جست‌وجوی استاد و پژوهشگر

Baoyu Zhouمشاهده پروفایل

استادیار

• Large-scale Continuous Optimization
• Constrained Stochastic Optimization
• Nonsmooth Optimization
• +۳ مورد دیگر

Baoyu Zhou is an Assistant Professor of Industrial Engineering at Arizona State University (ASU), School of Computing and Augmented Intelligence. He holds a PhD in Industrial and Systems Engineering from Lehigh University (2018–2022), an M.S. in Industrial Engineering from Lehigh University (2016–2018), and a B.E. in Mechanical Engineering from Shanghai Jiao Tong University (2012–2016). His research focuses on developing efficient algorithms for large-scale, stochastic, and constrained optimization problems, with contributions to sequential quadratic programming, nonsmooth optimization, and derivative-free methods. Before joining ASU, Zhou was a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business). He has received the Van Hoesen Family Best Publication Award and the Elizabeth V. Stout Dissertation Award. His work bridges optimization theory and practical applications, emphasizing scalability and robustness in complex systems. Zhou teaches courses such as IEE 470: Stochastic Operations Research at ASU and has guest-lectured at the University of Michigan. He actively contributes to the academic community through organizing conference sessions, reviewing for top journals, and participating in workshops at NeurIPS and SIAM. His group currently advises three PhD students focusing on optimization algorithms and their applications. Key research areas include large-scale continuous optimization, constrained stochastic optimization, and derivative-free methods. His publications span journals like SIAM Journal on Optimization and INFORMS Journal on Optimization, addressing challenges in nonlinear systems, variance reduction, and algorithmic convergence.

Ebrahim Sarabiمشاهده پروفایل

دانشیار

• Variational Analysis
• Optimization
• Parametric Optimization
• +۲ مورد دیگر

Ebrahim Sarabi is an Associate Professor in the Department of Mathematics at Miami University, located in Oxford, Ohio. He holds a Ph.D. in Applied Mathematics from Wayne State University. His primary research interests focus on variational analysis, optimization, and parametric optimization, with an emphasis on numerical algorithms and applications in continuous optimization. Dr. Sarabi's research explores advanced topics such as tilt-stable minimizers, epi-differentiability, and the role of subgradients in polyhedral functions. His work often intersects with second-order variational analysis and composite optimization problems. His contributions span theoretical developments and practical algorithmic advancements in optimization. His publications from 2020–2025 highlight trends in augmented Lagrangian methods, smoothness of subgradient mappings, and primal-dual convergence analysis. Notable themes include the study of partly smooth functions, spectral functions, and generalized Newton algorithms. No scientific awards were explicitly mentioned in the provided texts. He advises no listed students or research teams, though his work involves collaborations in parametric optimization and variational systems. His office is located in Bachelor Hall, and he can be reached via email at sarabim@miamioh.edu .

Albert S. Berahasمشاهده پروفایل

استادیار

• Nonlinear Optimization
• Machine Learning Optimization
• Stochastic Optimization
• +۵ مورد دیگر

Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan's College of Engineering. He joined the university in 2020 after completing postdoctoral positions at Lehigh University (2018-2020) and Northwestern University (2018). He holds a PhD in Engineering Sciences and Applied Mathematics from Northwestern University (2018), an MS in Applied Mathematics from Northwestern (2012), and a BSE in Operations Research and Industrial Engineering from Cornell University (2009). His research focuses on designing, developing, analyzing, and implementing algorithms for solving large-scale nonlinear optimization problems. His work spans multiple sub-fields including constrained optimization, optimization for machine learning, stochastic optimization, derivative-free optimization, and decentralized optimization. He is affiliated with the Michigan Institute for Data Science (MIDAS), the Michigan Institute for Computational Discovery and Engineering (MICDE), and the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM). Berahas has received numerous honors including the Charles Broyden Prize (2025), the Air Force Office of Scientific Research Young Investigator Program award (2025), the IISE Operations Research Division Teaching Award (2024), and the North Campus Dean's MLK Spirit Award for Community Building & Impact (2024). His recent publications demonstrate strong activity in developing novel optimization frameworks with theoretical guarantees for challenging problem settings. His research has been supported by significant grants including from the Office of Naval Research (ONR) and the Air Force Office of Scientific Research. He actively mentors PhD students and has successfully advised Jiahao Shi, who defended his dissertation in March 2025 and joined Amazon. Berahas is also engaged in community outreach, particularly through initiatives like Engage Detroit that aim to empower Detroit's next generation of engineers.

Andreas Malikopoulosمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Transportation Systems
• Control Theory
• +۴ مورد دیگر

Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.

• Nonlinear Optimization
• Stochastic Optimization
• Distributed Optimization
• +۱ مورد دیگر

Raghu Bollapragada is an Assistant Professor in Operations Research and Industrial Engineering at the University of Texas at Austin, with affiliations to the Oden Institute and Machine Learning Laboratory. His research designs algorithms for nonlinear optimization, including constrained, stochastic, and distributed methods with applications in machine learning. Supported by NSF, Argonne National Laboratory, and Lawrence Livermore National Laboratory, recent work (2024-2025) develops gradient tracking for decentralized systems, adaptive sampling techniques, and hessian averaging for nonconvex problems. He was elected Vice Chair of Nonlinear Optimization for INFORMS (2024-2026).

Frank E. Curtisمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Numerical Analysis
• +۳ مورد دیگر

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University, where he has been since 2009. He holds a B.S. in Mathematics and Computer Science from the College of William and Mary (2003), an M.S. and Ph.D. in Industrial Engineering from Northwestern University (2004 and 2007), and completed a postdoctoral fellowship at New York University’s Courant Institute (2007–2009). His research focuses on developing numerical methods for large-scale nonlinear optimization, with applications in machine learning, operations research, and energy systems. Key achievements include the 2021 SIAM/MOS Lagrange Prize for Continuous Optimization (with Bottou and Nocedal) and the 2018 INFORMS Computing Society Prize (with Burke, Lewis, and Overton). He has secured significant funding from the NSF, DoE, and ONR, including a TRIPODS grant and ARPA-E awards. His work on the ARPA-E Grid Optimization Competition earned second place in 2020. Curtis’s research interests span mathematical optimization, numerical analysis, and algorithm design. His recent articles emphasize stochastic optimization, fairness in machine learning, and robust algorithm development for constrained systems. He serves as an Area Editor for Mathematics of Operations Research and Associate Editor for multiple top journals, including Mathematical Programming and SIAM Journal on Optimization . Notable grants include DoE ASCR Early Career Awards and NSF TRIPODS funding for collaborative projects with Northwestern, Boston University, and Cornell. His OptML @ Lehigh team develops cutting-edge optimization tools and frameworks for real-world applications.

Harsha Gangammanavarمشاهده پروفایل

دانشیار

• Optimization under Uncertainty
• Stochastic Programming
• Large-Scale Computational Optimization
• +۳ مورد دیگر

Harsha Gangammanavar is an Associate Professor in the Department of Operations Research and Engineering Management (OREM) at Southern Methodist University (SMU), affiliated with the Data Science Institute. He holds a Ph.D. in Operations Research and M.S. in Electrical Engineering from The Ohio State University, and a B.E. in Electronics and Communication Engineering from Visvesvaraya Technological University, India. Education: Ph.D. in Operations Research, The Ohio State University M.S. in Electrical Engineering, The Ohio State University B.E. in Electronics and Communication Engineering, Visvesvaraya Technological University Research Interests: His work focuses on stochastic programming, large-scale computational optimization, and their applications in infrastructure systems, healthcare, and wireless communication. Key areas include optimization under uncertainty, stochastic decomposition methods, and scalable algorithms for power systems and renewable energy integration. Recent Activities & Awards: Recipient of NSF XTRIPODS grant for collaborative research in data science (2024). DOE Office of Science grant for multiscale stochastic optimization (2022). ONR grant for decomposition-based stochastic models in discrete-event systems (2022). Awarded INFORMS Undergraduate Student Paper Award (2021) and INFORMS Minority Affairs Poster Competition (2016). Advising & Grants: Current advisees include Ph.D. students Ishara A.A.D.H., Jackson Forner, Chhavi Sharma, and Zhiyuan Zhang. Former students Niloofar Fadavi, Sakitha Ariyarathne, and others have secured roles in industry and academia. Active in securing grants from AFOSR, ONR, DOE, and NSF. Labs & Teams: Leads research on stochastic optimization algorithms, power grid resilience, and healthcare applications through interdisciplinary collaborations. Open to motivated students for Ph.D. research in optimization and data science.

Miaolan Xieمشاهده پروفایل

استادیار

• Operations Research
• Stochastic Optimization
• Data Science
• +۴ مورد دیگر

Miaolan Xie is an Assistant Professor at Purdue University's Edwardson School of Industrial Engineering, joining in 2025. Currently, she is a postdoctoral Principal Researcher at the University of Chicago Booth School of Business, affiliated with the Healthcare Initiative. Her academic journey includes a Ph.D. from Cornell University (2019–2024) under Katya Scheinberg and a Master's and Bachelor's in Combinatorics & Optimization from the University of Waterloo. Prior to academia, she worked as a data scientist at Alibaba, Baidu, and PwC Consulting, and as a Givens Associate at Argonne National Laboratory. Education: Ph.D., Operations Research and Information Engineering, Cornell University (2019–2024) Master of Math, Combinatorics & Optimization, University of Waterloo (2014–2016) Bachelor of Math, Pure Math and Combinatorics & Optimization, University of Waterloo (2010–2014) Research Interests: Focuses on stochastic optimization, data science, nonlinear optimization, and machine learning applications in healthcare. Develops algorithms with rigorous theoretical guarantees for messy data scenarios. Current work emphasizes adaptive optimization methods with high probability complexity bounds, leveraging tools from statistics and stochastic processes. Awards: Second Place, 2023 Student Paper Prize (INFORMS Optimization Society) Second Place, Flash Talk Competition (YinzOR Student Conference) NSF Research Internship Funding (2022) Advising & Grants: Seeking PhD students for Fall 2025. Active in grant-funded research through NSF and institutional collaborations. Prior industry experience informs applied healthcare optimization projects. Labs & Teams: Leads a research group at Purdue, collaborating with healthcare and optimization experts. Affiliated with the Healthcare Initiative at the University of Chicago Booth School of Business.

Daniel W. Apleyمشاهده پروفایل

استاد

• Industrial and engineering statistics
• Statistical learning and data mining
• Predictive analytics
• +۷ مورد دیگر

Daniel W. Apley is Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering and Applied Science, Northwestern University, where he has served since 2003. He is Editor-in-Chief-Elect of Technometrics and previously Editor-in-Chief of the Journal of Quality Technology . He is also affiliated with Northwestern’s Master of Science in Machine Learning and Data Science Program. Education PhD Mechanical Engineering, University of Michigan, Ann Arbor MS Electrical Engineering, University of Michigan, Ann Arbor MS Mechanical Engineering, University of Michigan, Ann Arbor BS Mechanical Engineering, University of Michigan, Ann Arbor Research Interests Professor Apley is an industrial statistician whose work sits at the intersection of engineering modeling, statistical analysis, and predictive analytics. His major thrusts include statistical modeling of complex engineering and enterprise systems, machine learning for manufacturing data, quality engineering and Six Sigma methodologies, and computer-experiment–based design optimization under uncertainty. Recent applications span healthcare risk modeling, credit-risk analytics, materials microstructure prediction, and autonomous process control. Scientific Awards NSF CAREER Award IIE Transactions Best Paper Award (Quality & Reliability) Wilcoxon Prize for best practical application paper in Technometrics Teaching & Advising At Northwestern he teaches undergraduate courses in Statistical Methods for Quality Improvement, Introductory Statistics, and Statistical Tools for Data Mining, as well as graduate courses in Predictive Analytics, Engineering Applications of Data Mining, and Intermediate Statistics. His research has been supported by numerous industrial partners and federal agencies, underscoring a strong record of funded graduate and post-doctoral advising. Leadership & Service Beyond editorial roles, Professor Apley has chaired the Quality, Statistics & Reliability Section of INFORMS and served as Director of the Manufacturing and Design Engineering Program at Northwestern, shaping interdisciplinary curriculum and research initiatives.

Daniel P. Robinsonمشاهده پروفایل

استاد

• Computational Optimization
• Machine Learning
• Data Science
• +۳ مورد دیگر

Professor Daniel P. Robinson holds the position of Professor and MS Program Director in the Department of Industrial and Systems Engineering at Lehigh University. Previously, he served as a Postdoctoral Researcher at the University of Oxford and Northwestern University, and as an Assistant Professor at Johns Hopkins University. His research focuses on computational optimization and its applications in data science, machine learning, and computer vision, with a particular emphasis on healthcare and algorithm design. Education: Ph.D. in Mathematics from the University of California, San Diego; postdoctoral training at Oxford University and Northwestern University. Research Interests: Dr. Robinson’s work bridges mathematical optimization and data science, emphasizing algorithm design for continuous optimization problems. His areas include computational optimization, machine learning, data science, and computer vision applications. He has contributed to fair machine learning frameworks, stochastic optimization algorithms, and neural network compression techniques. His publications span top-tier journals and conferences such as Mathematical Programming, SIAM Journal on Optimization, and ICML. Notable grants include NSF funding for optimization research. He co-founded Johns Hopkins’ Mathematical Institute for Data Science (MINDS) and helped establish the JHU Master of Science in Data Science program. Scientific Awards: Twice recipient of the Professor Joel Dean Award for Excellence in Teaching. Advising & Grants: As MS Program Director, he oversees academic programs. His grants include over $1M from the Office of Naval Research and NSF support. He collaborates on projects like scalable subspace clustering and privacy-preserving machine learning. Labs/Teams: Leadership roles in Lehigh ISE’s optimization programs, fostering interdisciplinary research in data science and optimization.

Farnaz Adib Yaghmaieمشاهده پروفایل

استادیار

• Control Theory
• Reinforcement Learning
• Machine Learning for Control Systems
• +۵ مورد دیگر

Farnaz Adib Yaghmaie serves as an Assistant Professor and Docent at Linköping University's Department of Electrical Engineering (ISY), specializing in the Automatic Control division. Her research bridges control theory and machine learning, with significant contributions to reinforcement learning applications in complex systems. Education: Ph.D. in Electronic and Electrical Engineering, Nanyang Technological University (NTU), Singapore (2017, Best Thesis Award) Master's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2011) Bachelor's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2009) Her research focuses on redefining machine learning paradigms for control problems , particularly through reinforcement learning applications. Current projects include Foundation Models and RL for General-Purpose Control (2025-present), Online Control with Adversarial Noise (2022-present), and RL for Partially Observable Systems (2021-present). She explores how foundation models can enable control systems to generalize across diverse tasks and adapt to unseen scenarios, with applications in robotics and autonomous systems. Her work integrates large language models and generative AI into control frameworks, addressing fundamental challenges in sequential decision-making under uncertainty. Scientific Recognition: Best Thesis Award at Nanyang Technological University (2017) CENIIT research grant (2020) Co-founded SEDDIT competence centre (2024) As an educator, she teaches Ph.D.-level courses including Reinforcement Learning (2021-2024) and Advanced Robotics (2025), with her WASP course ranking as the second-best Ph.D. course in the program (3.9/5 evaluation). She actively mentors Ph.D. students including Abbas Pasdar, who is working on Foundation Models for General-Purpose Control. Her research is supported by grants including CENIIT and collaborations through the SEDDIT competence centre. She contributes to the academic community through workshops like the LINK-SIC Core Competence Workshops on Reinforcement Learning and by chairing sessions at major conferences including ECC 2025.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

• Edge Computing
• Internet of Things
• Vehicular Networks
• +۵ مورد دیگر

Muhammad Bilal is an Associate Professor at Zhejiang University's School of Computer Science, Department of Computer Science and Technology. His research spans multiple cutting-edge domains in computer science and engineering, with a particular focus on edge computing, Internet of Things, and vehicular networks. His research interests include Edge Computing , Internet of Things , Vehicular Networks , Federated Learning , Digital Twins , and Cloud Computing . His work addresses critical challenges in resource allocation, latency optimization, privacy preservation, and intelligent decision-making in distributed computing environments. He has made significant contributions to developing novel frameworks for service caching, computation offloading, and secure data processing in next-generation networks. His recent publications reveal a strong trend toward integrating artificial intelligence with edge and cloud infrastructure, particularly focusing on privacy-preserving techniques and resource-efficient algorithms. His work spans applications from intelligent transportation systems to healthcare monitoring and meteorological services, demonstrating the versatility and real-world impact of his research. Dr. Bilal has established a prolific publication record with numerous high-impact papers in top-tier journals and conferences, reflecting his active engagement in the academic community. His collaborative approach is evident through extensive co-authorship networks, particularly with researchers in China, Pakistan, and internationally.

Stephan Mescherمشاهده پروفایل

پژوهشگر

• Algebraic Topology
• Differential Geometry
• Robot Motion Planning
• +۵ مورد دیگر

Stephan Mescher is a Researcher at the Institute of Mathematics within the Faculty of Natural Sciences II at Martin Luther University Halle-Wittenberg. Based at the Georg Cantor House on Theodor-Lieser-Strasse in Halle (Saale), he conducts advanced research at the intersection of algebraic topology and differential geometry with applications in robotics. His research focuses on: Topological complexity and spherical complexities in motion planning Geodesic analysis in Riemannian and Finsler manifolds Morse theory and A-infinity algebra structures Closed geodesics and critical point theory Geometric properties of configuration spaces for robots Recent publications reveal a cohesive trajectory toward sequential topological complexity and fibered decompositions of cut loci, demonstrating how abstract topological invariants solve concrete problems in robot navigation. His work bridges pure mathematics with engineering applications through rigorous analysis of manifold structures. No scientific awards were documented in available sources. Information regarding student supervision, research grants, or collaborative teams was not provided in the source materials.

Andreas Waechterمشاهده پروفایل

استاد

• Large-scale nonlinear optimization
• Mixed-integer nonlinear optimization
• Software implementation of numerical algorithms
• +۴ مورد دیگر

Andreas Waechter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. He holds a Ph.D. in Chemical Engineering from Carnegie Mellon University (2002) and an M.S. in Mathematics from the University of Cologne (1997). Before joining Northwestern in 2011, he was a Research Staff Member at IBM Research (Yorktown Heights, NY). His research focuses on developing numerical algorithms for nonlinear continuous and mixed-integer optimization, with applications in power systems, scientific computing, and sustainability. Key contributions include the open-source solver Ipopt (winner of the 2011 Wilkinson Prize and 2009 INFORMS Computing Society Prize), and the Bonmin and Couenne solvers for mixed-integer optimization. Waechter has received numerous awards, including the 2017 Charles Broyden Prize and the 2020 ARPA-E Grid Optimization Competition award. His work emphasizes both theoretical advancements and practical software implementation, with applications ranging from power grid optimization to machine learning. Education: Ph.D., Chemical Engineering, Carnegie Mellon University, 2002 M.S., Mathematics, University of Cologne, 1997 His research interests span optimization algorithms, algorithmic differentiation, and interdisciplinary applications. He has advised over a dozen graduate students and collaborates with industry and academic institutions globally. Waechter's software packages are widely used in academia and industry, reflecting his commitment to open-source computational tools.