Massachusetts Institute of TechnologyUnited States
Luca Carlone is the Boeing Career Development Associate Professor in the Department of Aeronautics and Astronautics at MIT and a Principal Investigator at the Laboratory for Information & Decision Systems (LIDS) . He leads the SPARK Lab , focusing on developing certifiable perception algorithms for autonomous systems. PhD in Mechatronics (Polytechnic University of Turin, 2012) Research spans robotics, computer vision, and optimization Research Interests : Certifiable Perception algorithms for high-integrity systems High-level Perception (geometric, semantic, physical understanding) Efficient Perception methods for resource-constrained robots Scientific Contributions include: 2024 Outstanding Systems Paper Award (RSS) 2023 IEEE Transactions on Robotics King-Sun Fu Award 2021 NSF CAREER Award 2020 AIAA Advising Award 2019 Amazon Research Award Advising : Teaches graduate courses like Visual Navigation for Autonomous Vehicles and Robotics: Science and Systems . Collaborates with institutions including JPL, Caltech, and KAIST through the DARPA SubT Challenge.
David B. Shmoys is the Laibe/Acheson Professor of Business Management & Leadership Studies at Cornell University's School of Operations Research and Information Engineering (ORIE) and holds a joint appointment in the Department of Computer Science. He is a leading researcher in approximation algorithms, stochastic optimization, and computational sustainability. His work emphasizes data-driven decision-making in enterprise and society, with applications ranging from healthcare logistics to environmental conservation. Shmoys has advised 27 Ph.D. students and served as an editorial board member for top journals like Operations Research and Mathematics of Operations Research. Education: Ph.D. in Computer Science, University of California, Berkeley (1984). Postdoctoral positions at MSRI and Harvard University, followed by faculty roles at MIT before joining Cornell. Research Interests: Design and analysis of approximation algorithms for NP-hard problems, stochastic optimization, scheduling, facility location, and inventory management. Applications include computational sustainability, epidemiological modeling, and political districting optimization. Articles Trends: Focus on optimization under uncertainty, computational sustainability, and real-world applications like air ambulance routing, bike-sharing systems, and gerrymandering analysis. Notable works include the 2018 Wagner Prize-winning paper on bike-sharing analytics and the 2013 Lanchester Prize-winning textbook on approximation algorithms. Awards: Lanchester Prize (2013), Daniel H. Wagner Prize (2018), Fellowships from ACM, INFORMS, and SIAM, NSF Presidential Young Investigator (1987). Advising & Grants: Over 27 advised Ph.D. students, with former students at MIT, Waterloo, Brown, and D-Wave. Active in grants related to data science and sustainability. Labs/Teams: Involved in the Institute of Computational Sustainability at Cornell, focusing on applying algorithms to environmental challenges. Collaborates with organizations like Motivate (bike-sharing) and Ornge (air ambulance services).
Nina Balcan is a Professor at Carnegie Mellon University and holds the Cadence Design Systems Professorship in Computer Science. She is affiliated with the School of Computer Science, specifically the Machine Learning Department (MLD) and Computer Science Department (CSD). Her research spans foundational aspects of machine learning, artificial intelligence, theoretical computer science, algorithmic game theory, and interdisciplinary connections in learning theory. Machine Learning Artificial Intelligence Theoretical Computer Science Algorithmic Game Theory Multi-Agent Systems Data-Driven Algorithm Design Her recent work focuses on advancing algorithm design through machine learning, robustness in adversarial environments, and economic modeling. Key contributions include Learning to Branch (JACM 2024), Regret Minimization in Stackelberg Games (NeurIPS 2024), and Learning Accurate Decision Trees (UAI 2024, Outstanding Student Paper Award). She has pioneered novel approaches to data-driven optimization, semi-supervised learning, and privacy-preserving clustering. Nina has received prestigious accolades including ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award Her teaching at CMU includes graduate courses on machine learning, advanced machine learning, and specialized topics like algorithmic game theory.
Massachusetts Institute of TechnologyUnited States
Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.
Fatma Kılınç-Karzan is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business, with a courtesy appointment as Associate Professor of Computer Science. She is also affiliated with the Algorithms Combinatorics and Optimization (ACO) PhD Program and was a Visiting Scientist at Berkeley's Simons Institute for the Theory of Computing during Fall 2017. Her educational background includes a PhD from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering with a minor in Mathematics, supervised by Prof. Arkadi Nemirovski. She earned her B.S. and M.S. degrees from the Industrial Engineering Department of Middle East Technical University with a minor in Information Systems. Dr. Kılınç-Karzan's research spans mathematical optimization with emphasis on convex and non-convex optimization theory, algorithms, and applications. Her work bridges theoretical foundations with practical implementations in optimization under uncertainty (robust optimization, chance constraints, distributionally robust optimization), machine learning (preference learning from limited data), and business analytics. She develops foundational theory for large-scale optimization problems with applications in decision making under uncertainty and high-dimensional statistical inference. Analysis of her recent publications reveals a strong focus on convex hull characterizations, semidefinite programming relaxations, distributionally robust optimization, and online convex optimization frameworks. Her work demonstrates increasing integration of optimization theory with machine learning applications, particularly in developing data-driven approaches for decision making under uncertainty. NSF CAREER Award (2015) INFORMS Optimization Society Young Researcher Prize (2015) INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award (2014) BP Junior Faculty Chair (2014-2015) Faculty Giving Chair (2012-2013) Wimmer Fellowship (2012-2013) Dr. Kılınç-Karzan has successfully mentored numerous PhD students who have received prestigious awards, including the 2021 INFORMS Optimization Society Best Student Paper (1st prize) and multiple honorable mentions. Her research has been supported by significant grants including an NSF CAREER Award, an ONR grant (with S. Küçükyavuz), and an AFOSR grant. She serves on editorial boards for Mathematical Programming, Operations Research, Mathematics of Operations Research, and other leading journals, and has held leadership positions in professional societies including the Mathematical Optimization Society and INFORMS Computing Society. Through her affiliations with CMU's Tepper School, Computer Science Department, and ACO Program, she collaborates across disciplines to advance optimization theory and its applications. Her professional service includes committee chair roles for major INFORMS competitions and program committee leadership for international optimization conferences.
Illya V. Hicks is a Professor in the Computational and Applied Mathematics Department at Rice University. He holds a PhD from Rice University (2000) and a BS from Texas State University (1995). His research focuses on combinatorial optimization, integer programming, graph theory, and matroid theory, with applications in social networks, cancer treatment, and network design. He has advised numerous doctoral, post-doctoral, and masters students. Education: PhD and MA in Computational and Applied Mathematics, Rice University, 2000 BS in Mathematics, Texas State University, 1995 Research Interests: Utilizing graph decomposition techniques to solve NP-complete problems, including branch decompositions and matroid circuit problems. Applications include sensor network design, healthcare logistics, and algorithmic graph theory. Awards: Recognized with the 2015 Presidential Mentoring Award (Rice University), 2010 Forum Moving Spirit Award (INFORMS), and the 2005 Optimization Prize for Young Researchers. Grants and Projects: Includes NSF-funded research on branch decomposition techniques, submodular optimization, and healthcare service distribution. Active in promoting minority participation in operations research through travel grants and mentoring initiatives. Labs/Teams: Engaged in collaborative research on graph algorithms, combinatorial optimization, and interdisciplinary applications in healthcare and engineering.
Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.
Pinar Keskinocak serves as the H. Milton and Carolyn J. Stewart School Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. She co-founded and directs the Center for Health and Humanitarian Systems, demonstrating leadership in both academic administration and research innovation. Her career includes prior roles as College of Engineering ADVANCE Professor and interim associate dean for faculty development, alongside industry experience at IBM T.J. Watson Research Center. Her educational foundation includes a Ph.D. in Operations Research from Carnegie Mellon University and M.S. and B.S. degrees in Industrial Engineering from Bilkent University. This rigorous training underpins her interdisciplinary approach to complex societal challenges. Dr. Keskinocak's research pioneers operations research applications for societal impact , with dual emphases on health systems and humanitarian logistics . She develops advanced models for infectious disease dynamics (including COVID-19, malaria, and Guinea worm), vaccination strategies, disaster response, and supply chain optimization. Her work uniquely bridges engineering analytics with real-world implementation through partnerships with the CDC, American Red Cross, Carter Center, and Children's Healthcare of Atlanta. Analysis of her 2023-2025 publications reveals intensifying focus on eradication program modeling for polio and Guinea worm, with increasing integration of machine learning for predictive accuracy in vaccine distribution. A pronounced trend addresses health equity through targeted interventions for under-immunized populations in Sub-Saharan Africa, emphasizing resource allocation under constraints and cross-border coordination challenges. Her scientific recognition includes: INFORMS Fellow (2015) NSF CAREER Award (2001) Outstanding Professional Education Award, Georgia Tech (2018) Denning Award for Global Engagement, Georgia Tech (2016) Women in Engineering Excellence Teaching Award (2005) Moving Spirit Award, INFORMS (2005) Dr. Keskinocak has secured extensive project funding through collaborations with federal agencies and NGOs, directing research on pandemic response systems, vaccine logistics, and disaster debris management. While specific advisees aren't publicly cataloged, her leadership in the Center for Health and Humanitarian Systems mentors numerous graduate researchers through interdisciplinary projects. She significantly influences national policy via National Academies committees addressing supply chain resilience and vaccine distribution infrastructure. The Center for Health and Humanitarian Systems operates as a dynamic hub where engineering rigor meets humanitarian action, coordinating multi-institutional teams to develop deployable solutions for global health crises and disaster response systems.
University of Illinois Urbana-ChampaignUnited States
Chandra Chekuri is the Paul and Cynthia Saylor Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign, situated within the Grainger College of Engineering. He has been actively contributing to theoretical computer science for over two decades, with significant leadership roles including serving as Editor-in-Chief of the prestigious SIAM Journal on Computing since May 2025. His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras in 1993, followed by a Ph.D. in Computer Science from Stanford University in 1998. Prior to joining UIUC, he spent eight years as a Member of Technical Staff at Bell Labs, Lucent Technologies. Chekuri's research focuses on theoretical computer science with particular emphasis on the design and analysis of algorithms, discrete and combinatorial optimization, approximation algorithms, mathematical programming, and graph theory. His work explores fundamental connections between discrete structures and optimization problems, with applications spanning network design, data analysis, and computational complexity. His recent publications demonstrate a continued focus on hypergraph algorithms, submodular function optimization, and graph partitioning problems, showing how theoretical insights can yield practical algorithmic improvements. His approach often combines continuous relaxations with discrete rounding techniques to develop approximation algorithms for NP-hard problems. As Editor-in-Chief of SIAM Journal on Computing, Chekuri leads one of theoretical computer science's most respected publications, which covers analysis and design of algorithms, algorithmic game theory, computational complexity, and other mathematical aspects of computer science. His editorial leadership follows previous service as Associate Editor for several major journals including SIAM Journal on Computing, Mathematics of Operations Research, and Mathematical Programming. ACM Fellow (January 2024) Scott Fisher Teaching Award (for year 2022-23) from CS Department Chekuri has advised numerous PhD students to completion, including Kent Quanrud, Vivek Madan, Shalmoli Gupta, and Chao Xu, with several currently in progress such as Tanvi Bajpai, ElFarouk Harb, Rhea Jain, and Weihao Zhu. His teaching portfolio includes graduate courses on Randomized Algorithms, Approximation Algorithms, Algorithms for Big Data, and Combinatorial Optimization. He has served as Director of the Graduate Program in the Department of Computer Science from May 2014 to August 2017, demonstrating significant administrative leadership within the department.
Massachusetts Institute of TechnologyUnited States
Georgia Perakis is the John C Head III Interim Dean of MIT Sloan School of Management and a Professor of Operations Management and Operations Research & Statistics. She has been on MIT Sloan's faculty since 1998, contributing extensively to research in analytics/AI, optimization, and machine learning applications in pricing, supply chains, healthcare, and energy. Recognized as a leading academic, she has won numerous awards, including the INFORMS Fellow and Distinguished MSOM Fellow, along with multiple best paper awards. Perakis has supervised 30 PhD and 59 master's students, fostering lifelong academic relationships. Her administrative roles include co-director of the Operations Research Center and Associate Dean for Social and Ethical Responsibilities of Computing. She currently serves as Editor-in-Chief of M&SOM and has held editorial leadership roles in top journals like Operations Research and Management Science. Her education includes a BS in Mathematics from the University of Athens and advanced degrees in applied mathematics from Brown University. Research Interests : Perakis focuses on solving complex problems at the intersection of optimization and machine learning, with applications in retail promotions, healthcare operations (e.g., emergency department management), and energy systems. Her work emphasizes practical solutions to real-world challenges, leveraging data-driven analytics and prescriptive models. Recent projects include optimizing patient placement in emergency departments and modeling demand for new products in retail. Grants & Awards : Her accolades include the NSF CAREER Award, PECASE Award, and over a dozen best-paper recognitions. Notable contributions include a finalist position in the JD.com Competition (2019) and winning first place for Johnson & Johnson’s demand-prediction work (2018). She has also pioneered methodologies for equitable resource allocation in healthcare. Education & Leadership : Perakis holds leadership roles in interdisciplinary initiatives like the MIT Initiative on the Digital Economy and the Food Supply Chain Analytics and Sensing Initiative. Her teaching excellence is underscored by awards such as the Jamieson Prize and Teacher of the Year (MIT Sloan). She has directed major programs like the MIT Leaders for Global Operations and the Executive MBA program. Labs & Teams : She is affiliated with the Operations Research Center (an interdepartmental PhD program) and collaborates with institutions like UMass Memorial Hospital on healthcare optimization projects. Her work integrates ethics into AI development, emphasizing fairness, bias mitigation, and societal impact.
Neil Olver is an Associate Professor in the Department of Mathematics at the London School of Economics and Political Science (LSE), where he has held his current position since 2021. He is also affiliated with CWI in Amsterdam. His research focuses on combinatorial optimization, network design, algorithmic game theory, and probability, with applications to traffic networks and robust optimization. Olver has organized major events such as the Highlights of Algorithms conference and the Hausdorff Trimester Program on Discrete Optimization. He completed his PhD in Applied Mathematics at McGill University (2010), preceded by an MSc (2006) and BSc (2004) from the University of Cape Town and McGill University. His academic career includes roles at Vrije Universiteit Amsterdam (2013–2021) and MIT (2010–2013). Olver has been awarded grants from NWO (VIDI, TOP, VENI) and the NSF, supporting research on network optimization and robust design. His notable contributions include resolving the VPN Conjecture, advancing strongly polynomial algorithms for generalized flows, and analyzing dynamic traffic equilibria. Students advised include Dr. Dario Frascara and Dr. Madelon de Kemp. Current grants include a VIDI-funded project on 'Usable Algorithms for Network Design.'
Qifeng Li is an Associate Professor at the University of Central Florida specializing in Electrical Engineering with a focus on power and energy systems. His research integrates convex optimization and nonlinear dynamics to address challenges in renewable energy integration, microgrid stability, and energy-water-hydrogen nexus systems. Research Interests: Convex optimization, nonlinear systems, grid resilience, energy-water-food nexus Grants: NSF and DOE projects on microgrid stability and cross-system coordination Professional Roles: Editor for CSEE Journal, IEEE Battery Energy Storage Work Group member Honors: China National Scholarship 2012 His recent publications emphasize data-driven optimization methods, voltage stability analysis, and machine learning applications in power systems. Key trends include hybrid physics-data-driven models, stochastic disturbance analysis, and real-time grid control solutions. Scientific Awards: China National Scholarship (2012)
J. Cole Smith is the Dean of the College of Engineering and Computer Science at Syracuse University and holds the academic rank of Professor. He joined Syracuse in 2019 with a focus on advancing undergraduate and graduate education, research initiatives, and diversity, equity, and inclusion efforts. Previously, he earned a PhD in Industrial and Systems Engineering from Virginia Tech (2000) and a BS in Mathematical Sciences from Clemson University (1996). His research focuses on mathematical optimization, particularly mixed-integer programming and combinatorial optimization, with applications in network interdiction, logistics, national security, healthcare, and sports. His work has been supported by agencies such as the National Science Foundation, Office of Naval Research, and Defense Advanced Research Projects Agency. Recent research trends emphasize network interdiction under uncertainty, bilevel optimization, and large-scale optimization challenges. His publications in top journals like Operations Research and Mathematical Programming reflect these themes. Awards include INFORMS Fellow (2023) and recognition from Virginia Tech's Grado Department of Industrial and Systems Engineering. While his dean duties reduce direct research involvement, he remains active through collaboration with PhD students. His team's work addresses real-world problems in security, logistics, and healthcare, leveraging advanced algorithmic techniques.
University of Illinois Urbana-ChampaignUnited States
Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .
Florida Atlantic University - Boca RatonUnited States
Francesco Sica is an Assistant Professor in the Department of Mathematics and Statistics at Florida Atlantic University, with research focused on mathematical cryptology, public-key cryptography, and number theory. He earned his Ph.D. in Mathematics from McGill University (1998), M.Sc. from Scuola Normale Superiore (1993), and B.Sc. from the University of Pisa (1993). Education Ph.D. in Mathematics, McGill University, Canada (1998) M.Sc. in Mathematics, Scuola Normale Superiore, Italy (1993) B.Sc. in Mathematics, University of Pisa, Italy (1993) His research spans implementation and security analysis of elliptic curve cryptography , including isogeny-based problems and torsion point attacks. He has also contributed to pseudorandom number generation, multistorage solutions on elliptic curves, and cryptographic competitions. Recent publications highlight advancements in isogeny-based security (2024), cryptographic Olympiad frameworks (2022), and integer factorization techniques using number-theoretic hints (2021). Earlier work (2019) addressed secure bit extraction from Koblitz curves. Sica previously collaborated with the NESSIE project at Université catholique de Louvain and worked at a crypto lab at the National University of Singapore. His work bridges theoretical number theory with practical cryptographic applications.
