جست‌وجوی استاد و پژوهشگر

Deniz Gunduzمشاهده پروفایل

استاد

• Wireless Communications
• Information Theory
• Machine Learning
• +۳ مورد دیگر

Deniz Gündüz is a Professor of Information Processing at Imperial College London's Electrical and Electronic Engineering Department, leading the Information Processing and Communications Lab. He also serves as Deputy Head of the Intelligent Systems and Networks Group and holds a part-time faculty position at the University of Modena and Reggio Emilia. His research focuses on wireless communications, information theory, machine learning, and privacy, with significant contributions to semantic communication systems and AI-native networks. He has held visiting roles at Princeton University and the University of Padova. Dr. Gündüz is an Area Editor for IEEE Transactions on Communications and IEEE JSAC, and a former Distinguished Lecturer of the IEEE Information Theory Society. His awards include the IEEE Communication Society Early Achievement Award (2017), ERC Starting Grant (2015), and multiple best paper recognitions. He has organized major conferences and workshops, including the first MLCOM workshops on machine learning for communications. Education: B.S. (2002) from METU, Turkey; M.S. and Ph.D. (2004, 2007) from NYU Polytechnic School of Engineering. Prior roles include Research Associate at CTTC Barcelona, Consulting Assistant Professor at Stanford, and postdoctoral positions at Princeton. His work spans theoretical foundations and practical implementations, emphasizing interdisciplinary solutions for next-generation networks. Research Interests: Semantic communications, distributed learning, 6G systems, privacy-preserving techniques, and AI integration in communication networks. His recent work explores neural compression for cloud RAN, over-the-air computation, and federated learning optimizations. Awards and Roles: IEEE JSAC Series Editor (Machine Learning in Networks), IEEE Transactions on Wireless Communications Editor, and organizer of major conferences. His team's achievements include breakthroughs in joint source-channel coding and adversarial jamming defenses.

• Number Theory
• Combinatorics on Words
• Mathematical Analysis
• +۳ مورد دیگر

Lukas Spiegelhofer holds the academic rank of Professor in a position associated with the Chair of Mathematics, Statistics and Geometry. His research focuses on number theory, combinatorics on words, and mathematical analysis with particular emphasis on automatic sequences, digit statistics, and entropy-related problems. His work has been published in prestigious journals such as Israel Journal of Mathematics , Journal of the Australian Mathematical Society , and Journal d'analyse mathématique . Recent research explores topics like synchronization of automatic sequences, digit sum collisions across bases, and gap distributions in the Thue-Morse word. Spiegelhofer actively participates in academic activities including invited lectures at conferences such as the 2024 Thue-Morse workshop and combinatorics on words symposium. His research collaborations span international institutions and focus on interdisciplinary areas combining number theory with dynamical systems.

• Mathematical Physics
• Probability Theory
• Stochastic Processes
• +۳ مورد دیگر

Zhengye Zhou is an Assistant Professor in the Department of Mathematics at the University of Southern California Dornsife College of Letters, Arts and Sciences. Their research focuses on advanced mathematical frameworks for stochastic processes and quantum algebra. Research Interests: Interacting particle systems, orthogonal polynomial duality, asymptotic analysis, quantum groups, integrable systems, and non-commutative random surface growth. Their recent work explores asymptotics and duality in dynamic stochastic models, including multi-species asymmetric exclusion processes and higher-spin vertex models. This research intersects probability theory, mathematical physics, and algebraic structures. Zhengye Zhou contributes to theoretical developments in probability, particularly through rigorous analysis of Markov duality, unitary symmetries, and Gaussian fluctuations in stochastic systems. Notable publications span topics like dynamic ASEP, quantum group bialgebras, and boundary-driven exclusion processes.

• Partial Differential Equations
• Inverse Scattering Theory
• Riemann-Hilbert Problems
• +۳ مورد دیگر

Xin Zhou is an Associate Professor in the Department of Mathematics at Duke University, recognized with the George Polya Prize in 1998. His expertise lies in partial differential equations, inverse scattering theory, and Riemann-Hilbert problems. He has collaborated extensively with notable researchers such as Percy Deift, Alexander Its, and Stephano Venakides. Education: M.S. in Physics from the Chinese Academy of Sciences Ph.D. in Mathematics from the University of Rochester Research Interests: Development of Riemann-Hilbert methods for integrable systems Analysis of Painleve equations and random matrix models Applications of inverse scattering theory to nonlinear PDEs His work bridges mathematical analysis, theoretical physics, and applied mathematics, with contributions to asymptotic analysis and nonlinear wave dynamics. Key Collaborations: Richard Beals (Yale University) Percy Deift (Courant Institute, NYU) A.S. Fokas (Imperial College) Alexander Its (Indiana University-Purdue University) Awards: George Polya Prize (1998) Research Contributions: Pioneering work on steepest descent methods for oscillatory Riemann-Hilbert problems Advances in understanding asymptotic behavior of integrable systems Development of unified frameworks for orthogonal polynomials with varying weights Labs/Teams: Active collaborations across institutions, including work with the Duke Mathematics Department and international research groups.

Holger Reichمشاهده پروفایل

استاد

• Algebraic Topology
• K-Theory
• Homological Algebra
• +۳ مورد دیگر

Prof. Dr. Holger Reich is a Professor of Mathematics at the Free University of Berlin (FU Berlin), leading the Algebraic Topology group within the Department of Mathematics. He currently serves as the Research Dean of the Department of Mathematics and Computer Science (since 2021) and chairs the Berlin Mathematical School (BMS) Admissions Committee. Previously, he held roles such as Managing Director of the Mathematical Institute (2013–2023). His research focuses on algebraic K-theory, the Farrell-Jones conjecture, group rings, and homotopy theory, often collaborating with prominent mathematicians like Wolfgang Lück and Arthur Bartels. His work bridges algebraic topology, geometric group theory, and noncommutative geometry. Reich’s research interests include assembly maps, controlled algebra, topological cyclic homology, and isomorphism conjectures. He has contributed significantly to understanding algebraic K-theory in the context of hyperbolic groups and GL_n(Z). His publications often address foundational questions in algebraic topology and geometric topology, with applications to group theory and operator algebras. He has advised doctoral students and actively participates in academic governance, including roles on the Central Ethics Committee of FU Berlin and the BMS Committee. His interdisciplinary collaborations and leadership roles highlight his impact on both research and academic administration.

Ryan Williamsمشاهده پروفایل

استاد

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.

Catherine Jones Murphyمشاهده پروفایل

استاد

• Nanotechnology
• Materials Chemistry
• Biomedical Engineering
• +۴ مورد دیگر

Catherine Jones Murphy is a Professor and Head of the Chemistry Department at the University of Illinois. Her research focuses on nanotechnology, particularly gold nanorods and their applications in plasmonics, biomedical engineering, and surface chemistry. She leads the Murphy Research Group, which explores nanoparticle interactions with biological systems and their implications in medicine and environmental science. Director of the Chemistry Department's research efforts in nanomaterials. Recipient of prestigious awards, including the 2020 ACS Inorganic Chemistry Award and election to the National Academy of Sciences. Research Interests: Nanoparticle synthesis, plasmonic properties, biological impacts, and environmental behavior of nanomaterials. Her group's work on gold nanorods has been applied in medical imaging, cancer therapy, and environmental monitoring. Recent studies include self-assembly mechanisms, photothermal effects, and protein-nanoparticle interactions. Awards: Highlighted achievements include the 2020 ACS Award, 2019 Remsen Award, and contributions to the MoonArk project involving gold nanorods. Advising and Grants: Mentor to over 30 students and postdocs, with grants supporting research into nanoparticle toxicity, biomedical applications, and plasmonic materials. Labs/Teams: The Murphy Group collaborates with facilities like the Electron Microscopy Core and Laser Spectroscopy Core at UIUC. Their MoonArk initiative demonstrates interdisciplinary impact in space exploration and art.

• Probability Theory
• Mathematical Physics
• Random Matrices
• +۳ مورد دیگر

Dr. Theo Assiotis is a faculty member at the School of Mathematics, University of Edinburgh, joining in the 2020-21 academic year. His research focuses on probability theory, mathematical physics, random matrices, and branching graphs. He completed his undergraduate studies at the University of Cambridge and pursued further academic training through a PhD and postdoctoral work. His work bridges abstract algebraic structures with probabilistic methods, particularly in stochastic processes and integrable systems. Research interests include random matrices and their applications in statistical mechanics, as well as branching graphs linked to representation theory and Markov chains. Recent collaborations include a paper co-authored with two undergraduate students on characteristic polynomials of random matrices. He emphasizes the importance of perseverance in academic research, advising students to embrace challenges inherent in exploring uncharted mathematical domains. Teaching and supervision are central to his role, with particular enjoyment in mentoring students through research projects. While no specific awards are highlighted in the text, his contributions to probability theory and mathematical physics are evident through his extensive publication record.

Vishnu Lokhandeمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Vishnu Lokhande is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY, with a secondary affiliation at the Institute of Artificial Intelligence and Data Science (IAD). His research focuses on enhancing the reliability and robustness of deep learning models through innovative applications of optimization theory. PhD in Computer Science from University of Wisconsin-Madison (2023) BTech in Electrical Engineering from IIT Kanpur (2016) Dr. Lokhande's research spans Computer Vision , Machine Learning , Deep Learning , and Mathematical Optimization , with particular emphasis on constrained optimization methods for training deep neural networks. His work bridges theoretical foundations from statistics and mathematics with practical applications in vision and learning systems. His publication portfolio demonstrates significant contributions to constrained deep learning, particularly through his work on Conditional Gradient methods, which offer theoretical guarantees for model training while maintaining practical efficiency. Additional research areas include EEG signal processing for brain-computer interfaces, theoretical analysis of neural network convergence, and fairness-aware optimization techniques. During his doctoral studies at Wisconsin, Dr. Lokhande completed internships at both Google Research (twice) and Microsoft Research, gaining valuable industry experience that informs his applied research approach. He is actively recruiting PhD and MS students at UB and remains open to industrial collaborations as a consultant or part-time researcher.

• Mathematical Analysis
• Special Functions
• Orthogonal Polynomials
• +۵ مورد دیگر

Henrik Laurberg Pedersen is a Professor and Head of Section at the Department of Mathematical Sciences, University of Copenhagen , specializing in mathematical analysis, special functions, and complex analysis. His work bridges theoretical and applied mathematics. Research Interests: Special functions, orthogonal polynomials, complex analysis, generalized Stieltjes functions, and applications to dynamical systems. Teaching: Complex analysis at undergraduate and graduate levels, mathematics for life sciences, and linear algebra for computer science. Collaborations: Active in projects involving holomorphic dynamics, complex methods in dynamical systems, and geometry/global analysis. Publications focus on inequalities, gamma distribution quantiles, Bernstein functions, and Julia sets, with keywords spanning mathematical analysis, probability, and dynamical systems. Recent works emphasize connections between complex analysis and special functions.

Akil Narayanمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Approximation Algorithms
• +۳ مورد دیگر

Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.

• Machine Learning
• Computer Vision
• Robotics
• +۵ مورد دیگر

Qiang Qiu is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He actively contributes to the Vertically Integrated Projects (VIP) program, fostering interdisciplinary collaboration between faculty, students, and projects. His research spans machine learning, computer vision, robotics, and generative AI, with particular emphasis on diffusion models, transformer fine-tuning, and graph neural networks. Key themes include improving model generalizability through orthogonal low-rank embeddings, developing efficient parameter-tuning strategies, and advancing visuotactile manipulation in robotics. Recent publications reveal trends in attention control for text-to-image alignment, posterior sampling for diverse image generation, and federated learning security. His work addresses gradient conflicts in machine unlearning and explores multimodal sensing for robotic manipulation.

Xiaoliang Jinمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Machining of Composite Materials
• Additive Manufacturing
• +۵ مورد دیگر

Xiaoliang Jin is an Associate Professor and Canada Research Chair in Advanced Manufacturing within the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science. His research focuses on advanced manufacturing processes for next-generation materials and systems. His academic background includes: B.Sc. and M.Sc. from Beihang University Ph.D. from the University of British Columbia Dr. Jin's research spans cutting-edge manufacturing domains with emphasis on machining of composite materials (particularly carbon fiber reinforced polymers), hybrid additive-subtractive manufacturing , and advanced machining mechanics . His work integrates analytical modeling , experimental validation , and computational techniques to solve complex challenges in material processing, surface integrity, and process dynamics. Current investigations include vibration-assisted machining, micro-scale manufacturing, and real-time process monitoring systems. Analysis of his recent publications (2022-2025) reveals strong methodological trends: increasing application of machine learning for predictive modeling (especially in tool wear prediction), sophisticated residual stress characterization in additive manufacturing, and novel analytical frameworks for chip formation mechanics . His work bridges fundamental mechanics with industrial applications across aerospace and advanced materials sectors. His scientific recognition includes: Canada Research Chair in Advanced Manufacturing As a Canada Research Chair holder, Dr. Jin directs a significant research program securing substantial funding for advanced manufacturing innovation. He mentors graduate students in experimental and computational manufacturing research, with projects often involving industry partnerships to address real-world production challenges. His group develops specialized instrumentation for process monitoring and control. He leads the Advanced Manufacturing Processes (AMP) Laboratory (amp.mech.ubc.ca), which features capabilities for precision machining of composites and metals, vibration-assisted systems, hybrid additive-subtractive platforms, and dynamic process analysis. The lab supports fundamental research while maintaining strong connections to industrial manufacturing applications.

Michael V. Pakمشاهده پروفایل

استادیار

• Topological Quantum Computing
• Quantum Theory of Multi-Component Systems
• Matter-Antimatter Interactions
• +۴ مورد دیگر

Dr. Michael V. Pak is an Assistant Professor at the Air Force Institute of Technology's Graduate School of Engineering, Department of Engineering Physics, and affiliated with the Center for Directed Energy (CDE). His research bridges theoretical physics and quantum chemistry, focusing on quantization theory, topological quantum computing, and matter-antimatter interactions. He holds dual PhDs in Quantum Chemistry (Iowa State University, 2002) and Theoretical Physics (St. Petersburg State University, 1996), alongside an MS in Theoretical Chemistry (St. Petersburg State University, 1992). PhD, Quantum Chemistry, Iowa State University (2002) PhD, Theoretical Physics, St. Petersburg State University, Russia (1996) MS, Theoretical Chemistry, St. Petersburg State University, Russia (1992) Dr. Pak's research explores the quantum theory of multi-component systems, particularly methods to describe positron annihilation in complex environments and temperature-dependent beta decay modeling for nuclear explosion scenarios. His work on topological quantum computing emphasizes Majorana states and fault-tolerant gate operations, while nanowire architectures and Kohn-Sham systems under finite-temperature DFT are also key topics. His scientific awards include the Department of Energy Research Fellowship (2002), Nobel Foundation Scholarships (1992, 1995), and the V.A. Steklov Mathematical Institute Award (1992). He has published extensively on electron-positron correlation, hydrogen transfer, and quantum computational methods. At AFIT, Dr. Pak contributes to the Center for Directed Energy, advancing research in quantum theory and computational physics. His teaching interests align with theoretical physics and quantum mechanics education.

Bartosz Kołodziejekمشاهده پروفایل

دانشیار

• Probability Theory
• Statistics
• Stochastic Fixed Point Equations
• +۷ مورد دیگر

Bartosz Kołodziejek is an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology. His research lies at the intersection of probability theory and mathematical statistics, with a focus on stochastic fixed point equations, free probability, and graphical models. He actively publishes in top-tier journals and maintains a strong collaborative network. His research interests include: Stochastic fixed point equations and their applications Characterizations of probability distributions on symmetric and homogeneous cones Free probability and free convolution Graphical models and high-dimensional statistics Symmetry in Gaussian models and model selection Perpetuities and tail behavior of stochastic recursions The analysis of his recent publications reveals a consistent trend in theoretical probability and statistical methodology, particularly in high-dimensional inference, random matrix theory, and invariant models. His work often involves deep analytical techniques and connections across fields such as convex analysis, exponential families, and stochastic processes. He has no listed scientific awards on his homepage. While no students or grants are explicitly mentioned, his active publication record and software contributions (such as the gips R package) suggest ongoing research leadership. He collaborates with prominent researchers in probability and statistics across Europe. He is involved in the development of statistical software, notably contributing to the theoretical foundations of the gips package for Gaussian models with permutation symmetry, used in high-dimensional data analysis.