جستجوی استاد راهنمای دانشگاهی

Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.

• Algorithms
• Data Structures
• Dynamic Algorithms
• +۳ مورد دیگر

Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Discrete and Combinatorial Optimization
• +۶ مورد دیگر

Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.

• Fast Graph Algorithms
• Algorithm Design
• Minimum Cut Problems
• +۶ مورد دیگر

Quanquan Liu is an Assistant Professor of Computer Science at Yale University. His research focuses on algorithms for large data, dynamic and distributed graph algorithms, parallel computing, differential privacy, and Byzantine-resilient systems. He holds a PhD in Computer Science from MIT's Theory Group and has held postdoctoral positions at Northwestern University and MIT. Education: PhD in Computer Science, MIT (Advisors: Erik Demaine and Julian Shun) MEng in Computer Science, MIT B.S. in Computer Science and Math, MIT (Advisor: David Karger) Research Interests: Theory and practice of algorithms for large-scale data, dynamic/distributed graph algorithms, parallel and high-performance computing, differential privacy, and Byzantine-resilient algorithms. Recent Highlights: His work includes practical differentially private graph algorithms, efficient parallel algorithms for graph problems, and fair course allocation mechanisms. Notably, he received the Best Paper Award at SPAA 2022 for parallel dynamic graph algorithms. Service: PC member for PPoPP, ESA, SPAA, and ALENEX Coach for USA Computing Olympiad (USACO) and Northwestern's ICPC team Current Group: Advising PhD students Felix Zhou and Pranay Mundra, and Master's student Jinghua Sun.

• Algorithms for Large Data
• Dynamic Graph Algorithms
• Parallel and Distributed Computing
• +۴ مورد دیگر

Thatchaphol Saranurak is an Assistant Professor at the University of Michigan , specifically in the Computer Science and Engineering Division . Prior to this, he earned his PhD in Computer Science from KTH Royal Institute of Technology in 2018 under Danupon Nanongkai , followed by a postdoctoral research assistant professorship at Toyota Technological Institute at Chicago (2018-2020). Research Focus : His work bridges fundamental problems in graph theory, including Dynamic graph algorithms for max-flow and min-cut Expander graph decompositions and their applications Robust algorithms against adaptive adversaries Continuous optimization for combinatorial problems Scientific Contributions : He has made breakthroughs in deterministic graph algorithms, notably improving vertex connectivity bounds, developing near-linear time Gomory-Hu trees, and advancing dynamic matching algorithms. His research has been recognized by Sloan Research Fellowship NSF CAREER Award Presburger Award 2023 Teaching : He teaches courses like Expander and Graph Algorithms and Introduction to Algorithms (Winter 23, Winter 25). His lecture videos and notes are publicly available. Collaborations : He works with leading researchers including Sayan Bhattacharya , Joakim Blikstad , and Jason Li , with affiliations to institutions like TTIC , KTH , and SODA conferences.

• Fast graph algorithms in different computation models
• Robust algorithms against adaptive adversaries
• Continuous optimization for combinatorial problems
• +۳ مورد دیگر

Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. Previously, he was a Postdoctoral Member at the Institute for Advanced Study and earned his PhD from Stanford University under the supervision of Aaron Sidford. He completed his undergraduate studies at MIT, graduating in May 2018. His educational background includes: PhD in Computer Science, Stanford University (Advisor: Aaron Sidford) Bachelor's degree, Massachusetts Institute of Technology (graduated May 2018) Dr. Liu's research spans the intersection of mathematics and computer science, with particular focus on graph algorithms , optimization , high-dimensional geometry , and additive combinatorics . His work often develops novel algorithmic techniques that bridge theoretical insights with practical applications. He has made significant contributions to areas such as convex optimization, linear programming, and combinatorial problems. His teaching includes courses like "A Principled Approach to Optimization" (CS 15-759), which covers rigorous treatments of convex optimization topics including gradient descent, interior point methods, linear regression, linear programming, and sparsification. His extensive publication record in top-tier conferences (FOCS, STOC, SODA) demonstrates a consistent focus on developing almost-linear time algorithms for fundamental graph problems, optimization techniques, and combinatorial theorems. Recent work shows increasing emphasis on combinatorial lines, corners theorem, and k-CSP approximability, while maintaining strong connections to optimization theory and graph algorithms. Dr. Liu has received notable recognition for his work: National Defense Science and Engineering Graduate (NDSEG) Fellowship (2018-2021) Google PhD Fellowship (2022-2023) Best Paper award at FOCS 2022 for "Maximum Flow and Minimum-Cost Flow in Almost Linear Time" Best Student Paper at STOC 2021 for "Discrepancy Minimization via a Self-Balancing Walk" His research has been supported by prestigious fellowships including the NDSEG Fellowship and Google PhD Fellowship. His work on graph algorithms, optimization, and combinatorics involves collaborations with researchers across theoretical computer science and mathematics. His publications often involve co-authors from multiple institutions, suggesting active research collaborations across the field. Dr. Liu maintains an active research program with a focus on developing efficient algorithms for fundamental computational problems. His recent work continues to push the boundaries of what's computationally feasible in graph algorithms, optimization, and combinatorial mathematics, with particular emphasis on achieving almost-linear time complexity for challenging problems.

• graph algorithms
• optimization
• high-dimensional geometry
• +۳ مورد دیگر

Ken Ono is the STEM Advisor to the Provost and the Marvin Rosenblum Professor of Mathematics at the University of Virginia. He also holds courtesy appointments as Professor of Electrical and Computer Engineering, Professor of Data Science, and Professor of Statistics. Additionally, he is a Fellow of the Shannon Center for Advanced Studies and serves on the Institute for Advanced Study board of trustees and the National Security Agency Advisory Board. His educational background includes a Ph.D. in Mathematics from UCLA (1993) and a B.A. in Mathematics from the University of Chicago (1989). Throughout his distinguished career, Ono has held leadership roles including Vice President of the American Mathematical Society, Chair of the Mathematics Section of the American Association for the Advancement of Science, and Chairman of the Department of Mathematics at UVa. Ken Ono specializes in Number Theory, Combinatorics, Algebra, and Arithmetic Geometry, with recent work expanding into sports analytics and swimming optimization. His research has produced numerous publications on partition theory, modular forms, and related areas, continuing the mathematical legacy of Srinivasa Ramanujan. His recent publications demonstrate a continued focus on partition theory, modular forms, and their connections to prime numbers and combinatorial structures. The work shows sophisticated connections between classical number theory and modern mathematical physics, with applications in cryptography and data science. Guggenheim Fellowship David and Lucile Packard Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Presidential Early Career Award (2000) National Science Foundation Director's Distinguished Teaching Scholar (2005) University of Chicago Alumni Medal for Professional Achievement (2023) Fellow of the American Mathematical Society Ono has advised 34 PhD students and 17 postdocs, mentoring numerous award winners including 2 Breakthrough Mirzakhani Prize winners, 9 Morgan Prize winners, and 9 Schafer Prize winners. He founded and directs the Spirit of Ramanujan Global STEM Talent Search, which has awarded 125 budding scientists from 23 countries. His research grants have supported extensive REU programs, with continuous undergraduate research opportunities for 25 years. Beyond academia, Ono serves as a technical consultant for elite swimmers, has worked as an Associate Producer on the film The Man Who Knew Infinity , and is actively involved with the Infinity Arts Foundation. His interdisciplinary work spans from pure mathematics to practical applications in sports science, where he has developed data-driven approaches to optimize swimming performance and served as a consultant for Olympic medalists. He co-founded the Velocity Swim Camp with Olympic coach Todd DeSorbo, combining mathematical analytics with elite swimming training.

• Number Theory
• Combinatorics
• Algebra
• +۶ مورد دیگر

Timothy Duff is an Assistant Professor in the Mathematics Department at the University of Missouri's College of Arts and Science. He co-organizes the Math & Data Seminar and specializes in applied computational algebraic geometry for 3D reconstruction in computer vision. Research integrates algebraic geometry with machine learning and numerical analysis to solve geometric problems in imaging systems. Core interests include multi-view geometry, minimal solvers, and certified numerical methods. Recent publications emphasize efficient algorithms for camera calibration, 3D reconstruction, and polynomial system solving. Work frequently develops tools in Macaulay2 and addresses theoretical challenges in computer vision through algebraic frameworks.

• Algebraic Geometry
• Computer Vision
• Numerical Analysis
• +۱ مورد دیگر

James R. Lee is a Professor in the Department of Computer Science at the University of Washington. His research spans theoretical computer science, probability, and geometry. He has held visiting scientist roles at Microsoft Research (2023, 2018, 2017) and participated in programs at the Simons Institute (2023, 2020, 2018, 2017, 2014). Research Interests: Algorithms, complexity theory, convex optimization, metric embeddings, spectral graph theory, probability, stochastic processes, and the interplay between discrete and continuous analysis. Teaching: Courses on modern algorithms, quantum computing, optimization theory, and spectral methods in theoretical computer science. Scientific Contributions: Developed sparsification algorithms for generalized linear models and norms with near-linear size guarantees (STOC'24, FOCS'23). Extended Cheeger-type inequalities to higher eigenvalues (STOC'12, STOC'18). Proved super-polynomial lower bounds for LP/SDP relaxations in constraint satisfaction (STOC'15, FOCS'13). Disproved Benjamini-Papasoglou conjectures on annular separators (Discrete Comp. Geom. 2024). Advanced understanding of random walks in geometric and unimodular graphs (Israel J. Math. 2023, GAFA 2023). Scientific Awards: Best Paper Award, STOC 2015

• Algorithms
• Computational Complexity
• Convex Optimization
• +۴ مورد دیگر

Erik Demaine is a Professor in the Department of Electrical Engineering and Computer Science at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Theory of Computation group, and the Algorithms Group. His research spans algorithms, computational geometry, folding, robotics, complexity theory, and interdisciplinary connections between mathematics and art. He received the MacArthur Fellowship ("genius grant") for his work on folding and computation. Demaine's work includes co-authoring books such as Geometric Folding Algorithms and Games, Puzzles, and Computation . His art collaborations with his father Martin Demaine, including curved-crease sculptures, are in the permanent collections of the Museum of Modern Art (MoMA) and the Renwick Gallery. He is also involved in software projects like Coauthor and Cocreate, supporting collaborative research and education. His research interests include folding and unfolding of geometric structures, computational complexity of games, protein folding, and algorithm design. He has contributed to areas like data structures, robotics, and network computing, with a focus on bridging theoretical computer science and tangible applications. Demaine holds patents and has been recognized with numerous awards, including the NSERC Doctoral Prize and the Gödel Prize. His work often involves supercollaboration, emphasizing open problem-solving and interdisciplinary approaches.

• Algorithms
• Computational Geometry
• Complexity Theory
• +۵ مورد دیگر

Uriel Feige is a Professor in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science. Born in Jerusalem, he earned his BSc from the Technion and his MSc and PhD from the Weizmann Institute under the guidance of Adi Shamir. After postdoctoral research at Princeton University and IBM T.J. Watson Research Center, he joined the Weizmann Institute in 1992. From 2004-2007, he worked with Microsoft Research's Theory Group and continues to serve as a visiting researcher there. Education: BSc: Technion MSc: Weizmann Institute of Science (under Adi Shamir) PhD: Weizmann Institute of Science (under Adi Shamir) Professor Feige's research focuses on algorithms, computational complexity, and algorithmic game theory. His work spans approximation algorithms, hardness results, and beyond worst-case analysis of combinatorial optimization problems. He has made significant contributions to the understanding of random walks on graphs, particularly in analyzing cover times and developing approximation algorithms. His research also extends to allocation problems and fairness in algorithmic game theory, while previously working on cryptography and other theoretical topics. Professor Feige's publications reveal consistent contributions to theoretical computer science with a strong emphasis on fundamental algorithmic problems. His work bridges theoretical foundations with practical implications, particularly in approximation algorithms where he has established both upper and lower bounds for various problems. Many papers focus on the interplay between graph structure and algorithmic performance. Scientific Awards: 2001 Godel Prize 2005 SIAM Outstanding Paper Prize Professor Feige has advised numerous students documented in technical reports on his website. His research has been supported by various grants enabling his theoretical contributions. He actively teaches courses including algorithms, linear programming, combinatorial optimization, approximation algorithms, and algorithmic game theory through 2024-2025. Professor Feige maintains an active research program within the Department of Computer Science and Applied Mathematics at the Weizmann Institute, where his work intersects with multiple research groups focusing on theoretical computer science and algorithms.

• Algorithms
• Computational Complexity
• Algorithmic Game Theory
• +۴ مورد دیگر

Jean F. Honorio Carrillo is an Adjunct Professor at Purdue University's Department of Computer Science and a Senior Lecturer at the University of Melbourne's School of Computing and Information Systems. He specializes in machine learning theory, optimization, and their applications to combinatorial and non-convex problems. His research focuses on developing algorithms with theoretical guarantees for structured prediction, robustness, fairness, and federated learning. He has advised numerous students across multiple institutions and holds adjunct roles at Purdue's Statistics Department and MIT CSAIL. Roles: Senior Lecturer (Melbourne), Adjunct Professor (Purdue), Adjunct at MIT CSAIL Research Areas: ML Theory, Non-Convex Optimization, Fairness, Federated Learning Key contributions include breakthroughs in exact inference for structured prediction, optimization frameworks for NP-hard problems, and theoretical foundations for modern ML challenges. His work has been published in top venues like NeurIPS, ICML, and JMLR. He has secured grants from NSF and industry partners, including a 2021 NSF DMS grant for deep learning research. His students have gone on to postdoctoral roles at NUS and UChicago/CMU.

• Machine Learning
• Artificial Intelligence
• Optimization Theory
• +۵ مورد دیگر

Michael J. Fischer is a Professor of Computer Science at Yale University, renowned for foundational contributions to distributed systems theory, cryptography, and parallel algorithms. His work includes the seminal impossibility result for distributed consensus with faulty processes and the development of the parallel prefix algorithm fundamental to modern parallel computing. His educational background: B.S. in Mathematics, University of Michigan (1963) M.A. in Applied Mathematics, Harvard University (1965) Ph.D. in Applied Mathematics, Harvard University (1968) Fischer's research spans theoretical and applied computer science with emphasis on distributed systems (consensus protocols, fault tolerance), cryptography (information-theoretic security, card-based protocols), and electronic voting systems . His work bridges abstract theory with practical security applications, particularly in trust modeling for e-commerce. Current investigations focus on algorithmic approaches to establishing trust relationships in distributed environments. His publication trajectory reveals evolving expertise from early parallel algorithms (1970-80s) to distributed consensus breakthroughs (mid-1980s), then cryptographic protocols and secure e-voting systems (1985-2000s), with recent work synthesizing these domains through trust frameworks. Key thematic threads include fault tolerance in unreliable networks and verifiable security mechanisms. Scientific recognition: ACM Fellow Fischer has held significant leadership roles including Editor-in-Chief of the Journal of the ACM , chair of the Max-Planck-Institute for Computer Science International Scientific Advisory Board, and founding member of the Computing Research Association's subcommittee on Women in Computer Science. His advisory work extends to the National Science Foundation and Wuhan University's State Key Laboratory on Software Engineering. Outside academia, he actively participates in the Yale Figure Skating Club, having served multiple terms as president. He maintains international collaborations as Guest Professor at Wuhan University and through editorial roles including Acta Informatica , while continuing to teach core computer science courses from data structures to cryptography at Yale.

• Distributed Systems
• Cryptography
• Electronic Voting
• +۳ مورد دیگر

Daniel A. Klain is a Professor in the Department of Mathematics & Statistics at the University of Massachusetts Lowell , part of the College of Sciences. His career focuses on geometric and discrete mathematics, with significant contributions to Convex Geometry and its intersections with probability and combinatorics. Education: Ph.D. in Mathematics (1994), Massachusetts Institute of Technology B.S. in Mathematics (1990), Massachusetts Institute of Technology Research Interests span Convex Geometry, Geometric Tomography, Integral Geometry, and Combinatorics. His work explores geometric inequalities, valuations, and symmetrization techniques, often bridging classical geometry with modern probabilistic and discrete methods. Article Trends highlight his focus on Convex Geometry and Integral Geometry, with studies on Steiner symmetrization, shadow covering, and valuations. His publications also reflect interests in geometric probability, number theory, and educational insights. Scientific Awards and Grants: Mathematical Sciences Teaching Excellence Award (2010, 2003) Sigma Xi Young Faculty Award (2000) Jon A. Bucsela Prize in Mathematics (1990) NSF Graduate Fellowship (1990) National Merit Scholar (1986) NSF grants (2003, 1998, 1996) for convex geometry and geometric analysis Service and Collaborations: Active in teaching, research, and academic service, Klain has co-authored works in geometric probability and presented at numerous international workshops and seminars. His career integrates rigorous mathematical inquiry with educational innovation.

• Convex Geometry
• Geometric Tomography
• Integral Geometry
• +۳ مورد دیگر

Aravindan Vijayaraghavan is an Associate Professor in the Department of Computer Science at Northwestern University (affiliated with McCormick School of Engineering). He also holds courtesy appointments in the Industrial Engineering and Management Sciences (IEMS) department. Research interests include theoretical computer science , machine learning algorithms , quantum information , and combinatorial optimization under non-worst-case paradigms. He leads IDEAL (Institute for Data, Economics, Algorithms and Learning) as Site Director at Northwestern and former Institute Director (2023-24). Academic Background : PhD in Computer Science from Princeton University (advisor: Moses Charikar ) Bachelor's Degree in Computer Science from Indian Institute of Technology Madras Postdoctoral work at Courant Institute (NYU) and Carnegie Mellon University via Simons Collaboration grants Research Contributions : Developed smoothed analysis frameworks for random matrices with dependent entries Created sum-of-squares certificates for anti-concentration beyond Gaussian distributions Advanced quantum entanglement certification algorithms for subspaces Improved weak-to-strong generalization theory with data distribution expansion properties Designed error-tolerant e-discovery protocols for legal document classification Scientific Recognition : NSF CAREER Award NSF AITF Award (CCF-1637585, CCF-2154100) Google Research Scholar Program grant Amazon Research Awards program support Simons Postdoctoral Fellowship Academic Leadership : General Chair for FOCS 2024 Co-organizer of Junior Theory Workshop and Northwestern QTW series Active in program committees for COLT , ICML , NeurIPS , and STOC conferences Teaching Portfolio : CS262: Mathematical Foundations of CS (Continuous Mathematics for Computer Science) CS212: Mathematical Foundations of Computer Science (multiple offerings since 2015) CS496: Graduate Algorithms (since 2016) CS396/496: Quantum Computation & Information (co-taught with S. Rao) CS497: Machine Learning Theory (Spring 2025 offering)

• Theoretical Computer Science
• Machine Learning Theory
• Data Science
• +۷ مورد دیگر