جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Robotics
• Motion Planning
• +۸ مورد دیگر

Maxim Likhachev is a Professor at the Robotics Institute and National Robotics Engineering Center , both within the School of Computer Science at Carnegie Mellon University (CMU). His research focuses on Artificial Intelligence and Robotics , particularly in real-time graph search algorithms , planning under uncertainty , and high-dimensional mobile manipulation . He co-founded TravelWits.com and RobotWits (acquired by Waymo), and his lab develops the Search-based Planning Library (SBPL), integrated into ROS. Research Themes : Graph search-based planning for deterministic and probabilistic domains Decomposition of hard planning problems into sequential graph searches Incremental algorithms for dynamic environments Experience-driven heuristic optimization Scientific Awards : Gold Edison Award (2013) for industrial mobile manipulator project DARPA Urban Challenge Winner (2007) for autonomous SUV navigation Students : Supervises current PhD students including Itamar Mishani , Muhammad Suhail Saleem , and Rishi Veerapaneni , alongside Masters students like Gabriel Olin . Past advisees include Dhruv Saxena (now at Uber), Michael Phillips (now at Uber), and Shivam Vats (now at Waymo). Lab & Projects : Leads the Search-based Planning Laboratory at CMU, working on systems like K-MAX helicopter , PR2 robots , and industrial mobile manipulators for tasks including autonomous paint stripping and aerial navigation.

Dimitrios Skarlatosمشاهده پروفایل

استادیار

• Computer Architecture
• Security and Privacy
• Operating Systems
• +۱ مورد دیگر

Dimitrios Skarlatos is an Assistant Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research bridges computer architecture and operating systems with a focus on performance, security, and scalability. His work revolves around two central themes: (1) redesigning abstractions and interfaces between hardware and OS layers to improve performance and scalability, and (2) uncovering security vulnerabilities and building defenses at the hardware-OS boundary. Research Focus: Operating system security and vulnerability prevention Virtual memory systems and page table optimizations Serverless computing architectures Hardware-software co-design for security Datacenter resource management Dr. Skarlatos advises five PhD students and has published extensively on systems research. He received his PhD from the University of Illinois at Urbana-Champaign, where his dissertation received the ACM SIGARCH & IEEE CS TCCA Outstanding Dissertation Award for contributions to redesigning hardware-OS abstractions.

Todd Mowryمشاهده پروفایل

استاد

• Computer Architecture
• Systems
• Databases
• +۳ مورد دیگر

Todd Mowry is a Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on enhancing microprocessor-based system performance through parallelism exploitation, including single-chip multiprocessing and latency mitigation strategies. He leads initiatives like the STAMPede project, addressing challenges in compiler-driven parallelization and hardware-software co-design. His work spans computer architecture, systems, and databases, with a strong emphasis on compiler optimization, memory management, and scalable computing. Mowry has advised students including Sam Arch, Patrick Coppock, Hongyi Jin, Ruihang Lai, and Eliot Solomon. He teaches advanced courses such as 15745 (Fall 2025) and 15418 (Spring 2025), contributing to graduate-level education in computer systems. Recent research highlights include innovations in machine learning optimization (e.g., Relax framework), efficient LLM microservices, and architectural solutions for thread-safe metadata management. His publications address topics like UDF optimization, auto-batching in neural networks, and energy-minimal dataflow systems. Mowry's projects often involve collaboration across academic and industrial partners, driving advancements in both theoretical and applied computer systems. He has contributed to frameworks like CORA for tensor compilation and NVOverlay for non-volatile memory systems, emphasizing practical scalability and efficiency.

Marijn Heuleمشاهده پروفایل

دانشیار

• Satisfiability (SAT) Solving
• Formal Verification
• Combinatorial Mathematics
• +۵ مورد دیگر

Marijn Heule is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD from Delft University of Technology in the Netherlands. His academic career has been focused on advancing the theory and practice of satisfiability (SAT) solving and its applications. Dr. Heule's research primarily centers on solving hard combinatorial problems in formal verification, number theory, and extremal combinatorics. His work has made significant contributions to both theoretical foundations and practical implementations of SAT solvers. He has developed award-winning SAT solvers, and his preprocessing techniques are incorporated into state-of-the-art SAT solvers used worldwide. His current research focuses on two major challenges: exploiting high-performance computing for SAT solving and validating results from SAT solvers and related tools. Dr. Heule pioneered the "cube-and-conquer" paradigm, a novel parallel SAT solving approach that enables linear time speedups on many hard problems. His first publication on this topic won the best paper award at HVC 2011. He also designed a new proof format with a corresponding fast proof checker for SAT and QBF solvers. This proof logging format has been mandatory for SAT Competitions since 2013, significantly increasing confidence in tool correctness. His work on the Boolean Pythagorean Triples problem, which produced a 200 TB proof that was validated, demonstrated that proof logging and verification is feasible even for the most complex problems. Dr. Heule is one of the editors of the 900+ page "Handbook of Satisfiability," which has become a standard reference in the SAT community. He serves as an Associate Editor of the Journal on Satisfiability, Boolean Modeling and Computation and previously co-chaired the SAT 2015 conference in Austin. Best paper award at HVC 2011 for cube-and-conquer publication Multiple best paper awards/nominations at major conferences including IJCAR, SAT, TACAS Work featured in New York Times, Quanta Magazine, Nature, and other major media outlets Dr. Heule has advised several PhD students including Emre Yolcu, Joseph Reeves, Bernardo Subercaseaux, Cayden Codel, James Gallicchio, and Amar Shah. He has developed important software tools including DRAT-trim (a proof checking utility), QRAT-trim, and implementations of the cube-and-conquer paradigm that have become standard in the SAT solving community.

• Satisfiability (SAT) Solving
• Formal Verification
• Automated Reasoning
• +۵ مورد دیگر

Marijn J.H. Heule is an Associate Professor at the School of Computer Science , Carnegie Mellon University . He received his PhD from Delft University of Technology in the Netherlands. His research focuses on solving hard combinatorial problems through Satisfiability (SAT) solving , with applications in formal verification, number theory, and extremal combinatorics. Education: PhD in Computer Science, Delft University of Technology, Netherlands Heule's work addresses fundamental challenges in SAT solving, including: Exploiting high-performance computing via the cube-and-conquer paradigm Validating results from SAT solvers using novel proof formats His research has produced 15+ recent publications (2025-2021) on topics like automated reasoning, formal verification, and combinatorial proofs. Notable achievements include: Best Paper Award at HVC 2011 (Cube-and-Conquer) 200 TB proof for the Boolean Pythagorean Triples Problem Co-editor of the Handbook of Satisfiability He has advised multiple PhD students including Emre Yolcu , Joseph Reeves , and Bernardo Subercaseaux . His tools like DRAT-trim and QRAT-trim have become standard in proof validation.

Gregory Gangerمشاهده پروفایل

استاد

• Computer Systems
• Cloud Computing
• Distributed Systems
• +۶ مورد دیگر

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

Seth Goldsteinمشاهده پروفایل

دانشیار

• Programming Languages
• Systems
• Computer Architecture
• +۹ مورد دیگر

Seth Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on ensembles (massively scalable systems), distributed systems, reconfigurable computing, and programmable matter (Claytronics). He leads projects like BoLT (a decentralized lending platform) and Phoenix (computing without traditional processors). He has advised numerous graduate students and teaches courses including Compiler Design, Computer Architecture, and Cloud Computing. Research Interests : Goldstein explores systems with massive interacting components, such as molecular-scale devices, modular robotics, and economic systems. His work spans compilers (e.g., register allocation optimizations), time synchronization protocols for embedded systems, and algorithms for self-reconfiguring robots. Recent projects include BlockGraph (a secure distributed ledger) and peer code review methodologies. Key Contributions : Goldstein's work on Claytronics pioneered programmable matter, enabling shape-shifting materials. His BoLT platform addresses lending market inefficiencies using blockchain principles. He has also developed algorithms for modular robot coordination and energy-efficient self-reconfiguration. Labs/Teams : Goldstein directs research in the CMU Robotics Institute and collaborates with interdisciplinary teams on projects like the PipeRench and Phoenix architectures. His lab explores intersections between nanotechnology, distributed systems, and societal applications like new monetary systems.

Phil Gibbonsمشاهده پروفایل

استاد

• Big Data
• Parallel Computing
• Databases
• +۵ مورد دیگر

Phil Gibbons is a Professor in the Electrical & Computer Engineering and Computer Science Departments at Carnegie Mellon University. He holds a Ph.D. from UC Berkeley (1989) and has extensive industry experience at AT&T Bell Labs, Lucent Bell Labs, and Intel Research. His research focuses on parallel computing, distributed systems, databases, and machine learning, with a emphasis on algorithmic and systems-level innovations. He has led major initiatives like the Intel Science and Technology Center for Cloud Computing and contributed to projects such as IrisNet (a planetary-scale sensor network). Education : Ph.D. in Computer Science, University of California at Berkeley (1989) Research Interests : Gibbons' work spans big data analytics , high-performance computing , and cloud systems . He develops scalable algorithms and systems for emerging memory technologies, distributed ML, and robotics. Notable contributions include processing-in-memory (PIM) optimizations, pipeline parallelism for DNN training, and system architectures for robotic processors. Awards : IEEE Fellow (2014) ACM Fellow (2006) ACM Paris Kanellakis Theory and Practice Award (2019) Best Paper Award at NSDI 2006 Grants & Leadership : Co-PI of the $15M Intel STC for Cloud Computing (2011-2015) Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) Leadership roles in conferences like SPAA, EuroSys, and MLSys Teams & Labs : Active in robotics computing (RobotPerf benchmark), distributed ML systems, and hardware-software co-design initiatives.

Richard Sternمشاهده پروفایل

استاد

• Robust Speech Recognition
• Auditory Perception
• Signal Processing
• +۲ مورد دیگر

Professor Richard Stern holds joint appointments in Electrical and Computer Engineering, Computer Science, and Biomedical Engineering at Carnegie Mellon University. His research bridges auditory perception with speech technology, particularly in developing noise-robust speech recognition systems. His PNCC algorithm revolutionized feature extraction for speech recognition in noisy environments. Research emphases include: Auditory-inspired signal processing Robust automatic speech recognition Computational models of binaural hearing Music information retrieval Biomedical applications of audio analysis Recent work demonstrates growing interest in human-robot interaction and respiratory monitoring applications. Publications increasingly incorporate deep learning while maintaining foundations in auditory physiology. Honors include the IEEE Signal Processing Society 2019 Best Paper Award for PNCC research. Current projects investigate neural audio processing models and online learning for sound event detection.

Jonas Augustمشاهده پروفایل

پژوهشگر

• Computer Vision
• Medical Imaging
• Image Regularization
• +۳ مورد دیگر

Jonas August is a Project Scientist at the Robotics Institute of Carnegie Mellon University , focusing on geometry and uncertainty in computer vision and medical imaging. He employs probabilistic techniques like Markov processes and random fields to develop algorithms for medical image enhancement, including streak artifact removal in X-ray CT scans and vascular structure inference. Education: Ph.D., Yale University (2001) M.Phil., Yale University (1999) M.Sc., Yale University (1999) M.Eng., McGill University (1996) B.Eng., McGill University (1993) His research spans medical imaging , image regularization , and mathematical modeling , with applications in robotic perception and neural architecture. His work includes scalable regularized tomography algorithms and the development of DOC , a GNU/Linux cluster for medical imaging. He is supported by the National Science Foundation (Grant No. 0305719). August explores image statistics , curve inference , and object representation stability , contributing to contour fragment grouping and perceptual organization theories.

Seth Copen Goldsteinمشاهده پروفایل

دانشیار

• Programmable Matter
• Claytronics
• Reconfigurable Computing
• +۶ مورد دیگر

Seth Copen Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research spans multiple interdisciplinary domains focusing on ensembles - large collections of interacting agents. He has made significant contributions to reconfigurable computing, molecular-scale devices, programmable matter, and more recently, the impact of technology on labor markets and alternative monetary systems. Education: PhD in Computer Science, University of California at Berkeley BS, Princeton University Goldstein's research agenda has evolved from reconfigurable computing to molecular-scale devices and programmable matter, and most recently to social technology and alternative monetary systems. His early work focused on compiling high-level programming languages directly into configurations for computing ensembles of gates. He then investigated molecular-scale circuits and programmable matter - ensembles of computing elements that can be programmed to change physical properties. Since returning from a startup, he has shifted focus to ensembles of people, studying technology's impact on labor markets and innovation, and developing social technologies to reduce poverty and inequality. His publication trends show a consistent focus on modular robotics, distributed systems, and programmable matter. The research spans theoretical foundations, hardware implementation, and programming languages for large-scale ensembles. Recent work emphasizes energy efficiency, distributed algorithms, and novel programming paradigms for heterogeneous systems. The interdisciplinary nature of his work connects computer science, electrical engineering, and social sciences. Research Leadership: Claytronics project - creating ensembles of cooperating submillimeter robots Development of Meld and LDP programming languages for ensemble systems Phoenix project - computing without processors Building on Local Trust (BoLT) initiative Goldstein has advised numerous PhD students who have gone on to positions at major technology companies including Microsoft Research, Sun Microsystems, and NEC Labs. His work has been supported by significant research grants focusing on modular robotics, programmable matter, and distributed computing systems. He has taught core computer science courses including Compiler Design, Introduction to Computer Systems, and Cloud Computing.

• Combinatorial Optimization
• Distributed and Parallel Computing
• Coding Theory
• +۴ مورد دیگر

Bernhard Haeupler is a Professor at INSAIT (Institute for Computer Science, Artificial Intelligence and Technology) under Sofia University "St. Kliment Ohridski" since 2024. He is also a Post-Professor and Researcher at ETH Zurich 's Computer Science Department since 2020 and an Adjunct Professor at Carnegie Mellon University 's Computer Science Department since 2022. His roles span theoretical computer science, algorithm design, and distributed/parallel systems. Research Interests : Haeupler focuses on algorithms for combinatorial optimization, distributed and parallel computing, coding theory, graph/network algorithms, and information theory. His work bridges theoretical foundations with practical applications in network resilience, synchronization, and quantum/parallel optimization. Article Trends : His 15 most recent articles (2023-2025) emphasize distributed algorithms (e.g., dynamic routing, fault-tolerant spanners), parallel computing (e.g., low-congestion shortcuts, near-linear work), and coding theory (e.g., synchronization strings, insertion-deletion codes). Topics include graph theory , network design , and quantum shortest paths . Scientific Awards : ERC Starting Grant (2020) Sloan Research Fellow (2019) NSF CAREER Award (2018) ACM-EATCS Doctoral Dissertation Award (2014) George Sprowls Award for MIT PhD Thesis Advising & Grants : Haeupler has advised eight PhD students, including Jason Li (CMU, now at Google Research Zurich) and Nicolas Resch (University of Amsterdam). He has secured multiple NSF grants for projects like Distributed Optimization Beyond Worst-Case Topologies and New Coding Techniques for Synchronization Errors .

Kanat Tangwongsanمشاهده پروفایل

پژوهشگر

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +۶ مورد دیگر

Kanat Tangwongsan is a researcher and educator in computer science, with a PhD from Carnegie Mellon University and prior roles at IBM T.J. Watson Research Center. He has extensive experience in algorithm design, parallel computing, and self-adjusting computation, with publications in top-tier venues like DEBS, ICDE, SPAA, and TOCS. Education: PhD in Computer Science (Carnegie Mellon University), B.S. in Computer Science and Mathematics (Carnegie Mellon University) Research Interests: His work spans algorithms , parallel computing , streaming data structures , and self-adjusting programs , focusing on theoretical improvements and practical implementations for graph problems, convex hulls, and network design. Article Trends: His publications emphasize low-latency streaming , parallel SDD solvers , k-means clustering , and triangle enumeration , with applications in large-scale graph analysis, numerical systems, and cache-oblivious methods. Advising and Collaborations: He has collaborated with prominent researchers like Guy E. Blelloch, Martin Hirzel, and Anupam Gupta, contributing to open-source algorithm frameworks and educational materials.

• Algorithms
• Parallel Computing
• Graph Theory
• +۳ مورد دیگر

Claudson Ferreira Bornstein is an Adjunct Professor at the Computer Science Department of the Federal University of Rio de Janeiro (UFRJ) . He holds a PhD in Computer Science from Carnegie Mellon University. His research and teaching focus on algorithms, data structures, and combinatorial mathematics. Research Interests include: Algorithm design and optimization Parallel computing techniques Graph theory and elimination orders Combinatorial mathematics Contact Information : Email: cfb@cos.ufrj.br Institutional addresses in COPPE Sistemas and Instituto de Matematica at UFRJ Phone: +55-21-290-8091 (UFRJ) and +55-21-712-1504 (home)

Todd C. Mowryمشاهده پروفایل

استاد

• Computer Architecture
• Compiler Optimization
• Operating Systems
• +۶ مورد دیگر

Todd C. Mowry is a Professor in the Computer Science Department at Carnegie Mellon University. His work focuses on computer systems design across multiple domains, including hardware architecture, compiler optimization, operating systems, and database performance. He explores techniques for parallel processing, memory management, and efficient execution of machine learning workloads.