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Frank PfenningView profile

Professor

• Programming Languages
• Logic and Type Theory
• Logical Frameworks
• +5 more

Frank Pfenning is a Professor of Computer Science at Carnegie Mellon University, affiliated with the School of Computer Science and Computer Science Department. He has served in leadership roles including Director of Graduate Programs (2004-2008), Associate Dean for Graduate Education (2009-2010), and Head of the Computer Science Department (2013-2018). Education : PhD in Mathematics (Carnegie Mellon University, 1987), Fulbright Scholar, Technical University Darmstadt. Research Interests span programming languages, logic and type theory, logical frameworks, automated deduction, and trustworthiness in computing. His work on session types, linear logic, and substructural logics explores concurrency and communication safety. Current projects include Session Types (message-passing and shared memory concurrency) and C0 (safe imperative programming education). Recent Publications emphasize substructural type systems, adjoint logic, parametric subtyping, and the theoretical foundations of session types. These works bridge programming language design, formal verification, and distributed systems security. Awards : Herbert A. Simon Award for Teaching Excellence (School of Computer Science, 2002) Distinguished Paper Award (POPL 2024) Advising : Advised 29 PhD theses. Leadership : ACM SIGLOG Chair (2019-2022), LICS Steering Committee (2018-2023), CADE Trustee/President (1998-2004), CRA Committee on Best Practices (2013-2014). Labs and Teams : Collaborated on projects like Session Types (NSF/Mozilla support), C0 (MSR/Google-funded for teaching safe C), and foundational work in logical frameworks (Twelf).

Jonas AugustView profile

Researcher

• Computer Vision
• Medical Imaging
• Image Regularization
• +3 more

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.

Osman YaganView profile

Research Professor

• Communication Systems
• Network Science
• Cyber-Physical Systems
• +16 more

Osman Yagan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate roles in the School of Computer Science and core membership in CyLab Security and Privacy Institute, IDeaS, and Block Center. His research spans modeling, analysis, and optimization of computing systems using tools from applied probability, network science, and machine learning. PhD in Electrical and Computer Engineering, University of Maryland (2011) BS in Electrical and Electronics Engineering, Middle East Technical University (2007) Research Interests: Dr. Yagan focuses on statistical inference , resilient distributed machine learning , robustness of cyber-physical systems , secure IoT networks , and contagion processes in complex networks, including misinformation spread and epidemic modeling. His work integrates multi-armed bandit algorithms , reinforcement learning , and stochastic geometry . Scientific Contributions: He has received multiple fellowships (CIT Dean’s Early Career, IBM Academic Award), best paper awards (ICC 2021, IPSN 2022, ASONAM 2023), and co-authored 15+ recent articles on topics like federated learning, contagion dynamics, and network robustness. His research is funded by ONR, NSF, ARO, AFOSR, and industry grants. Advising: Dr. Yagan has mentored 23 PhD and Master’s students, including junior faculty at Nanyang Technological University and researchers at Meta, Intel, Bloomberg, and MIT. He has taught courses such as Advanced Probability & Statistics for Engineers and Applied Stochastic Processes. Collaborations: He works with institutions including Princeton, MIT, and industry partners like IBM and Intel, and is a Senior Member of IEEE.

Seth Copen GoldsteinView profile

Associate Professor

• Programmable Matter
• Claytronics
• Reconfigurable Computing
• +6 more

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.

• Natural Language Processing
• Machine Learning
• Unsupervised Learning
• +4 more

Taylor Berg-Kirkpatrick is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. He directs the BergLab research group, which focuses on developing machine learning methods for analyzing human language and artifacts, including historical documents, music, and multimodal data. His work bridges natural language processing, computational linguistics, and creative AI applications. Research Interests: Berg-Kirkpatrick's research spans unsupervised learning for language understanding, structured prediction models, and computational analysis of human artifacts. The BergLab specializes in: Deciphering historical documents and languages using computer vision and NLP Developing generative models for music and audio synthesis Creating novel methods for low-resource and unsupervised machine learning Cross-modal analysis between text, audio, and visual data Recent Publication Trends: His 15 most recent publications (2023-2024) demonstrate three key research vectors: advanced music generation using diffusion models and symbolic representations; computational analysis of historical artifacts including cuneiform tablets and early modern books; and foundational NLP work on model architectures, decoding methods, and multimodal systems. The work shows consistent innovation in generative AI, historical document processing, and efficient machine learning techniques. Awards and Honors: NSF CAREER Award for 'Modeling Language Evolution via Deep Probabilistic Factorization' (2022) Advising and Funding: Berg-Kirkpatrick has advised over 10 PhD students to completion on topics spanning NLP, music generation, and historical document analysis. His research is supported by the NSF CAREER grant and focuses on developing unsupervised methods for language evolution modeling. The BergLab maintains several open-source projects including Klavier (piano transcription), Ocular (historical document recognition), and Puck (high-speed parser). Lab Direction: The BergLab at UCSD develops computational methods for analyzing diverse human artifacts through interdisciplinary collaborations with historians, musicians, and archaeologists. Current projects include computational bibliography of early modern books, decipherment of historical ciphers, and generative models for symbolic music.

Christian KästnerView profile

Associate Professor

• Software Engineering for AI-Enabled Systems
• Open-Source Sustainability
• Software-Supply-Chain Security
• +7 more

Christian Kästner is an Associate Professor in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science, where he also serves as the director of the CMU Software Engineering Ph.D. Program. His work bridges software engineering and machine learning, focusing on the practical challenges of building production systems with ML components. His research spans several interconnected areas: software engineering for AI-enabled systems (particularly "Machine Learning in Production"), sustainability and fairness in open source communities, and software-supply-chain security. He investigates the limits of modularity and complexity caused by variability in software systems, with applications in quality assurance, interoperability, and feature interactions. His approach combines rigorous empirical research with program analysis and tool building. His recent publications reveal a strong trend toward ML systems engineering, with numerous papers on building reliable ML products, understanding open source sustainability challenges, and securing software supply chains. His work often involves large-scale empirical studies and the development of practical tools to address real-world software engineering problems. He has received multiple distinguished paper awards at top software engineering conferences including ICSE and FSE for his work on dependency abandonment, supply chain security, and collaboration challenges in ML systems. As an educator, he created and teaches the "Machine Learning in Production" course, which formed the basis for his MIT Press book of the same name. He has advised numerous PhD students and mentored many REU students, with several former students now holding faculty positions or working at major tech companies. His service to the community includes extensive program committee work for major software engineering conferences and editorial roles, demonstrating his significant influence in the field.

Vipul GoyalView profile

Associate Professor

• Cryptography
• Quantum Cryptography
• Security & Privacy
• +6 more

Vipul Goyal is an Associate Professor in the Computer Science Department at Carnegie Mellon University and a Senior Scientist at NTT Research . He previously spent 7 years at Microsoft Research before joining CMU. His academic journey includes a PhD from UCLA under advisors Rafi Ostrovsky and Amit Sahai , and a B.Tech. from IIT (BHU) Varanasi . Research Interests: Cryptography, Quantum Cryptography, Security & Privacy, Theoretical Computer Science Grants: NSF, DARPA, Department of Energy NETL, JP Morgan, Cisco, PNC, Ripple, DoS Networks, CyLab initiatives Education: PhD in Computer Science (UCLA, 2009) B.Tech. in Computer Science (IIT BHU Varanasi) His recent work focuses on Quantum Cryptography , Secure Multi-Party Computation , and Unclonable Security . Notable awards include the 2019 JP Morgan Faculty Award and the 2016 ACM CCS Test of Time Award . He advises the CMU Blockchain Group and has served on program committees for Crypto 2025 , FOCS 2024 , and others. Scientific Awards: 2019 JP Morgan Faculty Award 2016 ACM CCS Test of Time Award Microsoft Research Graduate Fellowship Google Outstanding Graduate Student Award Forbes 30 Under 30 in Science and Healthcare (2013) Students & Postdocs: He has mentored numerous PhD students and Postdocs , many of whom are now faculty at institutions like Tsinghua University, Max Planck Institute, and UT Austin. Former research assistants include Yifan Song and Elisa Masserova .

• Combinatorial Optimization
• Distributed and Parallel Computing
• Coding Theory
• +4 more

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 .

Peter ManoharView profile

Researcher

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +4 more

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Kanat TangwongsanView profile

Researcher

• Algorithms
• Parallel Computing
• Streaming Algorithms
• +6 more

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
• +3 more

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)

• Wireless Systems
• Satellite Networks
• Artificial Intelligence
• +3 more

Jingxian Wang is an NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS). He leads research in next-generation wireless systems, satellite networks, and AI-driven applications. Previously, he was a Research Scientist at Microsoft Research in Redmond, where he initiated the Smart Surface for 6G and Space project. He holds a Ph.D. from Carnegie Mellon University. His research spans wireless communication, backscatter technology, RFID systems, and soft robotics, with emphasis on low-latency networks and energy-efficient solutions. Work includes satellite traffic engineering, AI-enhanced LoRa reception, and wireless actuation for electronics-free robots. Publications demonstrate consistent focus on wireless innovation, with trends toward satellite networks (2025), generative AI in communication (2024), and soft robotics (2023). Earlier work established foundations in RFID and material sensing. Awards & Honors: ACM SIGMOBILE Doctoral Dissertation Award Microsoft Research Fellowship Emerging Rockstar in IEEE Pervasive Computing Best Paper Awards (UbiComp, IPSN, IJCAI) Research Highlights in ACM Communications (3×) and SIGMOBILE Team & Advising: He leads PhD students (Jinfan Liu, Kin Whye Chew, Jiahao Xu) and post-docs (Yijie Li, Zehua Sun), supported by initiatives like Microsoft's Accelerating Foundation Models program. The lab focuses on wireless systems, AI, and satellite networks.

Todd C. MowryView profile

Professor

• Computer Architecture
• Compiler Optimization
• Operating Systems
• +6 more

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.

Aditi RaghunathanView profile

Assistant Professor

• Machine Learning
• AI Safety
• Reliability of Large Language Models
• +7 more

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 .

• Distributed Systems
• Network Architectures
• Fault Diagnosis
• +3 more

Ronald Bianchini is an Adjunct Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University’s College of Engineering. His research focuses on Distributed Systems and Network Architectures, addressing challenges in peer-to-peer coordination, fault diagnosis in distributed systems, and scalable high-performance networks. He is affiliated with the university’s Department of Electrical and Computer Engineering. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (1989) MS in Electrical Engineering and Computer Engineering, Carnegie Mellon University (1986) BS in Electrical Engineering, Massachusetts Institute of Technology (1983) Research Interests: His work includes developing algorithms for distributed system diagnosis, such as the Distributed System Monitor (DSM) leveraging SNMP for network monitoring. Additionally, he contributed to the Tera switch architecture for Asynchronous Transfer Mode (ATM) networks, emphasizing robust performance under varying traffic conditions and scalability for large networks. Awards & Grants: No specific awards or grants are mentioned in the provided text. Labs & Teams: His research involves collaborations on projects like the DSM and Tera switch, though specific lab affiliations are not detailed.