جست‌وجوی استاد و پژوهشگر

• Computational Complexity
• Boolean Functions
• Communication Complexity
• +۵ مورد دیگر

Shachar Lovett is a researcher at the University of California, San Diego (UCSD), specializing in computational complexity, combinatorics, and theoretical computer science. His work spans advanced topics in communication complexity, pseudorandomness, and coding theory, often intersecting with problems in additive combinatorics and Boolean function analysis. Education : Not explicitly detailed in the provided text. Research Interests : Lovett's research focuses on computational complexity, particularly in communication and circuit complexity, combinatorial structures like sunflowers and high-dimensional expanders, and the analysis of Boolean functions through Fourier and Gowers norms. His work explores the limits of deterministic vs. randomized computation, the structure of codes over finite fields, and the interplay between additive combinatorics and theoretical computer science. Article Trends : His recent publications address exact vs. approximate representations of Boolean functions, quasipolynomial bounds in combinatorics, hypercontractivity in high-dimensional expanders, and advancements in the log-rank conjecture. These works emphasize connections between computational complexity, discrete mathematics, and pseudorandomness, often yielding improved bounds or novel frameworks for understanding Boolean function behavior. Scientific Awards : No specific awards or honors were mentioned in the provided text. Advising and Collaborations : Lovett collaborates extensively with researchers like Hamed Hatami, Kaave Hosseini, and Jiapeng Zhang, contributing to fields such as non-malleable codes, matrix multiplication algorithms, and communication complexity. No formal student advising details were provided.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Real Algebraic Geometry
• Combinatorics
• Convex Optimization
• +۱۰ مورد دیگر

Cynthia Vinzant is an Associate Professor in the Department of Mathematics at the University of Washington, College of Arts and Sciences. Her research lies at the intersection of real algebraic geometry, combinatorics, and convex optimization, with a focus on polynomials, determinants, and matroids. Ph.D., Mathematics, UC Berkeley, 2011 B.A., Mathematics and Neuroscience, Oberlin College, 2007 Her research interests include real algebraic geometry, combinatorics, convex optimization, tropical geometry, and spectrahedra. She studies the algebraic and combinatorial structures underlying optimization problems and geometric objects, particularly through the lens of hyperbolic and log-concave polynomials. Her recent publications span topics such as tropicalization of principal minors, determinantal representations, Fourier quasicrystals, and log-concave polynomials. These works demonstrate strong interdisciplinary connections across algebraic geometry, combinatorics, optimization, and mathematical physics. Sloan Research Fellowship (2020) Best Paper Award, STOC (2019) von Neumann Fellowship, IAS (2020–2021) Bernard Friedman Prize, UC Berkeley (2011) Rebecca Cary Orr Prize, Oberlin College (2007) She has advised several Ph.D. students including Tracy Chin, Jonathan Niño-Cortes, Joseph Rogge, Faye Pasley Simon, Michael Ruddy, Georgy Scholten, and Abeer Al Ahmadieh. She has received significant NSF funding, including a CAREER award (2020–2025) on determinantal, hyperbolic, and log-concave polynomials. She has taught courses in tropical geometry, convex algebraic geometry, and optimization at both the University of Washington and North Carolina State University.

• Communication Theory
• Channel Coding
• Low-Density Parity-Check (LDPC) Codes
• +۳ مورد دیگر

Richard D. Wesel is an Associate Dean for Academic and Student Affairs at a College of Engineering, where he has held leadership roles such as Vice-Chair of the Electrical Engineering Department. With Ph.D. and M.S. degrees in Electrical Engineering from Stanford University and MIT respectively, his research focuses on communication theory and channel coding, particularly low-density parity-check (LDPC) codes and turbo codes for efficient data transmission over noisy channels. Education: MIT (B.S., M.S.), Stanford (Ph.D.) Leadership: Associate Dean, Former Vice-Chair of Electrical Engineering Wesel's research explores advanced techniques for broadcast and multiple-access communication systems, emphasizing applications in wireless LANs, satellite communications, and optical networks. His work integrates machine learning with traditional decoding algorithms, as seen in recent publications on neural-network-optimized LDPC decoding and parallel trellis-stage-combining methods for high-throughput systems. His articles demonstrate a focus on finite-blocklength coding, including CRC-aided list decoding for convolutional and polar codes, and voltage optimization strategies for flash memory reliability. Wesel has authored over 100 publications and led a research group that won the 2006 Design Automation Conference's top award for optical multiple access design. Scientific Awards: National Science Foundation CAREER Award Okawa Foundation Award As an educator, Wesel received the TRW Excellence in Teaching Award in 2000 and has contributed to academic governance through roles on the Faculty Executive Committee and Undergraduate Council. His work bridges theoretical communication systems with practical implementations, including FPGA-based decoders and adaptive coding for fading channels.

Susanna de Rezendeمشاهده پروفایل

استادیار

• Computational Complexity
• Proof Complexity
• Circuit Complexity
• +۶ مورد دیگر

Susanna de Rezende is an Assistant Professor in the Department of Computer Science at Lund University (LTH). She is affiliated with the ELLIIT initiative on IT and mobile communication, the LTH Profile Area: AI and Digitalization, and the MIAO group collaborating with the University of Copenhagen. Her research focuses on computational complexity, proof complexity, circuit complexity, and communication complexity. She holds a PhD from KTH Royal Institute of Technology (2019) and a Master's from the University of São Paulo (2014). Her research explores connections between proof systems, circuit lower bounds, and communication complexity, with recent contributions to lifting theorems, automatability, and average-case hardness. She has received awards including the Stockholm Mathematics Centre Prize and Wallenberg Academy Fellow status. Current projects include funded PhD positions in theoretical computer science and editorial work for ZML: Zeitschrift für Mathematische Logik und Grundlagen der Mathematik. Key articles address proof complexity trade-offs, clique hardness in Sherali-Adams, and graph coloring challenges. Collaborations span institutions like the Czech Academy of Sciences and the Simons Institute. She advises multiple PhD students and collaborates on foundational research in complexity theory, supported by WASP, ELLIIT, and VR grants.

• Algorithms and Complexity
• Parameterized Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact. His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms , approximation algorithms , combinatorial optimization , scheduling , and algorithmic game theory . His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics. The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms . These works explore foundational problems in vector bin packing , integer programming , graph algorithms , and kernelization , while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering . He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including: STACS 2023 ESA 2024 FOCS 2023 ICALP 2024 IJCAI 2019–2025 AAAI 2018 SWAT 2018 He has successfully supervised several PhD students to completion, including Matthias Kaul , Roland Vincze , and Alexander Göke , many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031) , Hamburg Quantum Computing (2024–2029) , and Kernelization for Big Data , indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11) , fostering a research environment focused on high-impact algorithmic research.

Gaurav Rattanمشاهده پروفایل

استادیار

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +۵ مورد دیگر

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

Anuran Makurمشاهده پروفایل

استادیار

• Machine Learning Theory
• Information Theory
• Applied Probability
• +۴ مورد دیگر

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.

Donald R. Sheehyمشاهده پروفایل

دانشیار

• Geometric Algorithms
• Topological Data Analysis
• Computational Geometry
• +۴ مورد دیگر

Donald R. Sheehy is an Associate Professor of Computer Science in the College of Engineering at North Carolina State University. His research focuses on the intersection of geometric algorithms and topological data analysis, with significant contributions to computational geometry and persistent homology. Sheehy's research interests span geometric algorithms, topological data analysis, computational geometry, persistent homology, metric spaces, Voronoi diagrams, and Delaunay triangulations. His work bridges theoretical computer science with practical applications in data analysis, where he develops algorithms that extract meaningful topological information from complex datasets. His research has particular relevance for understanding the structure of high-dimensional data through geometric and topological lenses. Analysis of his recent publications reveals a strong focus on developing efficient algorithms for topological data analysis. His work on sparse filtrations, greedy permutations, and metric properties of persistence diagrams has advanced the field by providing computationally tractable methods for analyzing large datasets. Sheehy frequently explores how geometric structures like Voronoi diagrams and Delaunay triangulations can be adapted to topological contexts, creating bridges between classical computational geometry and modern data analysis techniques. Sheehy actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record in top venues like SOCG (Symposium on Computational Geometry) and SODA (Symposium on Discrete Algorithms). His work demonstrates a consistent trajectory of advancing both theoretical foundations and practical applications of geometric and topological methods in computer science.

Hemanshu Kaulمشاهده پروفایل

دانشیار

• Discrete Mathematics
• Operations Research
• Graph Theory
• +۵ مورد دیگر

Hemanshu Kaul is an Associate Professor of Applied Mathematics at Illinois Institute of Technology (IIT), part of the College of Computing. He serves as Co-Director of the M.S. in Computational Decision Science and Operations Research (CDSOR) program. His expertise spans Discrete Mathematics, Operations Research, Graph Theory, and Network Optimization, with applications in transportation, computer science, and engineering. Education: PhD in Mathematics from the University of Illinois at Urbana-Champaign (UIUC), MS in Mathematics from the Indian Institute of Technology Bombay. He has held roles including Distinguished Teaching Fellow (2016–2018) and AMS Project NExT Fellow (2007–2008). Research Interests : Focus on Graph Packing, DP-coloring, List Coloring, and algorithmic solutions for discrete optimization problems. His work bridges theoretical foundations with practical applications such as transportation networks and computer science systems. Publications & Grants : Over 50 publications in combinatorics and optimization, including NSF/NSA-funded projects like the EXCILL III Conference (2016–17). Recent work explores spectral Turán problems, DP-coloring algorithms, and longitudinal network models. Awards : Board of Trustees Award for Excellence in Teaching (2019) Excellence in Teaching Award (2017, College of Science, IIT) Interdisciplinary Research Grant (2009–2010, Transportation Networks) Advising & Leadership : Co-advisor for IIT's SIAM Student Chapter. Led restructuring of the Applied Math M.Sc. program (2018–19). Advised teams in the Mathematical Contest in Modeling (MCM), including a 2019 Meritorious Winner team for a disaster response system design. Labs & Collaborations : Involved in interdisciplinary projects combining applied math with computer science and engineering, including work on equitable public transit systems and network optimization.

Tselil Schrammمشاهده پروفایل

استادیار

• Algorithms
• Optimization
• Computational Complexity
• +۳ مورد دیگر

Tselil Schramm is an Assistant Professor at Stanford University in the Department of Statistics, with courtesy appointments in Computer Science and Mathematics. Her work bridges theoretical computer science and statistics, focusing on algorithm design and information-computation tradeoffs. Research interests include: Theoretical power of sum-of-squares semidefinite programs Fast spectral methods for high-dimensional estimation Characterizing computational complexity in statistical problems Interplay between different computation models Teaching: She has taught courses like Intro to Statistics , Machine Learning Theory , and The Sum-of-Squares Algorithmic Paradigm in Statistics since joining Stanford in 2022. Selected publications highlight: Robust algorithmic paradigms for high-dimensional data, computational barriers in low-degree polynomial estimation, and theoretical foundations of statistical query models. Scientific honors: NSF CAREER Award (2020s) COLT 2021 Best Paper Runner-up (Statistical query algorithms paper)

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Generative Programming
• Model-Based Development
• Software System Families
• +۷ مورد دیگر

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Spectral Graph Theory
• +۴ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.

Diego Regruto Tomalinoمشاهده پروفایل

دانشیار

• System Identification
• Robust Control
• Convex Relaxation Techniques
• +۵ مورد دیگر

Diego Regruto Tomalino is an active Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin, Italy, with contact details +39 0110907077 and diego.regruto@polito.it. He holds leadership roles in the IEEE Control Systems Society as Scientific Committee member of the Technical Committee on System Identification and Adaptive Control (2008-present) and served as its President (2013-2015). His work spans academic research, teaching, and international collaboration within control engineering. Education : Laurea in Electronic Engineering, Politecnico di Torino (2000) Ph.D. in Systems Engineering, Politecnico di Torino (2004) Research Interests : System Identification : Specializing in set-membership, sparse, and LPV techniques for nonlinear systems with applications in automotive and biomedical domains. Optimization & Control : Developing convex relaxation methods for model predictive control, robust control algorithms, and electrified vehicle propulsion systems. Control Education : Innovating through remote laboratories and industrial platforms to enhance engineering pedagogy globally. Publication Trends : Recent work (2021-2024) focuses on sparse optimization for cyber-physical security and electrified vehicles, extending set-membership identification to continuous-time systems, and educational frameworks. Dominant fields include control theory, optimization, and automotive applications with increasing emphasis on data-driven methods and sparse computing. Scientific Awards : No specific prizes, medals, or fellowships were documented in the provided texts. Advising and Grants : PhD Supervision : Alice Re (2024-present, sparse optimization for identification), Francesco Ripa (2023-present, electric vehicle propulsion management), Simone Pirrera (2021-2025, dynamical systems learning). Research Projects : Principal investigator for OpThermEV (2021-2022) on thermal management of electric vehicles, plus corporate-funded automotive research initiatives. Labs and Teams : Core member of the SIC (System Identification & Control) research group and LAB 11 at DAUIN. Leads international control education collaborations, notably a remote laboratory partnership between Asian and European institutions.