جست‌وجوی استاد و پژوهشگر

Lars Moberg Salbuمشاهده پروفایل

پژوهشگر ارشد

• Computational Geometry
• Algebraic Topology
• Parameterized Complexity
• +۳ مورد دیگر

Lars Moberg Salbu is a Postdoctoral Fellow at the Department of Informatics, University of Bergen. His research focuses on computational geometry, algebraic topology, and parameterized complexity, with contributions to graph theory, metric analysis, and algorithm design. He has collaborated with institutions such as the Department of Mathematics and Department of Computer Technology at Western Norway University of Applied Sciences. His work bridges theoretical mathematics and computer science, addressing challenges in topological data analysis and combinatorial optimization. Key publications include studies on transition graph dynamics, minimum bounded chains, and homology determination from data samples. Salbu’s research emphasizes interdisciplinary approaches, leveraging discrete mathematics and geometric principles to solve complex computational problems. No scientific awards are listed, but his active participation in international journals like IEEE Access and Mediterranean Journal of Mathematics highlights his academic contributions. Collaborations with co-authors such as Morten Brun and Belen Garcia Pascual underscore his collaborative approach to advancing theoretical frameworks in his field.

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.

Peter Chinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +۸ مورد دیگر

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

• Mathematical Ecology
• Ecological Modelling
• Population Dynamics
• +۳ مورد دیگر

Professor Sergei Petrovskii is a Chair in Applied Mathematics at the University of Leicester's School of Computing and Mathematical Sciences. His research focuses on mathematical ecology, ecological modeling, and complex systems analysis, with a particular emphasis on climate change impacts, oxygen depletion in oceans, and ecological catastrophes. He has published over 150 peer-reviewed papers and four books, including influential work on global anoxia and mass extinction dynamics. As Editor-in-Chief of Ecological Complexity (2011–2021) and Section Editor-in-Chief of Mathematics ' Mathematical Biology section since 2020, he has significantly shaped interdisciplinary research agendas. His research interests span modeling ecological transients, population dynamics, and invasive species spread. Key contributions include frameworks for landscape decision-making, stochastic models of protest dynamics, and the MPDE conference series he founded. Despite no explicit mention of awards, his editorial roles and prolific publishing underscore his academic influence. His work integrates mathematical modeling with real-world challenges, addressing issues like oxygen minimum zones and the socioeconomic dimensions of climate change. Publications highlight his exploration of transient dynamics, regime shifts, and ecological responses to environmental change. His interdisciplinary approach bridges ecology, epidemiology, and social systems, evidenced by studies on protest dynamics and pandemic modeling. While no lab names are explicitly stated, his research often involves collaborative projects like the Landscape Decisions initiative and MPDE conferences.

Holden Leeمشاهده پروفایل

استادیار

• Machine Learning
• Probability
• Theoretical Computer Science
• +۳ مورد دیگر

Holden Lee is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, where he joined in 2022. His research focuses on the theoretical foundations of machine learning, probability, and their intersections with theoretical computer science. He explores probabilistic methods in modern machine learning, including deep learning-based generative models and convergence guarantees for sampling algorithms like Markov Chain Monte Carlo. Prior to JHU, he was a postdoc at Duke University and a Simons Fellow at UC Berkeley. He holds a PhD in Mathematics from Princeton University and degrees from MIT and the University of Cambridge. Education: PhD in Mathematics, Princeton University, 2019 MASt in Pure Mathematics, University of Cambridge, 2014 BSc in Mathematics, MIT, 2013 Research Interests: Machine Learning Theory Probabilistic Sampling Methods Generative Models Statistical Learning Theory His work emphasizes theoretical rigor, particularly in understanding the success and limitations of deep learning algorithms and designing efficient sampling techniques beyond classical log-concave settings. Articles Trends: Lee’s recent publications (2022–2024) focus on advancing sampling algorithms for complex distributions, analyzing generative models, and improving convergence guarantees for methods like MCMC and score-based diffusion. His work bridges theory and practice, with applications in multimodal data, text generation, and dynamical systems. Awards: Simons Fellow at UC Berkeley (2021) Advising & Grants: Lee has contributed to projects at NeurIPS and collaborates on research in AI efficiency and theoretical guarantees. He teaches courses on probability and applied mathematics at JHU and Duke. Labs/Teams: His research group focuses on theoretical machine learning and probabilistic methods, with ongoing projects on scalable sampling algorithms and generative model analysis.

Jianqiang Chengمشاهده پروفایل

دانشیار

• Stochastic Programming
• Robust Optimization
• Distributionally Robust Optimization
• +۷ مورد دیگر

Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.

Darius Whelanمشاهده پروفایل

مدرس ارشد

• Mental Health Law
• Electronic Legal Resources
• E Law
• +۶ مورد دیگر

Darius Whelan is a Senior Lecturer in Law at University College Cork (UCC), where he serves as Director of the LLB programme. He holds a BCL from University College Dublin, an LL.M. in 'The Comparative Method and Law Reform', and a Ph.D. in 'Criminal Procedure and Mental Health' from Trinity College Dublin. His academic career spans over three decades, with roles at UCC since 1997, including Senior Lecturer since 2013. His research focuses on Mental Health Law, Employment Law, and Internet Regulation. Notable works include Mental Health Law and Practice: Civil and Criminal Aspects (2009) and contributions to the Mental Health Act, 2001 Toolkit (2022). He co-founded UCC's IT Law Clinic and is President of the Irish Mental Health Lawyers Association. Key grants include the Irish Research Council-funded 'Improving Human Rights for People with Mental Health Difficulties' (2021–2022) and a 2002 project on Employment Equality Law. Awards include Supreme Court citations for his research and UCC's President's Award for Innovative Teaching. Whelan has advised on mental health law reforms, participated in over 40 conferences, and collaborated with international organizations like Creative Commons. His teaching interests extend to Cybercrime, Copyright Law, and Digital Learning. He supervises Ph.D. students in areas like AI and Law, Employment Law in the Digital Age, and Human Rights in Mental Health. Labs/Teams: Director of UCC's IT Law Clinic; Board member of Sample Studios; Member of Creative Commons Ireland.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۳ مورد دیگر

Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.

Xiucai Dingمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Statistical Learning Theory
• Random Matrix Theory
• +۱۰ مورد دیگر

Xiucai Ding is a tenured Associate Professor in the Department of Statistics at the University of California, Davis, starting in 2025. He is also affiliated with the Graduate Group in Applied Mathematics (GGAM) at UC Davis. Previously, he was an Assistant Professor in the same department from 2020 to 2025 and a Research Associate at Duke University from 2018 to 2020. PhD in Statistics, University of Toronto (2014–2018), advised by Jeremy Quastel Research Associate, Duke University (2018–2020), with Hau-Tieng Wu Assistant Professor, UC Davis (2020–2025) Associate Professor (tenured), UC Davis (starting 2025) His research focuses on mathematical statistics and statistical learning theory, particularly applied random matrix theory, high-dimensional statistics, non-stationary and functional time series analysis, statistical optimal transport, and the statistical foundations of machine learning algorithms. His methodological work emphasizes nonparametric and sieve-based estimation, inference under complex dependencies, and applications to noisy, high-dimensional data. The recent publications and software tools (such as RMT4DS, Sie2nts, SIMle) reflect a consistent trend in developing theoretically grounded, computationally feasible tools for analyzing complex time series and high-dimensional covariance structures. His work bridges theoretical statistics with practical data science. His research has been supported by the National Science Foundation (NSF). Estimation and inference for precision matrices of nonstationary time series (2020) Auto-regressive approximations to non-stationary time series (2021) On the partial autocorrelation function for locally stationary time series (2022) He advises students and researchers through his role in the Department of Statistics and GGAM. He has taught courses such as STA 108 (Regression Analysis), STA 137 (Applied Time Series Analysis), STA 135 (Multivariate Data Analysis), STA 221 (Big Data & High Performance Statistical Computing), and STA 250 (Topics in Applied and Computational Statistics) at UC Davis. He previously taught at Duke University and the University of Toronto. He has developed several open-source R packages for statistical methodology: RMT4DS : Random matrix tools for data scientists (CRAN/GitHub) Sie2nts : Sieve methods for non-stationary time series (CRAN/GitHub) SIMle : Estimation and inference for nonlinear and non-stationary regression (CRAN/GitHub) UHDtst : Two-sample tests for high-dimensional covariance matrices (GitHub)

Cameron A. Myhrvoldمشاهده پروفایل

استادیار

• CRISPR-based technologies
• Viral RNA studies
• Host-pathogen interactions
• +۷ مورد دیگر

Cameron A. Myhrvold is an Assistant Professor of Molecular Biology at Princeton University, leading the Myhrvold Lab. His research focuses on CRISPR-based technologies to study viral and host RNA, with applications in detecting and combating RNA viruses like SARS-CoV-2. He specializes in developing Cas13-based tools for multiplexed nucleic acid detection, host-pathogen interaction analysis, and RNA perturbation. Education: A.B. in Molecular Biology from Princeton University (undergraduate), Ph.D. in Systems Biology from Harvard University. His postdoctoral work at Harvard involved pioneering CRISPR-Cas13 technologies, leading to innovations like CARMEN, CARVER, and SHINE. Research interests include: CRISPR-based RNA editing, quantitative nucleic acid detection, systems biology of viral infections, and translating technologies for global health. His lab emphasizes deploying diagnostics in resource-limited settings and studying viral dynamics across diverse tissues and infection conditions. Notable achievements: Named NEBEC Emerging Young Investigator. His work has advanced field-deployable viral diagnostics and multiplexed RNA analysis tools. Collaborates with institutions like the Broad Institute and the CDC on pandemic response initiatives. Current projects include developing quantitative CARMEN variants for gene expression analysis, high-throughput screening of Cas13-virus interactions, and applying CRISPR tools to study influenza and SARS-CoV-2 in animal models.

• Experimental Fluid Dynamics
• Data Assimilation
• Network Science
• +۳ مورد دیگر

Prof. Dr.-Ing. David E. Rival is a full Professor at the Institute of Fluid Mechanics within the Faculty of Mechanical Engineering at Technische Universität Braunschweig. His research spans interdisciplinary domains at the intersection of experimental fluid dynamics, data assimilation, network science, and bio-inspiration, with applications in renewable energy systems and bio-mimetic engineering. Former Associate Professor at Queen’s University, Canada Doctoral work on dragonfly flight aerodynamics at TU Darmstadt Alexander von Humboldt research fellowship recipient (2020) Postdoctoral associate at MIT studying shape morphing in nature Research chair at University of Calgary on atmospheric sensing His work focuses on unsteady flow phenomena, bio-inspired design, and advanced measurement techniques. Key projects include: Co-chairing NATO AVT task group on flow separation International collaborations with AFOSR, NATO, and ONR Development of cost-effective flow-tracking sensors for natural environments Investigations into shear-thinning suspension dynamics and vortex ring behavior Recent publications demonstrate a strong emphasis on: Large-scale particle tracking with natural light and UAVs Machine learning for sparse data reconstruction in fluid flows Soft coastal protection methods and ecohydraulics Advanced sensing techniques for atmospheric and industrial applications Scientific Awards: 2020: Alexander von Humboldt Research Fellowship Notable research achievements include textbook authorship on Biological and Bio-Inspired Fluid Dynamics (Springer) and media features in The Nature of Things (David Suzuki) and Discovery Channel’s Daily Planet .

Randy Bartelsمشاهده پروفایل

استاد

• Biomedical Imaging
• Optical Engineering
• Nonlinear Microscopy
• +۳ مورد دیگر

Randy Bartels is a Professor in the Department of Biomedical Engineering at the University of Wisconsin-Madison. His laboratory specializes in developing advanced biomedical imaging techniques to study complex biological phenomena and translate these methods into applications that enhance fundamental understanding of biology and disease treatments. Education: PhD, University of Michigan (2002) MS, University of Michigan (1999) BS, Oklahoma State University (1997) Research Interests: Bartels focuses on creating novel coherent nonlinear optical imaging modalities, such as spatial frequency modulation imaging (SPIFI), impulsive stimulated Raman scattering (ISRS), and synthetic aperture holography. His work emphasizes label-free imaging, optical scattering robustness, and computational enhancements for resolution and sensitivity. Scientific Awards: 2021 Institut Fresnel Visiting Professor 2013 American Physical Society Fellow 2011 Optical Society of America Fellow 2006 Presidential Early Career Award in Science and Engineering (PECASE) 2005 Sloan Research Fellow (Physics) 2004 NSF CAREER Award Recent Article Trends: Bartels' publications highlight innovations in label-free imaging, nonlinear microscopy, and computational techniques. Key themes include hyperspectral coherent Raman imaging, quantum-classical fusion for super-resolution, and robustness to optical scattering in biological and industrial applications. His work spans fundamental physics, engineering, and biomedical translation. Laboratory: Bartels leads a research group dedicated to advancing imaging technologies, with a focus on overcoming limitations in resolution, depth, and sensitivity through optical and computational methods.

• Quantum Computation
• Computational Complexity Theory
• Quantum Physics
• +۲ مورد دیگر

Adam Bouland is an Assistant Professor of Computer Science at Stanford University, where he leads the CS Theory Group. His research focuses on quantum computation, computational complexity theory, and their connections to physics. He completed his Ph.D. at MIT under Scott Aaronson, followed by postdoctoral research at UC Berkeley and the Simons Institute under Umesh Vazirani. His research explores fundamental questions in quantum computing including quantum complexity classes, quantum supremacy demonstrations, pseudorandom quantum states, quantum algorithm design, and connections to high-energy physics through AdS/CFT correspondence. Recent work investigates computational aspects of quantum systems, quantum learning theory, and noise resilience in quantum devices. Professor Bouland leads an active research group including 6 PhD students and 1 postdoctoral researcher, with joint appointments across Computer Science, Physics, and ICME departments. He regularly teaches graduate courses on Quantum Computation (CS259Q) and Quantum Complexity Theory (CS359D). His service includes program committee membership for major theoretical computer science conferences including FOCS, STOC, ITCS, and QIP.