جست‌وجوی استاد و پژوهشگر

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.

Suchuan Dongمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +۷ مورد دیگر

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).

Maxime Ferreira Da Costaمشاهده پروفایل

دانشیار

• Signal Processing
• Data Science
• Inverse Problems
• +۶ مورد دیگر

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Digital Circuits and Systems
• Energy-Efficient Circuits and Systems
• Embedded Systems
• +۷ مورد دیگر

Massimo Poncino is a Full Professor at the Department of Control and Computer Science (DAUIN) within the Faculty of Engineering at Politecnico di Torino. He serves as Scientific Advisor for the STMicroelectronics partnership and coordinates basic engineering subjects. A Senior Member of IEEE since 2012 and Fellow since 2012, he has served on editorial boards for IEEE Transactions on Computer-Aided Design, IEEE Design & Test of Computers, and ACM Transactions on Design Automation. Education: Laurea in Electronic Engineering (1989) and PhD in Computer and Systems Engineering (1993) from Politecnico di Torino Academic Career: Visiting Scientist University of Colorado (1993-1994), Researcher at Politecnico di Torino (1995-2001), Associate Professor at University of Verona (2001-2004), Full Professor at Politecnico di Torino (2006-present) His research focuses on energy-efficient digital systems , including design automation of SoCs, hardware-aware AI, battery management, cyber-physical systems, and embedded systems. Recent publications highlight advancements in digital twins for batteries , low-power neural network deployment , and IoT privacy . Scientific Awards: Recognition of Service Award - ACM (2013) Certificate of Appreciation - IEEE Circuits and Systems Society (2006, 2008, 2009) IEEE Fellow (2012-) Research Involvement: EU H2020, VI/VII Framework Programs evaluator Scientific Director for projects: Approxim@ction, EMBAI, DISLO-MAN, DAMASCO Member of EDA research group Teaching: Course director for Energy Management for IoT (2019-2025) Lecturer for Computer Science courses (2003-2025)

• Lattice Algorithms and Geometry of Numbers
• Linear and Integer Programming
• Extended Formulations
• +۳ مورد دیگر

Daniel Dadush is a part-time Professor of Geometry of Optimization at Utrecht University and leads the Networks & Optimization group at Centrum Wiskunde & Informatica (CWI). He has held previous positions as a Simons Postdoctoral Fellow at the Courant Institute of Mathematical Sciences (New York University) and a PhD in the ACO program (Algorithms, Combinatorics, and Optimization) at Georgia Tech. Research Interests: Lattice Algorithms, Geometry of Numbers, Linear/Integer Programming, Extended Formulations, Discrepancy Theory, Convex Optimization, Asymptotic Convex Geometry. Awards: Van Dantzig Prize (2020), Best Paper Award at CCC'20 (2020), Tucker Prize (2015). Advising: Supervised PhD/MSc students including Ben Bals, Samarth Tiwari, Sander Borst, Sophie Huiberts, Huck Bennett, and Yilin Li. Recent Publications: His 15 most recent articles focus on strongly polynomial algorithms, exact integer programming, convex optimization in the oracle model, matrix discrepancy, circuit diameter bounds, and integrality gaps, spanning journals and conferences like STOC, SODA, FOCS, and Mathematical Programming. Professional Activities: Organizer of the Dutch Day on Optimization (2022), co-organizer of workshops on Discrepancy Theory, Lattices, and Discrete Optimization at institutions like HIM Bonn and the Simons Institute. Served on program committees for STOC 2025, SODA 2024, and other major conferences. Teaching: Lectured on Interior Point Methods, Straight-Line Complexity, and courses in Continuous Optimization at Utrecht University and Mastermath.

Arijit Bishnuمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Randomized Algorithms
• Approximation Algorithms
• +۶ مورد دیگر

Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.

Walid Hubbiمشاهده پروفایل

دانشیار

• Power Systems
• Load Flow Analysis
• Control Systems
• +۱ مورد دیگر

Walid Hubbi is Associate Professor in Electrical and Computer Engineering at NJIT. He holds a PhD from Queen's University Belfast and degrees from the University of London and Aleppo University. His research focuses on power system analysis and control, particularly optimization techniques for reactive power compensation, load flow methodologies, and stability enhancement. Key contributions include fuzzy logic controllers for static VAR compensators, neural network applications for load modeling, and optimal placement strategies for grid control devices. His publications consistently address practical challenges in transmission efficiency, voltage stability, and measurement accuracy using computational intelligence and optimization frameworks.

Farhad Rachidiمشاهده پروفایل

استاد

• Electromagnetic Compatibility
• Lightning electromagnetics
• Lightning and EMP interaction with transmission lines
• +۴ مورد دیگر

Farhad Rachidi is a Full Professor at the Swiss Federal Institute of Technology (EPFL), where he serves as the Head of the Electromagnetic Compatibility (EMC) Laboratory within the School of Engineering's Department of Electrical Engineering. His research group has been active in EMC research since the early 1980s and maintains collaborations with numerous international institutions including Universities of Bologna and Rome (Italy), Uppsala University and KTH (Sweden), University of Toronto (Canada), University of Florida (USA), and others. Professor Rachidi's research spans electromagnetic compatibility, lightning electromagnetics, lightning and EMP interaction with transmission lines, electromagnetic time reversal, fault location, numerical computation of electromagnetic fields, and power line communications. His work integrates theoretical modeling with experimental validation, particularly in the context of lightning phenomena and electromagnetic interference. The research group develops innovative techniques such as electromagnetic time reversal for applications ranging from lightning detection to partial discharge localization in power systems. Analysis of his recent publications (2023-2025) reveals a strong focus on electromagnetic time reversal techniques, lightning physics and modeling, machine learning applications in electromagnetic phenomena, and advanced computational methods for electromagnetic field analysis. His work bridges fundamental electromagnetic theory with practical applications in power systems, atmospheric electricity, and security technologies. 2025 IEEE EMC Technical Achievement Award 2005 CIGRE Technical Committee Award 2006 Blondel Medal from SEE 2016 Berger Award from ICLP Best Paper Awards of IEEE Transactions on EMC (2016, 2018) Motohisa Kanda Award for most cited papers (2012-2018) 2024 Distinguished Honorary Professor at Tsinghua University 2014 Honorary Professor at Xi'an Jiaotong University Professor Rachidi has supervised numerous students through semester projects, diploma projects (equivalent to MS), and PhD programs at EPFL. His research is primarily sponsored by Swiss National Science Foundation, European Community programs, European Space Agency, Swiss Electrical Utilities, and private companies. He has served in leadership roles including President of the International Conference on Lightning Protection (2008-2014), Editor-in-Chief of IEEE Transactions on Electromagnetic Compatibility (2013-2015), and President of the Swiss National Committee of the International Union of Radio Science (2012-2020). The EMC Laboratory at EPFL, which he heads, maintains the Säntis lightning research facility and has been instrumental in advancing our understanding of lightning phenomena through direct measurements at instrumented towers. The group has developed innovative techniques including electromagnetic time reversal for fault location in power networks and lightning detection systems.

Yuri Faenzaمشاهده پروفایل

دانشیار

• Discrete Optimization
• Operations Research
• Mathematical Programming
• +۴ مورد دیگر

Yuri Faenza is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, with affiliations to the Data Science Institute (DSI) and Foundations of Data Science Center. His research bridges Discrete Optimization, Operations Research, and Computer Science, focusing on algorithmic theory and applications to Market Design (particularly School Choice) and Machine Learning. Key research areas include Knapsack , Matching , and Extended Formulations , with methodological work on polytope structures, greedy algorithms, and stochastic or semi-random models. His recent publications emphasize stable matching, optimization under uncertainty, and the intersection of discrete mathematics with data science. Scientific awards include the NSF CAREER award Meta Research Award . He has served on program committees for major conferences like ALGA, EC, APPROX, and IPCO, and is an Associate Editor for journals including Mathematical Programming and Discrete Optimization . Prior to Columbia, he held postdoctoral positions at the University of Brussels, EPFL, and University of Padua.

Marika Karbsteinمشاهده پروفایل

پژوهشگر

• Operations Research
• Mathematical Optimization
• Public Transport Planning
• +۳ مورد دیگر

Dr. Marika Karbstein is a Researcher at the Zuse Institute Berlin (ZIB), where she has been employed since April 2006. Affiliated with the DFG Research Center Matheon "Mathematics for key technologies" from 2006-2014, she earned her doctoral degree in Mathematics from Technische Universität Berlin in 2013. Her work bridges theoretical optimization and practical public transport applications. Her academic background includes: 1999-2005: Studies of Applied Mathematics (Wirtschaftsmathematik) at Technische Universität Berlin 2003-2006: Student assistant at Konrad-Zuse-Zentrum Berlin 2013: Doctoral degree in Mathematics (Dr. rer. nat), Technische Universität Berlin Dr. Karbstein specializes in mathematical optimization for public transport systems, focusing on strategic planning, service design, and passenger behavior modeling. Her research integrates timetabling, line planning, and passenger routing through advanced integer programming and network optimization techniques. She develops practical solutions for infrastructure design, crew scheduling, and fare planning, emphasizing real-world applicability in urban transport networks. Her methodology combines theoretical rigor with implementation-focused approaches to improve system efficiency and user experience. Her publication record shows consistent innovation in periodic timetable optimization and integrated transport modeling. Early work established foundations in Steiner tree problems for transport networks, evolving into sophisticated frameworks for passenger routing and line planning. Recent contributions emphasize direct connections and transfer minimization, reflecting industry needs for seamless passenger journeys. The research demonstrates strong interdisciplinary connections between operations research, computer science, and transportation engineering. Her scientific recognition includes: Diploma Thesis Award of the German Operations Research Society (2006) Runner-Up for Richard-Rado-Prize (2014) Dissertation Award of German Operations Research Society (2014) VBKI Wissenschaftspreis (2014) Tucker Prize Finalist (2015) Best Paper Award at ATMOS 2013 As a core member of the DFG Research Center Matheon, Dr. Karbstein contributed to major collaborative projects funded by the German Research Foundation. Her work with Ralf Borndörfer at ZIB has led to practical implementations including Potsdam's 2010 line plan optimization. While specific grants aren't detailed, her research has been sustained through institutional support at ZIB and Matheon's interdisciplinary framework. She actively collaborates with transport operators to translate theoretical advances into operational improvements. Dr. Karbstein operates within ZIB's renowned applied mathematics environment, leveraging its high-performance computing resources. Her work extends through Matheon's consortium connecting Berlin's mathematics community. Current research focuses on integrating passenger behavior models with real-time scheduling systems, advancing the frontier of data-driven transport optimization.

Vladimir Okhmatovskiمشاهده پروفایل

استاد

• Electromagnetics
• Computational Electromagnetics
• High-Performance Computing
• +۴ مورد دیگر

Dr. Vladimir Okhmatovski is a Full Professor in the Department of Electrical and Computer Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. His research focuses on fast algorithms in electromagnetics, quantum computing, and high-performance computing. He holds a Ph.D. from the Moscow Power Engineering Institute and has held academic and industry roles globally. He has received prestigious awards including the 2017 Intel Outstanding Researcher Award. Education: Ph.D., Antennas and Microwave Circuits, Moscow Power Engineering Institute, 1997 M.S., Radiophysics and Electronics, Moscow Power Engineering Institute, 1996 Research Interests: Dr. Okhmatovski develops advanced computational methods for electromagnetics, including tensor train decompositions and quantum algorithms. His work addresses challenges in signal integrity, layered media analysis, and inverse scattering. He leads efforts in applying quantum computing to solve matrix equations and has pioneered fast direct solvers using H-matrices. Awards: 2017 Intel Outstanding Researcher Award 1996 Best Young Scientist Report (VI International Conference on Mathematical Methods in Electromagnetic Theory) 2007 Outstanding ACES Journal Paper Award Advising & Grants: He mentors students in climate change research through collaborations with the Centre for Earth Observation Science. His funded projects include quantum computing applications and Arctic sea ice remote sensing. He chairs technical committees for IEEE MTT-S and AP-S. Labs/Teams: His research group focuses on electromagnetic modeling, quantum algorithms, and layered media analysis. Collaborations include the Churchill Marine Observatory for Arctic studies.

• Software Engineering
• Model Checking
• Formal Verification
• +۴ مورد دیگر

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

Ludovic Sacchelliمشاهده پروفایل

پژوهشگر

• Control Theory
• Sub-Riemannian Geometry
• Optimal Control
• +۷ مورد دیگر

Ludovic Sacchelli is an Inria researcher (CR) affiliated with the McTAO team at the Centre Inria d'Université Côte d'Azur and the Laboratoire J.A. Dieudonné of Université Côte d'Azur. His research spans control theory, sub-Riemannian geometry, and mathematical neuroscience, focusing on optimal control, observers, and estimation problems. His work on sub-Riemannian manifolds and control systems includes stabilization techniques for non-uniformly observable systems and applications to UAV control, neural fields, and bioprocess modeling. He has contributed to heat kernel analysis, line fields interpolation, and geometric models for sound processing. His recent publications address topics like distributed state estimation in neural models, polynomial state-affine control systems, and geometric algorithms for orientation field interpolation. He has also explored observability singularities in bilinear systems and stabilization of weakly contractive systems. Teaching roles include instructing Measure Theory , Stochastic Processes , and applied mathematics at institutions such as Université Côte d'Azur, Polytech Nice, Lehigh University, and École Polytechnique. His mentorship includes supervising a Masters research project on numerical implementation of line fields interpolation in 2019.

• Control systems
• Mechatronics
• Precision engineering
• +۳ مورد دیگر

Chun-Yi Su is a Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on advanced control systems, mechatronics, robotics, and precision engineering. He specializes in nonlinear control, hysteresis compensation, and adaptive systems applied to soft actuators, UAVs, and smart materials. Key research areas include: Control of dielectric elastomer actuators Fault-tolerant cooperative control of unmanned aerial vehicles (UAVs) Adaptive neural network-based control for nonlinear systems Data-driven approaches for power electronics and energy systems Recent work emphasizes: Soft robotics actuation mechanisms Fractional-order control strategies Resilient control under cyber-physical attacks Biomimetic robotic systems His lab develops innovative solutions for smart materials, mechatronic systems, and autonomous robotics. Applications span renewable energy systems, biomedical devices, and aerospace engineering. Active in collaborative research with industry partners.

Shamil Asgarliمشاهده پروفایل

استادیار

• Algebraic Geometry
• Arithmetic Geometry
• Finite Fields
• +۳ مورد دیگر

Shamil Asgarli is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University (SCU), College of Arts and Sciences. He holds a B.Sc. from the University of British Columbia (UBC) and a Ph.D. from Brown University (2019), advised by Brendan Hassett. Previously, he was a postdoctoral fellow at UBC under mentors Dragos Ghioca and Zinovy Reichstein. His research focuses on algebraic and arithmetic geometry, with recent explorations into algebraic graph theory. Core themes include geometry and combinatorics over finite fields, transverse hypersurfaces, blocking sets, and maximum cliques in Cayley graphs. Notable contributions involve Bertini-type theorems, Picard group analysis of quadric intersections, and studies of Frobenius nonclassical hypersurfaces. Teaching spans courses at SCU (e.g., Cryptography, Calculus), UBC, and Brown, including roles as instructor and teaching assistant for calculus, linear algebra, and number theory. He completed the Sheridan Center's Reflective Teaching Program (Certificate I). His work bridges pure mathematics with combinatorial applications, often leveraging finite field structures. Key collaborations include projects with Dragos Ghioca, Chi Hoi Yip, and Kuan-Wen Lai on transverse subspaces, blocking curves, and geometric irreducibility. His publications frequently address problems at the intersection of algebraic geometry and finite field theory, with applications to coding and cryptography.