جست‌وجوی استاد و پژوهشگر

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.

• Multi-scale Modeling
• Powder Forging
• Mechanical Engineering
• +۶ مورد دیگر

Mr. Yi Wang is a researcher at the Department of Mechanical Engineering, Imperial College London, specializing in Multi-scale Modeling of Powder Forging and related fields. His work bridges mechanical engineering, energy systems, and artificial intelligence. University: Imperial College London Department: Department of Mechanical Engineering Research Interests include electric vehicle-grid integration, microgrid resilience, reinforcement learning applications in energy systems, and multi-energy network optimization. His publications emphasize AI-driven strategies for low-carbon transitions and grid stability. Email: yi.wang12@imperial.ac.uk

• Machine Learning Systems
• Differential Privacy
• Adversarial Robustness
• +۳ مورد دیگر

Mathias Lecuyer is an Assistant Professor at the University of British Columbia (UBC), Department of Computer Science, where he leads the Systopia research group. His research focuses on trustworthy AI systems, including differential privacy, adversarial robustness, and causal machine learning. He holds a PhD from Columbia University and was a postdoctoral researcher at Microsoft Research. Education: PhD in Computer Science, Columbia University Postdoctoral Researcher, Microsoft Research (New York) Research Interests: Privacy-preserving machine learning (Differential Privacy) Certified adversarial robustness via randomized smoothing Causal inference for model generalization Systems for privacy and security in AI Awards: SOSP Distinguished Artifact Honourable Mention (2024) Google Research Award Supervised Students: PhD: Qiaoyue Tang, Saiyue Lyu, Frederick Shpilevskiy MSc: Mishaal Kazmi, Shadab Shaikh, Shiqi He Undergrad: Alain Zhiyanov, Jessica Bator, Ryan Shar, Eric Xiong, Joel Hempel Labs/Teams: Member of UBC S&P, TrustML, and CAIDA research groups.

Henry Addisonمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Climate Science
• Climate Modeling
• +۳ مورد دیگر

Henry Addison is a Senior Research Associate at the School of Geographical Sciences, University of Bristol. His research focuses on advancing climate science through machine learning techniques, particularly in the emulation of high-resolution regional climate models. He holds a BA, MMath, and MSc, and his work addresses critical challenges in climate prediction, extreme weather analysis, and computational efficiency of climate simulations. His research interests include developing machine learning frameworks to enhance the scalability and accuracy of climate models, with applications in understanding local climate change impacts, precipitation patterns, and environmental risk assessment. His recent studies emphasize diffusion models and emulation techniques for improving the resolution and accessibility of climate data. Publications highlight trends in machine learning-driven climate emulation, downscaling methodologies, and predictive modeling of extreme weather events. While no explicit awards or grants are mentioned, his work aligns with cutting-edge climate science and computational innovation.

ALBERTE REGUEIRA LOPEZمشاهده پروفایل

استادیار

• Environmental Biotechnology
• Metabolic Engineering
• Biorefinery Design
• +۳ مورد دیگر

Alberte Regueira López is an Assistant Professor at the Department of Chemical Engineering, Higher Technical School of Engineering, University of Santiago de Compostela. He holds a Doctorate in Chemical Engineering from the same institution, completing his thesis on Mechanistic understanding of mixed-culture fermentations by metabolic modeling in 2020, supervised by Dr. Juan Manuel Lema Rodicio and Dr. Miguel Mauricio Iglesias. His research focuses on environmental biotechnology, metabolic engineering, and biorefinery design, with an emphasis on microbial fermentation systems for the production of valuable chemicals from waste streams. Key research interests include metabolic modeling of mixed microbial cultures, optimization of biorefinery processes for organic waste valorization, and development of targeted production systems for volatile fatty acids (VFAs). He leads the BIOGROUP research team, investigating the synergistic interactions in microbial communities and applying systems biology approaches to improve bioconversion efficiency. His recent work explores the use of Clostridium species for gas fermentation, hollow fiber membrane biofilm reactors, and the integration of metabolic models with stochastic optimization for biorefinery design. He has pioneered methods to steer microbial production of isobutyric/n-butyric acids and lactic acid through process parameters like pH and substrate composition, while also advancing in-situ product removal strategies for bioreactor systems. Notable contributions include defining protein composition effects on microbial fermentation pathways, developing kinetic models for protein-to-VFA conversion, and applying superstructure optimization techniques for bioprocess design. His work bridges fundamental microbiology with applied engineering solutions for sustainable chemical production.

Lu Fengمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Lu Feng is an Associate Professor of Computer Science at the University of Virginia, affiliated with the Link Lab, a center specializing in Cyber-Physical Systems (CPS). She holds a Ph.D. in Computer Science from the University of Oxford. Her research focuses on ensuring safety and trustworthiness in CPS, with applications in medical devices, autonomous robotics, and smart cities. She has received prestigious awards including the NSF CRII Award (2018) and NSF CAREER Award (2020). Her work integrates formal methods, AI, and robotics to address challenges in CPS assurance and human-machine collaboration. Notable contributions include developing risk-assessment tools for heart failure patients, predictive monitoring frameworks for CPS, and trust-aware planning algorithms for autonomous systems. She has pioneered frameworks like DP-RuL for clinical decision support systems and IrrMap for precision agriculture. Her research bridges theoretical foundations (e.g., model checking, reinforcement learning) with practical applications in healthcare, transportation, and urban systems. She collaborates across disciplines, contributing to initiatives like the Link Lab’s smart city simulations and safety-critical medical CPS assurance. Education: Ph.D., Computer Science, University of Oxford Awards: NSF CRII (2018), NSF CAREER (2020) Labs: Link Lab (Cyber-Physical Systems Center) Focus Areas: Runtime safety, human-AI trust, medical device assurance, smart city systems

Michael Pittمشاهده پروفایل

استاد

• Statistical Methodology
• Bayesian Inference
• Sequential Monte Carlo Methods
• +۵ مورد دیگر

Professor Michael Pitt is a faculty member in the Department of Mathematics at King's College London, holding the rank of Professor of Statistics within the Faculty of Natural, Mathematical & Engineering Sciences. He completed his doctorate in Statistics at the University of Oxford (Nuffield College) in 1997, followed by postdoctoral research at Imperial College London. His career includes roles at the University of Warwick (1999-2016) as Assistant and Associate Professor before joining King's College. Education: PhD in Statistics, University of Oxford (1997) Postdoctoral Research, Imperial College London (1997-1999) Research Interests: Focuses on advanced statistical methodologies including particle filtering, sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), and their applications in financial econometrics and biostatistics. Key areas include: Development of computationally efficient algorithms for Bayesian inference Analysis of financial time series and stochastic volatility models Multivariate copula models for dependence structures Clinical outcome analysis in cardiology through statistical revascularization studies Publications: Recent work emphasizes methodological advancements in pseudo-marginal methods, correlated particle filtering, and adaptive sampling techniques. Notable contributions include applications in cardiac clinical outcomes and high-dimensional density modeling. Labs/Teams: Active member of the Research Centre for Non-Equilibrium Science (CNES) , focusing on interdisciplinary non-equilibrium systems, and part of the Statistics Group within the Department of Mathematics.

Andrey Bernsteinمشاهده پروفایل

مدرس

• Control and optimization of energy systems
• Data analytics for energy systems
• Adaptive decision-making in complex environments
• +۷ مورد دیگر

Andrey Bernstein is a Senior Researcher and Group Manager at the National Renewable Energy Laboratory (NREL) and a Lecturer at the University of Colorado Boulder. Since 2016 he has led NREL’s Grid Modernization research thrust on power systems engineering, focusing on decision-making, optimization and machine-learning methods for energy systems. Education: Ph.D. in Electrical Engineering, Technion – Israel Institute of Technology M.Sc. in Electrical Engineering, Technion – Israel Institute of Technology B.Sc. in Electrical Engineering, Technion – Israel Institute of Technology Research Interests: Bernstein’s work centers on the intersection of control theory, optimization, machine learning and power systems . Key themes include adaptive decision-making in complex stochastic environments, distributed optimization algorithms for real-time operation of distribution networks, market design for transactive energy systems, data-driven state estimation, and advanced inverter control strategies enabling high renewable penetration. His recent publications (2024-2025) collectively push toward decentralized, learning-based, and robust solutions for next-generation power grids. Themes span decentralized market mechanisms that respect network constraints, distributionally robust optimization for secure voltage control, dynamic pricing schemes maximizing social welfare in energy-sharing communities, and grid-forming inverter control strategies that reshape system-wide dynamic response. Scientific Awards & Honors: NREL Distinguished Member of Research Staff (2022) NREL President’s Award (2018) NREL President’s Award (2021) Affiliations & Service: Bernstein is an active member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Electrical and Electronics Engineers (IEEE), participating in multiple technical committees and collaborative networks within NREL and across international institutions.

Masoud H. Nazariمشاهده پروفایل

استادیار

• Power Systems
• Grid Optimization
• Resilience Engineering
• +۶ مورد دیگر

Dr. Masoud H. Nazari is an Assistant Professor in the Department of Electrical and Computer Engineering at Wayne State University's College of Engineering. His research focuses on resilience and optimization of modern power systems through applications of stochastic hybrid systems, machine learning, and control theory. He holds leadership positions including Chair of IEEE Smart Buildings, Loads, and Consumer Systems Architecture Subcommittee and serves on IEEE Technology Conferences editorial boards. Education: Ph.D. in Electrical & Computer Engineering/Engineering & Public Policy from Carnegie Mellon University (2012), with visiting research at Massachusetts Institute of Technology. M.S. in Engineering & Public Policy from Carnegie Mellon University (2010). Research Focus: Primary interests include modern power systems resilience, stochastic hybrid systems, large-scale grid optimization, AI-enabled community resilience assessment, cyber-physical systems, and IoT applications in energy management. His work bridges theoretical control systems with practical energy infrastructure challenges. Publication Trends: Recent articles demonstrate strong focus on distributed control systems, resilience engineering in power grids, AI/ML applications for anomaly detection and optimization, and microgrid management. Work frequently addresses communication failures, stochastic variations, and cyber-physical security challenges in energy systems. Awards and Honors: WSU Excellence in Teaching Award (2024) Top 10 Team - ARPA-E Grid Optimization Challenge (2022-23) DTE E-Challenge Winner (2022) IEEE Senior Member (2019) Best Paper Award - North American Power Symposium (2017) Research Leadership: Principal Investigator for NSF AMPS ($200K): Stochastic contingency detection in power systems Co-PI for DOE RACER ($1M): Energy-water nexus resilience planning PI for ARPA-E Grid Optimization Challenge ($400K) Led DTE E-Challenge project developing AI-controlled HVAC systems Research Group: Leads the Modern Power Systems research team with current PhD students Hamid Varmazyari and Antar Kumar Biswas. Former advisees include Dr. Siyu Xie (now Assistant Professor in China) and multiple graduate/undergraduate researchers. The group focuses on control algorithms, resilience analytics, and optimization frameworks for power infrastructure.

Maxime Cordyمشاهده پروفایل

پژوهشگر

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

Maxime Cordy is a researcher in Computer Science with a focus on Software Engineering and Formal Verification. He holds a PhD in Computer Science from the University of Namur, earned in 2014, and has engaged in visiting research at the University of Luxembourg (2017-2018). He also co-founded SkalUp as an R&D manager from 2015 to 2016. Education: Doctor of Science (University of Namur, 2014), Master in Computer Science (University of Namur, 2011) His research spans Software Product Lines , Model Checking , and Variability-Intensive Systems , emphasizing formal verification and automated analysis. He has contributed to over 53 research outputs with 949 citations and an h-index of 17. Recent publications include advancements in Featured Transition Systems , Mutation-Based Model Checking , and Machine Learning for Software Quality . He co-organized workshops like MaLTeSQuE 2019 and the Machine Learning and Software Engineering in Symbiosis workshop (2018). Scientific Awards: VAMOS 2024 Ten-Year Most Influential Paper Award (co-recipient) Maxime has collaborated extensively with institutions including the University of Luxembourg and co-authored works with leading researchers in formal methods and software engineering.

Lance Manuelمشاهده پروفایل

استاد

• Uncertainty Quantification
• Wind Energy
• Offshore Structures
• +۵ مورد دیگر

Lance Manuel is a Professor of Engineering at The University of Texas at Austin. His research focuses on uncertainty quantification in engineered systems, particularly wind energy and offshore structures. He has led projects related to wind turbine fatigue analysis, hurricane risk assessment, and climate change adaptation. Ph.D., Civil Engineering (Stanford University) M.S., Civil Engineering and Applied Mechanics (University of Virginia) B.Tech., Civil Engineering (Indian Institute of Technology, Bombay) His work bridges civil infrastructure with climate resilience, emphasizing probabilistic methods for structural reliability. Key areas include extreme climate modeling , floating offshore wind turbines , and fatigue damage prediction under non-stationary conditions. Recent publications highlight interdisciplinary trends in renewable energy systems and climate hazard quantification . He advises graduate students like Taemin Heo and Ding Peng Liu, whose work spans stochastic processes and offshore structural reuse.

• Task-Oriented Knowledge Acquisition
• Online Learning and Control
• Human-Robot Interaction
• +۴ مورد دیگر

Mahsa Ghasemi is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering, Purdue University, located in West Lafayette. She holds a B.Sc. in Mechanical Engineering from Sharif University of Technology (2014), an M.S.E. in Mechanical Engineering from The University of Texas at Austin (2017), and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin (2021). Her research focuses on task-oriented knowledge acquisition, online learning and control, human-robot interaction, trustworthy AI, and socially beneficial autonomy. She is affiliated with the Materials and Electrical Engineering Building at Purdue. Education: B.Sc., Mechanical Engineering, Sharif University of Technology (2014) M.S.E., Mechanical Engineering, UT Austin (2017) Ph.D., Electrical and Computer Engineering, UT Austin (2021) Her research interests span interdisciplinary areas including reinforcement learning, causal inference, control systems, and human-autonomy collaboration. Recent work emphasizes resilient cyber-physical systems, privacy-preserving multi-agent learning, and causal discovery in decision-making frameworks. Her articles address challenges in sensor selection, no-regret learning in bandits, and formal methods for autonomous systems. Publications highlight contributions to submodular optimization in hypothesis testing, robust sensor scheduling in intrusion detection, and adaptive experimental design for causal discovery. Her work bridges theoretical foundations with practical applications in robotics, cybersecurity, and AI ethics. No scientific awards or grants are explicitly listed in the provided data. She advises no listed students but collaborates on projects involving diverse planning and decision-making in constrained environments.

Joshua I Glaserمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +۷ مورد دیگر

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.

Leonardo Duenas-Osorioمشاهده پروفایل

استاد

• Infrastructure Resilience
• Quantum Computing Applications
• Network Reliability
• +۷ مورد دیگر

Leonardo Duenas-Osorio is a Professor of Civil and Environmental Engineering at Rice University, affiliated with the George R. Brown School of Engineering. He holds a Ph.D. from Georgia Institute of Technology (2005) and advanced degrees from MIT and Colombian universities. His research focuses on computational methods for infrastructure resilience, quantum computing applications in engineering, and risk assessment of interdependent systems. Key areas include network reliability, disaster management, and smart grid resilience. He leads the SISRRA research group, collaborating with statisticians, computer scientists, and engineers to address societal challenges. Recognitions include the NSF CAREER Award (2008), IASSAR Early Achievement Award (2017), and the 2020 Society for Risk Analysis Best Paper Award. His work is funded by NSF, DoD, NIST, and city initiatives. Current projects explore quantum algorithms for infrastructure reliability, socio-technical community resilience, and decentralized network restoration strategies. Education: B.S., Universidad de La Salle, 1996 M.S., Universidad de Los Andes, 1998 Professional Masters, Pontificia Universidad Javeriana, 2000 M.Eng., MIT, 2001 Ph.D., Georgia Tech, 2005 Research interests span infrastructure resilience quantification, quantum-classical optimization, and risk analysis of lifeline systems. He serves as Associate Editor for Natural Hazards Review and Structural Safety , and actively participates in ASCE committees. His group emphasizes interdisciplinary collaboration, addressing urban water security, hurricane risk economics, and topology optimization of critical networks.

Bahman Gharesifardمشاهده پروفایل

استاد

• Systems and Control
• Distributed Control and Optimization
• Geometric Control Theory
• +۹ مورد دیگر

Bahman Gharesifard is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada. He holds a Ph.D. from Queen's University (2009) and advanced degrees from Shiraz University (B.Sc., 2002; M.Sc., 2005). His research focuses on systems and control theory, with emphasis on distributed control, optimization, geometric control, and their intersections with network sciences, machine learning, and game theory. He has been recognized with the First Year Instructor Teaching Award in Engineering & Applied Science (2014 & 2016). His academic journey includes postdoctoral research at the University of California, San Diego (2009–2012) and the University of Illinois, Urbana-Champaign (2012–2013). His work bridges theoretical foundations of control systems with practical applications in distributed optimization, neural networks, and contagion models on networks. Recent research trends include advancing Lyapunov-based methods for reinforcement learning, analyzing structural controllability in sparse systems, and developing models for network dynamics using Pólya urn frameworks. His articles explore topics like averaged controllability, flexible-step MPC, and stability in distributed algorithms. Education: Ph.D., Queen's University (2009) M.Sc., Shiraz University (2005) B.Sc., Shiraz University (2002) Awards: Engineering & Applied Science First Year Instructor Teaching Award (2014) Engineering & Applied Science First Year Instructor Teaching Award (2016) He collaborates on projects involving secure distributed optimization, epidemic modeling via Pólya contagion networks, and neural network approximation guarantees. His lab contributes to theoretical control advancements with practical implications in robotics, energy systems, and AI.