projects indicate strong industry engagement and applied research leadership.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +۴ مورد دیگر

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

Christos Thrampoulidisمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning Theory
• Statistical Signal Processing
• +۳ مورد دیگر

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. Previously, he held the same position at the University of California, Santa Barbara (2018–2020) and was a Postdoctoral Researcher at MIT (2016–2018). He earned his Ph.D. and M.Sc. in Electrical Engineering from Caltech (2016 and 2012) and a Diploma in ECE from the University of Patras, Greece (2011). His research focuses on machine learning theory , optimization , high-dimensional statistics , and statistical signal processing . Specific interests include the theoretical foundations of deep learning, neural collapse phenomena, and the analysis of overparameterized models. He has contributed to understanding imbalanced data challenges, model compression, and safe optimization techniques. Key academic achievements include the Qualcomm Innovation Fellowship (2014) and the A. Mentzeolopoulos Fellowship . He teaches graduate courses on Optimization and Signals & Systems at UBC. His research group (MILD Group) actively explores topics like language model geometry, federated learning, and algorithmic reasoning in transformers. Notable publications include work on neural-collapse geometry, imbalance-aware learning, and the theoretical underpinnings of transformers. He advises multiple Ph.D., M.Sc., and undergraduate students, fostering a collaborative environment centered on equity, diversity, and inclusion.

Salar Fattahiمشاهده پروفایل

استادیار

• Optimization
• Machine Learning
• Robust Matrix Recovery
• +۴ مورد دیگر

Salar Fattahi is an Assistant Professor at the University of Michigan, affiliated with the College of Engineering’s Department of Industrial and Operations Engineering. He holds additional appointments with the Michigan Institute for Computational Discovery and Engineering (MICDE), Michigan Institute for Data Science (MIDAS), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). PhD in Industrial Engineering and Operations Research from UC Berkeley M.Sc. in Electrical Engineering from Columbia University B.Sc. in Electrical Engineering from Sharif University of Technology Research Focus: Developing scalable computational methods for structured optimization and machine learning problems by exploiting sparsity, low-rankness, and benign landscape properties. Applications span gene regulatory networks, power systems, and brain connectivity modeling. 2025: Parametric algorithms for MIQPs over trees 2024: Triple Component Matrix Factorization for global/local/noise separation 2023: Robust subspace recovery and dictionary learning Scientific Recognition: NSF CAREER Award (2023) INFORMS Best Paper Awards (2023, 2024) Dean’s MLK Spirit Award (2024) MICDE Catalyst Grant (2021) Academic Service: Associate Editor for INFORMS Journal on Data Science; Area Chair for NeurIPS, ICML, and ICLR. Mentored students including Jianhao Ma (now Tsinghua University), Geyu Liang (Amazon), and Aaresh Bhathena. Research supported by NSF, ONR, MICDE, MIDAS, START, and DEI Faculty grants.

• Machine Learning Theory
• Statistical Physics
• Neuroscience
• +۵ مورد دیگر

Stefano Sarao Mannelli is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at Chalmers University of Technology and University of Gothenburg. He also holds a Visiting Lecturer position at the University of the Witwatersrand. His research group focuses on fundamental aspects of learning in biological and artificial systems, with emphasis on bias generation, optimization dynamics, and comparative neuroscience. Education: Ph.D. in Theoretical Physics, Université Paris-Saclay (2020) M.Sc. in Electronic Engineering, Politecnico di Torino (2017) M.Sc. in Physics of Complex Systems, Politecnico di Torino/SISSA (2016) M2 in Physique Théorique, Paris Diderot/UPMC/ENS Cachan (2016) B.Sc. in Mathematics for Engineering, Politecnico di Torino (2014) Research: Dr. Mannelli develops model-based approaches to reduce complex machine learning problems into analytically tractable frameworks. His core interests include: 1) Bias amplification mechanisms in AI systems, 2) Learning differences between biological and artificial neural networks (continual/transfer/curriculum learning), and 3) Optimization in high-dimensional landscapes. His work bridges statistical physics, neuroscience, and deep learning theory. Publication Trends: Recent articles (2024-2025) predominantly analyze curriculum learning dynamics, bias propagation in optimization, and theoretical comparisons between biological and artificial learning systems. Methodologically, they combine statistical physics frameworks with control theory and high-dimensional analysis. Awards: Academic Grant (CM Lerici Foundation, 2025) Travel Grants (Guarantor of Brains, G-Research 2024) UK–IT Trustworthy AI Exchange Programme (Alan Turing Institute, 2023) SCGB Conference Award (Simons Foundation, 2023) Ph.D. Scholarship (CEA, 2017-2020) Team & Funding: Leads a research group with 2 PhD students and 1 postdoc. Secured significant funding for international workshops including Analytical Connectionism (£42K, 2023; $152K, 2024) and High-Dimensional Methods (135,500 SEK, 2025).

• Optimal Transportation
• Mathematical Economics
• Mathematical Physics
• +۳ مورد دیگر

Brendan Pass is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on optimal transportation theory, with applications in mathematical economics, mathematical physics, and density functional theory. He is a core member of the Kantorovich Initiative, an interdisciplinary group dedicated to advancing optimal transport methodologies and their practical implementations. His work bridges pure mathematics with applied fields, addressing complex problems in multi-marginal transport, economic equilibrium modeling, and geometric analysis. Pass’s academic contributions span theoretical frameworks for optimal transport between spaces of differing dimensions, cyclic cost structures, and robust risk management. He explores intersections with diverse disciplines, including econometrics, financial mathematics, and geometric probability. His publications often address uniqueness conditions for Monge solutions, Wasserstein metrics, and barycenter problems in metric spaces. He has co-authored works on topics ranging from hedonic pricing models to vectorial martingale transport. Recent research trends include leveraging optimal transport for inequality measurement, robust optimization, and data denoising. His methodologies emphasize structural analysis and algorithmic design, with applications in crop root systems, quantum chemistry (via density functional theory), and option pricing. Pass frequently collaborates with institutions globally, contributing to both theoretical advancements and real-world problem-solving. Teaching responsibilities include advanced mathematics courses such as Math 156 (Calculus for Business/Economics) and Math 300 (Boundary Value Problems). His scholarly output includes over 50 peer-reviewed articles, conference contributions, and edited volumes. Current projects explore ODE characterizations of transport problems, distributional robustness, and partial identification in econometric models.