Approximation Algorithms Professors and Supervisors in Qatar | ApplySharif

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David Woodruffمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

David Woodruff is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on algorithms, complexity theory, machine learning, and theoretical computer science. He is particularly known for contributions to communication complexity, data stream algorithms, graph algorithms, and numerical linear algebra. His work bridges foundational theory with practical applications in privacy, security, and efficient computation. Research Interests: Algorithms and Complexity Machine Learning Data Stream Algorithms Communication Complexity Sketching and Randomized Numerical Linear Algebra Recent publications emphasize adversarial robustness in sketching, space-efficient streaming algorithms for graph problems, and eigenvalue approximation techniques. His work on privacy-preserving GWAS and transformer optimizations showcases interdisciplinary impact. He advises three PhD students and teaches courses in algorithms (15451, 15651, 15851). Advising and Grants: Active mentor to Honghao Lin, Hoai-An Nguyen, and Madhusudhan Pittu. His research has been supported by grants focusing on streaming algorithms, communication complexity, and machine learning foundations.

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Zhihao Jiaمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.

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Ryan O'Donnellمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.

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Richard Pengمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

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Arun Kumar Kuchibhotlaمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Arun Kumar Kuchibhotla is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), within the Dietrich College of Humanities and Social Sciences. His research focuses on foundational aspects of statistical inference, machine learning, and high-dimensional data analysis. Education : While specific details are not listed here, his academic trajectory aligns with roles in top-tier institutions. His research interests span Causal Inference , Statistical Machine Learning , High-Dimensional Statistics , and Nonparametric Methods . He explores theoretical guarantees for algorithms, fair machine learning applications, and algorithmic forecasting of extreme events. Notable areas include: Development of robust statistical methods for online algorithms and M-estimation. Innovations in conformal prediction, confidence regions, and self-normalization techniques. Advancements in fair risk assessments in criminal justice through optimal transport and conformal prediction. Recent work emphasizes dimension-agnostic adaptive inference and censored time-to-event analysis , with applications to climate science and policy. His publications in 2025 highlight breakthroughs in concentration inequalities, extreme heat wave forecasting, and doubly robust calibration methods. Advising & Grants : No specific advisees or grants are listed here, but his research aligns with CMU’s emphasis on interdisciplinary projects. He collaborates with groups like the CSAFE and DELPHI labs, focusing on applied statistics and societal impact. Labs/Teams : Active contributor to CMU’s CSAFE Lab Group (statistical forensics) and DELPHI Lab Group (epidemiological forecasting).

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Aaditya Ramdasمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Aaditya Ramdas is an Associate Professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. He is also a visiting academic at Amazon Research. His work bridges theoretical statistics and practical machine learning, focusing on algorithms with strong guarantees for post-selection inference, game-theoretic statistics, and predictive uncertainty quantification. Applied domains include privacy, neuroscience, genetics, and auditing. He has received prestigious awards such as the Sloan Fellowship, IMS Peter Gavin Hall Prize, and NSF CAREER Award. His research spans foundational areas like sequential testing, multiple hypothesis testing, and conformal prediction, with recent work emphasizing anytime-valid inference and robust statistical methods. Affiliations: Dietrich College of Humanities and Social Sciences, Amazon Research Labs/Teams: DELPHI Lab Group (specializing in public health forecasting), CSAFE Lab Group (computational statistics). Key contributions include advancing e-value based methods for hypothesis testing and developing scalable techniques for uncertainty quantification in machine learning models. His work often intersects interdisciplinary fields, such as auditing AI systems for fairness and privacy compliance.

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David Choiمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

David Choi is an Associate Professor at the Heinz College, part of Carnegie Mellon University's Dietrich College of Humanities and Social Sciences, with a courtesy appointment in the Department of Statistics. His expertise lies in statistics and machine learning applied to network data, including community detection and causal inference in social networks. He holds a PhD in Electrical Engineering from Stanford University (2004) and has held roles at MIT Lincoln Laboratory, Harvard University, and UC Berkeley. Research focuses on network models involving latent variables, exploratory data analysis, and interference effects in network experiments. His work bridges statistical methodology with practical applications in public policy and social sciences. Recent studies include analyzing Medicaid expansion impacts, single-cell network construction, and Alzheimer's disease microglia regulation. Prominent publications address network clustering, dynamic network analysis, and causal inference in experimental settings. Technical reports explore structured blockmodels and exposure mappings in experiments. Contact: Hamburg Hall 2118B, davidch@andrew.cmu.edu.

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Christopher Harshawمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Christopher Harshaw is an Assistant Professor in the Statistics Department at Columbia University. His research focuses on causal inference and algorithm design, particularly in improving the design and analysis of randomized experiments, including experiments with interference and sequential experiments. He holds a PhD in Computer Science from Yale University and completed postdoctoral fellowships at MIT/UC Berkeley (via FODSI) and the Simons Institute for the Theory of Computing. Education: PhD in Computer Science (Yale University), Postdoctoral Fellowships at MIT/UC Berkeley and the Simons Institute. Research Interests: At the intersection of computation and statistics. Key areas include causal inference methodologies, algorithmic tools for experimental design, and submodular optimization. His work emphasizes balancing covariates, handling interference in experiments, and developing efficient experimental frameworks. Notable Achievements: Received the Best Paper Award at the CML4Impact NeurIPS 2022 Workshop and the Best Paper Award at CISRC 2016. His contributions include the Gram-Schmidt Walk Design for covariate balancing and the Conflict Graph Design for causal effect estimation under interference. Labs/Teams: Collaborations include work on software packages such as GSWDesign.jl and SubmodularGreedy.jl, advancing tools for statistical experimentation and submodular optimization.

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Benjamin Moseleyمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Benjamin Moseley is the Carnegie Bosch Associate Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University (CMU), with a courtesy appointment in the School of Computer Science's Machine Learning Department. He is also a consulting professor at the start-up Relational AI. Education: PhD in Computer Science from the University of Illinois, advised by Chandra Chekuri. Prior roles include Assistant Professor at Washington University in St. Louis (2014–2017), Research Assistant Professor at Toyota Technological Institute at Chicago (2012–2014), and visiting positions at Sandia National Laboratories and Yahoo Labs. Notable affiliations include the NSF-funded Algorithms, Combinatorics, and Optimization (ACO) PhD program. Research focuses on algorithm design, analysis, and evaluation, with emphasis on operations research, theoretical computer science, and machine learning. Current projects include algorithmic foundations of machine learning, distributed algorithm design, and scheduling/logistics optimization. His work bridges theoretical rigor and practical applications, addressing challenges in big data analysis, streaming algorithms, and approximation techniques. Recognition includes multiple best paper awards (IPDPS 2015, SPAA 2013, SODA 2010), top-tier NeurIPS presentations, and prestigious grants from NSF, ONR, Google, Bosch, and others. His research has been supported by a NSF CAREER Award, Google Faculty Research Awards, and the Carnegie-Bosch Chair. Teaching contributions include developing the Business Analytics and Optimization undergraduate minor at Tepper, courses for UBA/MBA/MSBA programs, and被评为“Top 50 Undergraduate Business School Professor” by Poets and Quants. He emphasizes pedagogy integrating advanced algorithms with business applications. Labs/Teams: Active in Relational AI collaborations and affiliated with CMU's machine learning and operations research research groups. His work frequently intersects with industrial partners like Google, Yahoo, and Bosch.

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Prasad Tetaliمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.

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Tom Bohmanمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

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Boris Bukhمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Boris Bukh is a Professor of Mathematics at Carnegie Mellon University (CMU), affiliated with the Department of Mathematical Sciences within the Mellon College of Science. He holds a Ph.D. from Princeton University and has held postdoctoral positions at the University of Cambridge and Churchill College. His research focuses on combinatorics, discrete geometry, extremal graph theory, and geometric selection theorems. He has received prestigious awards such as the Sloan Research Fellowship and the NSF CAREER Award. His work spans topics like Turán problems, geometric configurations, and algebraic methods in combinatorics. Recent publications explore extremal graph structures, convex polytopes in restricted point sets, and applications of random algebraic constructions to computational complexity. Bukh organizes events like the Math Kangaroo competition, fostering mathematics engagement among students. Key contributions include advancements in Ramsey theory, coding theory, and the intersection of combinatorics with geometry. His research often bridges theoretical insights with computational techniques, addressing problems in graph density, geometric incidences, and discrete optimization.

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Anupam Guptaمشاهده پروفایل

استاد مدعو

Carnegie Mellon UniversityQatar

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

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Pravesh Kothariمشاهده پروفایل

استاد مدعو

Carnegie Mellon UniversityQatar

Pravesh Kothari serves as an Adjunct Professor in the Computer Science Department at Carnegie Mellon University, focusing on theoretical computer science and algorithmic foundations of average-case computational problems. His research centers on designing efficient algorithms and establishing rigorous evidence for algorithmic thresholds in problems spanning theoretical computer science, statistics, and allied fields. Key contributions include the development of the sum-of-squares method which bridges proof complexity and semidefinite programming relaxations for optimization challenges, detailed in his monograph Semialgebraic Proofs and Efficient Algorithm Design with Pitassi and Fleming. Recent publications reveal consistent focus on constraint satisfaction problems, small-set expansion, and sparse statistical estimation, demonstrating strong integration of theoretical computer science with statistical learning theory through semidefinite programming frameworks. Major recognitions include: NSF CAREER Award (2021-2026) for The Nature of Average-Case Computation Sloan Fellowship (2022) Current research is primarily supported by the NSF CAREER Award, enabling investigation into computational thresholds and efficient algorithm design for average-case problems through theoretical frameworks. His Fall 2021 CMU lecture notes document practical applications of these methodologies.

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Zachary Liptonمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu

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