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Howie ChosetView profile

Professor

• Robotics
• Medical Robotics
• Snake Robots
• +11 more

Howie Choset is a Professor of Robotics at the Robotics Institute, Carnegie Mellon University. He directs the Undergraduate Robotics Minor and leads the Biorobotics Laboratory, where his research focuses on snake robots, motion planning, and medical robotics. He is also affiliated with the Manufacturing Futures Institute. Ph.D., Mechanical Engineering, California Institute of Technology (1996) M.S., Mechanical Engineering, California Institute of Technology (1995) B.S.E., Computer Science and Engineering, University of Pennsylvania (1990) B.S., Economics, The Wharton School of Business (1990) Choset's research centers on robotics for confined and complex environments, particularly through the development of snake robots. His work integrates mechanism design, path and motion planning, and estimation to enable applications in surgery, manufacturing, infrastructure inspection, and search and rescue. He is a pioneer in medical robotics and has co-founded Medrobotics to commercialize minimally invasive surgical robots. His recent publications highlight a strong focus on ergodic exploration, multi-agent systems, motion planning under uncertainty, and medical robotics. Themes include optimizing robot trajectories for information gathering, solving complex path planning problems with dynamic obstacles, and advancing autonomous systems for disaster response and space exploration (e.g., the EELS robot for Enceladus). MIT Technology Review Top 100 Innovators under 35 (2002) Best Paper Award, RIA (1999) Best Paper Award, ICRA (2003) Best Paper, IEEE Bio Rob (2006) Best Video, ICRA (2011) Nominations for best papers at ICRA, IROS, and CLAWAR Choset has advised numerous students, many of whom have won top awards. His lab has received significant funding for robotics research, including projects in surgical robotics, additive manufacturing, and autonomous exploration. He is the lead author of the textbook Principles of Robot Motion and is actively involved in educational innovation through custom robotics labs. He leads the Biorobotics Laboratory at CMU, which develops advanced robotic systems like snake robots and the EELS (Exobiology Extant Life Surveyor) robot for NASA missions. The lab collaborates with industry and government agencies on applications ranging from surgery to space exploration.

Sridhar R. TayurView profile

Academic

• Operations Research
• Quantum Computing
• Healthcare Operations
• +3 more

Sridhar R. Tayur is the Ford Distinguished Research Chair and University Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business. He holds a Ph.D. in Operations Research from Cornell University and a B.Tech. in Mechanical Engineering from IIT Madras. His research focuses on quantum computing applications in operations research, healthcare systems optimization, and supply chain management. He has held visiting roles at MIT, Stanford, and Cornell, and founded companies like SmartOps and OrganJet. His recent work spans quantum-inspired optimization algorithms, healthcare decision support systems, and fair resource allocation policies. He has contributed to over 110 publications, including high-impact papers in Management Science , Operations Research , and IEEE Transactions . Awards include INFORMS Fellow and NAE membership. He teaches courses in quantum integer programming, healthcare operations, and service management at the Tepper School. Education: Ph.D. (Cornell), B.Tech. (IIT Madras) Research Labs: Quantum Technology Group, OrganJet Key Awards: INFORMS Fellow, NAE Member, MSOM Distinguished Fellow Teaching: MBA Operations Management, PhD Quantum Optimization, Healthcare Systems His interdisciplinary work bridges quantum computing, healthcare policy, and logistics, supported by collaborations with industry and government institutions.

Jignesh M. PatelView profile

Professor

• Data Management
• Database Systems
• Machine Learning
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University , focusing on Data Management , System Efficiency (e.g., Scalable Data Platforms), and Human Efficiency (e.g., LLM-Based Query Interfaces). His work bridges Database Systems , Machine Learning , and Human-Computer Interaction . Co-founder of four startups: Paradise (1997), Locomatix (2007), Quickstep (2015), and DataChat (2017). Member of SIGMOD 2025 (AE) , CIDR 2024 (Co-Chair) , and other program committees. Research Interests include efficient data analysis algorithms , LLM-based data interaction , and systems security . His group develops platforms combining scalability and user productivity . Scientific Awards include Best Paper Awards at SIGMOD and VLDB, and Fellowships from AAAS, ACM, and IEEE. He also received Teaching Awards at CMU. Professional Activities feature co-founding startups , serving on program committees , and teaching courses like Database Systems and Advanced Database Systems at CMU.

Nina BalcanView profile

Professor

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +5 more

Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.

• Machine Learning
• Artificial Intelligence
• Combinatorial Optimization
• +13 more

Yiming Yang is a Professor at the Language Technologies Institute and Machine Learning Department within the School of Computer Science at Carnegie Mellon University , where he has held faculty positions since 2003. His research spans foundational and applied aspects of machine learning , artificial intelligence , and scientific computing . Professor, Carnegie Mellon University (2003–Present) Associate Professor, Carnegie Mellon University (1996–2003) Yang's research focuses on LLM-based problem-solving agents , combinatorial optimization , and scalable oversight frameworks . His work explores diffusion models, Langevin dynamics, and Fourier neural operators for NP-hard problems, while advancing reinforcement learning techniques for self-play supervision and principle-driven fine-tuning of large language models. Recent publications highlight his contributions to code synthesis , PDE solving , and multi-agent reinforcement learning . Key methodologies include demonstration-guided control, retrieval-augmented reasoning, and test-time scaling laws. His team has developed frameworks like FEEDER for efficient in-context learning and μTransfer-FNO for zero-shot hyperparameter transfer in PDE solvers. Notable scientific achievements include: Best Student Paper Runner Up (2013) Best Theoretical Paper Award (1994) Best Theoretical Paper Award (1993) Yang has mentored over 20 PhD students and postdocs, including Shengyu Feng , Zhiqing Sun , and Aman Madaan , across domains like graph learning , extreme multi-label classification , and language model alignment .

Amritanshu PandeyView profile

Assistant Professor

• Grid Simulation and Optimization
• Grid Cybersecurity
• Modeling of Future Energy Systems
• +3 more

Amritanshu Pandey is an Assistant Professor in Electrical Engineering at the University of Vermont, with a part-time adjunct appointment in Electrical and Computer Engineering at Carnegie Mellon University. His research focuses on enhancing the efficiency, reliability, and security of electric grids through methods in circuit theory, optimization, and machine learning. He pioneered the SUGAR simulation engine for power systems and collaborates globally on grid challenges. Research Interests: Pandey's work spans renewable integration, grid cybersecurity, digital twins, and decarbonization. Key projects include developing algorithms for large-scale grid optimization, anomaly detection, electric vehicle infrastructure modeling, and cyber-resilient energy systems. His research addresses real-world challenges in rapidly evolving grids across Asia and Africa. Awards: Best Paper Award, IEEE PES General Meeting (2017, 2021) Best-of-the-Best Paper Award, IEEE PES General Meeting (2021) Best Student Paper Runner-up, ECML-PKDD (2018) Students & Labs: He advises 7 PhD students and has graduated 11 advisees (PhD/MS/BS). His lab focuses on power systems innovation, including the SUGAR simulation framework and projects on grid cybersecurity and sustainable electrification.

• Operations Research
• Optimization
• Combinatorial Optimization
• +6 more

Michael Trick is the Senior Associate Dean for Faculty and Research and Higgins Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on combinatorial optimization, sports scheduling, constraint programming, and operations research applications. He has contributed to seminal work on sports timetabling (e.g., scheduling college basketball conferences) and the traveling tournament problem. Trick's work bridges theoretical advancements with practical applications, including integer programming, branch-and-price methods, and stochastic dynamic programming. He has led initiatives in operations research education and served as President of INFORMS, emphasizing the field's societal impact. His research also spans voting systems, optimization for newspaper zoning, and algorithmic design for complex scheduling problems. Trick's expertise spans academic leadership, research methodology, and cross-disciplinary applications. His articles address topics like auction design for spectrum allocation, bike-sharing logistics, and robust scheduling strategies. He collaborates across academia and industry, contributing to both theoretical advancements and real-world operational challenges. His work often highlights the practical utility of mathematical programming techniques in diverse domains.

R RaviView profile

Professor

• Discrete Optimization
• Operations Research
• Computer Science
• +4 more

R Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University's Tepper School of Business. He holds a B.E. from IIT Madras and M.S./Ph.D. from Brown University. Active since 1995, he served as Associate Dean for Intellectual Strategy and Chair of the Future Educational Delivery Committee. He currently directs Analytics Strategy and leads the Center for Intelligent Business. His research focuses on discrete optimization, network optimization, and applications in supply chain logistics and online advertising, supported by NSF, Google, and others. He has advised over two dozen doctoral students and developed graduate courses like Network Optimization. He received the 30-year Test of Time Award (2023) and is an INFORMS Fellow. His academic roles include editorial leadership in Operations Research and FOCS chairmanship.

• Energy Systems
• Electricity Markets
• Energy Storage
• +12 more

Ramteen Sioshansi is a Professor in the Department of Engineering and Public Policy, the Department of Electrical and Computer Engineering, and Heinz College at Carnegie Mellon University, where he also serves as Associate Department Head for Graduate Affairs and Director of the Carnegie Mellon Electricity Industry Center. He is additionally an Adjunct Professor at The Ohio State University’s Department of Integrated Systems Engineering. His work bridges engineering, economics, and policy with a focus on energy systems and market design. His research interests include energy storage, electricity market design, renewable integration, decarbonization, grid resilience, and energy justice. He employs advanced optimization and modeling techniques to analyze how energy systems can be operated and regulated more efficiently and equitably. His work often addresses the economic and policy challenges of integrating low-carbon technologies and distributed energy resources. Ramteen's recent publications highlight a strong trend in analyzing the role of energy storage in providing multiple grid services, reforming market designs to accommodate long-duration storage, and ensuring equitable access to resilient energy. His work spans technical modeling, economic analysis, and policy implications, frequently appearing in high-impact journals such as Current Sustainable/Renewable Energy Reports and The Energy Journal . Energy Justice Through Energy Storage Long-Duration Energy Storage (LDES) Policy Electric Vehicle Integration Demand Flexibility and Aggregators Hydrogen and Carbon Removal Economics Small Modular Reactors and Nuclear Policy Ramteen has received recognition as a top researcher in energy and energy storage domains. He actively contributes to the academic community through INFORMS, particularly in the Energy, Natural Resources, and the Environment (ENRE) section, and encourages nominations for prestigious awards like the Harold Hotelling Medal. He advises graduate students, including recent PhD graduate Dr. Hyeong Jun Kim and former student Joe Duggan. His research is supported by collaborations across academia, national labs (e.g., Argonne), and industry. He frequently engages with media on topics such as natural gas supply risks, EV integration, and the future of nuclear energy. Ramteen leads the Carnegie Mellon Electricity Industry Center, a hub for interdisciplinary research on electricity markets and policy. He is also a Faculty Affiliate at the Wilton E. Scott Institute for Energy Innovation, where he contributes to CMU’s broader energy innovation ecosystem.

Egon BalasView profile

Professor

• Integer Programming
• Disjunctive Programming
• Mathematical Optimization
• +3 more

Egon Balas was a University Professor of Industrial Administration and Applied Mathematics, and The Thomas Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business. He held dual Ph.D.s in economics (University of Brussels, 1967) and mathematics (University of Paris, 1968). Balas pioneered advancements in integer programming and disjunctive programming, revolutionizing optimization theory. His early life included two imprisonments: first for opposing the Nazis via the communist party, then post-war under Stalinist purges. He fled to the West in 1956, eventually joining CMU in 1967 where he spent over five decades as a faculty member. His seminal work included the Additive Algorithm (1965) for timber-harvesting optimization and foundational contributions to disjunctive programming. A symposium and memorial service were held at CMU in 2019 celebrating his legacy, featuring talks on topics like global optimization and machine learning applications in MIP. His books *Will to Freedom* (memoir) and *Disjunctive Programming* (technical) highlighted his dual academic and historical impact.

Gianni A. Di CaroView profile

Academic

• Swarm Intelligence
• Swarm Robotics
• Multi-robot Systems
• +5 more

Gianni A. Di Caro is an Associate Teaching Professor in the Computer Science Department at Carnegie Mellon University in Qatar, where he leads cutting-edge research in swarm intelligence and multi-robot systems. His work bridges theoretical foundations with real-world applications in environmental monitoring, search and rescue, and agricultural automation through significant QNRF-funded projects. His research focuses on swarm robotics, human-swarm interaction, and collective intelligence, developing adaptive algorithms for heterogeneous robot teams operating in complex environments. Key contributions include distributed path planning, map learning via adaptive sampling, and emotion-inspired coordination models that enhance team resilience and efficiency without centralized control. Recent publications reveal a strong trend toward practical implementations of swarm intelligence, particularly in underwater inspection (TARMEM project), greenhouse farming (Greenhouse-5.0), and forest navigation. His work increasingly integrates machine learning for autonomous decision-making while emphasizing human-robot collaboration in mission-critical scenarios. Scientific recognition includes: Most Entertaining Video award at the 2016 AAAI Video competition Professor Di Caro actively mentors students, advising Ph.D. candidate Eduardo Feo-Flushing and senior thesis projects by award-winning students Fatma Tlili (Best Project Award for deep learning crack detection) and Rohith Pillai (Best CS Project Award for survivable path planning). His research program is sustained by major grants including QNRF's $600,000 TARMEM project and the Greenhouse-5.0 initiative for AI-driven agricultural robotics. He directs the TARMEM project developing aquatic/aerial robot teams for marine monitoring and leads the Greenhouse-5.0 consortium applying swarm robotics to automated greenhouse farming, positioning his lab at the forefront of field-deployable swarm systems.

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

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

Gerard CornuejolsView profile

Professor

• Integer Programming
• Combinatorial Optimization
• Packing and Covering
• +4 more

Gerard Cornuejols is a Professor at Carnegie Mellon University's Tepper School of Business, where he maintains an active research program in integer programming and combinatorial optimization. His office is located in Tepper School Room 4107 at 5000 Forbes Avenue, Pittsburgh, PA 15213. Professor Cornuejols' research focuses on integer programming and combinatorial optimization, particularly packing and covering problems. His work bridges theoretical mathematics with practical applications in financial optimization and network design. He has made significant contributions to polyhedral combinatorics, developing fundamental insights into the structure of integer programs and their relaxations. His research often explores the connections between graph theory, linear programming, and discrete optimization, with applications ranging from financial portfolio management to network flow problems. His publication record demonstrates consistent high-impact contributions across both theoretical and applied optimization. Recent work shows a strong trend toward dyadic programming, fractional packings, and the study of ideal clutters, while maintaining connections to practical financial applications. The research spans pure combinatorial theory to applied financial optimization, reflecting his ability to connect abstract mathematical structures with real-world problems. Fulkerson prize 2000 for best paper in Discrete Mathematics SIAM Outstanding Paper Prize 2004 Most frequently cited paper published in Discrete Optimization 2005-2010 Professor Cornuejols has established himself as a leading figure in optimization through his influential books, extensive publication record, and collaborative research with prominent mathematicians including Michele Conforti, Giacomo Zambelli, and Ahmad Abdi. His work on the strong perfect graph theorem and ideal clutters has shaped modern combinatorial optimization theory.

Siddharth PrasadView profile

Researcher

• Mechanism Design
• Market Design
• Optimization
• +4 more

Siddharth Prasad is a Researcher at the Toyota Technological Institute at Chicago (TTIC), focusing on the intersection of mechanism design, market design, optimization, and machine learning. His work leverages integer programming and operations research to address complex problems in artificial intelligence and economic systems. Education: PhD in Computer Science from Carnegie Mellon University, advised by Nina Balcan and Tuomas Sandholm. B.S. in Mathematics and Computer Science from Caltech. Prasad's research emphasizes revenue-optimal combinatorial auctions, algorithm learnability, and data-driven optimization. He applies techniques like Gomory cuts and branch-and-cut to improve market mechanisms and machine learning systems. Recent work trends include AI-driven auction design, integer programming advancements, and applications of machine learning in market shrinkage and uncertainty scenarios. His publications span top venues like IJCAI, AAAI, NeurIPS, and CP. Scientific Awards: Best poster award (honorable mention) at MIP workshop, 2024.

Ignacio GrossmannView profile

Professor

• Process Systems Engineering
• Discrete-Continuous Optimization
• Process Synthesis
• +3 more

Ignacio E. Grossmann is the R.R. Dean University Professor in the Department of Chemical Engineering at Carnegie Mellon University . He obtained a B.S. from Universidad Iberoamericana (1974), followed by M.S. (1975) and Ph.D. (1977) in Chemical Engineering from Imperial College London. Member & Former Director, Center for Advanced Process Decision-Making (industrial consortium with 20+ companies) Foreign Academic, Royal Academy of Exact, Physical & Natural Sciences of Spain (2025) 700+ publications, textbooks: Advanced Optimization in Process Systems Engineering and Systematic Methods of Chemical Process Design (co-authored with Larry Biegler & Art Westerberg) His research interests span: Mixed-Integer/Nonlinear Programming (MILP/MINLP) General Disjunctive Programming (GDP) Global Optimization & Multiperiod Optimization Applications in chemical process design, energy systems, and decarbonization Honors & Awards : Multiple AIChE awards (Computing, Walker, Warren Lewis, Sustainable Engineering) 2015 Sargent Medal (IChemE) 2025 Foreign Academic of Spanish Royal Academy 2019 Top Cited Scientist (Worldwide #53, National #38 in CS/Electronics) Education & Legacy : 68 Ph.D. and 34 M.S. students mentored. Honorary doctorates from 9 institutions including RWTH Aachen (Germany), University of Maribor (Slovenia), and Universidad Nacional del Sur (Argentina).