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Feras SaadView profile

Assistant Professor

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +4 more

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

Ruben MartinsView profile

Research Professor

• Constraint Programming
• Program Synthesis
• Software Analysis
• +3 more

Ruben Martins is an Assistant Research Professor and Master’s Program Director at the School of Computer Science, Carnegie Mellon University. He holds a Ph.D. from the Technical University of Lisbon, followed by postdoctoral research at the University of Oxford and UT Austin. His work focuses on constraint programming, program synthesis, and formal verification, with applications in improving programmer productivity and automating data science tasks. Education: Ph.D. in Computer Science, Technical University of Lisbon (2013) Postdoctoral Researcher, University of Oxford (2014-2015) Postdoctoral Researcher, UT Austin (2015-2017) Ruben's research bridges constraint programming with program synthesis, aiming to automate tasks such as vulnerability detection, code repair, and SQL synthesis. He developed Open-WBO , a MaxSAT solver that won multiple gold medals and is used in real-world optimization scenarios like seating arrangements for events. His work integrates large language models (LLMs) with traditional methods to enhance software analysis and debugging. Awards: Distinguished Paper Award at PLDI 2018 Gold Medal in MaxSAT Competitions for Open-WBO Advising & Grants: Ruben advises Master’s students and directs courses such as 15639, 15604, and others. His research has been supported by grants focusing on program synthesis and cybersecurity. Labs/Teams: Leads development of Open-WBO and contributes to projects like Crabtree (Rust API testing) and Pryde (evasion attack analysis).

Evan Spotte-SmithView profile

Assistant Professor

• Computational Chemistry
• Data Science
• Sustainability
• +4 more

Evan Spotte-Smith is a computational scientist focusing on sustainable materials and energy systems. In Fall 2025, he will join Carnegie Mellon University as an Assistant Professor in Chemical Engineering. Previously, he completed his Ph.D. in Materials Science and Engineering at UC Berkeley under Kristin Aslaug Persson, specializing in computational simulations, machine learning, and reaction networks. His research addresses climate change through decarbonizing chemical manufacturing, improving battery technologies, and establishing ethical standards for AI in chemistry. He is a CBI Post-Doctoral Fellow, leveraging this role to advance machine learning in catalysis and electrochemistry. Education: Ph.D., Materials Science & Engineering, UC Berkeley (Advisor: Kristin A. Persson); Postdoctoral Research at CBI, CMU. Research Interests: Computational chemistry, data-driven materials discovery, sustainability in chemical processes, electrochemical systems, and ethical AI frameworks for chemical sciences. He explores how machine learning can accelerate the design of energy storage materials while addressing environmental impacts. Recent articles focus on machine learning ethics, electrochemical reaction cascades, battery degradation mechanisms, and sustainable electrolyte design. His work bridges fundamental chemistry with industrial applications, aiming to translate lab discoveries into scalable environmental solutions. Awards: CBI Post-Doctoral Fellowship, which provides mentorship and resources for early-career researchers in computational and data science. Grants/Advising: His upcoming faculty position will expand collaborations with CBI and CMU’s energy initiatives. Current focus includes decarbonizing feedstock chemicals and improving plastic recycling via electrochemistry. Labs/Teams: Active in CMU’s Center for Behavioral and Integrated Data Science (CBI), collaborating with Bosch and other industry partners on applied electrochemical challenges.

Jignesh PatelView profile

Professor

• Systems
• Data-Intensive and Cloud Computing
• Databases
• +3 more

Jignesh Patel is a Professor in the Computer Science Department at Carnegie Mellon University, specializing in database systems and data-intensive computing. His research focuses on hardware-software synergy for high-performance databases and democratizing data analytics through no-code interfaces. He co-founded DataChat, a startup focused on intuitive data analytics platforms. He holds fellowships from AAAS, ACM, and IEEE, along with teaching awards. His work emphasizes building systems that leverage novel hardware and user-friendly interfaces. Research interests include scalable data platforms, LLM-based query interfaces, and optimizing database performance through hardware collaboration. Notable projects include the Quickstep data platform and the Ava conversational interface. Awards: Fellow of AAAS, ACM, IEEE; Multiple Teaching Awards Labs/Teams: CRISP (Intelligent Storage and Processing), DataChat startup

• Machine Learning
• Artificial Intelligence
• Catalysis
• +6 more

Gabriel Gomes is an Assistant Professor in the Departments of Chemistry and Chemical Engineering at Carnegie Mellon University, where he leads the Gomes Group research program focused on the intersection of machine learning and chemistry. His work centers on developing new chemical reactions, catalysts, and materials using state-of-the-art machine learning and automated synthesis techniques. Dr. Gomes received his Ph.D. in Chemistry from Florida State University in 2018 under Professor Igor V. Alabugin, where he researched stereoelectronic effects. Prior to joining CMU in 2022, he was a Banting Postdoctoral Fellow at the University of Toronto in the Matter Lab led by Professor Alán Aspuru-Guzik. He earned his B.S. in Chemistry from Federal University of Rio de Janeiro, Brazil in 2013. His research program focuses on developing autonomous platforms for reaction discovery, with emphasis on catalysis. Gomes aims to create inverse design frameworks where algorithms can propose new functional catalysts from scratch based on desired properties. His work integrates quantum mechanics with machine learning to teach computers chemical intuition, enabling novel approaches to catalyst design and chemical synthesis. He strongly advocates for the integration of AI with human creativity to transform chemical research. Gomes' recent publications demonstrate a clear trend toward integrating large language models with chemical discovery, developing transferable machine learning potentials for catalysis, and advancing molecular representations with quantum chemical insights. His work spans from fundamental quantum chemistry to practical applications in biocatalysis and materials design. Scialog Fellow (2023) C&EN Talented Twelve (2022) NSERC Banting Postdoctoral Fellowship (2020-2021) CAS SciFinder Future Leaders Program (2018) ACS COMP Chemical Computing Group Excellence Award (2018) FSU's Graduate Student Research and Creativity Award (2018) IBM Ph.D. Scholarship (2017) Dr. Gomes mentors several graduate students including Robert MacKnight and Daniil Boiko, who have contributed to projects like the Catnip web application for biocatalyst prediction. His research has received funding through prestigious programs like the Scialog Automating Chemical Laboratories awards. Gomes actively promotes open science initiatives and has contributed to the Open Reaction Database to improve data accessibility for machine learning in chemistry. The Gomes Group operates at the cutting edge of AI-driven chemistry research, developing tools like Coscientist that bridge the gap between natural language prompts and actual chemical reactions. Their work on autonomous reaction discovery systems represents a significant step toward fully automated chemical laboratories where machines can 'dream of molecules' with specific desired properties.

Andrew PavloView profile

Associate Professor

• Databases
• Distributed Systems
• Data-Intensive Computing
• +3 more

Andrew Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University , part of the School of Computer Science . His research focuses on database systems, particularly self-driving architectures, transaction processing, and large-scale analytics. He is a member of the CMU Database Group and Parallel Data Laboratory. His awards include the NSF CAREER (2019), Sloan Fellowship (2018), and ACM SIGMOD Jim Gray Dissertation Award (2014). He co-founded OtterTune, a database tuning startup, though it later ceased operations. Current research interests emphasize autonomous database systems, query optimization, and distributed computing. Recent publications (2024) highlight work on self-driving DBMS, null representation in columnar formats, and UDF optimization techniques. Awards: NSF CAREER Award (2019) Sloan Fellowship (2018) ACM SIGMOD Jim Gray Dissertation Award (2014) Advising: Mentors students in database systems, including Sam Arch, Wan Shen Lim, and William Zhang. Labs/Teams: Leads the Database Group and collaborates with the Parallel Data Laboratory.

Dominik MoritzView profile

Assistant Professor

• Human-Centered Machine Learning
• Data Science
• Interactive Systems
• +6 more

Dominik Moritz is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University and Machine Learning researcher at Apple. He leads the Data Interaction Group and develops systems for interactive data visualization. Ph.D. in Computer Science from University of Washington (2019) B.S. in Computer Science from Hasso Plattner Institute (2013) His research focuses on: Human-Centered Machine Learning Data Visualization Toolkits Interactive Systems Design Accessibility in Visual Analytics Scalable Web-Based Visualization Recent publications address visualization design knowledge formalization (Draco), cross-filtering frameworks (Mosaic), and accessibility evaluation (Chartability). He has received Best Paper Honors at VIS and EuroVis conferences. Professional Collaborations: Co-founder of Vega-Lite visualization grammar Collaborations with Jeff Heer, Bill Howe, Jock MacKinlay Projects with Microsoft Research and Google Research

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.

Adam PererView profile

Associate Professor

• Human-Centered AI
• Data Visualization
• Healthcare Informatics
• +4 more

Adam Perer is an Associate Professor at Carnegie Mellon University, where he is a member of the Human-Computer Interaction Institute within the School of Computer Science. He serves as Co-Director of the Data Interaction Group and holds leadership positions as Area Papers Chair at IEEE VIS and Visualization Subcommittee Papers Chair at ACM CHI. Previously, he worked as a Research Scientist at IBM Research. Ph.D. in Computer Science from the University of Maryland, College Park Perer's research integrates data visualization and machine learning techniques to create visual interactive systems that help users make sense of big data. His work focuses on human-centered data science, extracting insights from clinical data to support data-driven medicine, and facilitating human-AI collaboration. He investigates how people engage with and make decisions using data, designing new interfaces to interact with complex information while assisting impactful domains drowning in data. His recent publications reveal a strong trend toward healthcare applications of AI and visualization, particularly in clinical decision support and overdose prevention. There's also a significant focus on explainable AI (XAI), with multiple papers examining how imperfect explanations affect human-AI collaboration and decision-making in critical contexts like healthcare. His work consistently bridges visualization theory with practical applications in high-stakes domains. Best Paper Honorable Mention for 'Dead or Alive: Continuous Data Profiling for Interactive Data Science' (VIS 2023) Best Paper for 'Neo: Generalizing Confusion Matrix Visualization' (CHI 2022) Most Reproducible Paper Award for 'SQLShare' (SIGMOD 2016) Perer actively mentors students across all levels, with PhD students focusing on human-AI collaboration in healthcare settings, visualization techniques, and clinical decision support systems. His lab receives funding for projects related to human-centered AI, data visualization in healthcare, and explainable machine learning systems. The Data Interaction Group, which he co-directs, focuses on empowering everyone to analyze and communicate data through interactive systems. His research has been supported by collaborations with medical institutions and appears in premier venues for visualization, human-computer interaction, and medical informatics. Current projects include Eye into AI (improving XAI interpretability), Predicting and Visualizing Overdose Risk, and AI applications in intensive care units.

Jay D. AronsonView profile

Professor

• Science and Technology Studies
• Human Rights
• Forensic Science
• +3 more

Jay D. Aronson is a Professor of Science, Technology, and Society in the Department of History at Carnegie Mellon University. He is also the founder and director of the Center for Human Rights Science at CMU. His research and teaching focus on the intersections of science, technology, law, media, and human rights, with particular emphasis on forensic science, digital evidence, and post-conflict justice. Ph.D. in History of Science and Technology, University of Minnesota (2004) Pre- and post-doctoral fellow, Harvard University’s John F. Kennedy School of Government Aronson’s work explores how technological innovations—especially digital evidence and DNA profiling—shape human rights practices, legal accountability, and public policy. He has authored or co-authored several books, including Death in Custody: How America Ignores the Truth and What We Can Do About It (2023), Who Owns the Dead? The Science and Politics of Death at Ground Zero (2016), and Genetic Witness: Science, Law, and Controversy in the Making of DNA Profiling (2007). He co-edited New Technologies for Human Rights Law and Practice (2018) and Counting Civilian Casualties (2013). His recent research delves into the use of computer vision and machine learning for analyzing human rights media, shooter localization via social media videos, and ethical challenges in forensic DNA databases. He collaborates with computer scientists and statisticians to improve tools for civilian casualty recording and digital evidence authentication. Aronson also investigates the role of user-generated content in transitional justice and the politics of memorialization after mass disasters.

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

Alon LavieView profile

Academic

• Machine Translation
• Natural Language Processing
• Computational Linguistics
• +9 more

Dr. Alon Lavie is a Consulting Professor at the Language Technologies Institute (LTI) within Carnegie Mellon University (CMU), where he has been a faculty member since 1996. He previously served as a Research Professor at CMU from 1996 to 2015. Lavie is also currently the VP of AI Research at Phrase, leading AI R&D teams across Pittsburgh, Prague, and Edinburgh. His work bridges academic research and industrial application in machine translation (MT) and natural language processing (NLP). Academic Affiliations: CMU (Consulting Professor, 1996–present), Unbabel (VP of Language Technologies, 2015–2019), Amazon (Senior Manager, 2015–2019) Industry Roles: Co-founded Safaba Translation Solutions (2009–2015), acquired by Amazon Research Interests: Lavie’s research focuses on Machine Translation and Natural Language Processing , particularly syntax-driven and hybrid approaches to MT, human feedback integration, and evaluation metrics. His projects include: AVENUE/LETRAS: Developing MT systems for minority languages (Mapudungun, Quechua, Inupiaq). METEOR: Leading the decade-long development of this highly correlated MT evaluation metric. COMET: Directed development of a neural MT evaluation metric at Unbabel. MEMT: Pioneering multi-engine MT combination frameworks. GRASP: Robust parsing of grammatical relations in spoken language corpora. Scientific Awards: Recipient of the 2021 Makoto Nagao IAMT Award of Honour for contributions to MT. Former president of the International Association for Machine Translation (2013–2015) and Association for Machine Translation in the Americas (2008–2012). Advising & Leadership: Supervised 23 graduate students, including PhD recipients Ricardo Rei (2024) and Austin Matthews (2019). Served as General Chair for AMTA 2010/2012, MT Summit 2015, and as president of ACL’s SIGParse (2008–2013).

• Systems
• Databases
• Distributed Systems
• +6 more

David Andersen is a Professor in the Computer Science Department at Carnegie Mellon University, with research spanning systems, databases, distributed systems, networking, and security. He holds a Ph.D. and M.S. from MIT, and B.S. degrees in Computer Science and Biology from the University of Utah. Education: Ph.D./M.S., MIT (Computer Science); B.S., University of Utah (Computer Science, Biology). His research focuses on networked systems , emphasizing robustness , energy efficiency , and scalable architectures . Key projects include FAWN (low-power clusters), XIA (secure internet architecture), and MemC3 (memory-optimized hashing). Recent publications highlight trends in machine learning integration , storage innovations , and datacenter networking . Professional activities include leadership roles in conferences like OSDI, SOSP, and NSDI, and advisory positions in DARPA’s ISAT group. He is also founder and CTO of BrdgAI and previously co-founded a deep-learning startup and an ISP. Personal interests include running, triathlons, and rock climbing, with notable contributions like the Pi Searcher and running route guides for Pittsburgh and Boston.

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 .

Evan Walter Clark Spotte-SmithView profile

Adjunct Professor

• computational chemistry
• electrochemistry
• materials science
• +5 more

Evan Walter Clark Spotte-Smith is an Adjunct Professor in the Department of Chemical Engineering at Carnegie Mellon University (CMU) and an incoming Ad Astra Fellow and Assistant Professor of Digital Chemistry at University College Dublin (UCD). Their research focuses on computational simulations, data science, and electrochemistry, particularly in battery materials, catalysis, and sustainability. They hold a Ph.D. and M.S. from UC Berkeley and a B.S. from Columbia University. Education: Ph.D. (2023), M.S. (2021) in Materials Science & Engineering from UC Berkeley; B.S. (2019) in Materials Science & Engineering from Columbia University. Research Interests: Leveraging machine learning and reaction networks to study electrochemical systems, battery degradation mechanisms, and sustainable technologies. Specific areas include Li-ion/Mg-ion batteries, electrolyte decomposition, and ethics in AI for science. Key Contributions: Developed tools like RNMC for reaction network simulations, contributed to the Materials Project database, and co-founded the CoReACTER research group. Leads efforts on ethical AI in chemical sciences. Awards: Carnegie Bosch Institute Fellowship (2024), Winton-Kavli Research Pitch Competition Prize (2023), NSF Honorable Mentions (2020, 2019). Advising & Grants: Mentored multiple students in computational chemistry and materials science. Secured funding including a $500k Toyota grant and high-performance computing allocations. Collaborates with Rachel Kurchin (CMU) and Samuel Blau (LBNL). Labs/Teams: CoReACTER (anti-oppressive research group), ACME group (UC Berkeley), and the Materials Project.