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.
Matteo Cremonesi is an Assistant Professor in the Department of Physics at Carnegie Mellon University's Mellon College of Science, where he has been faculty since 2023. His research focuses on high energy physics experiments, particularly dark matter searches at the Large Hadron Collider (LHC) and the development of advanced machine learning techniques for particle physics data analysis. He is an active member of the Compact Muon Solenoid (CMS) experiment at CERN. Dr. Cremonesi's educational background includes: Ph.D. in Physics from the University of Oxford (2014) M.S. in Physics from the University of Rome II (2012) B.S. in Physics from the University of Rome II (2009) Before joining Carnegie Mellon, Dr. Cremonesi held several prestigious research positions: LHC Physics Center Artificial Intelligence Fellow & Research Associate at University of Notre Dame (2021-) Research Associate at Fermi National Accelerator Laboratory (FNAL) (2015-2021) Fellow at Harvard University Department of Physics (2013) As an experimental particle physicist, Dr. Cremonesi's research centers on studying fundamental particles and searching for physics beyond the Standard Model, with particular emphasis on dark matter. His work involves analyzing data from the CMS experiment at CERN's LHC, where he has made significant contributions to dark matter searches using missing transverse momentum (MET) techniques. Dr. Cremonesi has pioneered the application of graph neural networks for MET reconstruction, significantly improving the sensitivity of dark matter searches. He previously contributed to top quark physics research as a member of the Collider Detector at Fermilab (CDF) experiment, helping to discover a predicted production mechanism of the top quark. Dr. Cremonesi's publication record demonstrates a consistent focus on advancing particle physics methodology and searching for new physics phenomena. His recent work shows an increasing emphasis on machine learning applications in high energy physics, particularly graph neural networks for particle reconstruction tasks. His research spans both theoretical aspects of particle physics and practical detector development, with a strong focus on data analysis techniques that push the boundaries of what can be measured at particle colliders. The breadth of his publications indicates active collaboration across multiple experimental fronts within the CMS collaboration. Professional Recognition: Selected as CMS delegate to the LHC Dark Matter Working Group Former leader of the MET group of the CMS experiment (2020-2022) Dr. Cremonesi has been actively involved in advancing the technical capabilities of particle physics experiments, particularly in the areas of data analysis frameworks and machine learning applications. His work on the Coffea framework and Big Data technologies for High Energy Physics analysis demonstrates his commitment to developing innovative computational approaches for handling the massive datasets generated by modern particle physics experiments. His leadership of the MET group at CMS highlights his recognized expertise in a critical area of particle physics analysis. Dr. Cremonesi's laboratory work primarily involves collaboration with the CMS experiment at CERN, where he contributes to the development and operation of detector systems and analysis frameworks. His research group at Carnegie Mellon focuses on applying advanced machine learning techniques to particle physics problems, with particular emphasis on real-time data processing for trigger systems and offline analysis improvements.
David E. Bernal Neira is an Adjunct Professor at Carnegie Mellon University's Tepper School of Business, focusing on Operations Management and Quantum Computing. His research integrates optimization, quantum algorithms, and chemical engineering applications. Education: PhD in Chemical Engineering, Carnegie Mellon University (2021) BS in Physics, Universidad de los Andes (2018) MS in Chemical Engineering, Universidad de los Andes (2016) BS in Chemical Engineering (Cum Laude), Universidad de los Andes (2014) Research Interests: He specializes in Process Intensification, Quantum Algorithm Design, Nonlinear Optimization, and Machine Learning applications in chemical systems. His work emphasizes scalable solutions for industrial challenges, from refinery planning to quantum hardware efficiency. Publication Trends: Recent articles (2020-2023) reveal a strong focus on quantum computing advancements (35%), convex MINLP methodologies (40%), and chemical process optimization (25%). Key themes include algorithm scalability, hybrid modeling, and cross-disciplinary quantum applications. Awards: Best Talk Award, Quantum Computing Workshop (AIChE/DTU, 2022) AIChE CAST Directors’ Student Presentation Finalist (2020) Mark Dennis Karl Teaching Award (CMU, 2019) Cum Laude in Chemical Engineering (UniAndes, 2014) Advising and Labs: While specific student engagements are undisclosed, his research involves collaborations across computational chemistry and quantum information groups. No dedicated lab is mentioned, but affiliations center on CMU's optimization and quantum initiatives.
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 .
Gabe Gomes is an Assistant Professor in the Departments of Chemistry and Chemical Engineering at Carnegie Mellon University (CMU). He joined CMU in 2022, leading the Gomes Group, which focuses on merging machine learning, artificial intelligence, and automated synthesis to accelerate the discovery of new chemical reactions, catalysts, and materials. Previously, he was a postdoctoral researcher at the University of Toronto under Prof. Alán Aspuru-Guzik, where he developed AI-driven methodologies for catalyst design and earned a Banting Postdoctoral Fellowship. Education : Ph.D. in Chemistry, Florida State University (2018) B.Sc. in Chemistry, Federal University of Rio de Janeiro (2013) Research Interests : His work bridges quantum mechanics and machine learning to automate chemical discovery, emphasizing catalysis, energy storage, and sustainable chemistry. Key projects include developing predictive models for biocatalysts (e.g., Catnip tool) and advancing autonomous chemical experimentation systems like Coscientist. He explores how AI can enhance molecular design, reaction pathway prediction, and green chemistry innovations. Notable Contributions : Co-developed Catnip , a machine learning tool predicting enzyme-substrate compatibility for biocatalysis. Designed Coscientist , an AI system aiding chemists in experiment planning and hypothesis generation. Recipient of Scialog Fellow (2023), C&EN Talented Twelve (2022), and Banting Fellowship (2020-2021). Labs and Teams : He leads the Gomes Group at CMU, fostering collaborations across disciplines to advance AI-driven chemistry and sustainable energy solutions.
John Kitchin is a Professor of Chemical Engineering at Carnegie Mellon University, affiliated with the College of Engineering. He leads the Kitchin Research Group, focusing on catalysis, energy systems, and the integration of machine learning into scientific discovery. His work bridges computational modeling and experimental validation, with contributions to surrogate models, materials development, and self-driving laboratories. Education: B.S. Chemistry (NCSU), M.S./Ph.D. Chemical Engineering (University of Delaware). Postdoctoral fellowship at the Fritz Haber Institute (Berlin). Tenure-track faculty since 2006. Research interests include catalyst design, energy storage, molecular simulation, and AI-driven experimentation. Key projects involve optimizing hydrogen infrastructure and accelerating discovery via graph neural networks and surrogate modeling. Awards include DOE Early Career Award (2010), Presidential Early Career Award (2011), and AIChE Innovation Award (2023). He holds the John E. Swearingen Professorship and has authored over 100 peer-reviewed articles. Labs/Teams: Kitchin Research Group, collaborating with industry and academia on materials and energy projects. Develops open-source tools like litdb and Claude-Light for scientific automation.
Geoffrey J. Gordon is a Professor in the Machine Learning Department at Carnegie Mellon University and affiliated with the Robotics Institute. His research spans multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of complex data, computational learning theory, and game theory. He leads the SELECT lab (SEnse, LEarn, and aCT), focusing on predictive state representations, spectral learning, and applications in robotics. His recent work integrates deep learning with controlled dynamical systems and optimization, as seen in publications at AAAI and AISTATS. Research Interests: Multi-agent systems and game theory Reinforcement learning and dynamical systems Statistical models for high-dimensional data Spectral learning and quantum Markov models Scientific Awards: Best paper award at ICML 2010 Teaching: 10-405/605: Machine Learning with Large Datasets (2023) 10-606/607: Mathematical/Computational Background for ML (2022, 2017) 10-701: Intro to Machine Learning (2021, 2014) Labs & Teams: SELECT Lab (SEnse, LEarn, and aCT) Collaborations with Stanford Robotics Lab, AUTON Lab, and others
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 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.
Sridhar Tayur is the Ford Distinguished Research Chair and Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business, with a courtesy professorship in Electrical and Computer Engineering. He holds a Ph.D. from Cornell University and an undergraduate degree from IIT Madras. His research focuses on healthcare operations, quantum computing, and supply chain optimization. Notable recognitions include INFORMS Fellow, MSOM Distinguished Fellow, and membership in the National Academy of Engineering (NAE). Education: Ph.D. in Operations Research and Industrial Engineering, Cornell University Bachelor's in Mechanical Engineering, IIT Madras (Distinguished Alumnus Award) Research Interests: His work bridges operations research with cutting-edge fields like quantum computing and healthcare systems. Key areas include optimizing organ transplantation policies, applying AI to supply chains, and developing quantum algorithms for real-world problems. He emphasizes practical solutions for systemic challenges in healthcare equity and operational efficiency. Publications Trend: Recent articles highlight advancements in quantum-inspired optimization, fair liver allocation models, and AI-driven supply chain transformations. His work often intersects interdisciplinary domains such as quantum computing for healthcare logistics and sustainability-driven operational strategies. Awards: INFORMS Fellow MSOM Society Distinguished Fellow Elected to National Academy of Engineering (NAE) Advising & Grants: No explicit student advisement or grant details provided, though his research indicates active collaboration in interdisciplinary projects at CMU. Labs/Teams: Associated with CMU’s quantum computing research initiatives and healthcare operations groups, though specific lab affiliations are not detailed in the text.
Zhihao Jia is an Assistant Professor in the Computer Science Department at Carnegie Mellon University (CMU). He is affiliated with the CMU Catalyst Group and the Parallel Data Lab, focusing on advancing systems for machine learning, quantum computing, and large-scale data analytics. Previously, he was a research scientist at Facebook and earned his PhD from Stanford University (2020), advised by Alex Aiken and Matei Zaharia. His bachelor's degree is from Tsinghua University's Special Pilot CS Class under Andrew Yao. His research emphasizes accelerating deep learning computations on modern hardware and optimizing quantum circuits for intermediate-scale quantum devices. Notable contributions include speculative reasoning techniques, efficient LLM serving systems, and quantum circuit simulators. He teaches advanced courses such as 15418 and 15618 at CMU. Zhihao advises PhD students including Zhuoming Chen, Zikun Li, and Xinhao Cheng. His work bridges systems research with emerging applications, aiming to enhance computational efficiency and scalability. He collaborates on projects like Specexec, Helix, and Atlas, addressing challenges in distributed computing and quantum simulation.
Ryan O'Donnell is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He specializes in theoretical computer science, with a focus on algorithms, complexity theory, quantum computing, and probability. His research includes Fourier analysis of Boolean functions, constraint satisfaction problems, and quantum information theory. He teaches courses such as 15251 (Fall 2025), 15754 (Spring 2025), and others. His work spans quantum tomography, pseudorandomness, and algorithmic design. Recent publications explore topics like quartic quantum speedups, uniformity testing, and explicit expanders. He advises PhD students William He, Noah Singer, and Jingxun Liang. His research interests bridge foundational theory and practical applications, including healthcare informatics and cryptographic tools. He actively contributes to academic conferences and program committees, reflecting his leadership in the field.
Alan McGaughey is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads the Nanoscale Transport Phenomena Laboratory, where his research bridges mechanical engineering, materials science, physics, and chemistry to study atomic-level transport of mass, momentum, and energy. His work emphasizes phonon, photon, electron, and fluid particle dynamics using advanced simulation techniques. Bachelor of Engineering, McMaster University (1998) Master of Applied Science, University of Toronto (2000) Ph.D., University of Michigan (2004) Post-doctoral training, University of Florida Alan McGaughey's research interests center on nanoscale thermal transport , with applications in energy technologies , materials for energy efficiency , and multiscale modeling . His lab develops molecular- and meso-scale simulation methods, including molecular dynamics, lattice dynamics, density functional theory, and Boltzmann transport equation modeling. Key research areas include thermal transport in nanostructures and interfaces, hybrid organic-inorganic materials, electrocaloric cooling, and liquid-vapor phase change. The team also applies machine learning to accelerate materials discovery and property prediction. The recent publications (2023–2025) reflect a strong focus on thermal conductivity prediction in diverse systems—from polymers and 2D materials to disordered crystals and thin films. The work integrates first-principles simulations , uncertainty quantification , and machine learning to uncover fundamental mechanisms of phonon transport and interfacial heat transfer. A recurring theme is the role of structural disorder —static, dynamic, or rotational—in modulating thermal properties. Air Force Office of Scientific Research Young Investigator Program (2009) Benjamin Richard Teare Teaching Award (2014) National Academy of Engineering’s Frontiers of Engineering Education Symposium (2015) Professor of the Year by MechE seniors (2012, 2015, 2017) 2019 & 2024 College of Engineering Faculty Awards 2021 Viskanta Fellowship, Purdue University McGaughey has advised numerous Ph.D. and Master’s students, many of whom have gone on to impactful research careers. His group has secured funding from agencies such as the Department of Defense and the Department of Energy, including Scott Institute seed grants for energy research. He collaborates extensively with experimentalists, including Jonathan Malen, Reeja Jayan, Chris Wilmer, and others, ensuring strong theory-experiment integration. He is also involved in educational innovation and was named faculty chair-elect for the College of Engineering. The Nanoscale Transport Phenomena Laboratory is a vibrant research group that combines computational modeling with interdisciplinary collaboration to advance fundamental understanding and enable next-generation thermal materials and devices.
Maria Kurnikova is a Professor of Chemistry at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. She holds concurrent roles as Co-Director of the Molecular Biophysics & Structural Biology (MBSB) Graduate Program and affiliated faculty in the Joint CMU-Pittsburgh Computational Biology Program. Her research focuses on computational chemistry, molecular modeling, and theoretical biophysics, particularly in understanding protein structure-function relationships through high-performance computing and multi-scale modeling approaches. Education: M.Sc., Moscow Institute of Physics and Technology, 1984–1990 Ph.D., University of Pittsburgh, 1998 Postdoctoral Fellowships: University of Tel Aviv (1998–1999) and University of Pittsburgh/NIST (1999–2001) Research Interests: Dr. Kurnikova's work spans computational modeling of membrane proteins, ion channels, and drug design. Her group develops hierarchical models to bridge atomistic and macroscale processes, with applications in understanding ligand binding, channel gating, and ion selectivity. Current projects include: Membrane protein receptors and ion channel dynamics In silico drug design using machine learning and free energy simulations Development of accurate force fields for molecular interactions Awards: 2024 Department of Chemistry Distinguished Alumni Award (University of Pittsburgh) 2002 Research Corporation Innovation Award Collaborations & Resources: Active user of NSF/NIH supercomputing facilities (e.g., Pittsburgh Supercomputing Center) Collaborations with Olexandr Isayev’s group on drug design methodologies Labs & Teams: The Kurnikova Research Group specializes in theoretical and computational approaches to biological macromolecules, with ongoing projects in TRPM7 channel regulation, AMPA receptor allostery, and heme transfer mechanisms.
