جست‌وجوی استاد و پژوهشگر

• Data Mining
• Machine Learning
• Relational Learning
• +۱۲ مورد دیگر

Jennifer Neville is a Senior Principal Researcher at Microsoft Research Redmond and holds the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. With over 100 publications and 10K citations, her research spans data mining, machine learning, and AI algorithms for relational and networked domains including social networks, epidemiology, and web analytics. Education: BS in Computer Science, University of Massachusetts Amherst (2000) MS in Computer Science, University of Massachusetts Amherst (2004) PhD in Computer Science, University of Massachusetts Amherst (2006) Her work focuses on relational learning techniques that exploit connections between entities to enhance pattern discovery. Recent research explores large language models (LLMs), emphasizing alignment with user intent through interaction at scale, while addressing statistical biases from graph structures. Selected scientific awards include the NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s 10 to Watch in AI (2008). She served on the AAAI Executive Council (2015-2018) and chaired multiple conferences including SIAM Data Mining (2019) and ACM Web Search (2016). Contact: neville@cs.purdue.edu jenneville@microsoft.com

• Verification and Validation
• Search-Based Software Engineering
• Autonomous Driving
• +۳ مورد دیگر

Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.

Jesper Velgaard Olsenمشاهده پروفایل

استاد

• Quantitative Proteomics
• Mass Spectrometry
• Cell Signaling
• +۳ مورد دیگر

Jesper Velgaard Olsen is a Professor and Deputy Center Director at the University of Copenhagen's Novo Nordisk Foundation Center for Protein Research (CPR) and leads the Olsen Group. His research focuses on quantitative, high-resolution mass spectrometry-based proteomics, particularly in characterizing signaling networks regulated by phosphorylation and other post-translational modifications. The group develops advanced offline peptide fractionation and enrichment methods combined with high-resolution Orbitrap tandem mass spectrometry to improve robustness and reproducibility in proteomic analysis. The Olsen Group investigates functional selectivity in cell signaling, where different growth factors binding to the same receptor activate distinct pathways. They identified tyrosine phosphorylated residues as molecular switches determining cell fate and optimized workflows enabling deep human proteome analysis comparable to RNA-seq. Their work spans technology development and biological applications in diseases like acute myeloid leukemia. Key group members include PhD Fellows, PhD Students, and Researchers such as Agnete Witness Præst Jensen, Charlotte Hjort, and Kristina Bennet Emdal. For collaboration or inquiries, contact Olsen via email jesper.olsen@cpr.ku.dk .

• Control Systems
• Dynamic System Modeling
• System Identification
• +۵ مورد دیگر

Professor Quanmin Zhu is a Professor in Control Systems at the School of Engineering, University of the West of England (UWE), Bristol, UK, holding this position since 2004. His academic career spans over four decades, including roles as Lecturer at Qiqihar University (China, 1983-1986), Post-doctoral Researcher at University of Sheffield (UK, 1989-1994), Lecturer at University of Brighton (UK, 1994-1997), and Lecturer/Reader at Aston University (UK, 1997-2004). His educational background includes: MSc in Engineering from Harbin Institute of Technology, China (1980-1983) PhD from University of Warwick, UK (1986-1989) Professor Zhu's research centers on dynamic system modeling, identification, control, and simulation, with pioneering contributions to nonlinear control systems, robust control methodologies, and U-model based control frameworks. His work bridges theoretical advances with practical applications in robotics, renewable energy systems, and industrial automation, emphasizing model-free and adaptive control solutions for complex nonlinear dynamics. Analysis of his 2021-2025 publications reveals a dominant focus on robust control for uncertain nonlinear systems, with significant contributions to sliding mode control, multi-agent coordination, and cyber-physical security. His research increasingly integrates machine learning techniques (e.g., actor-critic reinforcement learning) while maintaining core expertise in optimization-based control algorithms applied to UAVs, robotic manipulators, and wind energy systems. His professional honors include: Chartered Engineer (CEng) Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) As an academic leader, Professor Zhu serves as President/Founder of the International Conference on Modelling, Identification and Control (ICMIC), Editor/Founder of Elsevier's Book Series on Emerging Methodologies in Modelling and Control, and University Ambassador for UK-China educational collaboration. His research group secures substantial grants in control theory applications, with ongoing projects in U-model control platforms and international partnerships. He leads the Control Systems research group at UWE, driving innovation in the U-control platform and its industrial applications. His team maintains strong international collaborations, particularly with Chinese institutions, and actively develops the Elsevier Book Series as a key publication channel for emerging control methodologies.

Pavel Panchekhaمشاهده پروفایل

استادیار

• Programming Languages
• Program Synthesis
• Verification
• +۴ مورد دیگر

Pavel Panchekha is an Assistant Professor in the School of Computing at the University of Utah, where he holds the Warnock Chair for Junior Faculty. His research spans programming languages, web browsers, and numerical analysis, with a focus on developing programming language techniques to address challenges across computer science. Dr. Panchekha received his educational training at prestigious institutions: PhD in Computer Science from the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Michael D. Ernst and Zachary Tatlock BS in Mathematics from MIT Panchekha's research program has two major thrusts. First, he works on web browser internals , with projects including fuzzing layout invalidation, multi-tenant garbage collection, and optimizing 2D graphics. He is also authoring a textbook on web browsers that informs much of this research. Second, he focuses on automatic numerical analysis , with projects such as automatic accuracy improvement, synthesis via term rewriting, scalable static accuracy analysis, and math library implementation. He leads the FPBench and Herbie projects, which are major deployments of his research. His scholarly output demonstrates consistent contributions across programming languages, verification, and numerical methods. Recent work shows a growing emphasis on bidirectional typing systems, layout invalidation in browsers, and robust floating-point error analysis. His publications reveal a trajectory from foundational work on floating-point accuracy (notably the Herbie tool that won a Distinguished Paper Award at PLDI 2015) toward more comprehensive systems for program synthesis, verification, and browser optimization. Panchekha has received significant recognition for his research contributions: NSF Fellowship ARCS Foundation Fellowship Adobe Research Fellowship Wissner-Slivka Foundation Fellowship 2015 PLDI Distinguished Paper Award for work on the Herbie numerical analysis and repair tool As an advisor, Panchekha mentors a substantial group of students across multiple levels. He currently advises six students: Marisa Kirisame (PhD), Bhargav Kulkarni (PhD), Yumeng He (PhD), Artem Yadrov (MS), Jesus Ponce (BS), and Jonas Regehr (BS). Previously, he has advised over twenty students including PhD candidates like Ian Briggs and numerous MS and BS students. His advising spans theoretical topics in programming languages and practical applications in web browsers and numerical computing. Panchekha leads research groups focused on programming languages applications to web browsers and numerical analysis. His work on the Herbie tool for floating-point accuracy improvement has become influential in the programming languages community, and his more recent work on browser internals is shaping how researchers understand and optimize modern web rendering engines. He is currently developing a textbook on web browsers that aims to synthesize knowledge about browser architecture and implementation.

• Information Theory
• Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Oliver Kosut is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has worked since August 2012. He was promoted to Associate Professor in 2018 and received the NSF CAREER award in 2015. His research spans information theory, machine learning, cybersecurity, and power systems, with a focus on theoretical foundations and applications to privacy, security, and smart grid resilience. Education: B.S. in Electrical Engineering and Mathematics from MIT (2004), Ph.D. in Electrical and Computer Engineering from Cornell University (2010) His recent work explores differential privacy, adversarial robustness in decentralized networks, and information-theoretic approaches to cybersecurity. He advises graduate students with strong mathematical backgrounds, particularly those interested in fundamental theory for applied problems. Scientific accolades include the IEEE Information Theory Society Distinguished Lecturer (2023–2024) and NSF CAREER award. Key research areas: Information Theory, Privacy, Machine Learning, Power System Security Students: Obai Bahwal, Atefeh Gilani, Naima Tasnim (current); Nima Bazargani, Andrea Pinceti, Jingwen Liang, Zhigang Chu, Fatemeh Hosseinigoki, Nematollah Iri, Kousha Kalantari, Roozbeh Khodadadeh (former)

Santosh S. Vempalaمشاهده پروفایل

استاد

• Algorithms
• Convex Geometry
• High-Dimensional Sampling
• +۴ مورد دیگر

Santosh S. Vempala is the Frederick P. Storey II Chair and Professor of Computer Science at Georgia Institute of Technology's College of Computing with joint appointments in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and the School of Mathematics. He teaches courses including CS6150: Computing for Good (C4G) and CS6550/CS8803DAA: Continuous Algorithms: Optimization and Sampling. His research spans multiple interconnected domains: Algorithmic convex geometry and high-dimensional sampling Continuous optimization methods Computational models of brain function Randomized algorithms with applications to machine learning Vempala's recent publications reveal a strong focus on developing efficient algorithms for high-dimensional problems, particularly logconcave sampling and convex body integration. His work bridges theoretical computer science with practical applications in optimization and neuroscience, with increasing attention to the intersection of theoretical frameworks and brain computation models through his collaboration with Christos Papadimitriou. He leads the Computing for Good (C4G) initiative which applies computational approaches to social challenges, including projects like Safe and Easy Passwords!, LifeNet, C4G BLIS, and Shelter-to-Home that address problems in resource-constrained settings. Vempala currently advises PhD students Xinyuan Cao, Mirabel Reid, Max Dabagia, and Yunbum Kook, and has authored influential books including 'Spectral Algorithms' and 'The Random Projection Method' that have shaped research in algorithmic convex geometry. His tutorials at major conferences, including STOC 2015 on 'Sampling and Volume Computation in High Dimension' and FOCS 2020 on 'Computation in the Brain,' demonstrate his leadership in connecting theoretical computer science with broader scientific challenges.

• Machine Learning
• Reinforcement Learning
• Bandit Algorithms
• +۵ مورد دیگر

Dr. Kevin G. Jamieson is a faculty member at the University of Washington , School of Computer Science , with prior affiliations at the University of California, Berkeley (Department of Electrical Engineering and Computer Sciences) and the University of Wisconsin-Madison (Department of Electrical and Computer Engineering). His work spans machine learning, reinforcement learning, bandit algorithms, and robotics. Current university: University of Washington Academic rank: Professor His research focuses on: Bandit algorithms and sequential decision-making Optimization in non-stationary environments Reinforcement learning with real-world applications Multi-agent systems and game theory Efficient data selection for multimodal learning Human-in-the-loop AI systems Recent publications highlight his expertise in pure exploration strategies, robotic manipulation, and bridging simulation-to-reality gaps in RL. He has mentored numerous collaborators, though formal student advising details are not explicitly listed here. No scientific awards are mentioned in the provided data.

• Risk Management
• Risk Analysis
• Uncertainty Quantification
• +۱۲ مورد دیگر

Roger Flage is a Professor of Risk Management at the University of Stavanger, affiliated with the Faculty of Science and Technology and the Department of Security, Economics and Planning. His research focuses on foundational and applied aspects of risk analysis, uncertainty quantification, and decision-making under uncertainty, with applications in critical infrastructure, environmental systems, and offshore energy. Roger Flage's research interests lie at the intersection of risk science, safety engineering, and decision theory. He investigates how uncertainty—especially epistemic uncertainty and assumptions—affects risk assessments, and advocates for more transparent and robust frameworks. His work spans theoretical advances, such as the treatment of 'black swan' events and the concept of 'real risk', as well as practical applications in offshore safety, power systems, and geohazards. He emphasizes the integration of data-driven methods, AI, and digital twins while critically assessing their limitations and associated security risks. His recent publications show a strong trend toward integrating dynamic, data-rich, and interdisciplinary approaches to risk analysis. Themes include the role of time in risk, AI applications, infrastructure interdependencies, and environmental risk in the oil and gas sector. He frequently publishes in top-tier journals like Risk Analysis , Reliability Engineering & System Safety , and Safety Science , often in collaboration with leading scholars such as Terje Aven and Seth Guikema. No scientific awards are mentioned in the provided text. Roger Flage has supervised or collaborated with several researchers, though no formal list of advisees is provided. His work is supported through academic collaborations and institutional affiliations rather than explicit grant mentions. He is actively involved in advancing risk science methodology, particularly in the treatment of assumptions and uncertainty, and contributes to both theoretical foundations and real-world applications in safety-critical domains. He is associated with research groups and collaborative networks at the University of Stavanger, particularly within the Department of Security, Economics and Planning. His work often involves interdisciplinary teams focusing on risk in complex engineered systems, including energy, transportation, and environmental systems.

Mathias Beiglboeckمشاهده پروفایل

استاد

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +۴ مورد دیگر

Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.

Anna Stuhlmacherمشاهده پروفایل

استادیار

• Power Systems
• Distributed Energy Resources
• Optimization
• +۳ مورد دیگر

Anna Stuhlmacher is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. Her research focuses on the optimization of uncertain distributed energy resources (DERs) and the coordination of the power grid with other critical infrastructure systems including water and agricultural networks. Dr. Stuhlmacher received her academic credentials from prestigious institutions: PhD in Electrical Engineering, University of Michigan MS in Electrical Engineering, University of Michigan BS in Electrical Engineering, Boston University Her research program addresses a critical challenge in modern energy infrastructure: how distributed generation, storage, and flexible loads can be optimized to provide grid flexibility while coordinating with other essential infrastructure systems. Dr. Stuhlmacher specializes in modeling and optimizing the inherent flexibility and uncertainty propagation between power systems and other infrastructure systems such as drinking water, wastewater treatment, and agricultural systems. This interdisciplinary approach is vital for improving grid reliability, particularly during periods of network stress, by increasing demand flexibility through coordinated management of multiple infrastructure systems. Dr. Stuhlmacher's publication record reveals a consistent progression from fundamental optimization techniques for water distribution networks to more complex systems involving wastewater treatment biogas and agrivoltaics. Her research demonstrates sophisticated application of advanced optimization methods including chance-constrained programming, robust optimization, and machine learning techniques like input convex neural networks to address uncertainty in coupled infrastructure systems. The majority of her work focuses on the water-power nexus, with recent expansion into agrivoltaics as renewable energy and food production compete for land resources. Her notable achievements include: Best paper award for the Electric Energy Systems Track, HICSS 2025 Dr. Stuhlmacher actively secures research funding as Principal Investigator on multiple significant grants including an NSF award focused on biogas from wastewater treatment, a PSERC award on flexible load dispatch (as Co-PI with Georgia Tech researchers), and a Michigan Tech Research Excellence Fund grant on agrivoltaics. While she indicates she is not actively seeking graduate students for the 2025-26 academic year, she remains open to working with exceptional students with strong foundations in power systems and mathematics. Her undergraduate teaching includes courses on Distributed Energy Resources, Electrical Energy Systems, and Power System Optimization, building on her previous teaching experience at the University of Michigan. Her research leverages Michigan Tech's DOE-designated Regional Test Center for Emerging Solar Technologies, particularly for her agrivoltaics research. She has established connections with national laboratories including NREL, where she interned during her PhD studies, and maintains active collaborations with researchers at institutions like Georgia Tech. Her work bridges theoretical optimization techniques with practical applications that have immediate relevance to utility companies and infrastructure operators.

• Structural Engineering
• Structural Health Monitoring
• Civil Engineering
• +۳ مورد دیگر

Dr. Andy Nguyen is a Senior Lecturer in the School of Engineering at the University of Southern Queensland. He holds a PhD from Queensland University of Technology (QUT), an MEng from the National University of Civil Engineering (NUCE), and a BEng from NUCE. His research focuses on structural health monitoring, integrating machine learning and deep learning techniques to assess infrastructure integrity. Key areas include damage detection in bridges, pavements, and buildings, as well as sustainable construction materials like bamboo. Nguyen leads projects such as the 'Next Generation Living Laboratory for Engineering Education and Engagement,' emphasizing real-world applications of technology in civil infrastructure. His work spans crack detection algorithms, finite element model updating, and vibration-based structural analysis. He collaborates on AI-driven solutions for autonomous vehicle object detection and smart maintenance planning. Nguyen’s contributions include over 50 peer-reviewed publications and active supervision of postgraduate research in composite materials and transport infrastructure. His research outputs highlight advancements in computational mechanics, sensor technologies, and data-driven methods for infrastructure resilience. Nguyen’s expertise bridges civil engineering challenges with cutting-edge machine learning, advancing both theoretical and applied solutions for sustainable and safe structures.

Long Nguyenمشاهده پروفایل

استاد

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Michael Qizhe Shiehمشاهده پروفایل

استادیار

• Large Language Models
• Deep Learning
• Natural Language Processing
• +۴ مورد دیگر

Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.

Bruno Castro da Silvaمشاهده پروفایل

استادیار

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Bruno Castro da Silva is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He holds a PhD in Computer Science from UMass Amherst (2014), and MSc and BSc degrees from the Federal University of Rio Grande do Sul (UFRGS), Brazil. Prior to UMass, he was an Associate Professor at UFRGS and a postdoctoral researcher at MIT's Aerospace Controls Laboratory. His research focuses on reinforcement learning (RL), robotics, and AI safety, aiming to develop algorithms that ensure safe and autonomous task decomposition while meeting user-specified safety criteria. Key areas include hierarchical policies, active learning, and biologically-plausible mechanisms. He has published in top venues like ICML, NeurIPS, and Science, and received awards such as the Best Paper at RLDM 2022 and Distinguished Reviewer distinctions. He teaches courses in reinforcement learning and machine learning at UMass, emphasizing accessibility and safety. His work also extends to fostering diversity in STEM education. He leads the Autonomous Learning Laboratory and collaborates with organizations like Adobe Research and the Laboratory of Computational Neuroscience in Rome.