جستجوی استاد راهنمای دانشگاهی

Philip Koopmanمشاهده پروفایل

دانشیار

• Autonomous Vehicles
• Safety Engineering
• Embedded Systems
• +۵ مورد دیگر

Philip Koopman is an Associate Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, specializing in autonomous vehicle safety and dependable embedded systems. His work focuses on ensuring safety in self-driving technologies, including standards development (e.g., ANSI/UL 4600), risk assessment, and fault-tolerant software design. Education & Affiliations: Carnegie Mellon University: Faculty in ECE since [year not explicitly listed, but content suggests 20+ years] Principal author of UL 4600 safety standard for autonomous products Former advisor to graduate students (no longer accepting new advisees) Research Interests: Autonomous vehicle safety validation and assurance Reliability of embedded systems software Cyclic redundancy check (CRC) algorithms and error detection Ethical and legal frameworks for automated systems Standards for vehicle automation (e.g., SAE J3016) Key Contributions: Authored influential books on safety in autonomous systems, CRCs, and embedded software Developed safety case methodologies for autonomous vehicles Testified before U.S. Congress on self-driving car safety Labs/Teams: Leads research initiatives on safety-critical systems, including collaboration with industry and government agencies. Maintains active engagement through blogs (Safe Autonomy, Better Embedded Software) and standards bodies.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +۲ مورد دیگر

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

Mor Harchol-Balterمشاهده پروفایل

استاد

• Systems
• Distributed Systems
• Networking
• +۳ مورد دیگر

Roles and Affiliations : Mor Harchol-Balter is the Bruce J. Nelson Professor of Computer Science at Carnegie Mellon University, leading the Department's PhD program (2008-2011). She holds ACM and IEEE Fellowships, and her work focuses on performance analysis and design of distributed systems, scheduling policies, and queueing theory. Her research challenges conventional system design assumptions, emphasizing mathematical rigor and practical implementation. Education : Ph.D. in Computer Science from UC Berkeley (1996), joined CMU in 1999. Extensive teaching history includes courses like Performance Modeling and Probability and Computing . Research Interests : Performance analysis of computer systems, stochastic modeling, scheduling algorithms (e.g., SRPT, Whittle Index), load balancing, and distributed systems design. She explores high-variability workloads, cache throughput optimization, and redundancy in systems. Key contributions include queueing theory innovations like RDR, RRR, and CAP techniques. Grants and Collaborations : Active in NSF-funded projects on scheduling, cloud computing, and cache networks. Her work bridges theory and practice, with implementations like SyNC and AutoScale adopted by industry. Labs and Teams : Leads the SQUALL research group (Scheduling and QUeueing Around Lunchtime) and collaborates with industry (e.g., Facebook's AutoScale adoption). Her research has produced influential textbooks on performance modeling and probability for computing.

Rashmi Korlakai Vinayakمشاهده پروفایل

دانشیار

• Distributed Systems
• Cloud Computing
• Fault Tolerance
• +۲ مورد دیگر

Rashmi Korlakai Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, where she leads the TheSys research group and contributes to the Parallel Data Lab (PDL). Her research focuses on robustness and resource efficiency in data systems across storage, computation, and communication. She employs a multidisciplinary approach combining computer systems, information theory, and machine learning to develop next-generation data systems. Research interests span: Fault tolerance and efficiency in storage systems High-performance caching architectures Machine learning systems optimization Coding theory applications in distributed systems Recent publications demonstrate a strong emphasis on distributed computing optimization, GPU utilization, and novel algorithms for web caching and storage management. Her work consistently addresses scalability challenges in large-scale systems. Awards include prestigious fellowships from Sloan Foundation and IEEE, with industry recognition from Meta, VMware, and Google. Current projects involve collaborations with NetApp, Facebook, Cisco, Intel, and Cloudera. She advises PhD and Master's students working on systems optimization projects, and has developed industry-adopted solutions now integrated into platforms like Apache Hadoop and VMware.

Nathan Beckmannمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Energy-Efficient Computing
• +۴ مورد دیگر

Nathan Beckmann is a Courtesy Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, part of the College of Engineering. His research focuses on computer architecture, embedded systems, energy-efficient computing, and memory hierarchy design. He explores topics such as dataflow architectures, near-data computing, and cache optimization to enhance performance and reduce energy consumption in modern systems. His work spans both theoretical and applied aspects, including hardware-software co-design for reconfigurable systems, low-power IoT architectures, and novel caching strategies for emerging memory technologies like flash and MRAM. Key contributions include frameworks for ultra-low-power CGRAS, energy-minimal dataflow SoCs, and sustainable caching solutions. Dr. Beckmann's recent articles emphasize innovations in spatial dataflow programming models, task parallelism control, and polymorphic cache hierarchies. His research often targets real-world applications in edge computing, embedded systems, and high-performance datacenters.

Ken Maiمشاهده پروفایل

استاد پژوهش

• High-Performance Circuit Design
• Secure IC Design
• Radiation Hardening
• +۴ مورد دیگر

Ken Mai is a Principal Systems Scientist in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.S., M.S., and Ph.D. in Electrical Engineering from Stanford University. His research focuses on high-performance circuit design, secure IC design, radiation hardening, reconfigurable computing, and computer architecture. He has received prestigious awards including the NSF CAREER Award and the George Tallman Ladd Research Award. Education: Ph.D., Electrical Engineering, Stanford University, 2005 M.S., Electrical Engineering, Stanford University, 1997 B.S., Electrical Engineering, Stanford University, 1993 Research Interests: Dr. Mai’s work addresses challenges in nanometer-scale CMOS technology, including interconnect delay, leakage, and soft errors. His projects include secure IC design against hardware attacks, robust memory techniques using digital communications, 100GHz logic via cryogenic cooling, and bio-implantable computing platforms. He collaborates with neurosurgeons at the University of Pittsburgh for medical applications. Key Projects: Secure IC Design: Countermeasures against invasive/non-invasive attacks Robust Memory Design: Error correction codes for resilience 100GHz Logic: Cryogenically cooled CMOS for high-speed applications Bio-Implantable Systems: Low-power, high-density computing for medical devices Awards: NSF CAREER Award George Tallman Ladd Research Award Eta Kappa Nu Excellence in Teaching Award Teaching and Advising: He teaches courses like 18-322 (Digital Circuits), 18-617 (Memory Systems), and advises students including Mudit Bhargava and Mark McCartney. His lab, the VLSI Design Group, develops tools and methodologies linking circuit design to architectural design. Contact: Email: kenmai@ece.cmu.edu Website: Carnegie Mellon VLSI Design Group

• 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.

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.

Umut Acarمشاهده پروفایل

دانشیار

• Programming Languages
• Parallel Computing
• Algorithms and Complexity
• +۳ مورد دیگر

Umut Acar is an Associate Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on programming languages, parallel computing, and algorithms, with an emphasis on raising the level of abstraction for software development while ensuring efficiency. He leads efforts in dynamic and incremental computation, parallelism optimization, and quantum computing systems. His work spans theoretical foundations and practical implementations, including systems like Atlas for quantum circuit simulation and Atomique for neutral atom quantum compilers. He advises students on advanced topics such as quantum algorithms and parallel programming models. Notable awards include Best Paper and Distinguished Paper recognitions at top conferences like SC and ISCA. Education: PhD in Computer Science (University of Chicago, 2005) Key Projects: Development of self-adjusting computation frameworks, parallel algorithms for dynamic data, and quantum compiler optimization tools. Grants: Collaborative research grants from NSF for parallelism and quantum computing initiatives. Dr. Acar’s contributions bridge theory and practice, enabling scalable software systems for modern hardware architectures and emerging technologies like quantum computing.

Alan Scheller-Wolfمشاهده پروفایل

استاد

• Stochastic Processes
• Operations Management
• Energy and Sustainability
• +۵ مورد دیگر

Alan Scheller-Wolf is the Richard M. Cyert Professor of Operations Management at Carnegie Mellon University's Tepper School of Business. His research focuses on stochastic processes applied to energy sustainability , supply chain management , renewable energy , and healthcare operations , including split liver transplantation and child welfare systems . He is affiliated with the Wilton E. Scott Institute for Energy Innovation. PhD in Operations Research (1996), Columbia University BS in Mathematics and Computational Science (1989), Stanford University His recent work emphasizes data-driven optimization , fairness in resource allocation , and algorithmic approaches for complex systems like server farms and quantum networks . Publications highlight applications in organ procurement , remanufacturing , and public service operations .

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.

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.