جستجوی استاد راهنمای دانشگاهی

Frederike Dümbgenمشاهده پروفایل

استادیار

• Robotics
• Optimization
• Machine Learning
• +۱۲ مورد دیگر

Frederike Dümbgen is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, starting in Spring 2026. She is currently a researcher with the Willow team at Inria Paris, focusing on optimization for robotics, and previously served as a postdoctoral fellow at the University of Toronto's Robotics Institute. Education: Ph.D. in Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2021) M.Sc. in Mechanical Engineering, EPFL (2016) B.Sc. in Mechanical Engineering, EPFL (2013) Her research centers on improving the efficiency and safety of robots operating in the physical world through principled optimization and machine learning methods. She emphasizes certifiable and globally optimal algorithms to build reliable foundations for next-generation robotics in domains such as autonomous vehicles, assistive technology, and manufacturing. Her work bridges robotics, control systems, and artificial intelligence, with a strong focus on mathematical rigor and scalability. The 15 most recent publications highlight a consistent trajectory in robotics-focused optimization, particularly in state estimation, SLAM, pose estimation, and data-driven methods. These works frequently employ semidefinite programming, convex relaxations, and Koopman-based linearization, demonstrating a deep integration of theoretical optimization with practical robotic applications. Keywords across these papers include robotics, optimization, machine learning, and estimation, with subfields like certifiable algorithms, global optimality, and sensor fusion recurring throughout. Dr. Dümbgen has not yet had scientific awards listed in the provided text. She has not yet advised any named students in the provided materials, and there is no mention of grants or funding sources. However, her research trajectory and publication record suggest active involvement in competitive research environments. Her experience includes internships at Disney Research and ABB, and her master’s thesis was completed at ETH Zürich’s Autonomous Systems Lab. She is currently affiliated with the Willow research team at Inria Paris, a group known for foundational work in computer vision, machine learning, and robotics. This team emphasizes mathematical rigor in algorithm design, aligning closely with her focus on certifiable and globally optimal methods.

Christopher McCombمشاهده پروفایل

دانشیار

• Sociotechnical Systems
• Machine Learning for Engineering Design
• Human-AI Collaboration
• +۱۷ مورد دیگر

Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.

Anupam Guptaمشاهده پروفایل

استاد مدعو

• Algorithms
• Complexity
• Artificial Intelligence
• +۴ مورد دیگر

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.

Yang Liuمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Graph Algorithms
• Optimization
• +۴ مورد دیگر

Yang P. Liu is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. 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. His research focuses on theoretical computer science and mathematics, emphasizing graph algorithms, optimization, high-dimensional geometry, and additive combinatorics. He has received notable awards including the A.W. Tucker Prize and Google PhD Fellowship, alongside multiple best paper recognitions at major conferences like FOCS, STOC, and ITCS. Education: PhD in Computer Science from Stanford University (2023); BS from MIT (2018). Research Interests: Graph Algorithms Optimization (especially convex and high-dimensional) Algorithmic Techniques in Additive Combinatorics Geometric and Structural Aspects of Computation Teaching: Currently instructing CS 15-759: A Principled Approach to Optimization (Spring 2025), covering topics like gradient descent, interior-point methods, and sparsification techniques. Course emphasizes rigorous mathematical foundations. Awards: Recognized for contributions to optimization theory and algorithmic complexity. His work bridges discrete mathematics and continuous optimization paradigms.

Pravesh Kothariمشاهده پروفایل

استاد مدعو

• Theoretical Computer Science
• Algorithms
• Average-Case Complexity
• +۳ مورد دیگر

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.

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Computer Graphics
• +۳ مورد دیگر

Yue Jiang is an incoming Assistant Professor at the University of Utah (Fall 2025) and is completing their PhD at Aalto University and the Finnish Center for Artificial Intelligence (FCAI). Their research focuses on computational user interface understanding, eye tracking, and adaptive GUI layouts. They have held roles such as Accessibility Chair for CHI2023/2024 and have organized workshops on computational UI methodologies. Education : PhD in Intelligent Systems (Aalto University & FCAI, Finland) Visiting PhD Student (CMU's BIG Lab, 2024) Master's in Computer Graphics (UMD, USA) Bachelor's in Computer Science & Mathematics (U of Toronto, Canada) Research Interests : Developing human-centered technologies for adaptive UIs, eye tracking analysis, and AI-driven HCI. Key projects include Graph4GUI, EyeFormer, and computational methods for GUI layout optimization. Awards : Meta PhD Fellowship (2023-2025) Google Europe Students with Disabilities Scholarship (2022) CHI2022 Best Paper Honorable Mention Heidelberg Laureate Forum Young Researcher (2024) Service : PC Member for VL/HCC2025, CHI2026 Associate Chair for CHI2025/2026 Organized three Computational UI Workshops at CHI Labs & Collaborations : Collaborates with Prof. Jeffrey Bigham (CMU), Prof. Wolfgang Stuerzlinger (SFU), and Prof. Christof Lutteroth (U of Bath). Former internships at Apple AIML Lab and Adobe Research.

Zachary Liptonمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۵ مورد دیگر

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

Weina Wangمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +۷ مورد دیگر

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .