جست‌وجوی استاد و پژوهشگر

Negin Alemazkoorمشاهده پروفایل

استادیار

• Infrastructure Systems
• Artificial Intelligence
• Renewable Energy
• +۷ مورد دیگر

Negin Alemazkoor is an Assistant Professor at the University of Virginia's School of Engineering and Applied Science, specializing in interdisciplinary research on infrastructure resilience. Her work focuses on developing AI-driven methodologies for analyzing interconnected systems like power grids, urban flood models, and transportation networks under uncertainty. Key areas include enhancing grid reliability through multi-fidelity modeling, hurricane evacuation equity analysis, and precision-compression techniques for large-scale data. She co-leads a NSF-funded initiative to democratize AI education in high schools. Her research integrates graph neural networks, physics-informed models, and machine learning to address challenges in energy systems, environmental monitoring, and disaster response. Notable projects include hurricane-induced power outage risk analysis under climate change and precision guarantees for smart-meter data analytics. She emphasizes computational efficiency and multi-fidelity approaches to balance accuracy with resource constraints. Recent contributions span AI applications in flood forecasting, renewable energy integration, and infrastructure cybersecurity. Her NSF grant aims to create inclusive AI curricula, reflecting her commitment to education and societal impact. She is affiliated with UVA Engineering’s research initiatives on resilient systems and data-driven decision-making.

Sijia Gengمشاهده پروفایل

استادیار

• Power Systems
• Control Theory
• Nonlinear Dynamical Systems
• +۴ مورد دیگر

Sijia Geng is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University (JHU) and a core researcher at the Ralph O’Connor Sustainable Energy Institute (ROSEI). She directs the Power and Energy Network Systems Analysis (PENSA) Laboratory and co-leads the NSF-funded Electric Power Innovation for a Carbon-free Society (EPICS) Center as co-PI. Her research focuses on integrating control theory, mathematical analysis, and optimization to enhance renewable energy utilization and grid resiliency. Education: Ph.D. and M.S. (ECE & Mathematics) from the University of Michigan-Ann Arbor (2016–2022), B.S. in Automation from Harbin Institute of Technology (2016). Postdoctoral work at MIT (2022) and visiting scholar roles at Purdue University (2015) and Pacific Northwest National Lab (2018). Research Interests: Dynamic analysis of inverter-based power systems, nonlinear control theory, data-driven decision-making, and multi-energy systems. Her work emphasizes achieving autonomous, resilient energy systems through advanced computational tools and theoretical frameworks. Awards: Best Paper Award at MIT/Harvard Applied Energy Symposium (2022), MIT Rising Stars in EECS (2021), Barbour Scholarship (2021), Towner Prize (2018), and Gerald and Esther Forrest Fellowship (2016). She is active in IEEE and INFORMS, organizing sessions at PES General Meeting and CISS conferences. Grants & Collaborations: Funded by NSF, DOE, MIT Energy Initiative, and industry. Leads global initiatives through EPICS, collaborating with UK, Australian, and international stakeholders. Co-leads ROSEI’s Grid pillar to advance fossil-free energy systems. Labs & Teams: Directs PENSA Lab, affiliated with JHU’s Data Science and AI Institute, Applied Mathematics & Statistics, and Computer Science departments.

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Frederi G. Viensمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Quantitative Finance
• +۷ مورد دیگر

Frederi G. Viens is a Professor of Statistics at Rice University, where he leads research in probability theory, stochastic processes, and their applications to finance, climate science, and agro-ecology. Previously, he was a full professor at Michigan State University (2016–2022) and Purdue University (2000–2015), serving as Department Chair and Director of Actuarial Science. His work bridges theoretical mathematics with practical problems in agriculture, economics, and nuclear physics. Education: Ph.D. Mathematics, University of California, Irvine (1996) M.S. Mathematics, University of California, Irvine (1991) Maîtrise de Mathématiques Pures, Université de Paris VII, France (1991) Research Interests: Probability Theory & Stochastic Analysis Quantitative Finance & Actuarial Science Climate Science & Bayesian Statistics Agro-ecology & Agricultural Economics His collaborative projects include climate modeling, nuclear physics simulations, and sustainable crop diversity initiatives like the DRIVES network. Awards & Honors: Fellow of the Institute of Mathematical Statistics (2012) Franklin Fellow, U.S. State Department (2010) Purdue College of Science Research Award (2013) Grants & Collaborations: Funded by the NSF, USDA, and private donors, Viens has organized major conferences and serves on editorial boards for journals like Annals of Finance . He advises transnational research groups, including Sustainability Lake Chad , addressing agrarian sustainability in West Africa. Labs & Initiatives: Founding member of the DRIVES agro-ecology collaborative and moderator of the Seminar on Stochastic Processes.

Diane Guignardمشاهده پروفایل

استادیار

• Numerical Analysis
• Applied Mathematics
• Computational Mathematics
• +۶ مورد دیگر

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

Gudrun P. Kiesmüllerمشاهده پروفایل

استاد

• Supply Chain Management
• Inventory Management
• Spare Parts Inventory Management
• +۳ مورد دیگر

Prof. Dr. Gudrun P. Kiesmüller is a full professor of Operations Management at TUM Campus Heilbronn since 2019. Previously, she held full professorships at Kiel University (Supply Chain Management) and Otto von Guericke University Magdeburg (Operations Management). She studied mathematics at Julius-Maximilians-University of Würzburg and later worked as a postdoc and assistant professor at Eindhoven University of Technology. Her research focuses on supply chain management, inventory management (particularly spare parts), maintenance process planning, and manufacturing system design. She develops optimization approaches for decision support, with publications in journals like IISE Transactions and Production and Operations Management . Key awards include an Honorary Doctorate (2023), ISIR Service Award (2022), and multiple teaching and reviewer awards. She has contributed to advancing stochastic inventory models and operational efficiency in complex systems. Her work integrates theoretical rigor with practical applications, addressing challenges in inventory routing, buffer allocation, and component reliability optimization. Current research emphasizes dynamic maintenance planning and capital goods design.

Felix Motzoiمشاهده پروفایل

دانشیار

• Quantum Technologies
• Optimal Control Theory
• Machine Learning
• +۶ مورد دیگر

Prof. Felix Motzoi is an Associate Professor at the University of Cologne and Division Leader & Head of the 'Automatic Optimization, Control and Design' group at the Peter Grünberg Institute (PGI-8) in Jülich. His research focuses on advancing quantum technologies, including superconducting and semiconducting architectures, trapped cold atoms/ions, Rydberg qubits, and long-range entanglement. He leads theoretical efforts in quantum control theory, machine learning applications, hardware co-design, and error mitigation strategies. Key research areas include developing optimal control methodologies (e.g., DRAG, STA), numerical optimization, and dynamics modeling for quantum systems. His work bridges theoretical frameworks with experimental implementations, emphasizing practical solutions for scalable quantum computing. Recent publications highlight innovations in quantum gate design, error suppression via pulse shaping, and hybrid optimization techniques combining machine learning with physics-driven approaches. His team collaborates across disciplines to address challenges in qubit coherence, entanglement stabilization, and robust quantum processing.

Tijani CHAHEDمشاهده پروفایل

استاد

• Network Optimization
• Edge Computing
• Machine Learning in Networks
• +۴ مورد دیگر

Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).

Andrew Rau-Chaplinمشاهده پروفایل

استاد

• High Performance Computing
• Parallel Algorithms
• Data Warehousing
• +۱۴ مورد دیگر

Andrew Rau-Chaplin is a Professor and Dean of the Faculty of Computer Science at Dalhousie University, where he leads the Risk Analytics Lab and contributes significantly to research in high performance computing, parallel algorithms, and risk analytics. He is affiliated with the Institute for Big Data Analytics and has a strong academic and administrative presence. Education: Postdoc - DIMCS (Princeton, Rutgers, Bell Labs) PhD - Carleton University (1993) MCS - Carleton University (1990) BCS - York University (1986) His research focuses on applying parallel and high performance computing to data-intensive domains such as data warehousing, OLAP, catastrophe modeling, and risk analytics. He emphasizes both algorithmic design and practical system implementation, with a strong grounding in experimental evaluation. His work spans theoretical studies and real-world applications in finance, bioinformatics, and geospatial systems. The 15 most recent publications reflect a consistent focus on parallel data processing, OLAP optimization, indexing techniques (e.g., Hilbert curves), and risk modeling. Key themes include scalable data cube computation, view selection, adaptive coding, and spatial analytics, demonstrating expertise in both algorithmic innovation and systems-level performance. He has served on numerous scientific committees and grant panels, including NSERC and Compute Canada, and has been a journal editor for JPDC and DMTCS. Dr. Rau-Chaplin has supervised a wide range of graduate students in areas including risk analytics, GPU computing, text analytics, and parallel algorithms. His lab has received funding for postdoctoral, graduate, and undergraduate research positions. He teaches courses such as Parallel Computing, Software Engineering, Data Structures, and Risk Analytics, and has developed software tools like LaHave, Clustal XP, and Digital Coliseum. His lab, the Risk Analytics Lab, focuses on integrating analytics, risk management, and HPC for challenges in catastrophe modeling and financial risk. The lab leverages technologies such as stochastic simulation, optimization, and spatial OLAP.

Nils Wildeمشاهده پروفایل

استادیار

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +۱۶ مورد دیگر

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

Jacob Fishمشاهده پروفایل

استاد

• Multiscale Computational Science
• Computational Mechanics
• Homogenization Methods
• +۵ مورد دیگر

Jacob Fish is the Robert A.W. and Christine S. Carleton Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He directs the Multiscale Science and Engineering Center and leads Columbia's Computational Science and Engineering initiative (iCSE), coordinating 65+ faculty. With 35 years of pioneering research, he specializes in multiscale computational methods bridging aerospace, automotive, and healthcare industries. His research integrates multiscale computational science with applications in: Homogenization and reduced-order methods for complex materials Stochastic modeling of heterogeneous systems Coupled thermo-chemo-electro-mechanical processes Data-physics driven frameworks for industrial processes Recent work emphasizes AI-enhanced modeling for composites, porous media, and environmental systems. His 15 most recent publications (2023-2025) demonstrate strong trends toward: Data-physics integration in manufacturing (e.g., resin transfer molding) Multiscale environmental applications (canopy flows, CO2 mineralization) Advanced numerical methods (discontinuous Galerkin, solver-free homogenization) Digital twin development for composite lifecycle management Scientific Awards & Honors: 2018 JSCES Grand Prize 2010 IACM Computational Mechanics Award 2005 USACM Computational Structural Mechanics Award 2003 Rensselaer Research Award Fellowships: AAM, USACM, IACM Two Best Paper awards He founded the commercial Multiscale Designer software suite (250+ global clients) and secured major grants including an NSF-DFG collaboration on thermoplastic interfaces. His textbooks are used in 200+ universities worldwide. Leads the Multiscale Science and Engineering Center focusing on industrial-scale computational challenges and mentors researchers through Columbia's iCSE initiative. Former President of USACM and current IACM Vice-President for the Americas.

Aryeh Kontorovichمشاهده پروفایل

استاد

• Theoretical Machine Learning
• Probability
• Statistics
• +۴ مورد دیگر

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

• Lattice Algorithms
• Geometry of Numbers
• Linear Programming
• +۵ مورد دیگر

Daniel Dadush is a part-time Professor at Utrecht University and a senior researcher at Centrum Wiskunde & Informatica (CWI) , where he leads the Networks & Optimization group. His research spans lattice algorithms, integer programming, convex optimization, and discrepancy theory, with a focus on theoretical and algorithmic advancements. PhD in Algorithms, Combinatorics, and Optimization (ACO) from Georgia Tech (2012) Simons Postdoctoral Fellow at Courant Institute, NYU (2012-2014) His work bridges discrete and continuous optimization, exemplified by breakthroughs like Strongly Polynomial Algorithms for Linear Programming (STOC 2024) and Interior Point Methods Are Not Worse Than Simplex (FOCS 2022). Recent publications emphasize randomized algorithms, integrality gaps, and high-dimensional geometry. Scientific Awards : ERC Starting Grant (2019-2024) NWO Veni Grant (2015-2018) Van Dantzig Prize (2020) A.W. Tucker Prize for Best Thesis (2015) INFORMS Optimization Society Student Paper Prize (2011) He mentors PhD students and postdocs, including Ben Bals , Samarth Tiwari , and Sophie Huiberts , and co-organizes major conferences like ISMP 2027 and Dutch Day on Optimization . His teaching includes courses on Interior Point Methods and Learning-Augmented Algorithms.

Alina Eneمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Optimization
• +۳ مورد دیگر

Alina Ene is an Associate Professor in the Department of Computer Science at Boston University. Her research focuses on theoretical computer science, algorithms, optimization, and theoretical machine learning, with recent work emphasizing submodular optimization and convex/non-convex optimization techniques. Key research areas include Submodular optimization Convex and non-convex optimization Stochastic gradient methods Streaming and parallel algorithms Her recent publications analyze high-probability convergence in optimization, adaptive gradient methods, and submodular function applications in machine learning and network design. Current advisees include PhD students Fabian Spaeh and Ta Duy Nguyen. She has previously mentored students and postdocs such as Andrew Suh, Adrian Vladu, and Rafael da Ponte Barbosa. Alina has taught courses like CS 237 (Probability in Computing), CS 531 (Advanced Optimization Algorithms), and CS 591 E2 (Convex Optimization Algorithms) at Boston University, and earlier courses at the University of Warwick.

Mengdi Wangمشاهده پروفایل

استاد

• Reinforcement Learning
• Machine Learning Theory
• Generative AI
• +۴ مورد دیگر

Mengdi Wang is a Professor at Princeton University with primary appointments in the Department of Electrical and Computer Engineering and the Center for Statistics and Machine Learning, and courtesy appointments in the Department of Computer Science and Omenn-Darling Bioengineering Institute. She co-directs Princeton AI for Accelerated Invention and is affiliated with the Princeton ML Theory Group and Princeton Language+Intelligence Initiative, with prior visiting roles at DeepMind, IAS, and Simons Institute. Her educational background includes a PhD in Electrical Engineering and Computer Science (with Mathematics minor) from MIT (2013), advised by Dimitri P. Bertsekas at LIDS, and undergraduate studies in Automation at Tsinghua University: PhD: MIT, Electrical Engineering and Computer Science (2013) Bachelor: Tsinghua University, Automation Her research establishes theoretical foundations for machine learning with emphasis on reinforcement learning algorithms, generative AI, and large language models. She investigates data-driven stochastic optimization, statistical limits of reinforcement learning, representation learning, and diffusion models, developing provably robust algorithms for complex systems. Her work bridges theoretical guarantees with real-world applications in healthcare, biotech drug discovery, fintech, and scientific acceleration, focusing on how AI can transform discovery processes across disciplines. Her scientific contributions are recognized by prestigious awards: Young Researcher Prize in Continuous Optimization (Mathematical Optimization Society, 2016) Princeton SEAS Innovation Award (2016) NSF Career Award (2017) Google Faculty Award (2017) MIT Tech Review 35-Under-35 (China region, 2018) WAIC YunFan Award (2022) Donald Eckman Award (American Automatic Control Council, 2024) Professor Wang actively mentors students and recruits undergraduate interns, visitors, and postdocs for her research group. Her work is supported by major grants from NSF, AFOSR, NIH, ONR, Google, Microsoft C3.ai, FinUP, RVAC Medicines, MURI, and GenMab. She serves as Program Chair for ICLR 2023 and Senior Area Chair for NeurIPS, ICML, and COLT, while editing for Harvard Data Science Review and Operations Research. She leads Princeton AI for Accelerated Invention, which develops AI-driven solutions for scientific discovery, collaborating closely with Princeton's ML Theory Group and Language+Intelligence Initiative to advance algorithmic innovation and interdisciplinary applications.