جست‌وجوی استاد و پژوهشگر

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

Geir Dullerud serves as the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign, holding affiliate appointments in Computer Science and Electrical and Computer Engineering within the College of Engineering. He is a core faculty member at the Coordinated Science Laboratory and actively contributes to the Center for Autonomy initiative. Education Ph.D. in Information Engineering, Cambridge University, England, 1994 M.A.Sc. in Electrical Engineering, University of Toronto, Canada, 1990 B.A.Sc. in Engineering Science, University of Toronto, Canada, 1988 Research Focus : Professor Dullerud pioneers theoretical frameworks for cyber-physical systems, specializing in networked control architectures , stochastic hybrid systems , and optimization-driven machine learning . His work bridges formal verification with practical implementations in robotics and autonomous systems, emphasizing security-aware control and privacy-preserving algorithms for distributed environments. Recent breakthroughs integrate reinforcement learning with classical control theory to address scalability challenges in complex CPS. Publication Trends : Analysis of his 15 most recent publications (2021-2024) reveals three dominant threads: (1) Machine learning control (60% of works) focusing on reinforcement learning convergence and safety guarantees; (2) Formal verification (25%) for stochastic hybrid systems using model reduction; (3) Cybersecurity applications (15%) including anonymous networks and privacy-preserving mechanisms. This evolution demonstrates strategic expansion from foundational control theory into AI-integrated CPS. Scientific Recognition IEEE Fellow (2008) and ASME Fellow (2011) NSF CAREER Award (1999) W. Grafton and Lillian B. Wilkins Professorship (2016) Xerox Faculty Research Award (2005) Multiple plenary/keynote invitations (IWFASA 2019, CAV 2017, IWOTA 2016) Academic Leadership : Dullerud has advised numerous graduate students while securing sustained funding through NSF grants and industry partnerships. His Willett Faculty Scholar designation (2002-present) and Technical Committee leadership in IEEE Control Systems Society underscore institutional impact. Current teaching includes advanced courses in nonlinear systems (ECE 528) and convex control methods (ME 561), consistently ranked excellent by students. Research Infrastructure : He leads projects within UIUC's Coordinated Science Laboratory, leveraging resources from the Center for Autonomy to develop testbeds for cooperative robotics and networked control systems. His group maintains active collaborations with Stanford, KTH Stockholm, and industry partners in autonomous vehicle development.

Filip Elvanderمشاهده پروفایل

استادیار

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

Rob Phillipsمشاهده پروفایل

استاد

• Biophysics
• Molecular Biology
• Cellular Biology
• +۵ مورد دیگر

Rob Phillips is the Fred and Nancy Morris Professor of Biophysics, Biology, and Physics at the California Institute of Technology (Caltech). He received his B.S. from the University of Minnesota (1986) and Ph.D. from Washington University (1989). He has held positions at Caltech since 1997, including Clark Millikan Visiting Assistant Professor (1997), Professor (2000-2011), and Morris Professor (2012-present). University: California Institute of Technology Departments: Biophysics, Biology, Physics Email: phillips@pboc.caltech.edu His research focuses on developing quantitative theoretical models to describe biological phenomena, with experimental validation. Key areas include genome organization, energy flow in biological systems, and ecological roles of viruses. His lab collaborates across disciplines, integrating Biology, Bioengineering, Biochemistry, Applied Physics, and Physics. Recent publications address microtubule dynamics, gene regulation out of equilibrium, and phage-host interactions. His work combines theoretical frameworks with precision experiments, emphasizing active matter physics and non-equilibrium biophysics. Scientific Awards: Fred and Nancy Morris Professorship The lab trains graduate students from multiple departments, though specific advisee names are not listed. Research is supported by his professorship and institutional resources at Caltech.

Olivier Fercoqمشاهده پروفایل

استاد

• First-order optimization algorithms
• Primal-dual methods
• Coordinate descent methods
• +۷ مورد دیگر

Olivier Fercoq is a Professor at Télécom Paris in the Information Processing and Communication Laboratory (LTCI) . He holds a PhD in Applied Mathematics from École Polytechnique (2012) and a Master's degree from Paris 6 and ENSTA Paris. His research focuses on first-order optimization algorithms , primal-dual methods , coordinate descent , and applications in web ranking , chronotherapy for cancer , and statistical learning . Education : PhD in Applied Mathematics (École Polytechnique), Master's (Paris 6), Engineering degree (ENSTA Paris) Research Themes : Optimization for machine learning, primal-dual algorithms, web ranking, chemotherapy scheduling His recent work spans stochastic optimization , nonconvex-nonconcave minimax problems , and scalable semidefinite programming . Articles highlight advancements in primal-dual coordinate descent , power-aware signal processing , and duality gap analysis . Awards include the IMA Leslie Fox Prize (2015) and the Gaspard Monge PhD Thesis Award (2013) . Olivier has supervised numerous PhD students including Eyal Vayness (2024-), Iyad Walwil (2022-), and Eugène Ndiaye (2016-2018). He organized the Paris-Saclay Junior Conference on Data Science and Engineering (2018) and co-led the workshop Optimization, Distributed Computation, and Machine Learning (2016) . His software contributions include APPROX for parallel coordinate descent and tools for scikit-learn .

Nicolas Vergneمشاهده پروفایل

مدرس

• Regulated Markov chains
• Poisson approximation
• DNA barcoding
• +۱۰ مورد دیگر

Nicolas Vergne is a University Lecturer at the University of Rouen, where he has been actively teaching mathematics, statistics, and bioinformatics courses since at least 2010. His academic affiliation centers around the Laboratoire de Mathématiques Raphaël Salem (LMRS) at the University of Rouen, where his office is located (M.2.10, 2nd floor). His educational background includes: Doctorate in Applied Mathematics (2003-2008) from University of Evry-Val-d'Essonne DEA AMIB (Application of Mathematics and Computer Science to Biology) (2002-2003) Dual Masters in Mathematics and Mathematical Engineering (2001-2002) Bachelor's degree in Mathematics (2000-2001) DEUG MIAS (Mathematics and Computer Science Applied to Science) (1998-2000) BAC S (Scientific, Specialty Mathematics) (1997-1998) Vergne's research spans the intersection of probability theory, statistics, and biological applications. His primary focus involves developing and applying Markov chain models (including regulated, drifting, and semi-Markov variants) to analyze biological sequences and genomic data. He has made significant contributions to DNA barcoding methodology, Poisson approximation techniques for rare word detection in DNA sequences, and statistical approaches for RNA sequencing data analysis. His work bridges theoretical probability with practical bioinformatics applications, particularly in the areas of reliability analysis, survival modeling, and mixture models for high-throughput genomic data. Analysis of his publication record reveals a consistent trajectory from theoretical probability foundations toward increasingly applied bioinformatics research. His earliest work focused on rigorous mathematical treatments of Markov chains and Poisson approximation theory. Over time, his research evolved to address concrete biological problems including DNA sequence analysis, DNA barcoding, microarray analysis, and RNA-Seq data processing. A notable trend is his development of specialized R packages (SMM, DRIMM'R, WebDRIMM) that translate theoretical models into practical tools for the bioinformatics community. His most recent publications demonstrate expertise across multiple domains including cancer genomics, marine microbiology (diatom research), and rheumatoid arthritis biomarker discovery. Vergne has extensive teaching experience across multiple levels of the French university system, supervising both undergraduate and graduate students. His teaching portfolio includes probability, statistics, real analysis, biostatistics, and specialized bioinformatics courses. He has served as an apprenticeship supervisor for CNRS apprentices in bioinformatics and supervised multiple third-year interns. His laboratory affiliations include the LMRS at University of Rouen, the Bioinformatics Unit 900 of the Curie Institute, and the MIA unit of INRA in Jouy-en-Josas, reflecting his interdisciplinary approach spanning mathematics, statistics, and biology.

Haojin Yangمشاهده پروفایل

استاد

• Efficient AI
• Edge AI
• Deep model acceleration and compression
• +۷ مورد دیگر

Dr. Haojin Yang is a Professor and group leader of the Multimedia and Machine Learning Research Group at the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He works under the supervision of Prof. Dr. Christoph Meinel and leads research in efficient AI, edge computing, and deep learning applications. His research group includes several PhD students and scientific coworkers focusing on cutting-edge machine learning problems. Dr. Yang's research interests span Efficient AI systems with emphasis on edge computing, binary neural networks, deep model acceleration and compression, computer vision applications, and multimodal data retrieval. His work bridges theoretical advancements with practical applications in medical imaging, weather forecasting, and digital humanities. He has made significant contributions to the field of binary neural networks through frameworks like BMXNet and BITorch. His recent publications demonstrate a strong focus on optimizing neural networks for efficiency, particularly in binary and low-bit implementations, while maintaining performance. His work spans computer vision, natural language processing, and multimodal learning, with particular attention to real-world deployment constraints like computational efficiency and energy consumption. BEST PAPER AWARD at the 22nd IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2024) for 'Low-Bit CUTLASS GEMM Template Auto-Tuning Using Neural Network' Dr. Yang actively supervises numerous PhD and Master's students, with several having completed their degrees and moved to prominent positions at companies like Amazon AI, German Aerospace Center, and MILA-Quebec AI Institute. His research is supported by collaborations with SAP, Wildenstein Plattner Institute, and various academic institutions. He also contributes to the academic community as a reviewer and program committee member for top conferences including ICML, NeurIPS, and CVPR.

• Power System Engineering
• Energy Management
• Microgrid Optimization
• +۷ مورد دیگر

Pratik Harsh is a Post-doctoral Research Associate at Durham University , UK, affiliated with the Department of Engineering and the Electrical Power research group. His work addresses global challenges in energy systems through the lens of UN Sustainable Development Goals 7 (Affordable Energy) and 12 (Sustainable Consumption). Education: Ph.D. in Electrical Engineering (2018–2023) from Indian Institute of Technology Kharagpur , with a focus on "Optimal energy management of reconfigurable microgrid using demand response program" Research Interests Pratik specializes in power system engineering with particular emphasis on: Advanced energy management systems for microgrids and virtual power plants Innovative grid reconfiguration strategies for distribution networks Electric vehicle integration (G2V/V2G) in smart grids Application of metaheuristic optimization to energy systems Development of digital twin technologies for integrated energy systems Creation of resilient protection frameworks for hybrid microgrids Publication Trends Pratik's research output demonstrates a clear focus on: Evolution from traditional congestion management (2018) to sophisticated digital twin applications (2025) Progression from basic EV charging coordination to priority-ordered smart charging strategies Development of scalable protection frameworks using modern graph learning techniques Integration of deep learning (LSTM networks) for energy profile identification Advancement of stochastic incentive-based demand response programs for VPPs Application of multi-objective optimization to peer-to-peer energy trading systems UN Sustainable Development Goals His research directly contributes to: SDG 7 : Affordable and clean energy SDG 12 : Responsible consumption and production

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

Kay Hamacherمشاهده پروفایل

استاد

• Mathematical modeling
• Data analytics
• Computational biology
• +۳ مورد دیگر

Kay Hamacher is a Professor of Computational Biology & Simulation at the Department of Biology , with co-appointments in the Department of Computer Science and Department of Physics at Technische Universität Darmstadt . His research bridges mathematical modeling , biostatistics , and privacy-enhancing technologies for biomedical data. Develops advanced algorithms for stochastic global optimization in molecular systems Co-investigator in HiGHmed , CORD , and Athene consortia for secure medical data analysis Principal investigator of the Biotite project for computational biology tools His work focuses on: Computational biophysics of ion channels (e.g., HCN4) and protein complexes Secure data science for personalized medicine and genome-wide studies Algorithm development in metaheuristics and dynamic graph analysis Publications span 2024–2022 with breakthroughs in: HCN channel structural modeling Privacy-preserving epistasis analysis Hydrogen atom placement in molecular simulations Secure multi-party computation frameworks Statistical complexity measures Scientific awards include: Best Paper Award (GECCO2021) for genetic algorithm niching DeGBS Best-Paper-of-the-Year (2021) for DNA damage response research As an advisor, he mentors Magnus Behringer and Jan Krumbach , while leading a lab that integrates privacy, algorithms, and structural biology . Collaborations include CompuGene , iNAPO , and CORD-MI initiatives.

• Artificial Intelligence
• Fault Detection and Diagnosis
• Metamodeling
• +۸ مورد دیگر

Nicola Pedroni is an Associate Professor at the Department of Energy (DENERG) of Politecnico di Torino, Italy. His research focuses on advanced computational methods for nuclear safety, uncertainty quantification, and artificial intelligence applications in risk assessment. He has held external academic roles at institutions including MIT, École Centrale Paris, and CEA Saclay. Research Interests: Artificial Intelligence, Monte Carlo Methods, Nuclear Safety, Uncertainty Quantification, Probabilistic Risk Assessment, Resilience Analysis His recent work spans inverse uncertainty quantification, fault detection in safety-critical systems, and metamodeling techniques for nuclear reactor analysis. He serves on editorial boards for Frontiers in Nuclear Engineering , Energies , and Proceedings of the Institution of Mechanical Engineers, Part O . Pedroni leads the ARISTOTELES project on AI and stochastic simulation for critical infrastructure resilience and contributes to the EU-funded ENDURANCE project for molten salt reactor safety. He supervises PhD student George Afful and teaches courses in nuclear plant modeling and risk analysis.

Jose A. Ramosمشاهده پروفایل

استاد

• Control Systems
• Mechatronics
• System Identification
• +۴ مورد دیگر

Jose A. Ramos is a Professor in the Engineering Department at Nova Southeastern University's College of Computing & Engineering. He holds a Ph.D. and is affiliated with the Engineering department within the College of Computing & Engineering. Research Areas: Control systems, mechatronics, system identification, signal processing, stochastic processes, multivariate statistics, optimization theory.

• Modular Construction
• Stochastic Programming
• Construction Logistics
• +۷ مورد دیگر

Pei-Yuan (Leo) Hsu is a Lecturer specializing in modular construction, stochastic programming, and construction logistics. His research focuses on optimizing modular construction supply chains through stochastic programming and robust optimization, addressing uncertainties in off-site and hybrid construction methods. He also applies Design for Manufacturing and Assembly (DfMA) and lean principles to enhance sustainability. Current research includes reducing embodied carbon in modular supply chains, optimal wireless charging facilities for public transport, and reinforcement learning for humanitarian logistics. Completed projects include risk-averse modular supply chain design, root cause analysis for construction schedule deviations, and BIM integration for progress tracking. His work bridges theoretical optimization models with practical applications in construction management, emphasizing sustainability and efficiency. Key contributions include multi-stage stochastic programming frameworks for logistics planning and fault tree analysis for schedule deviation diagnosis.

Liaqat Ali Shahمشاهده پروفایل

پژوهشگر

• Hydrological Modeling
• Flood Risk Management
• GIS Applications in Water Engineering
• +۵ مورد دیگر

Liaqat Ali Shah is a Doctoral Researcher specializing in hydrological modeling and water engineering. His work integrates physical principles and data-driven approaches with Geographic Information Systems (GIS) to simulate water dynamics across rivers, groundwater systems, and rainfall-runoff processes. Current research focuses on nature-based solutions for flood risk management, hydrodynamic modeling, and climate-streamflow relationships. Doctoral Researcher in Water Engineering His research expertise spans hydrological modeling, climate change impact assessment, and water quality analysis. He employs GIS tools and advanced calibration techniques to enhance model reliability for sustainable water resource management and flood prediction. Recent publications address machine learning applications in material science, statistical significance of streamflow elasticity, watershed-water quality linkages, and pandemic-related pollution changes. His work emphasizes spatially explicit modeling and path analysis techniques. Collaborative projects include climate-hydrology interactions in Pakistan's major rivers, multi-scale environmental assessments, and vegetation-climate feedback mechanisms. Research outputs aim to improve flood forecasting and inform climate adaptation strategies.

Rebecca Morrisonمشاهده پروفایل

استادیار

• Dynamical systems
• Probabilistic graphical models
• Calibration, validation, and uncertainty quantification

Rebecca Morrison is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder. Her research focuses on dynamical systems, probabilistic graphical models, and calibration, validation, and uncertainty quantification. She actively mentors PhD students in Computer Science and collaborates with postdoctoral researchers in interdisciplinary fields like geology and climate science. Research Interests Dr. Morrison specializes in the intersection of dynamical systems and probabilistic modeling, with a particular emphasis on quantifying uncertainty in computational models. Her work spans theoretical developments in nonlinear transformations and practical applications in mechanical systems and ecological modeling. Publications Trends Her recent publications (2022-2025) demonstrate expertise in stochastic modeling, graphical model inference, and nonlinear dynamical systems. Key themes include model-form error analysis, non-Gaussian transformations, and validation frameworks across engineering and biological systems. Research Group Current: Nate Holland, Noah Peterson, Ujas Shah (CS PhDs); Jack Shaw (CS + Geology Postdoc) Former: Rileigh Bandy (CS PhD, 2024); Teo Price-Broncucia (CS PhD, 2025)

Sait Tuncمشاهده پروفایل

استادیار

• Operations Research
• Healthcare Analytics
• Queuing Theory
• +۶ مورد دیگر

Sait Tunc is an Assistant Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech, part of the College of Engineering. His research focuses on stochastic modeling, queuing theory, game theory, and machine learning applied to healthcare operations, medical decision making, and portfolio investment. He leads the Healthcare Analytics Research Group and is affiliated with the Translational Biology, Medicine & Health (TBMH) graduate program. Education Ph.D., Industrial and Systems Engineering, University of Wisconsin-Madison (2016) M.S., Industrial and Systems Engineering, University of Wisconsin-Madison (2015) M.S., Electrical and Electronics Engineering, Koç University, Istanbul, Turkey (2012) B.S., Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Turkey (2010) Research Interests Tunc’s work bridges operations research and healthcare systems, addressing challenges in organ transplantation, pandemic response, and medical diagnostics. He develops incentive mechanisms to optimize healthcare operations and reduce inefficiencies, leveraging game theory, Markov decision processes, and interpretable machine learning models. Recent contributions include kidney allocation algorithms, university reopening strategies during pandemics, and breast cancer screening guidelines. Article Trends His publications emphasize healthcare operations optimization, with a focus on kidney transplantation policies, pandemic preparedness, and diagnostic decision-making. Key themes include incentive mechanisms, predictive modeling, and systems engineering approaches to improve healthcare outcomes. Scientific Awards CAREER Award: Optimizing Healthcare Operations through Incentive Mechanism Design (2024) Advising & Grants Tunc mentors PhD students in applied mathematics and healthcare analytics. He actively seeks funding for interdisciplinary projects at the intersection of healthcare, operations research, and policy design. Current work includes NSF-supported research on incentive-compatible transplantation systems. Labs & Teams He directs the Healthcare Analytics Research Group , collaborating with clinicians and policymakers to translate data-driven insights into actionable healthcare solutions.