Distributed and Parallel Computing Professors and Supervisors in Qatar

Zhihao JiaView profile

Assistant Professor

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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Benjamin MoseleyView profile

Associate Professor

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 TetaliView profile

Professor

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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Soummya KarView profile

Professor

Carnegie Mellon UniversityQatar

Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.

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William KuszmaulView profile

Assistant Professor

Carnegie Mellon UniversityQatar

William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University. His research interests include theory and algorithms and complexity , with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role. His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials. His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024) , suggesting involvement in both introductory and advanced computer science education.

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