Natural Language Processing & Large Language Models Professors and Supervisors in Qatar

Aditi Raghunathanمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Aditi Raghunathan is an Assistant Professor in the Computer Science Department at Carnegie Mellon University , with affiliations to the Machine Learning Department . She holds a PhD from Stanford University (2021) , advised by Percy Liang , and a B.Tech in Computer Science from IIT Madras (2016) . Her research focuses on advancing the scientific understanding of frontier models , addressing their reliability, safety, and unlearning challenges. Education PhD, Stanford University (2021) B.Tech, IIT Madras (2016) Research Themes Overtraining effects in LLMs Memorization sinks for unlearning Algorithmic creativity beyond next-token prediction Scalable frameworks for AI safety Her work at ICML 2025 revealed critical limitations in LLM pre-training, including catastrophic overtraining and imperfect unlearning due to entangled memorization-generalization circuits. She has developed seed-conditioning and multi-token learning to enhance structured diversity and creativity in models. Scientific Recognition includes: NSF CAREER Award Arthur Samuel Best Thesis Award at Stanford Google Research Scholar Forbes 30 Under 30 in Science She supervises PhD and Master’s students such as Jacob Mitchell Springer , Christina Baek , and Taeyoun Kim , and actively collaborates with CMU graduate students. Her teaching includes courses on Trustworthy AI and Graduate Artificial Intelligence .

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Ruslan Salakhutdinovمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Ruslan Salakhutdinov is a UPMC Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University's School of Computer Science. His research focuses on Deep Learning, Probabilistic Graphical Models, and Large-scale Optimization, with applications spanning artificial intelligence, computer vision, and robotics. His recent publications demonstrate strong focus on multimodal systems, diffusion models, reinforcement learning, and self-supervised techniques. Research trends show consistent innovation in generative AI methods and their application to robotics and decision-making systems. Awards include Microsoft Faculty Fellow Sloan Fellowship Directs research group advising over 30 PhD/Master's students. Secured funding from NSF, Google, and SAP Labs. Leads the Machine Learning Department's research initiatives with international collaborations.

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Amir Barati Farimaniمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Dr. Amir Barati Farimani is an Associate Professor at Carnegie Mellon University's College of Engineering, jointly appointed in Mechanical Engineering and Biomedical Engineering. His work bridges machine learning , data science , and molecular dynamics simulations to solve complex problems in bioengineering and materials science. Education : B.S. and M.S. in Mechanical Engineering from Ferdowsi University and Tehran TMU University, followed by a Ph.D. in Mechanical Science and Engineering from the University of Illinois at Urbana-Champaign (2015). Postdoctoral Training : Stanford University, where he combined machine learning with molecular dynamics to study GPCR activation mechanisms. The Barati Lab at CMU focuses on two core research areas: (1) using molecular dynamics (MD) simulations and statistical learning to analyze bio-molecule interactions with synthetic materials, and (2) applying dimensionality reduction techniques to decode high-dimensional MD data for protein conformation studies. His lab emphasizes AI-driven physical modeling and computational discovery . Recent publications highlight his lab's expertise in transformer-based models for material property prediction, protein language models (e.g., GPCR-BERT), and physics-informed neural architectures for solving differential equations. Collaborative projects span desalination membranes , peptide engineering , and additive manufacturing . Scientific Awards : Stanley I Wise Best Thesis Award (2015). Dr. Barati Farimani's work is supported by interdisciplinary collaborations and open-source tools like AugLiChem and FaultNet , reflecting his commitment to advancing AI-powered engineering and democratizing scientific workflows.

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Woojun Kimمشاهده پروفایل

پژوهشگر ارشد

Carnegie Mellon UniversityQatar

Woojun Kim is a Postdoctoral Fellow at the Robotics Institute of Carnegie Mellon University, working in the Advanced Agent - Robotics Technology Lab. His research focuses on multi-agent reinforcement learning, human-agent collaboration, and autonomous navigation systems. Key research areas include: Multi-agent systems for decentralized robotics Large language model integration in robotic tasks Collision avoidance and path planning algorithms Offline reinforcement learning frameworks His work spans robotics, computer science, and artificial intelligence, with recent publications addressing collaborative strategies, geometric decomposition for grasping, and domain adaptation in visual learning tasks.

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Nihar Shahمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Nihar Shah is Associate Professor with joint appointments in Machine Learning and Computer Science at Carnegie Mellon University's School of Computer Science. His research spans statistical learning, game theory, and peer review systems, with applications in fairness and robustness of AI systems. His publication record demonstrates strong focus on improving scientific evaluation systems through machine learning approaches. Recent experimental work examines bias detection, LLM-generated content identification, and peer review optimization. Significant honors include NSF CAREER, Google Research Scholar, and JP Morgan Faculty Research awards. He leads research on AI applications for scientific quality assessment deployed in evaluation of over 100,000 research papers.

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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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Eric Xingمشاهده پروفایل

استاد

Carnegie Mellon UniversityQatar

Eric Xing is a Professor in the Machine Learning Department at Carnegie Mellon University, with an affiliation to the Computer Science Department. His research focuses on advancing artificial intelligence, particularly in machine learning, bioinformatics, and interdisciplinary applications. He advises three students: Aviv Bick, Lingjing Kong, and Yonghao Zhuang. His recent work spans cutting-edge topics such as graph neural networks, generative AI, biosecurity safeguards, and scalable language model evaluation. Notable contributions include foundational models for RNA prediction, efficient video diffusion systems, and ethical frameworks for AI deployment. He actively bridges AI with fields like genomics, healthcare, and urban planning through collaborative projects. Xing’s research often emphasizes practical applications, such as edge computing-optimized inference systems and open mathematical corpora. His work has implications for both technical advancements and societal challenges, reflecting a commitment to impactful, interdisciplinary AI research.

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Yo Joong Choeمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Yo Joong Choe is an Assistant Professor of Decision Sciences at INSEAD (Singapore), with a joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University. His research bridges statistics, machine learning, and natural language processing, focusing on game-theoretic statistics, sequential inference, and representation geometry in large language models. Current role: Assistant Professor at INSEAD Education: Joint Ph.D. in Statistics and Machine Learning (CMU), advised by Aaditya Ramdas Previous role: DSI Faraco Family Postdoctoral Fellow at University of Chicago His research addresses challenges in anytime-valid inference for sequential experiments and representation analysis in LLMs. A recent paper formalizes combining e-values under asymmetric information using adjusters, with applications to financial data testing. He has mentored data science projects at University of Chicago and CMU, including a team winning the 2023–2024 Award for Excellence in Data Science. Scientific awards 2023–2024 Award for Excellence in Data Science (as mentor) Teaching experience Guest lectures at University of Chicago and Georgetown University TA roles for courses in financial time series, machine learning, and probability

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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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Alex Reinhartمشاهده پروفایل

دانشگاهی

Carnegie Mellon UniversityQatar

Alex Reinhart is an Associate Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. from CMU and a BS in Physics from the University of Texas at Austin. His research focuses on statistical pedagogy, natural language processing, and applications of spatio-temporal data analysis to crime prediction and radiation detection. Reinhart’s work emphasizes improving statistical education through innovative methods like writing in the age of AI and psychology case studies. He also explores the intersection of large language models and human-like text generation. His contributions include the book Statistics Done Wrong , which critiques common statistical errors in scientific research. His recent publications address pandemic-related challenges, such as vaccine hesitancy and real-time data analysis through surveys like the US COVID-19 Trends and Impact Survey. His research spans statistical software development (e.g., the pseudobibeR package) and interdisciplinary applications in public health, biomechanics, and education.

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Lining Yaoمشاهده پروفایل

دانشیار

Carnegie Mellon UniversityQatar

Dr. Lining Yao is a Cooper-Siegel Associate Professor of Human-Computer Interaction at Carnegie Mellon University's School of Computer Science, directing the Morphing Matter Lab. She holds courtesy appointments in Mechanical Engineering and Materials Science & Engineering, and is part of the Softbotics initiative. With a PhD from MIT Media Lab (2017), her research focuses on programmable materials, sustainable design, and interdisciplinary fabrication. Yao is a UNIDO eco-design instructor, CMU Inclusive Teaching Fellow, and NSF CAREER Award recipient. Education: PhD in Media Arts & Sciences, MIT Media Lab (2017) Bachelor's/Master's (unspecified in text) Research Interests: Yao's work bridges material science, computation, and design to create adaptive systems. Key themes include morphing materials for sustainability, soft robotics, food engineering, and human-plant interaction. Her lab explores biodegradable actuators, self-folding devices, and energy-harvesting systems. Key Contributions: Notable projects include self-burying seed carriers (Nature 2023), morphing pasta (Science Advances 2021), and the Thermorph 4D printing system (CHI 2018). She co-founded MorphingMatter4Girls, promoting STEM engagement. Awards: NSF CAREER Award 9 Best Paper/Talk Awards Wired UK Fellowship Labs & Future Work: The Morphing Matter Lab develops eco-conscious materials and systems. Upcoming directions include LLM-driven generative design, programmable degradation mechanisms, and interdisciplinary physical AI systems.

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Eswaran Subrahmanianمشاهده پروفایل

استاد پژوهش

Carnegie Mellon UniversityQatar

Eswaran Subrahmanian is a Research Professor at Carnegie Mellon University (CMU), affiliated with the Engineering Research Accelerator and the Department of Engineering and Public Policy within the College of Engineering. He concurrently serves as a Visiting Honorary Professor at the International Institute of Information Technology, Bangalore and an Adjunct Faculty member at Ahmadabad University. His roles include part-year work at the National Institute of Standards and Technology (NIST), USA, focusing on smart networks and societies. Education: Ph.D. in Public Policy & Information Systems, Carnegie Mellon University (1987) M.S. in Computer Science, University of South Carolina (1979) B.E. (Hons) in Chemical Engineering, Birla Institute of Technology & Science (1976) Research Focus: Subrahmanian's work centers on socio-technical systems design, decision support systems, and design theory. He explores intersections of technology, public policy, and societal impact through projects with industry leaders like Boeing, Bosch, and the World Bank. His theoretical contributions include the PSI framework and categorical models for complex systems analysis. Publications & Awards: With over 90 peer-reviewed articles and co-edited books, he co-authored We Are Not Users: Dialogues, Diversity, and Design (MIT Press, 2020). Honors include the Steven Fenves Award (2006), AAAS Fellowship (2008), and ACM Distinguished Scientist (2013). Advising & Innovation: Co-founder of Fields of View , a nonprofit designing games/simulations for policy challenges, and involved in initiatives like the National Health Information Systems Project. His work emphasizes inclusive design methodologies and interdisciplinary collaboration. Labs & Teams: Active in the Engineering Research Accelerator and collaborates across disciplines to address urban systems, energy policy, and ICT for sustainable development.

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Francis Ogokeمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

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