جست‌وجوی استاد و پژوهشگر

R. Raviمشاهده پروفایل

استاد

• Operations Research
• Computer Science
• Combinatorial Optimization
• +۴ مورد دیگر

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.Tech from IIT Madras and a Ph.D. from Brown University. His research focuses on combinatorial optimization, network design, and their applications in business and technology, particularly in supply chains, logistics, and online advertising. Ravi has held prestigious roles including Associate Dean for Intellectual Strategy, Chair of the Future Educational Delivery Committee, and Director of Analytics Strategy. He is an INFORMS Fellow (2017) and recipient of the 30-year Test of Time Award (2023) for his 1989 STOC paper. His work is supported by NSF, ONR, and industry grants. Ravi has advised over 25 Ph.D. students and contributed to numerous editorial and conference leadership roles. Education: Bachelor's: Computer Science, IIT Madras (1989) Master's and Ph.D.: Computer Science, Brown University (1991, 1993) Postdoctoral Fellowships: Princeton University and UC Davis (1993-1995) Research Interests: Optimization models for business-technology intersections, including supply chains, logistics, and online advertising. His methods blend theory with practical applications, leveraging funding from NSF, Google, and others. Awards & Honors: INFORMS Fellow (2017) NSF Career Award ACM STOC 30-Year Test of Time Award (2023) Current Roles: Director of Analytics Strategy at Tepper School, Amazon Scholar (since 2021), and adjunct faculty at CMU's Computer Science Department.

Fatma Kilinc-Karzanمشاهده پروفایل

استاد

• Operations Research
• Analytics
• Machine Learning
• +۸ مورد دیگر

Fatma Kilinc-Karzan is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, and holds the Frank A. and Helen E. Risch Faculty Development Chair. She is also an Associate Professor of Computer Science (by courtesy) and affiliated with the Algorithms, Combinatorics, and Optimization (ACO) PhD Program. Her career includes visiting roles at institutions like the Simons Institute at UC Berkeley and extensive professional service on editorial boards and conference committees. PhD in Industrial and Systems Engineering (minor in Mathematics) from Georgia Institute of Technology B.S. and M.S. in Industrial Engineering (minor in Information Systems) from Middle East Technical University Research Interests : Her work focuses on convex optimization , structured nonconvex optimization , and their applications in optimization under uncertainty (robust optimization, chance constraints), machine learning (preference learning from limited data), and business analytics . She explores theoretical aspects like semidefinite programming (SDP) relaxations, convex hull characterizations, and algorithmic efficiency for large-scale problems. Article Trends : Her recent publications emphasize semidefinite programs , rank-one function optimization , and chance-constrained programming with applications in portfolio optimization , healthcare , and recommender systems . Key methodologies include perspective reformulation , submodularity , and first-order algorithms . Scientific Awards : 2015 INFORMS Optimization Society Prize for Young Researchers 2014 INFORMS JFIG Best Paper Award Advising and Grants : She has advised over a dozen PhD students, many of whom won awards like the INFORMS Optimization Society Best Student Paper Prize. Her research is supported by grants including an NSF CAREER Award , ONR grant , and AFOSR grant . She collaborates with institutions like IBM and the Simons Institute.

David E. Bernal Neiraمشاهده پروفایل

استاد مدعو

• Optimization in Chemical Engineering
• Quantum Computing Algorithms
• Process Systems Engineering
• +۲ مورد دیگر

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.

Nikolaos Sahinidisمشاهده پروفایل

استاد مدعو

• Global Optimization
• Process Systems Engineering
• Machine Learning
• +۲ مورد دیگر

Nikolaos Sahinidis holds adjunct faculty position at Carnegie Mellon University and Gary C. Butler Family Chair at Georgia Tech. His research develops optimization algorithms for process systems engineering, machine learning applications, and chemical product design. An NAE member, he received multiple awards including the Beale-Orchard-Hays Prize for computational optimization breakthroughs. Key research domains: ALAMO machine learning framework for simulation optimization Global optimization of mixed-integer nonlinear programs Environmentally benign chemical design Protein structure prediction algorithms

Willem-Jan Van Hoeveمشاهده پروفایل

استاد

• Operations Research
• Constraint Programming
• Integer Programming
• +۷ مورد دیگر

Willem-Jan Van Hoeve is the Carnegie Bosch Professor of Operations Research at the Tepper School of Business , Carnegie Mellon University . He also holds a courtesy appointment at the Heinz College, Carnegie Mellon University. His research focuses on methodologies in operations research , including mathematical optimization , constraint programming , decision diagrams , integer programming , and machine learning , with applications to network design , scheduling , vehicle routing , data mining , healthcare operations , and analytical marketing . He pioneered decision diagrams for optimization and developed the Decision Diagrams for Optimization research project with source code and benchmarks. His funded research includes National Science Foundation (2019-2024, 2011-2015), Office of Naval Research (2021-2024, 2018-2021), and Google Faculty Research Awards (2015, 2011). He has consulted for FedEx Ground, Exxon Mobil, PNC Bank, Bosch/Siemens, Charter Steel, and non-profit organizations. Academic Service and Leadership: Board of Directors, INFORMS Computing Society (2019-2021) Chairperson, INFORMS Student Competition Committee (2018-2020) Steering Committee, CPAIOR Conference Series Executive Committee, Association for Constraint Programming (2013-2016) Associate Editor roles at INFORMS Journal on Computing , Operations Research , and Artificial Intelligence Scientific Awards: INFORMS Computing Society Harvey J. Greenberg Research Award (2020) IISE Transactions Best Application Paper (2022) EJOR Best Paper Award (2019) Best paper award at CP Conference (2006) Best student paper award at CP Conference (2004) Key Academic Contributions: He has advised PhD students David Bergman, Andre Cire, Christian Tjandraatmadja, and others. His work includes co-authorship of the book Decision Diagrams for Optimization (Springer, 2016), and development of software like HADDOCK and Seq2Pat library.

Charles Joseph Argueمشاهده پروفایل

مدرس

• Algorithms
• Combinatorics
• Optimization
• +۴ مورد دیگر

Charles Joseph Argue is a Lecturer in Mathematics at Yale University since July 2022, specializing in Algorithms, Combinatorics, and Optimization. He received his PhD in Mathematics from Carnegie Mellon University, where he was advised by Anupam Gupta. Specialties: Online algorithms, convex geometry, convex optimization, combinatorial optimization Current research focuses on information-theoretic questions in algorithms, convex optimization, and their applications to theoretical computer science. The trends in his publications emphasize algorithmic design for convex and combinatorial optimization problems, with significant contributions to competitive algorithms for convex body chasing and packing integer programs. His work spans both theoretical foundations and practical applications. Scientific awards include: SODA 2020 Best Paper SODA 2019 Best Paper At Carnegie Mellon University, he taught courses in multivariate analysis, matrix algebra, discrete mathematics, and calculus. Since 2016, he has coached the Western Pennsylvania ARML Team, mentoring high school and middle school students in mathematics competitions.

Tuomas Sandholmمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Algorithms and Complexity
• +۱۲ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University, where he also holds appointments in the Machine Learning Department, the Ph.D. Program in Algorithms, Combinatorics, and Optimization, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He serves as Co-Director of CMU AI and Director of the Electronic Marketplaces Laboratory. Dr. Sandholm's research spans artificial intelligence, economics, and operations research, with particular focus on algorithms and complexity, game theory, machine learning, and electronic commerce. His work involves developing incentive-compatible market mechanisms and efficient algorithms for executing those mechanisms, as well as designing software agents that act optimally in electronic marketplaces. His research has led to significant commercial applications, including systems fielded by CombineNet, Inc. that handled over $60 billion in spend. Sandholm's recent publications reveal a strong emphasis on solving large-scale games, equilibrium computation, and applying these techniques to real-world problems. His work shows a progression from theoretical foundations to practical implementations, with applications ranging from poker AI (Libratus and Pluribus) to kidney exchange systems. His research group has pioneered techniques in game abstraction, regret minimization, and equilibrium computation that have pushed the boundaries of what's possible in imperfect-information games. Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) IJCAI John McCarthy Award (2021) Robert S. Engelmore Award (2021) Minsky Medal (2019) Science Breakthrough of the Year Runner-Up (2019) As an advisor, Sandholm has mentored numerous PhD students who have gone on to prestigious positions at institutions like MIT, Stanford, and CMU. His lab has secured substantial research funding for projects including solving large-scale games and heart transplantation policy optimization. The Electronic Marketplaces Laboratory he directs has been instrumental in developing algorithms that run the national kidney exchange for UNOS, resulting in approximately 10,000 life-saving transplants. Sandholm is also the founder and CEO of multiple companies including Strategy Robot, Inc., Strategic Machine, Inc., and Optimized Markets, Inc., which apply his research to defense, intelligence, business strategy, and advertising markets.

• Artificial Intelligence
• Optimization
• Game Theory
• +۷ مورد دیگر

Tuomas Sandholm is the Angel Jordan University Professor of Computer Science at Carnegie Mellon University (CMU), holding appointments in the Computer Science Department, Machine Learning Department, Algorithms, Combinatorics, and Optimization (ACO) Ph.D. Program, and the CMU/University of Pittsburgh Joint Ph.D. Program in Computational Biology. He co-directs CMU AI and leads the Electronic Marketplaces Laboratory. His research focuses on artificial intelligence, economics, and operations research, with notable contributions to game theory, optimization, and kidney exchange systems. Sandholm has pioneered algorithms for electronic marketplaces, including the first superhuman AI in poker (Libratus and Pluribus) and kidney exchange optimization. He has founded multiple companies, including CombineNet, Strategic Machine, and Strategy Robot, and has over 500 peer-reviewed publications and 29 patents. His awards include the Vannevar Bush Faculty Fellowship, AAAI Award for AI for Humanity, and honorary doctorate from the University of Zurich. Education: Ph.D. and M.S. in Computer Science Dipl. Eng. (M.S. with B.S. included) in Industrial Engineering and Management Science Research Interests: Artificial Intelligence, Optimization, Game Theory, Kidney Exchange, Electronic Commerce, Multiagent Systems, Market Design, and Healthcare Applications. His work bridges theoretical advancements with real-world applications, particularly in strategic reasoning, organ transplantation, and defense/intelligence systems. Recent Articles: Focus on solving large-scale games, kidney exchange optimization, and AI-driven decision-making. Key areas include imperfect-information game solving (e.g., poker AIs), algorithmic fairness in healthcare markets, and combinatorial auction design. Awards & Honors: Vannevar Bush Faculty Fellowship (2023) AAAI Award for AI for the Benefit of Humanity (2023) Fellowships: ACM, AAAI, INFORMS, AAAS Edelman Laureate (2005) Over 100+ awards, including honorary doctorates and industry recognitions Advising & Grants: Advised PhD students now in top positions at MIT, Stanford, and CMU. Secured grants for AI game-solving and healthcare policy optimization. Current openings for PhD students in game-solving and kidney exchange policy. Labs & Teams: Electronic Marketplaces Laboratory (EML) and CMU AI Initiative. Collaborates with industry partners (e.g., Baidu, Google) and government agencies on strategic AI applications.

Prasad Tetaliمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Theory of Computing
• +۶ مورد دیگر

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.

Peter Zhangمشاهده پروفایل

استادیار

• Optimization Theory
• Supply Chain Management
• Transportation Systems
• +۳ مورد دیگر

Peter Zhang is an Assistant Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, with a courtesy appointment in Civil and Environmental Engineering. He holds a PhD in Engineering Systems from MIT (2019), an MASc in Mechanical and Industrial Engineering from the University of Toronto (2013), and a BASc in Engineering Science from the same university (2011). His research focuses on optimization theory, applied to supply chains, transportation systems, health, and socio-technical systems. He emphasizes robust optimization, dynamic and bilevel problems, and their applications in policy design and high-stakes decision-making. Education PhD in Engineering Systems, MIT (2019) MASc in Mechanical and Industrial Engineering, University of Toronto (2013) BASc in Engineering Science, University of Toronto (2011) Research Interests Robust optimization and its applications Transportation safety and last-mile delivery systems Supply chain resilience and policy design Machine learning integration with optimization Awards & Recognition INFORMS Koopman Prize (2020) First Place, INFORMS Junior Faculty Forum (2022) Ford Engineering Excellence Award (2015) INFORMS Daniel H. Wagner Prize (2014) Advising & Mentorship Supervised PhD students including Hao Hao and Guanting Wu, with postdoctoral mentorship for Ningji Wei and Alberto Japón Sáez. Research teams have participated in datasets like the U.S. Household Commuting Dataset and NASA’s Blue Skies Competition. Labs & Collaborations Collaborates on transportation fairness, hydrogen aviation decarbonization, and school bus optimization through interdisciplinary projects involving data science and policy analysis.

Eduardo Feo Flushingمشاهده پروفایل

استاد آموزشی

• Robotics
• Multi-Agent Systems
• Machine Learning
• +۴ مورد دیگر

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.