Probabilistic Programming Professors and Supervisors in Qatar

Fabio Cozmanمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Fabio Cozman is a Full Professor at the School of Engineering, University of São Paulo (USP), and Director of the Center for Artificial Intelligence at USP. He holds a PhD from Carnegie Mellon University (1996) and completed his engineering degree at Poli-USP. His research spans artificial intelligence, machine learning, and probabilistic reasoning, with significant contributions to knowledge representation under uncertainty, credal networks, and physics-informed AI. Current research focuses on AI interpretability , AI and societal impact , and ocean hazard prediction . Key publications cover probabilistic logic programming , credal networks , and Bayesian network extensions . He is a founding member and former president of the Society for Imprecise Probability Theory and Applications (SIPTA) and has served as Associate Editor for multiple AI journals. His work integrates theoretical advancements in probabilistic modeling with practical applications in robotics, computer vision, and environmental systems, particularly focused on the Brazilian maritime territory through projects like the Blue Amazon Brain (BLAB) architecture.

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Jan Hoffmannمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Jan Hoffmann is an Associate Professor in the Computer Science Department at Carnegie Mellon University. His research develops quantitative program analysis techniques to ensure software reliability, efficiency, and security. Key focus areas include resource-aware programming languages, probabilistic program verification, and scalable static analysis methods for modern computing environments. Dr. Hoffmann leads the Carnegie Mellon Resource Analysis Group, advising PhD students on projects spanning energy-aware computing, secure compilation, and probabilistic inference. His work bridges formal methods with practical system design, emphasizing compositional verification and automated reasoning. Recent publications advance CUDA kernel optimization, programmable Markov chain Monte Carlo methods, and robust resource bound synthesis. This research establishes foundations for predictable performance in concurrent systems and AI-driven applications.

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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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Gonzalo Menaمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Gonzalo Mena is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. His research focuses on applying statistical and machine learning methods to life sciences problems, particularly in neuroscience and epidemiology. He has prior experience as a Florence Nightingale Bicentennial Fellow and Tutor at the University of Oxford, a Postdoctoral Fellow at Harvard University, and holds a Ph.D. in Statistics from Columbia University and a B.Sc. in Mathematical Engineering from Universidad de Chile. His work bridges cutting-edge neurotechnology data analysis with large-scale epidemiological surveillance, emphasizing valid inference from biased datasets. He is a leader in statistical optimal transport, developing methodologies to measure distributional distances and inform scientific discovery. His research spans the DELPHI Lab Group and intersects with computational neuroscience, genomics, and public health policy. Key research areas include Alzheimer's disease modeling via multimodal cell atlases, Bayesian methods for neurodegenerative dynamics, and pandemic impact analysis (e.g., socioeconomic disparities in Covid-19 outcomes). His recent publications highlight interdisciplinary applications of optimal transport theory and permutation inference in neuroscience data analysis. Mena has contributed to foundational statistical frameworks for population size estimation and neuronal identification in C. elegans. His work combines methodological innovation with real-world applications in healthcare and public health surveillance.

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Ann Leeمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Ann Lee is a Professor and Co-Director of the Ph.D. Program in the Department of Statistics & Data Science at Carnegie Mellon University, with a joint appointment in the Machine Learning Department. She has been at CMU since 2005, after serving as the J.W. Gibbs Assistant Professor of Applied Mathematics at Yale University. Her academic journey began with a PhD in Physics from Brown University in 2002, preceded by BSc and MSc degrees in Engineering Physics from Chalmers University of Technology in Sweden. Dr. Lee's educational background includes: PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on developing statistical methodology for complex data and problems in the physical sciences, with particular emphasis on trust-worthy scientific inference and reliable uncertainty quantification. She works at the intersection of classical statistics and machine learning, developing methods for simulation-based inference and experimental design. Dr. Lee is especially interested in likelihood-free inference, calibrated probabilistic forecasting, and interpretable diagnostics of generative models, with applications in astronomy and hurricane intensity guidance involving satellite imagery and large surveys. Dr. Lee co-founded the STAMPS (STAtistical Methods for the Physical Sciences) research group with Mikael Kuusela in 2018, which is transitioning to a CMU Research Center in Fall 2024. The group hosts public colloquia-style webinars and weekly research meetings for students and faculty at CMU and UPitt. Her notable scientific contributions have been recognized through: Her paper "Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery" being selected for The Best of AOAS session at the 2023 Joint Statistical Meeting Her student Luca Masserano winning the ASA Best Student Paper Award at the 2023 Joint Statistical Meeting Her student Alex Shen winning a Poster Award at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023 Dr. Lee has advised numerous PhD students to completion, with research spanning statistical machine learning, high-dimensional statistics, and applications in physical sciences. She has secured research funding supporting her work on statistical methods for physical sciences and has collaborated extensively with researchers across disciplines. Her current research projects focus on advancing likelihood-free inference methods and applying them to challenging problems in climate science and astronomy. She leads the STAMPS research group, which is becoming a CMU Research Center in Fall 2024, and organizes workshops including the PHYSTAT-SBI workshop on "Simulation Based Inference in Fundamental Physics" and the Hammers & Nails workshop on "Frontiers in Machine Learning in Cosmology, Astro & Particle Physics."

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Peter Freemanمشاهده پروفایل

عضو هیئت علمی

Carnegie Mellon UniversityQatar

Peter Freeman is an Associate Teaching Professor and Director of the Undergraduate Program in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He holds a B.A. in Physics (1989, University of California) and a Ph.D. in Astronomy and Astrophysics (1997, University of Chicago). Before joining CMU in 2004, he worked as a scientific programmer for the Chandra X-ray Telescope mission, specializing in data analysis methodologies. His research focuses on astrostatistics, developing advanced statistical techniques for analyzing complex astronomical datasets. Key areas include source detection algorithms, cosmic microwave background mapping, photometric redshift estimation, and galaxy morphology analysis. He has collaborated with faculty and students from CMU and the University of Pittsburgh on interdisciplinary projects, integrating machine learning and computational methods into astrophysical research. Freeman is actively involved in educational initiatives such as the STAMPS (Statistical Pedagogy & Educational Research) group and the Carnegie Mellon Sports Analytics Camp (CMSAC). His work emphasizes authentic, community-engaged learning experiences, particularly in undergraduate laboratory courses and data science curricula. His recent publications span astrostatistics, ecological studies (e.g., Coqui frog acoustic analysis), and pedagogical innovations in STEM education. He has contributed to major projects like the Rubin Observatory Legacy Survey of Space and Time (LSST) and the CANDELS galaxy structure classification effort.

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Stephen Smithمشاهده پروفایل

استاد پژوهشی

Carnegie Mellon UniversityQatar

Stephen Smith is a Research Professor at the Robotics Institute, Carnegie Mellon University. He directs the Intelligent Coordination and Logistics Laboratory and holds affiliations with Traffic21, Metro21, and the TSET University Transportation Center. His research focuses on AI-driven solutions for planning, scheduling, and coordination in complex systems, including transportation infrastructure, robotics, and manufacturing. Research interests include constraint-based optimization, multi-agent coordination (e.g., robot teams), robust planning under uncertainty, and adaptive traffic control systems like Surtrac. Applications span smart cities, autonomous vehicles, and distributed resource management. He collaborates with municipal partners on projects such as pedestrian safety apps and snowplow route optimization. His work bridges theoretical advances with real-world deployment, emphasizing scalability and practicality. Notable contributions include decentralized traffic control systems, learning-based planning assistants, and frameworks for distributed decision-making in uncertain environments. Current and former advisees include PhD students Hsu-kuang Chiu and Jayanth Krishna Mogali, alongside master’s students like Viraj Parimi. Projects include the PedPal smartphone app and the RADAR cognitive assistant.

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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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Dave Andersenمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Dave Andersen is an Associate Professor at the School of Computer Science, Carnegie Mellon University , with research focusing on memory and power-efficient computing, robust distributed systems, and networked environments. He also serves as CTO of Enriched Ag . Education: Ph.D. and M.S. in Computer Science from MIT (2001, 2004), B.S. in Computer Science and Biology from the University of Utah (1995). Research Trends: Explores systems design in the post-Moore's Law era, emphasizing concurrency, low-latency geo-replicated storage, and RDMA in datacenters. Key projects include MemC3, Eiger, Cuckoo Filter, and FAWN. Teaching: Courses in Advanced OS, Distributed Systems, Low-Power Computing, and Network Security. Professional Service: Program committee roles at SOSP, NSDI, SIGCOMM, and DARPA ISAT advisory group. Personal: Active in running, climbing, and outdoor activities with detailed route guides for Pittsburgh and Boston.

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