جست‌وجوی استاد و پژوهشگر

A. Stephen Morseمشاهده پروفایل

استاد

• Control Systems
• Network Science
• Multi-Agent Systems
• +۵ مورد دیگر

A. Stephen Morse is the Dudley Professor of Electrical & Computer Engineering at Yale University. He has been affiliated with Yale since 1970 and holds memberships in prestigious organizations such as the National Academy of Engineering and the Connecticut Academy of Science and Engineering. His research focuses on control systems, including hybrid systems, network science, multi-agent coordination, and sensor networks. He has received numerous awards, including the Bellman Control Heritage Award (2013) and the IEEE Technical Field Award (1999). Morse earned his BSEE from Cornell University, MS from the University of Arizona, and PhD from Purdue University. His work emphasizes logic-based switching, vision-based control, and distributed algorithms for autonomous systems. He has contributed to foundational papers on multi-agent consensus and formation control, as well as sensor network localization. Current projects include swarming dynamics and reactive control strategies for autonomous vehicles. His scientific contributions span over 200 publications, with recent work addressing distributed control algorithms, climate impact modeling, and game-theoretic network analysis. Morse advises graduate students like Ming Cao and Jia Fang, and his research group explores cutting-edge topics in systems theory and robotics.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Hien Quoc Ngoمشاهده پروفایل

دانشیار

• Massive MIMO
• Cell-Free Massive MIMO
• Cooperative Communications
• +۴ مورد دیگر

Dr. Hien Quoc Ngo is a Reader at Queen's University Belfast and a UKRI Future Leaders Fellow. He specializes in wireless communications, particularly in massive MIMO, cell-free massive MIMO, and cooperative systems. His research focuses on improving spectral efficiency, security, and energy efficiency in next-generation networks. Education: B.S., Electrical Engineering, Ho Chi Minh City University of Technology (2007) M.S., Electronics and Radio Engineering, Kyung Hee University (2010) Ph.D., Communication Systems, Linköping University (2015) Research Interests: Dr. Ngo's work spans massive MIMO systems, cell-free architectures, physical layer security, and millimeter-wave technologies. He has pioneered studies on channel estimation, power allocation, and interference management in distributed networks. Awards & Recognition: IEEE ComSoc Stephen O. Rice Prize (2015) IEEE ComSoc Leonard G. Abraham Prize (2017) Best PhD Award from EURASIP (2018) UKRI Future Leaders Fellowship (2019) Multiple AMiner Most Influential Scholar Awards (2022-2024) Grants & Projects: Lead on the Future Communications Hub in All-Spectrum Connectivity (UKRI-funded) Principal Investigator for Cell-Free Massive MIMO for ISAC Labs & Teams: He leads the Wireless Communications Research Group at Queen's University, focusing on 5G/6G technologies and intelligent systems.

Renaud Lambiotteمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Temporal Networks
• +۷ مورد دیگر

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Dong Gongمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

• Optimal Control
• Aerospace Engineering
• Robotics
• +۳ مورد دیگر

Ran Dai is a Professor in the Department of Aeronautics and Astronautics at Purdue University's College of Engineering. His research focuses on optimal control theory, trajectory optimization, and robotics applications, with an emphasis on aerospace systems and energy-efficient solutions. He leads the Autonomous Optimization Lab (AOL) and has contributed extensively to advancements in learning-based control, mixed-integer programming, and deployable space systems. His work spans applications such as spacecraft guidance, unmanned vehicle path planning, and energy management for solar-powered systems. Notable contributions include algorithms for fuel-optimal powered descent, real-time trajectory optimization, and origami-inspired deployable mechanisms. He holds a Ph.D. in Aerospace Engineering and has published over 100 peer-reviewed articles. Research interests include: Optimal control and trajectory optimization Reinforcement learning for decision-making Autonomous systems and robotics Energy-efficient aerospace engineering Recent work emphasizes meta-reinforcement learning frameworks and adaptive optimization engines for complex systems.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Chun-Liang Li is a research scientist at Apple MLR and an affiliate assistant professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His work bridges machine learning theory with practical applications in computer vision and natural language processing, focusing on efficient model training and representation learning. His educational background includes: Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019), supervised by Prof. Barnabás Póczos B.S. and M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013), supervised by Prof. Hsuan-Tien Lin Li's research centers on generative models and representation learning , with significant contributions to document understanding (FormNet series), multimodal systems (Pic2word), and large language model efficiency . His work consistently addresses real-world challenges like reducing training costs while maintaining performance, as seen in distillation techniques and synthetic data optimization. Analysis of his 2022-2024 publications reveals three dominant trends: (1) LLM efficiency through curriculum training and model updating, (2) structural document understanding via graph-based methods, and (3) multimodal representation learning for vision-language tasks. These reflect his cross-cutting approach to improving model scalability and applicability. His scientific recognition includes: IBM Ph.D. Fellowship (2018) Best student paper runner-up at IJCAI (2017) Double first-place wins in KDD Cup Tracks (2011, 2013) While specific grant details aren't listed, his award-winning KDD Cup performances and extensive publication record suggest strong funding support. He collaborates widely with students and researchers, though formal advisees aren't specified. His current roles at Apple MLR and UW position him at the industry-academia interface for cutting-edge AI development. At Apple, Li contributes to the Machine Learning Research group's core vision-language projects, while his UW affiliation enables academic mentorship and cross-institutional collaboration on foundational ML research.

• Theoretical Computer Science
• Mathematics
• Spectral Graph Theory
• +۵ مورد دیگر

Daniel Alan Spielman is the Sterling Professor of Computer Science at Yale University , where he has been affiliated since 2006. He serves as Chair of the Department of Statistics and Data Science and is Co-Director of the Yale Institute for Network Science. His research spans theoretical computer science and mathematics, focusing on algorithms, spectral graph theory, and numerical linear algebra. Educational background: Bachelor of Arts in Mathematics and Computer Science from Yale University (1992) PhD in Applied Mathematics from MIT (1995) Research interests include smoothed analysis of algorithms , graph-based codes , spectral graph theory , and numerical linear algebra . His work on the Kadison–Singer problem and graph theory applications has been groundbreaking. Scientific awards and honors include: Gödel Prize (2008, 2015) Nevanlinna Prize (2010) MacArthur Fellowship (2012) Pólya Prize (2014) Breakthrough Prize in Mathematics (2022) Election to the National Academy of Sciences (2017) His publications highlight advancements in network science , operator theory , and algebraic combinatorics , with a focus on algorithmic solutions and mathematical proofs.

Jian Kangمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +۱۱ مورد دیگر

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

Steffen Bondorfمشاهده پروفایل

استاد

• Network Calculus
• Deterministic Networking
• Performance Modeling
• +۴ مورد دیگر

Steffen Bondorf is a Professor of Distributed and Networked Systems in the Faculty of Computer Science at Ruhr University Bochum, Germany. His research focuses on performance modeling and analysis of deterministic networking, particularly using network calculus to provide rigorous performance guarantees for networked systems. Dr. Bondorf received his B.Sc., M.Sc., and Dr.-Ing. (Ph.D.) in Computer Science from TU Kaiserslautern (TUK), where he was part of the distributed computer systems lab. He was the first student to enroll in a B.Sc. program at TUK before finishing secondary education. After graduation, he held positions as postdoctoral researcher, lecturer and Carl-Zeiss Fellow at TUK, research fellow at the National University of Singapore, and visiting researcher at the University of Toronto. From fall 2018 to fall 2019, he was funded by an ERCIM Alain-Bensoussan fellowship at NTNU Trondheim, Norway, and spent time at INRIA Paris. His research interests include network calculus, deterministic networking, performance modeling, computer networks, distributed systems, and P2P overlays. Dr. Bondorf's work has significantly advanced the field of network calculus, particularly in developing methods for analyzing FIFO feedforward networks, multicast flows, and P2P overlay structures. His recent research has integrated machine learning techniques with network calculus to improve analysis accuracy and efficiency, creating hybrid approaches that leverage the strengths of both methodologies. Dr. Bondorf was appointed as assistant professor at Ruhr University Bochum on October 1, 2019, becoming the university's first hire in the joint tenure-track program for the promotion of young scientists. Since December 1, 2021, he has been a tenured full professor at RUB. Carl-Zeiss Fellow ERCIM Alain-Bensoussan fellowship At RUB, Dr. Bondorf teaches Advanced Topics in Networking, Distributed Systems, Deterministic Network Calculus, and Seminar Distributed and Networked Systems. His research group develops tools and methodologies for deterministic network analysis with applications in industrial networking, real-time systems, and time-sensitive communications. His work bridges theoretical networking principles with practical implementations, contributing to the advancement of deterministic networking standards and protocols.

• Theoretical Computer Science
• Sublinear Algorithms
• Complexity Theory
• +۴ مورد دیگر

Elena Grigorescu is an Adjunct Associate Professor in the Department of Computer Science at Purdue University, where she has been a faculty member since Fall 2012. Her research program spans theoretical computer science with a focus on foundational algorithmic challenges in large-scale data processing and computational limits, maintaining strong connections to cryptography, communications, and optimization applications. Her educational background includes a PhD from the Massachusetts Institute of Technology (MIT), establishing her expertise in rigorous theoretical frameworks. Professor Grigorescu's research emphasizes designing algorithms that operate in sublinear time or space for massive datasets, analyzing complexity of error-correcting codes and lattices, and exploring information-theoretical computation limits. Current investigations integrate differential privacy with learning-augmented techniques to solve online optimization problems, network design challenges, and data stream processing bottlenecks. Her work bridges abstract theory with practical implementations in cryptographic systems and quantum computing paradigms, demonstrating consistent innovation in algorithmic foundations. Analysis of her recent publications (2022-2025) reveals a dominant focus on sublinear-time algorithms, particularly at the intersection with differential privacy and machine learning augmentation. Key contributions include novel spanner constructions for network design, privacy-preserving clustering frameworks, and breakthroughs in trace reconstruction and coding theory. A pronounced trend shows increasing integration of learning-based predictions to enhance classical online algorithms for packing/covering problems while maintaining theoretical guarantees, alongside sustained contributions to error-correcting code analysis and graph-theoretic foundations. No specific scientific awards or major fellowships were documented in the provided materials, though her publication record in premier venues like STOC, FOCS, and APPROX/RANDOM indicates significant peer recognition. Professor Grigorescu actively mentors graduate students in theoretical computer science research, guiding investigations in sublinear algorithms, complexity theory, and coding theory. Her collaborative projects involve interdisciplinary teams across institutions, focusing on cryptographic applications and quantum information theory, though specific grant details were not included in the source texts. Ongoing work suggests expansion into quantum algorithm design and privacy-preserving machine learning frameworks. While dedicated laboratory facilities were not specified, her research operates within Purdue's theoretical computer science group, leveraging university-wide computational resources and fostering collaborations through conference participation and workshop organization.

Mattia Bianchiمشاهده پروفایل

مدرس

• Control Theory
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Mattia Bianchi is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland. He is affiliated with the Automatic Control Laboratory under Prof. Florian Dörfler, with office location at ETL I 34, Physikstrasse 3, Zurich. His research focuses on developing distributed, efficient, and robust methods for decision and control problems in complex network systems, including power grids and cognitive radio networks. Bachelor’s degree in Information and Communication Engineering (2016), University of L’Aquila, Italy Master’s degree in Systems Engineering (2018), University of L’Aquila, Italy PhD in Systems and Control (2018–2023), TU Delft, The Netherlands Postdoctoral researcher (2023–present), ETH Zurich, Switzerland His methodological approach integrates operator theory, learning algorithms, game theory, and data-driven control. Key research themes include uncovering common structures in optimization and control algorithms, with applications in distributed feedback optimization, Nash equilibrium seeking, and stabilization of constrained systems. Current work explores partial-decision information frameworks and linear convergence guarantees. For detailed information on his publications, visit his Google Scholar profile . Mattia actively supervises Master’s theses and semester projects, inviting candidates to contact him with their academic credentials.

Haoming Shenمشاهده پروفایل

استادیار

• Stochastic Optimization
• Integer Programming
• Data-Driven Decision Making
• +۴ مورد دیگر

Haoming Shen is an Assistant Professor in the Department of Industrial Engineering at the University of Arkansas, College of Engineering. He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor, along with master's degrees in Electrical and Computer Engineering and Mathematics from the same institution. His bachelor's degree is in Electrical Engineering from Xi'an Jiaotong University. Dr. Shen's research focuses on stochastic optimization and integer programming with applications in power grids and transportation systems. His work centers on data-driven decision-making under uncertainty, particularly using Wasserstein ambiguity sets for chance-constrained programming. His research has significant implications for optimizing critical infrastructure systems where uncertainty must be rigorously accounted for. His publications demonstrate a strong trajectory in optimization theory with applications to power systems. His work on Wasserstein ambiguity models for chance constraints has been published in top venues including Mathematical Programming and the IEEE Conference on Decision and Control. His 2022 paper on Wasserstein two-sided chance constraints with application to optimal power flow received an Honorable Mention in the INFORMS Optimization Society Best Student Paper Competition. Honorable Mention award in the 2022 INFORMS Optimization Society Best Student Paper Competition Rackham Professional Development DEI Certificate Dr. Shen actively engages in Diversity, Equity and Inclusion initiatives. While specific information about his advisees is not provided in the available materials, his research program appears to be actively developing with multiple recent publications in optimization theory and applications.