جست‌وجوی استاد و پژوهشگر

Minshuo Chenمشاهده پروفایل

استادیار

• Generative AI
• Diffusion Models
• Reinforcement Learning
• +۳ مورد دیگر

Minshuo Chen is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. He previously served as an Associated Research Scholar in the ECE department at Princeton University, collaborating with Prof. Mengdi Wang. His research focuses on developing methodologies and theoretical foundations in generative AI, reinforcement learning, and optimization. He holds a Ph.D. from Georgia Tech (supervised by Prof. Tuo Zhao and Wenjing Liao), a Master's from UCLA, and a Bachelor's from Zhejiang University. Key research areas include diffusion models for distribution estimation, foundations of learning (approximation and optimization), and reinforcement learning applications in complex systems. He has presented at major conferences like INFORMS 2024 and NeurIPS 2023, and serves as an area chair for NeurIPS 2023. His recent work emphasizes theoretical guarantees for diffusion models, including statistical rates and optimization perspectives. He has received awards such as the ARC-TRIAD Student Fellowship and William S. Green Fellowship. Collaborations include studies on POMDPs, policy evaluation, and manifold learning.

Xudong Chenمشاهده پروفایل

دانشیار

• Computational Methodologies
• Cyberphysical Systems & Internet of Things
• Machine Learning & Artificial Intelligence
• +۳ مورد دیگر

Xudong Chen is an Associate Professor in the Department of Electrical & Systems Engineering at Washington University in St. Louis, part of the McKelvey School of Engineering. Previously, he held an Assistant Professor position at the University of Colorado, Boulder. He earned a BS in Electronics Engineering from Tsinghua University (2009) and a PhD in Electrical Engineering from Harvard University (2014). His research focuses on control theory, decision theory, dynamical systems, stochastic processes, and network science, with a particular emphasis on large-scale multi-agent systems. Applications span quantum systems, smart materials, neuroscience, social science, robotics, drones, and spacecraft. His work develops advanced mathematical tools and engineering methods to address challenges in these complex systems. Notable Awards: 2023 A.V. Balakrishnan Early Career Award 2021 Donald P. Eckman Award NSF CAREER Award (2021) AFOSR Young Investigator Award (2020) Chen has secured grants including NSF support for graphon-based structural system theory and AFOSR funding. He leads a research group and maintains a lab website for collaborative projects. His work bridges theoretical foundations with practical applications across diverse engineering domains.

Keenan Craneمشاهده پروفایل

دانشیار

• Computer Science
• Mathematics
• Scientific Computing
• +۷ مورد دیگر

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Fangwei Siمشاهده پروفایل

استادیار

• Biophysics
• Bacterial Physiology
• Cellular Adaptation
• +۷ مورد دیگر

Fangwei Si is the Cooper-Siegel Assistant Professor of Physics at Carnegie Mellon University's Department of Physics, with courtesy appointments in Biomedical Engineering. His research focuses on uncovering biological laws through quantitative biophysics , integrating microfluidics , imaging , and physical modeling . He previously held postdoctoral positions at The Scripps Research Institute and University of California, San Diego, and earned his Ph.D. in Mechanical Engineering from Johns Hopkins University. Ph.D.: Johns Hopkins University (2015) B.S.: Peking University (2009) His research bridges cell surface biophysics , cellular adaptation , and bacteria-phage interactions , emphasizing how cells optimize fitness through precise membrane organization and component redundancy . Current projects explore mechanical compression effects , quantitative adaptation principles , and phage-host coevolution . The lab's articles reveal trends in cell size control (2017-2019), mechanosensation (2018), and stochastic modeling (2020-2021), extending to machine learning approaches (2025) and high-throughput imaging (2024). Key methods include microfluidics , genetic modulation , and physical modeling . At CMU, Si leads the Experimental Cell Biophysics Lab, mentoring Ph.D. students like Mo Zhou and Christopher Aldrich , alongside postdocs and undergraduates. His lab received NIH and NSF grants in 2023 to advance research on microbial systems.

• Causal Machine Learning
• Topological Data Analysis
• Riemannian Optimization
• +۵ مورد دیگر

Qian Li is a Lecturer in Computing at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University, Australia. She holds a Ph.D. from the Chinese Academy of Sciences and M.Sc. degrees from Shandong University and the University of Luxembourg. Her research focuses on causal machine learning, topological data analysis, and optimal transport, with applications in computer vision, data science, and recommendation systems. She has published over 50 articles in top-tier venues like IEEE Transactions and ACM conferences. Education Ph.D., Chinese Academy of Science (CAS) MSc (Research), Shandong University MSc (Research), University of Luxembourg Research Interests Dr. Li explores causal reasoning for machine learning, leveraging mathematical tools like Riemannian geometry and optimal transport to address challenges in robustness and interpretability. Her work spans causal inference, counterfactual fairness, and explainable AI, with applications in healthcare, energy, and commerce. Recent projects include causal-based recommendation systems and topological data analysis techniques. Key Achievements Secured a $120k grant from China's National Natural Science Foundation (2020-2024). Lead researcher on AI-driven solar energy storage projects with UNSW and Providence Asset Group. Recipient of prestigious scholarships including Chinese National Graduate Scholarship (2016, top 1%). Grants & Students Current Ph.D. students include Xiangmeng Wang and Tri Dung Duong. She has supervised graduates like Yangyang Shu (Adelaide University Research Associate) and Jun Yin (UTS). Labs & Teams Leads research in causal AI and topological data analysis, collaborating with institutions like UTS and the University of Melbourne.

Mohamed Ibnkahlaمشاهده پروفایل

استاد

• Wireless Sensor Networks
• Internet of Things (IoT)
• Cognitive Radio Networks
• +۵ مورد دیگر

Dr. Mohamed Ibnkahla is a Full Professor and NSERC/Cisco Senior Industrial Research Chair in Sensor Networks for the Internet of Things (IoT) at Carleton University's Department of Systems and Computer Engineering. He holds a Ph.D. and HDR from the National Polytechnic Institute of Toulouse (INP), France. His research focuses on IoT, wireless sensor networks, cognitive radio systems, and adaptive signal processing, with applications in smart cities, healthcare, energy, and transportation. He has led numerous industry and government-funded projects, including the Carleton-Cisco IoT Testbed. Education: Ph.D. and HDR (INP Toulouse, 1996/1998), Engineering and MSc in Electronics/Signal Processing (INP Toulouse, 1992). He previously served at Queen’s University (2000–2015) and INP Toulouse (1996–1999). Research Interests: IoT security, energy harvesting, cognitive radio networks, smart grid communication, and machine learning for wireless systems. His work spans theoretical contributions (e.g., 5 books on signal processing and IoT) and applied innovations (e.g., sensor network architectures for environmental monitoring). He has published over 150 peer-reviewed papers, 4 books, and supervised ~40 graduate students. Awards include the INP Leopold Escande Medal (1997) and Ontario’s Prime Minister’s Research Excellence Award (2000). His lab develops solutions for IoT trust management, edge computing, and decentralized access control using blockchain. Key Projects: Cisco-funded IoT sensor networks for smart cities, NSF-funded cognitive radio projects, and collaborations on eHealth systems. His research addresses IoT security challenges in healthcare, transportation, and energy grids, emphasizing scalable, energy-efficient solutions.

Ji Liuمشاهده پروفایل

دانشیار

• Distributed Control
• Multi-Agent Systems
• Distributed Optimization
• +۴ مورد دیگر

Ji Liu is an Associate Professor in the Department of Electrical and Computer Engineering at Stony Brook University. He is affiliated with the Department of Applied Mathematics and Statistics, the Institute for AI-Driven Discovery and Innovation, and the Institute for Engineering-Driven Medicine. He holds a Ph.D. from Yale University and completed postdoctoral research at the University of Illinois at Urbana-Champaign and Arizona State University. His research focuses on distributed control, multi-agent systems, distributed optimization, and cyber-physical systems, with applications to social networks, epidemic models, and quantum networks. Key areas of investigation include consensus algorithms, network topology design, and resilient reinforcement learning. Recent work explores topics like entanglement routing in quantum networks, decentralized multi-armed bandits, and bi-virus disease dynamics. His contributions span theoretical frameworks and practical implementations in domains such as smart grids, medical prosthetics, and secure microgrid control. Education: Ph.D. in Electrical Engineering, Yale University (201X) Postdoctoral Research: UIUC (Coordinated Science Lab), ASU (School of ECEE) Labs: Active in distributed systems, quantum control, and networked epidemic modeling

• Nanoelectronic Circuit Design
• Neuromorphic Computing
• Hardware Security
• +۴ مورد دیگر

Garrett Rose is a Professor and Department Head in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK). He holds a B.S. in Computer Engineering from Virginia Tech (2001), and M.S. and Ph.D. in Electrical Engineering from the University of Virginia (2003/2006). Prior to UTK, he served as Assistant Professor at NYU Polytechnic (2006–2011) and Senior Electronics Engineer at the Air Force Research Lab (2011–2014). His research focuses on nanoelectronic circuit design, neuromorphic computing, hardware security, and memristor-based systems. He leads the SENECA Research Group and the TENNLab initiative, exploring applications in neuromorphic architectures, hardware security primitives (e.g., PUF devices), and device modeling. Recent work emphasizes memristor-driven neuromorphic systems, secure FPGA designs, and in-memory computing. Grants include projects on neuromorphic target detection and nanotechnology-based security solutions. Rose actively mentors students and collaborates on co-design methodologies for real-world neuromorphic applications. Education: Ph.D. Electrical Engineering, University of Virginia, 2006 M.S. Electrical Engineering, University of Virginia, 2003 B.S. Computer Engineering, Virginia Tech, 2001 Research Interests: Dr. Rose’s work spans neuromorphic hardware design, including memristor-based neural networks and spiking systems. He investigates hardware security through nanoscale devices like memristors for PUFs and side-channel resistant circuits. His team develops novel memristor models and explores applications in reconfigurable computing and energy-efficient architectures. Recent efforts focus on neuromorphic vision systems, robotic navigation, and neuromorphic processors with co-design frameworks. Grants & Projects: "Ground-roaming autonomous neuromorphic targeter" (2020) "Secure Backup and Restore for IoT using Nanotechnology" (2020) "Physically Unclonable Reconfigurable Computing System (PURCS)" (2020)

Gaofeng Jiaمشاهده پروفایل

دانشیار

• Structural Engineering
• Risk Assessment
• Infrastructure Resilience
• +۴ مورد دیگر

Gaofeng Jia is Associate Professor in Civil and Environmental Engineering at Colorado State University's Walter Scott, Jr. College of Engineering. He specializes in natural hazard risk assessment, infrastructure resilience, and uncertainty quantification. Research integrates simulation-based approaches with high-performance computing for assessing complex engineering systems. Core areas include infrastructure deterioration modeling, wave energy converter optimization, tsunami evacuation planning, and surrogate modeling techniques. Recent work emphasizes physics-informed machine learning for engineering applications. Publications demonstrate consistent methodological innovation in uncertainty quantification across domains including seismic engineering, coastal hazards, renewable energy systems, and structural reliability. Articles employ advanced computational statistics, Bayesian methods, and multi-fidelity modeling. Young Researcher Best Paper Award Best Student Paper Award

• Optimization and optimal control
• Inverse problems
• Mathematical imaging
• +۵ مورد دیگر

Karl Kunisch is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz and serves as Scientific Director of the Radon Institute of the Austrian Academy of Sciences in Linz. With a distinguished career spanning several decades, he has established himself as a leading researcher in optimization and control theory. Prof. Kunisch completed his PhD and Habilitation at the Technical University of Graz in 1978 and 1980, respectively. His academic journey includes significant positions at Brown University's Lefschetz Center for Dynamical Systems, INRIA Rocquencourt, Universite Paris Dauphine, and he previously served as a professor of numerical mathematics at the Technical University of Berlin. Research Interests: Prof. Kunisch's research focuses on optimization and optimal control, inverse problems and mathematical imaging, numerical analysis and applications, with current emphasis on life sciences applications. His specific areas include Optimal Control of Partial Differential Equations, Nonsmooth Optimization in Function Spaces, and Applications of Optimization and Control in the Life Sciences. His work bridges theoretical mathematics with practical applications across various scientific domains. His recent publications demonstrate a continued focus on advancing the theoretical foundations of optimal control while developing practical numerical methods. Key trends include work on infinite horizon control problems, feedback stabilization techniques, applications to PDE-constrained optimization, and the integration of machine learning approaches with traditional control theory. His research group actively explores connections between theoretical developments and applications in the life sciences. Scientific Recognition: W.T. and Idalia Reid Prize 2021 SIAM Fellow (2017) European Research Council Advanced Grant (2015) Alwin Walther Medaille (2008) SIAM Outstanding Paper Prize (2006) Prof. Kunisch has made substantial contributions to the mathematical community through his editorial work, serving as editor for prestigious journals including SIAM Journal on Control and Optimization, SIAM Journal on Numerical Analysis, and the Journal of the European Mathematical Society. He leads the Research Group on Optimization and Optimal Control at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) and is involved in the ERC-Project OCLOC "From Open to Closed Loop Control".

Josephine V. Carstensenمشاهده پروفایل

دانشیار

• Structural Design Optimization
• Sustainable Materials Engineering
• Carbon Emissions Reduction
• +۴ مورد دیگر

Josephine V. Carstensen is the Gilbert W. Winslow Career Development Associate Professor in the Department of Civil and Environmental Engineering at MIT. She holds a B.Sc. and M.Sc. from the Technical University of Denmark, and an M.S.E. and Ph.D. from Johns Hopkins University. Her research focuses on leveraging digitalization and AI to revolutionize structural design, particularly in topology optimization and sustainable materials engineering. She teaches Senior Civil and Environmental Engineering Design (1.013) and Topology Optimization of Structures (1.583). Her lab, the Carstensen Group, develops algorithms for human-AI collaboration in design, emphasizing manufacturability and sustainability. Notable achievements include NSF CAREER Award (2021), CEE Maseeh Teaching Award (2021), and Denmark-America Fellowship (2012). Recent work includes minimizing carbon footprints in truss designs and automating hyperparameter tuning in topology optimization. Her research spans structural mechanics, material architecture, and carbon reduction strategies. Current projects explore AI-driven design frameworks, reclaimed material utilization, and timber-steel hybrid systems. The lab’s experimental work includes 3D-printed concrete beams and waterjet-cut steel reinforcement testing. Key Research Themes: Sustainable infrastructure, topology optimization, embodied carbon reduction, AI-human collaboration Affiliations: MIT Sustainable Materials & Infrastructure Faculty, MIT Department of Civil and Environmental Engineering Lab Focus: Interdisciplinary engineering solutions for next-generation structural systems

Haiyan Huangمشاهده پروفایل

استاد

• Computational Biology
• Applied Statistics
• High Dimensional Data Analysis
• +۳ مورد دیگر

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics. Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics. Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility. Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work. Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems. Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.

Do Young Eunمشاهده پروفایل

استاد

• Distributed Optimization
• Machine Learning
• Network Modeling
• +۵ مورد دیگر

Do Young Eun is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University (NC State), with affiliations in Computer Science and Operations Research. He holds a Ph.D. from Purdue University and M.S./B.S. degrees from KAIST, Korea. His research focuses on distributed optimization for machine learning, network modeling, and algorithms for social/wireless networks, with applications in epidemic analysis and graph analytics. Education: Ph.D. in Electrical and Computer Engineering, Purdue University (2003) M.S. in Electrical Engineering, KAIST (1997) B.S. in Electrical Engineering, KAIST (1995) Research Interests: Distributed optimization and machine learning Network modeling and performance analysis Epidemic modeling and control Graph analytics and social network analysis Stochastic processes and algorithms Highlighted Awards: NSF CAREER Award (2006) Outstanding Paper Award, ICML 2023 Best Paper Awards at IEEE ICCCN (2005), IPCCC (2006), NetSciCom (2015) Best Student Paper Award, ACM MobiCom 2007 Advising & Grants: Current advisees include Jie Hu, Yi-Ting Ma, and Feiya Xiang NSF Grant (2024–2027): 'Toward Maximally Efficient Sampling and Optimization for Decentralized Learning' Supervised over 10 Ph.D. students, many in academic or industry leadership roles Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of networking, machine learning, and stochastic systems, with collaborations in academia and industry.

Aritra Mitraمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Control Theory
• +۶ مورد دیگر

Aritra Mitra is an Assistant Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from Purdue University (2020), an M.Tech. from IIT Kanpur (2015), and a B.E. from Jadavpur University (2013). Before joining NC State, he was a postdoctoral researcher at the University of Pennsylvania. His research focuses on enabling reliable, efficient learning and decision-making in large-scale distributed systems, addressing challenges like computation, communication constraints, and adversarial robustness. Key areas include control theory, machine learning, signal processing, and network science. Education: Ph.D., Electrical and Computer Engineering, Purdue University (2020) M.Tech., Electrical Engineering, Indian Institute of Technology Kanpur (2015) B.E., Electrical Engineering, Jadavpur University (2013) Research Interests: Dr. Mitra’s work bridges theoretical foundations with practical applications in distributed systems. He designs algorithms for federated learning, reinforcement learning, and adversarial robustness, with applications in control systems and networked environments. Recent efforts emphasize finite-time analysis of TD learning, heterogeneous federated systems, and resilient control under communication constraints. His contributions often integrate tools from stochastic approximation, optimization, and signal processing. Publications: His articles explore cutting-edge topics like federated TD learning, robust system identification under heavy-tailed noise, and distributed multi-agent optimization. Recent trends highlight advancements in asynchronous algorithms, delay-adaptive systems, and model-free control under communication bottlenecks. Grants & Labs: While specific grants are not detailed, his research aligns with themes in distributed computing and control, suggesting potential involvement in NSF or industry-funded projects. No lab-specific details are provided in the text.

Chanan Singhمشاهده پروفایل

استاد

• Reliability of Electric Power Systems
• Integration of Renewable Energy Sources
• Cyber-Physical Systems Reliability
• +۳ مورد دیگر

Chanan Singh is a distinguished academic serving as a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He holds the Irma Runyon Chair and is a Regents Professor. His affiliations include the College of Engineering and a Guest Professorship at Tsinghua University's Department of Electrical Engineering (2010–2015). Dr. Singh earned his Ph.D. in Electrical Engineering from the University of Saskatchewan, alongside M.S. and B.S. degrees from the same institution and Punjab Engineering College, respectively. His research focuses on reliability and security of electric power systems , including renewable energy integration and cyber-physical systems resilience. He pioneered methodologies for hurricane impact analysis, cyber-malfunction modeling, and wind farm optimization. Key achievements include the IEEE-PES Roy Billinton Award (2010), PMAPS Merit Award (2008), and Fellow of IEEE (1991). His work has been recognized globally, including through over 20 major awards and fellowships. Dr. Singh advises students like Hangtian Lei and leads funded projects on power system resilience. He is affiliated with the Electric Power System Group , advancing interdisciplinary research in energy systems and reliability engineering.