جستجوی استاد راهنمای دانشگاهی

• Applied Probability
• Inference and Algorithms on Networks
• Random Graphs
• +۱ مورد دیگر

Souvik Dhara is an Assistant Professor in Purdue University's Edwardson School of Industrial Engineering with a courtesy appointment in Mathematics. His research develops probabilistic frameworks and spectral algorithms for analyzing complex networks, addressing fundamental questions about hidden patterns and microscopic-to-macroscopic behavior in graph-structured data. Key research areas include: 1) Community detection in networks using spectral methods 2) Phase transitions in random graphs 3) Large deviation theory for network properties 4) Graph limit theory applications to non-parametric statistics. His work on spectral algorithms has established optimal recovery bounds for planted substructures under censored data conditions. Honors include the Stieltjes Prize for best mathematics PhD thesis in the Netherlands, Simons-Berkeley Fellowship, AMS-Simons Travel Grant, and multiple industry fellowships. He mentors students in random graph theory and stochastic processes, with upcoming transition to Georgia Tech as Tennenbaum Early Career Assistant Professor.

• Mathematical Finance
• Stochastic Processes
• Financial Mathematics
• +۳ مورد دیگر

Dr. Anna Aksamit is a Lecturer in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on mathematical finance and stochastic processes, particularly the theoretical foundations of information modeling in financial markets. She specializes in enlargement of filtration theory, arbitrage detection, and stochastic control under uncertainty. Notably, she co-authored the book Enlargement of Filtration with Finance in View (2017), which explores applications of filtration theory in financial modeling. Her work includes collaborations on projects such as 'Information and Uncertainty in Financial Modelling' (2024) and 'Can green investors drive the transition to a low emissions economy?' (2022), funded by the University of Sydney and the Australian Research Council. She has published extensively in top-tier journals like Finance and Stochastics and Stochastic Processes and their Applications , addressing topics ranging from robust pricing-hedging duality to sensitivity analysis of stochastic processes. Aksamit’s contributions span both theoretical advancements (e.g., martingale representations, random times decomposition) and applied finance (e.g., green finance frameworks, mean-field games for sustainable finance). No scientific awards are explicitly listed in her profile, though her research has been recognized through multiple grant awards. She holds a doctoral thesis titled Random Times, Enlargement of Filtration and Arbitrages (2014) from Université d'Evry Val d'Essonne.

Alessandro Abateمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Stochastic Hybrid Systems
• Formal Verification
• +۵ مورد دیگر

Professor Alessandro Abate holds the position of Professor of Verification and Control in the Department of Computer Science at the University of Oxford. He is affiliated with St Hugh’s College as a Fellow and Tutor, and serves as a Faculty Fellow at the Alan Turing Institute in London. His academic journey includes roles as an Assistant Professor at TU Delft and postdoctoral research at Stanford University and SRI International. He earned a Laurea (summa cum laude) from the University of Padua, followed by an MS and PhD from UC Berkeley. His research focuses on the analysis, verification, and control of stochastic hybrid systems, with applications in cyber-physical systems, safety-critical autonomy, smart energy, and systems biology. He integrates model-based techniques with machine learning and Bayesian methods. Notable contributions include formal methods for safety-critical systems, probabilistic verification, and control synthesis. Abate has received significant recognition, including IEEE Fellow status (2024) and leadership roles in conferences like ADHS and l4DC. His work bridges theoretical advancements and practical implementations, addressing challenges in autonomous systems, energy networks, and biological modeling. Current research initiatives include certified AI systems, safe reinforcement learning, and the aggregation of renewable energy resources. He advises a cohort of over 20 doctoral and postdoctoral researchers, fostering innovation in formal methods and their real-world applications.

Peter Feldhütterمشاهده پروفایل

استاد

• Finance
• Corporate Finance
• Financial Economics
• +۵ مورد دیگر

Peter Feldhütter is a Professor of Finance in the Department of Finance at Copenhagen Business School (CBS). He holds a visiting position at the University College London (UCL) School of Management. His research focuses on corporate finance, credit markets, and quantitative finance with particular emphasis on ESG investing, corporate bond liquidity, and risk management. Key contributions include analyzing sustainability-linked bonds, capital structure theories, and credit spread dynamics. He has received prestigious awards such as the Jack Treynor Prize (2015) and the Outstanding Paper Award from Wharton’s Jacobs Levy Equity Management Center (2014). His work frequently addresses empirical asset pricing puzzles and regulatory impacts on financial markets, leveraging advanced econometric methods. Recent research trends include exploring ESG’s role in capital structure decisions, liquidity dynamics in corporate debt markets, and the valuation of creditor control mechanisms in bonds. His studies have informed policy discussions, including analyses cited in New York Times and Financial Times articles on post-Subprime Crisis liquidity changes. Awards: Jack Treynor Prize, Wharton’s Outstanding Paper Award, Society of Quantitative Analysts Award Key Themes: Credit risk modeling, liquidity measurement, ESG finance, and market microstructure Grants/Consulting: Research collaboration with Oliver Wyman on Volcker Rule impacts His work spans academic journals like the Journal of Financial Economics and Review of Financial Studies , with active engagement in policy-relevant financial research.

Jozsef Baloghمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Extremal Combinatorics
• +۵ مورد دیگر

Jozsef Balogh is a Professor in the Department of Mathematics at the University of Illinois, holding the J. Andrew and Susan Langan Scholar title. He is affiliated with the College of Liberal Arts & Sciences and focuses on Combinatorics, Graph Theory, and related areas. His research emphasizes extremal combinatorics, additive combinatorics, and the development of the container method for hypergraphs, which has applications in Ramsey theory and theoretical computer science. Education: Ph.D. in Mathematics from the University of Memphis (2001). He has held prestigious fellowships including the Simons Fellowship (2013–2014) and Marie Curie Fellowship (2013–2017), and received the George Pólya Prize in Combinatorics (2016) from SIAM. His NSF CAREER Award (2008–2013) supported work in modern combinatorics. Research highlights include resolving conjectures on maximal triangle-free graphs, maximal sum-free sets, and union-free sets using the container theorem. His work bridges probabilistic methods and combinatorial structures, influencing areas like discrete geometry and algorithm design. Recent publications address extremal graph problems, Ramsey-type tilings, and probabilistic thresholds in combinatorial systems. Awards and grants reflect his impactful contributions, while his advising and mentorship are central to his academic role. Ongoing research focuses on expanding applications of container methods and exploring new frontiers in extremal combinatorics.

Marius Jungeمشاهده پروفایل

استاد

• Functional Analysis
• Operator Algebras
• Quantum Information Theory
• +۴ مورد دیگر

Marius Junge is a Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on operator algebras, noncommutative probability, quantum information theory, and related areas. He holds a PhD and Habilitation from Christian-Albrechts-Universität Kiel (1991, 1996). His research interests include functional analysis, operator spaces, noncommutative martingales, Lindbladians, quantum games, and entanglement. He has been recognized with awards such as the American Mathematical Society Fellowship (2017) and served as an Associate at the Center for Advanced Study (2010-2011). Recent publications explore topics like open quantum systems, quantum entropy decay, and classification of von Neumann algebras. His work intersects theoretical mathematics with applications in quantum information science through collaborations with IQUIST and the quantum exchange program. Key contributions span operator space embeddings, noncommutative Lp-spaces, and quantum game theory. Collaborations include researchers at institutions like the University of Illinois and international partners. His research bridges pure mathematics with cutting-edge quantum technologies.

Kay Kirkpatrickمشاهده پروفایل

استاد

• Quantum Mechanics
• Statistical Mechanics
• Probability Theory
• +۶ مورد دیگر

Kay Kirkpatrick is a full Professor of Mathematics and Physics at the University of Illinois, Urbana-Champaign. She holds affiliations with the Department of Mathematics and the Department of Physics within the College of Liberal Arts & Sciences. Her roles include being a Blackwell Scholar and Helen Corley Petit Scholar. Kirkpatrick’s research focuses on quantum and statistical mechanics, nonlinear quantum equations, and applications to physics and biology, including dark matter dynamics, biophysical systems, and computational models of biological computation. She has contributed to understanding Bose-Einstein condensation, topological insulators in microtubules, and the channel capacity of ribosomes. Education: Ph.D. in Mathematics from UC-Berkeley (2007), B.S. in Mathematics from Montana State University. Additional training includes a Study in a Second Discipline Fellowship in Neuroscience (2020–2021). She has held postdoctoral positions at MIT and NYU, and visiting roles at CRM Montréal and MIT. Research interests span quantum statistical mechanics, nonlinear Schrödinger equations, Gibbs measures, and computational biology. Notable awards include the Simons Fellowship, NSF CAREER Award, and Tenney Peck Teaching Award. Kirkpatrick has mentored students and led projects in percolation theory and phase transitions through Illinois Geometry Lab initiatives. Her recent publications (2022–2024) address topics like Bose-Einstein condensation in dark matter, ribosome channel capacity, and fractional quantum models. She teaches advanced courses in machine learning and partial differential equations, emphasizing interdisciplinary applications and effective communication.

Yuan Liuمشاهده پروفایل

استادیار

• Number Theory
• Group Theory
• Arithmetic Geometry
• +۲ مورد دیگر

Yuan Liu is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign. He holds a Ph.D. from the University of Wisconsin-Madison and a Bachelor’s degree from Peking University, China. His research focuses on number theory, group theory, and arithmetic geometry, with support from NSF Grant DMS-2200541. Key contributions include studies on Galois groups, class groups, and monodromy in algebraic structures. Education: Ph.D. in Mathematics, University of Wisconsin-Madison Bachelor’s in Mathematics, Peking University Research interests emphasize algebraic number theory, arithmetic geometry, and group theory. His recent work explores the statistical properties of Galois extensions and cohomological structures in number fields. He has published in top-tier journals like Inventiones Mathematicae and Algebra and Number Theory . Grants and Awards: NSF Grant DMS-2200541 (current research support)

Sayar Karmakarمشاهده پروفایل

استادیار

• Time Series Analysis
• High-Dimensional Statistics
• Forecasting
• +۹ مورد دیگر

Sayar Karmakar is an Assistant Professor in the Department of Statistics at the University of Florida. His research focuses on time series analysis, high-dimensional statistics, econometrics, and neural networks. He holds a B.Stat and M.Stat from the Indian Statistical Institute and a PhD in Statistics from the University of Chicago. Dr. Karmakar has served on editorial boards of journals like Statistical Papers and Sankhya A , and has received grants from NSF and AMS Simons, totaling over $200,000. His work spans theoretical contributions in posterior consistency, time-varying models, and applied research in cyberbullying analysis and climate change. Education B.Stat, Indian Statistical Institute (Kolkata) M.Stat, Indian Statistical Institute (Kolkata) PhD in Statistics, University of Chicago Research Interests Dr. Karmakar’s research explores dependence structures in data, including time series (stationary/non-stationary), econometric models (GARCH/ARCH), neural networks, and spatial/spatiotemporal systems. His work emphasizes robust statistical methods, such as change-point detection, posterior consistency, and model-free predictions. Recent projects include analyzing cryptocurrency volatility, climate risks in financial markets, and cyberbullying trends during the pandemic. Grants & Awards NSF DMS Grant (ATD Program: $200,000, 2021-2024) AMS Simons Travel Grant (2021-2024) Early Career Advisory Board Member, Journal of Multivariate Analysis Teaching He has taught courses at the University of Florida (Probability, Time-Series Forecasting) and University of Chicago (Statistical Methods, Probability Theory). He was nominated as Best Tutor in Statistics at the University of Chicago (2014-2016). Labs & Collaborations Dr. Karmakar collaborates with researchers in epidemiology, cybersecurity, and climate science. His lab focuses on developing algorithms for large geospatial datasets and analyzing complex systems like cryptocurrency networks and disease propagation.

Shiqian Maمشاهده پروفایل

استاد

• Optimization
• Machine Learning

Shiqian Ma is a Professor of Computational Applied Mathematics and Operations Research at Rice University. His research focuses on optimization, machine learning, and their applications. He holds a Ph.D. from Columbia University, an M.S. from the Chinese Academy of Sciences, and a B.S. from Peking University. Education: Ph.D. in Industrial Engineering and Operations Research, Columbia University (2011) M.S. in Computational Mathematics, Chinese Academy of Sciences (2006) B.S. in Mathematics, Peking University (2003) Research Interests: Dr. Ma specializes in optimization theory and algorithms, particularly in stochastic approximation, distributed/decentralized optimization, and applications in machine learning. His work addresses challenges in non-convex optimization, Riemannian geometry-based methods, and high-dimensional data analysis. Advising and Grants: While no specific advisees or grant details are listed, his academic role suggests involvement in mentoring graduate students and securing research funding in computational mathematics and operations research. Labs/Teams: Not explicitly mentioned, but his research likely involves collaborations within Rice's computational and applied mathematics community.

Cong Maمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Reinforcement Learning
• Transfer Learning
• +۳ مورد دیگر

Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright. His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion. Education: PhD in Operations Research & Financial Engineering, Princeton University (2020) BEng in Electrical Engineering, Tsinghua University (2015) Awards: SIAM Activity Group on Imaging Science Best Paper Prize (2024) Teaching: STAT 253/317: Introduction to Probability Models (Winter 2025) STAT 28000: Optimization (Winter 2025) Machine Learning (Autumn 2022) His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.

• Statistical Machine Learning
• High-Dimensional Statistics
• Likelihood-Free Inference
• +۳ مورد دیگر

Summary Ann B. Lee is a Professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University (CMU). She serves as Co-Director of the Ph.D. Program in Statistics. Previously, she was the J.W. Gibbs Assistant Professor at Yale University and a visiting researcher at Brown University. Her research focuses on developing statistical methods for complex data in physical sciences, including trust-worthy uncertainty quantification, likelihood-free inference, and applications in astronomy, climate science, and hurricane dynamics. Education: Ph.D. in Physics from Brown University (2002); M.Sc./B.Sc. in Engineering Physics from Chalmers University of Technology (Sweden). Key Research Interests: - Scientific Machine Learning - Uncertainty Quantification (UQ) - Likelihood-Free Inference - Tropical Cyclone Analysis - High-Dimensional Data Modeling She leads the STAMPS (STAtistical Methods for Physical Sciences) research group, which bridges classical statistics and machine learning. Recent work includes methods for estimating ocean thermal responses to hurricanes, probabilistic forecasting, and diagnostics for generative models. Her team collaborates with climate and astrophysics communities, hosting public webinars and symposia. Advising: Supervised over 15 PhD students, including graduates now in academia and industry. Current advisees include Luca Masserano and Alex Shen. Labs/Teams: Co-directs the STAMPS Research Center at CMU, launching in Fall 2024 as a university-wide initiative.

Michael Widomمشاهده پروفایل

استاد

• Condensed Matter Theory
• Quasicrystals
• Metallic Glasses
• +۷ مورد دیگر

Michael Widom is a Professor of Physics at Carnegie Mellon University's Mellon College of Science, with a courtesy appointment as Professor of Materials Science & Engineering. His research spans condensed matter theory, focusing on novel materials in both condensed matter and biological physics settings. Education: PhD in Physics, University of Chicago (1983) B.A. with honors in Physics, Cornell University (1980) Professor Widom's research focuses on theoretical modeling of novel materials using methods of statistical mechanics, quantum mechanics, and computer simulation. His work addresses structure, stability, and properties of materials including liquid metals, metallic glasses, quasicrystals, and biomolecular structures. His research group employs first-principles total energy calculations coupled with statistical mechanics to model entire ensembles of probable structures. Analysis of Professor Widom's recent publications (2023-2025) reveals a strong focus on high-entropy alloys, quasicrystals, and computational materials science. His work spans multiple disciplines including condensed matter physics, materials science, and biophysics, with particular emphasis on structure-property relationships in complex metallic systems. The research employs advanced computational techniques including first-principles calculations, molecular dynamics simulations, and machine learning approaches to understand thermodynamic properties and phase behavior. Scientific Awards: Fellow, American Physical Society (1997) Fellow, American Association for the Advancement of Science (2008) Alfred P. Sloan Fellowship (1991) APDIC Best Paper Award (2005) W.R. Harper Fellowship (1983) Professor Widom advises graduate students including Hassan Albuhairan and Abir Mahmud, and collaborates with visiting scientists such as Marek Mihalkovic from Slovakia. His research has been supported by various grants that enable computational studies of complex materials systems. The Widom Research Group maintains several software tools for materials simulation including Wavetrans (for extracting electronic wavefunctions), MCMD (a hybrid Monte Carlo/molecular dynamics method), and an enthalpy database with formation enthalpies of many compounds. The group's research spans metal alloys (quasicrystals, metallic glasses) and biological physics (viral capsids, RNA structure).

David Jeffreyمشاهده پروفایل

استاد

• Symbolic computation
• Computer-Algebra Systems
• Microhydrodynamics
• +۳ مورد دیگر

David Jeffrey is a Professor and SMSS Outreach and Research Director in the Department of Mathematics at the University of Western Ontario. His research focuses on symbolic computation, computer-algebra systems (e.g., Maple), and microhydrodynamics. He has contributed extensively to the theory and applications of the Lambert W function, matrix factorization, and numerical algorithms. His work bridges mathematical theory and computational tools, addressing challenges in fluid mechanics, nonlinear systems, and special functions. Jeffrey’s research includes studies on Riemann surfaces, branch cuts, and the implementation of mathematical algorithms. He has developed software tools for comprehensive integrals and rational orthogonal matrices. His collaborations span disciplines, including contributions to the MIP*=RE paper in quantum computing and the analysis of Stokes resistance functions. Publications highlight advancements in parameterized integral computation, vector algebra systems, and the application of symbolic methods to engineering problems. His work emphasizes algorithmic efficiency and the practical implementation of mathematical theories in software systems.

James A. Mingoمشاهده پروفایل

استاد

• Free probability
• Random matrices
• Operator algebras
• +۵ مورد دیگر

James A. Mingo is a Professor in the Department of Mathematics and Statistics at Queen's University. His research focuses on free probability, random matrices, operator algebras, and related areas. He holds a Ph.D. from Dalhousie University. Education: Ph.D. (Dalhousie University) Research Interests: Mingo's work explores the interplay between free probability and random matrices, with applications to operator algebras and mathematical physics. Key topics include infinitesimal freeness, asymptotic distributions of random matrices, and combinatorial structures in non-commutative probability. His contributions bridge abstract algebraic frameworks with probabilistic methods, addressing questions in both theoretical and applied contexts. Grants and Collaborations: Mingo collaborates widely, as evidenced by his co-authored papers with researchers like Roland Speicher, Mihai Popa, and Daniel Munoz George. His research has been supported by grants from institutions like the Simons Foundation. Teaching and Service: Mingo has taught courses such as STAT/MTHE 353 and MATH 892 at Queen's University, reflecting his commitment to both undergraduate and graduate education. He also organizes research seminars and workshops in free probability and random matrices.