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Tze Meng LowView profile

Research Professor

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +3 more

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Akshitha SriramanView profile

Assistant Professor

• Computer Architecture
• Hardware-Software Co-design
• Data Center Computing
• +4 more

Akshitha Sriraman is an Assistant Professor in the Department of Electrical and Computer Engineering and Computer Science at Carnegie Mellon University. She holds a PhD in Computer Science from the University of Michigan (2021) and an M.S. in Embedded Systems from the University of Pennsylvania (2015). Her research focuses on bridging computer architecture and systems software to design efficient, sustainable, and equitable data center systems, emphasizing hardware-software co-design. Her work has been deployed in real hyperscale data centers, influencing Intel’s Alder Lake CPU architectures and Infrastructure Processing Unit designs. Recipient of the NSF CAREER Award (2024), Intel Rising Star Award (2023), and multiple dissertation prizes. Advances include reducing global carbon emissions via server design and introducing equity as a first-order design concern in web systems. Advises PhD and Master’s students on topics like sustainable computing, equitable systems, and hardware optimization. Key contributions span energy-efficient data center architectures, bias-free scheduling systems, and cloud GPU power management. She leads a research group prioritizing socially responsible computing and has received grants from NSF, AWS, and industry partnerships.

Karl CraryView profile

Associate Professor

• Programming Languages
• Security and Privacy
• Type Theory
• +4 more

Karl Crary is an Associate Professor at the Computer Science Department of Carnegie Mellon University , where he also serves as Director of Doctoral Programs . His research focuses on Programming Languages , Security and Privacy , and Mechanized Metatheory , with emphasis on applying Type Theory to software verification and compiler design. Research Type Safety Proofs Certified Code Compiler Implementation His recent publications explore Substructural Parametricity , Hashgraph Consensus Verification , and Focused Logic applications. He has advised students including Derek Dreyer , Chris Martens , and Tom Murphy , with software projects like Istari proof assistant and CM-Lex/CM-Yacc for hygienic code generation. Current teaching includes Constructive Logic (15-317/657) and HOT Compilation (15-417).

George AmvrosiadisView profile

Research Professor

• Distributed Systems
• Cloud Storage
• High Performance Computing (HPC)
• +4 more

George Amvrosiadis is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with a courtesy appointment in Computer Science. He is a core member of the Parallel Data Lab and spends part of his time at Amazon S3 as an Amazon Scholar. Education: 2016 - Ph.D., Computer Science, University of Toronto 2009 - BA, Computer Science, University of Ioannina Research Interests: His work focuses on distributed systems , operating systems , data analysis , cloud computing , and storage technologies . He explores high performance computing (HPC), zoned storage , systems security , and storage solutions for machine learning . Scientific Trends: Articles highlight innovations in storage systems, including zoned storage , HPC data services , and machine learning infrastructure . Research spans distributed systems , I/O optimization , and data integrity in large-scale environments. Scientific Awards: DeltaFS project received the R&D 100 Award from R&D World Magazine Teaching & Service: He co-teaches graduate courses on storage and cloud systems, serves on program committees for top conferences (SOSP, OSDI, FAST), and mentors students in systems research and infrastructure projects.

Cayden CodelView profile

Researcher

• Programming Languages
• Formal Verification
• Satisfiability (SAT) Solvers
• +3 more

Cayden Codel is a Researcher at Carnegie Mellon University's Computer Science Department , focusing on programming languages and formal verification. His work bridges theoretical logic with practical applications in automated reasoning and constraint solving. Research Interests: Programming Languages, Formal Verification, Satisfiability (SAT) Solvers, Satisfiability Modulo Theories (SMT), Automated Theorem Proving, Machine Learning Institution: Carnegie Mellon University (CMU) His publications emphasize formal verification for logical systems, SMT/SAT solvers , and reinforcement learning applications. Articles from 2024-2019 reveal a trajectory from foundational logic to real-world dataset design (e.g., Minecraft-based AI research). Thesis Advisors: Marijn Heule, Jeremy Avigad Contact: ccodel@andrew.cmu.edu

Beidi ChenView profile

Assistant Professor

• Machine Learning Systems
• Efficient Machine Learning
• Large Language Models
• +3 more

Beidi Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a visiting research role at FAIR/Meta. She holds a Ph.D. in Computer Science from Rice University (2020) and a B.S. in Electrical Engineering and Computer Science from UC Berkeley (2015). Her research focuses on optimizing machine learning algorithms and models for modern hardware, emphasizing efficiency in large-scale systems. She has been recognized as a Rising Star in EECS by MIT (2019) and UIUC (2021), and her work has received multiple best-paper awards, including runner-up at ICML 2022 and awards at IISA 2018 and USENIX LISA 2014. Education: Ph.D., Computer Science, Rice University (2020) B.S., Electrical Engineering & Computer Science, UC Berkeley (2015) Her research interests span machine learning systems , algorithm-system co-design , and large language models (LLMs) . She explores techniques like structured sparsity, efficient inference, and hardware acceleration to enhance the scalability and performance of ML models. Recent work includes optimizing LLM context lengths, speculative decoding, and memory-efficient training methods. Her publications highlight advancements in LLM acceleration (e.g., 'Megalodon' for unlimited context), memory optimization ('Headinfer'), and scalable training frameworks ('FlexGen'). She also contributes to federated learning and edge deployment solutions for LLMs. Awards: Best Paper Runner-up, ICML 2022 Best Paper Award, IISA 2018 Best Paper Award, USENIX LISA 2014 Rising Star in EECS (MIT 2019, UIUC 2021) Chen collaborates across academia and industry, focusing on bridging algorithmic innovation with practical hardware constraints. Her lab emphasizes scalable, efficient systems for modern AI challenges.

Mor Harchol-BalterView profile

Professor

• Systems
• Distributed Systems
• Networking
• +3 more

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.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +2 more

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.

Wenting ZhengView profile

Assistant Professor

• Secure Systems
• Applied Cryptography
• Privacy-Preserving Computation
• +4 more

Wenting Zheng is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU), with a courtesy appointment in the Electrical and Computer Engineering Department. She co-founded Opaque Systems and serves as a core faculty member at CyLab Security and Privacy Institute. Ph.D. in Electrical Engineering and Computer Science (EECS) from UC Berkeley M.Eng. and Bachelor’s degrees from MIT under Barbara Liskov Her research focuses on system security and applied cryptography , particularly systems enabling “sharing without showing.” Key areas include secure cloud computation, collaborative privacy-preserving analytics, and practical cryptographic frameworks for machine learning. Recent work emphasizes encrypted AI (e.g., Cinnamon), secure multi-party computation (e.g., Silph), and private information retrieval (e.g., PIANO). Notable scientific honors include the Berkeley Fellowship (2014-2016) , IBM Research Fellowship (2017-2018) , and the USENIX Security 2021 Distinguished Paper Award . She has advised numerous Ph.D. and Master’s students, including collaborators at CMU and UC Berkeley. Teaching: Distributed Systems, Secure Computer Systems, Cryptosystems: Theory and Practice Research grants from NSF, AWS, Cisco, Google, Samsung, and CMU CyLab Co-founder of DARE, a diversity-focused research mentorship program

Charlie GarrodView profile

Academic

• distributed database systems
• computer science education
• teaching assistant mentorship
• +6 more

Charlie Garrod is an Associate Teaching Professor in the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. His academic career focuses primarily on undergraduate computer science education and related areas such as teaching assistant and faculty mentorship. Institution: Carnegie Mellon University School: School of Computer Science Department: Software and Societal Systems Department Position: Associate Teaching Professor Dr. Garrod received his B.S. in Computer Science from Harvey Mudd College in 2001 and completed both his M.S. and Ph.D. in Computer Science at Carnegie Mellon University in 2006 and 2008, respectively. His educational background laid the foundation for his expertise in distributed database systems and software engineering education. His research interests center around computer data management systems, with particular emphasis on the performance, scalability, and power management of highly scalable distributed systems. As a teaching professor, his research program is intentionally focused on undergraduate and Master's student research projects rather than large-scale independent research. His work bridges theoretical computer science with practical applications in transportation information systems, mobile location services, and database scalability. Dr. Garrod's publication history reveals a strong trajectory in distributed systems and database technologies, with recent work shifting toward educational topics such as mentoring teaching assistants. His publications span prestigious venues including ACM Transactions on Algorithms, VLDB, ICDE, and SIGCHI, with his work on the Tiramisu transportation system earning a CHI 2011 Honorable Mention. His research demonstrates consistent contributions to database caching, query optimization, and consistency management for web applications. CHI 2011 Honorable Mention for 'Field Trial of Tiramisu: Crowd-Sourcing Bus Arrival Times to Spur Co-Design' As an educator, Dr. Garrod has mentored numerous students through formal courses and undergraduate research projects. He has developed and taught a wide range of courses including software design and engineering, algorithms and data structures, theoretical computer science, distributed systems, and databases. His teaching philosophy emphasizes practical application of theoretical concepts, with a focus on preparing students for real-world software engineering challenges. He has been actively involved in developing pedagogical approaches for computer science education, particularly in the mentorship of teaching assistants. Dr. Garrod maintains an active teaching schedule, currently instructing courses such as 15-210 Parallel and Sequential Data Structures and Algorithms, 17-480/17-780 API Design, and 07-070 Teaching Techniques for Computer Science. His extensive teaching portfolio demonstrates deep commitment to computer science education across multiple institutions and course levels.

Rashmi Korlakai VinayakView profile

Associate Professor

• Distributed Systems
• Cloud Computing
• Fault Tolerance
• +2 more

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 BeckmannView profile

Professor

• Computer Architecture
• Embedded Systems
• Energy-Efficient Computing
• +4 more

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.

Guannan QuView profile

Assistant Professor

• Control Theory
• Optimization
• Machine Learning
• +7 more

Guannan Qu is an Assistant Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He holds a B.S. from Tsinghua University (2014) and a Ph.D. in Applied Mathematics from Harvard University (2019). Before joining CMU, he was a CMI and Resnick Postdoctoral Scholar at Caltech (2019–2021). His research focuses on control theory, optimization, and reinforcement learning (RL), with applications to power systems, multi-agent systems, IoT, and cyber-physical systems. Key areas include scalable RL for networked systems, stability-aware learning, and model-based RL for nonlinear control. Education: Ph.D., Applied Mathematics, Harvard University, 2019 B.S., Electrical Engineering, Tsinghua University, 2014 Research Interests: Machine learning and reinforcement learning for large-scale systems Control and optimization of networked systems (e.g., power grids, robotics) Model-based and model-free control synthesis Scalable distributed algorithms for decentralized decision-making Applications in smart grids, IoT, and transportation systems Awards: NSF CAREER Award (2023) Caltech Simoudis Discovery Award Amazon AI4Science Fellowship IEEE SmartGridComm Best Student Paper Award AAAIP 2025 Workshop Best Paper Award Advising & Grants: Advises PhD students in RL and control (e.g., Alex Deweese, Ziyi Zhang) Recipient of NSF EPCN, C3 AI Institute, and CMU CyLab grants Developed scalable RL frameworks for voltage control and inverter coordination Labs & Teams: Active in CMU’s robotics and power systems research groups, collaborating with industry partners on real-world applications of ML and control theory.

Guy E. BlellochView profile

Professor

• Parallel Computing
• Programming Languages
• Algorithms and Complexity
• +5 more

Guy E. Blelloch is the U.A. and Hellen Whitaker University Professor at Carnegie Mellon University , affiliated with the Computer Science Department . He focuses on the intersection of algorithms and programming languages within parallel computing , with significant experimental and theoretical contributions. His recent work includes the PSCICO project (with Gary Miller, Bob Harper, Peter Lee) exploring high-level programming constructs in geometric algorithms, and continued development of the NESL programming language for parallel computation. Research spans Parallel Garbage Collection Work-Efficient Scheduling Deterministic Parallel Algorithms Processing-in-Memory Models Key publications since 2022 show trends in dynamic data structures , cache-oblivious algorithms , and concurrent memory management , with applications in graph processing (PIM-tree), geometric computation (tree-based sorting), and multiversioning systems. His work often bridges theory and practice, demonstrated in frameworks like Ligra , GBBS , and Aspen for large-scale graph analysis. Scientific awards include ACM Paris Kanellakis Theory and Practice Award (2023) IEEE Charles Babbage Award (2021) Best Paper Awards at PPoPP 2022, SPAA 2021 SCS Doctoral Dissertation Award (Honorable Mention) He has advised numerous PhD students including Magdalen Dobson (current student on nearest-neighbor problems), Daniel Anderson (parallel batch-dynamic algorithms), and Julian Shun (graph processing frameworks). His teaching includes advanced courses on Parallel and Sequential Data Structures and Parallel and Concurrent Algorithms .

Rupert CroftView profile

Professor

• Cosmology
• Astrophysics
• Structure Formation
• +5 more

Rupert Croft is a Professor of Physics at Carnegie Mellon University, affiliated with the Mellon College of Science and Department of Physics. His research focuses on astrophysics and cosmology, particularly studying the universe through simulations and observational data. Education D. Phil. in Astrophysics, Oxford University (1995) B.Sc. in Physics, Imperial College (1991) His work explores the formation of cosmic structures, dark matter, galaxy clusters, and intergalactic filaments using computer simulations. He collaborates with observational astrophysicists to analyze data from telescopes and surveys, aiming to constrain cosmological theories. Recent publications emphasize gravitational waves from black hole mergers, Lyman-alpha forest analysis, super-resolution simulations, and multi-messenger astronomy. Key keywords include cosmology, astrophysics, computational physics, and machine learning. Scientific Awards Leverhulme Visiting Professor, Oxford University (2012) Rupert Croft has held academic positions at Carnegie Mellon University since 2001, including Assistant Professor (2001–2004), Associate Professor (2004–2014), and Professor (2014–present). He utilizes parallel computing facilities at CMU for cosmological simulations.