Stephen Smith is a Research Professor at the Robotics Institute, Carnegie Mellon University. He directs the Intelligent Coordination and Logistics Laboratory and holds affiliations with Traffic21, Metro21, and the TSET University Transportation Center. His research focuses on AI-driven solutions for planning, scheduling, and coordination in complex systems, including transportation infrastructure, robotics, and manufacturing. Research interests include constraint-based optimization, multi-agent coordination (e.g., robot teams), robust planning under uncertainty, and adaptive traffic control systems like Surtrac. Applications span smart cities, autonomous vehicles, and distributed resource management. He collaborates with municipal partners on projects such as pedestrian safety apps and snowplow route optimization. His work bridges theoretical advances with real-world deployment, emphasizing scalability and practicality. Notable contributions include decentralized traffic control systems, learning-based planning assistants, and frameworks for distributed decision-making in uncertain environments. Current and former advisees include PhD students Hsu-kuang Chiu and Jayanth Krishna Mogali, alongside master’s students like Viraj Parimi. Projects include the PedPal smartphone app and the RADAR cognitive assistant.
Dr. Richard Randall is an Associate Professor of Music Theory at Carnegie Mellon University's School of Music and holds a faculty appointment at the Center for the Neural Basis of Cognition and the Neuroscience Institute. His research focuses on the cultural, technological, and psychological foundations of musical experiences, with particular emphasis on auditory perception, cognitive processes, and the intersection of music with social and political contexts. He directs the Music Experience Lab (MEL), which explores interdisciplinary questions about music's role in human life, including projects centered on Romani musicians in the Balkans and the ethical dimensions of music technology. Randall co-founded the Listening Spaces Project, examining how technology shapes contemporary musical practices, and led initiatives like the Pittonkatonk festival, advocating for music as a public good. His work bridges neuroscience, media studies, and ethnomusicology, supported by grants such as the Rothberg Research Award and NIH funding. Affiliations: School of Music, Center for the Neural Basis of Cognition, Neuroscience Institute Key Projects: MEL, Listening Spaces Project, Romani Drummers Project, Pittonkatonk Education: (Not explicitly stated in provided text) Randall's research spans neuroimaging studies of auditory perception (e.g., auditory scene analysis) to cultural critiques of digital music distribution. His lab emphasizes collaborations with artists, activists, and technologists to address systemic issues like cultural representation and labor rights in music. He co-edited 21st Century Perspectives on Music, Technology, and Culture and developed educational programs such as the Young Musicians Collaborative, fostering community engagement through music. His recent publications explore topics like predictive coding models of musicality perception and the impact of low-level auditory features on grouping strength. Awards include the NIH grant T32-MH19983. Randall also leads the experimental ensemble Bombici, merging Balkan folk traditions with electronic improvisation. Grants: Rothberg Research Award, NIH grant T32-MH19983, Fine Foundation, Sprout Fund Labs/Teams: Music Experience Lab (MEL), Listening Spaces Project, Bombici collective
Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.
Gregory S. Rohrer is the W.W. Mullins Professor of Materials Science and Engineering at Carnegie Mellon University , where he has been a faculty member since 1990. He served as Department Head from 2005 to 2021 and was named University Professor in 2025. His research focuses on the structure and properties of interfaces in crystalline materials, particularly grain boundaries, and their impact on material behavior. Education : Ph.D. in Materials Science and Engineering from the University of Pennsylvania (1989), B.S. in Physics from Franklin and Marshall College (1984) Rohrer's research spans advanced materials processing, ceramics, energy materials, metallurgy, and sustainable energy. He has pioneered work on quantifying atomic and microstructural mechanisms in high-temperature materials, with significant contributions to grain boundary crystallography and microstructure prediction. His recent research page details applications in materials for energy production and catalysis. His publications (over 350) include groundbreaking studies on grain boundary energy, phase transformations, and microstructural evolution. Key awards include the Outstanding Educator Award (2025), Cyril Stanley Smith Award (2025), and MRS Fellow (2024). He has also held leadership roles as Coordinating Editor for Acta Materialia and Board Member of the American Ceramic Society. As a dedicated educator, he authored the textbook Structure and Bonding in Crystalline Materials and maintains extensive student/instructor resources. His NSF-funded research has driven innovations in predicting material microstructures and understanding high-temperature deformation mechanisms.
Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.
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.
Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.
Kaushik Dayal is a Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University's College of Engineering. He leads the Multiscale Mechanics Research Group and is affiliated with several interdisciplinary centers including the Center for Nonlinear Analysis, the Center for the Mechanics and Engineering of Cellular Systems, the NextManufacturing Center, and the Wilton E. Scott Institute for Energy Innovation. His research bridges theoretical and computational mechanics with applications in materials science, energy, and environmental systems. Ph.D., Mechanical Engineering, California Institute of Technology (2007) M.S., Aeronautics, California Institute of Technology (2001) B.Tech., Naval Architecture, Indian Institute of Technology Madras (2000) His research focuses on theoretical and computational multiscale methods , particularly in modeling the behavior of materials across atomic to continuum scales. Key areas include non-equilibrium response , electromagnetic effects , phase-field modeling of fracture , poroelasticity , soft active materials , and data-driven inverse design . He investigates phenomena such as microstructure evolution, dislocation dynamics, surface growth, and material behavior under extreme conditions. His work integrates mechanics with chemistry, robotics, and climate resilience, often leveraging machine learning and Bayesian inference. His recent publications reveal a strong trend in computational mechanics of heterogeneous and functional materials , with emphasis on phase-field models, multiscale homogenization, and instability exploitation in soft electromechanical systems. Many studies involve collaboration with national labs and cross-departmental teams, reflecting a highly interdisciplinary approach. McGaw Graduate Fellowship in Mechanical Engineering Army Research Laboratory Journeyman Fellowship Adamson Fellowship Bushnell Doctoral Fellowship Mao Yisheng Outstanding Dissertation Award MIT Postdoctoral Fellowship for Engineering Excellence Center for Machine Learning and Health Fellowship Dowd Doctoral Fellowship Steinbrenner Doctoral Fellowship Dunlap Awardee Dayal has advised numerous PhD students, many of whom have gone on to postdoctoral positions at institutions such as Caltech, MIT, Johns Hopkins, and Los Alamos National Laboratory. His research is supported by major grants from the Department of Defense (MURI program), Air Force Research Laboratory, and other federal agencies. He actively promotes education through teaching assistant awards and participation in Rising Stars workshops. He leads the Multiscale Mechanics Research Group , a vibrant team engaged in cutting-edge research on material modeling, soft robotics, energy materials, and environmental mechanics. The group emphasizes open scientific exchange, interdisciplinary collaboration, and innovation in computational methods.
Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.
Rachel Dzombak is the Distinguished Service Professor of Design and Innovation and Senior Innovation Advisor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. She also holds teaching roles in the Human-Computer Interaction Institute, Tepper School of Business, and College of Engineering. Her work focuses on AI ethics, innovation strategy, and digital transformation, with an emphasis on responsible AI adoption in public and private sectors. Dr. Dzombak earned her PhD in Civil and Environmental Engineering from UC Berkeley, followed by a postdoctoral fellowship at Berkeley’s Haas School of Business. She holds a BS in Bioengineering from Penn State University. Her research spans AI governance, circular systems, and sustainable engineering, with notable contributions to design thinking, technology policy, and social entrepreneurship. She founded Outcome x Design, a consultancy advising organizations on AI adoption and innovation processes. Previously, she led digital transformation initiatives at CMU’s Software Engineering Institute and contributed to Berkeley’s Master’s in Development Engineering program. Her articles and engagements emphasize ethical AI practices, systemic accountability, and bridging technology with societal needs. Collaborations include the DoD’s National Security Innovation Network and projects in Kenya’s healthcare sector. Dzombak’s teaching and advisory work aim to foster innovation that addresses global challenges while prioritizing human and environmental well-being.
William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University. His research interests include theory and algorithms and complexity , with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role. His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials. His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024) , suggesting involvement in both introductory and advanced computer science education.
Dr. Eduardo Feo Flushing is an Assistant Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science , based in Pittsburgh, PA. His role involves academic instruction and research at the intersection of software engineering, societal computing, and intelligent systems. Research Focus: His work spans robotics, distributed AI, and networked systems, with applications in critical domains. Primary research themes include: Multi-robot coordination for spatially distributed tasks Machine learning in healthcare diagnostics and renewable energy Wireless network optimization for mobile systems Human-robot collaboration under uncertainty Publication Trends: Recent articles (2021-2025) demonstrate a strong emphasis on applied machine learning (LLMs for medical ECG, deep learning for solar panel inspection) and advanced robotics (indoor mapping, task allocation in communication-constrained environments). Earlier work (2016-2020) focused on foundational aspects of multi-robot coordination, optimization, and wireless network resilience.
Dunja Mladenic is a researcher at the Jožef Stefan Institute 's Department of Knowledge Technologies in Ljubljana, Slovenia. She has held visiting positions at Carnegie Mellon University 's School of Computer Science in 1996-1997 and 2000-2001, where she worked on text and data mining projects. Coordinator of the European project Sol-Eu-Net (2005) Tutorial chair for ECML/PKDD-2005 and ICML-2003 Key contributor to CMU's Text Learning Group Her research focuses on Text Mining , Data Mining , and Machine Learning , particularly in Web navigation assistance and intelligent agent design. She developed the Text Mining Group 's Personal WebWatcher system that uses machine learning to highlight interesting hyperlinks based on user behavior. Her publication trends show a strong emphasis on text learning and intelligent agent design, with technical reports and conference papers exploring feature selection, classifier comparison (kNN vs Naive Bayes), and document representation techniques for web browsing assistance. She maintains dual email contact through CMU and Jožef Stefan Institute . Her work spans multiple disciplines including biomedical data analysis, discrete event simulation, and encyclopedia typesetting using TeX.
David Farber is an Adjunct Professor of Internet Studies at the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. He played a pivotal role in early Internet development through initiatives like CSNet, NSFNET, NREN, and the NSF Gigabit Testbed. His work laid foundational infrastructure for modern Internet expansion. Key contributions to distributed systems and global networking adoption Recipient of prestigious SIGCOMM and John Scott Awards Active in Internet Society governance and national technology policy Contact: 5000 Forbes Avenue, Pittsburgh, PA 15213
Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu
