جست‌وجوی استاد و پژوهشگر

Massimiliano Tamborrinoمشاهده پروفایل

دانشیار

• Stochastic Processes
• Approximate Bayesian Computation (ABC)
• Parallel-in-Time Numerical Methods
• +۴ مورد دیگر

Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.

• Structural Biomechanics
• Musculoskeletal Modeling
• Finite Element Analysis
• +۳ مورد دیگر

Andrew Phillips is a Professor in Structural Biomechanics at Imperial College London's Department of Civil and Environmental Engineering within the Faculty of Engineering. He leads the Structural Biomechanics Group and coordinates the MEng Civil Engineering degree program. His research focuses on musculoskeletal modeling, finite element analysis, and bioinspired structures. Phillips holds a PhD from the University of Edinburgh (2005) and an MEd from Imperial College (2014). Key affiliations include the Additive Manufacturing Network, Centre for Blast Injury Studies, and Musculoskeletal Medical Engineering Centre. His work integrates computational methods with biomechanical principles, addressing topics like bone adaptation, trauma injury effects, and wearable sensor technologies. Notable contributions include developing motoneuron-driven muscle models and synthetic motion capture datasets via generative adversarial networks (GANs). Phillips also organizes the Parametric Engineering Course with Arup and SimplyRhino. Recent publications emphasize bone health in amputees, neural drive estimation, and energy-absorbing metamaterials. His 2018 European Society of Biomechanics SM Perren Award recognizes outstanding contributions to hip dysplasia biomechanics. Research trends highlight interdisciplinary approaches merging machine learning with traditional biomechanics to solve clinical and engineering challenges.

• Incentive-Aware Machine Learning
• Social Computing
• Online Learning
• +۵ مورد دیگر

Chara Podimata is the Class of 1942 Career Development Assistant Professor of Operations Research and Statistics at MIT Sloan School of Management. She holds a joint appointment as a Lead Researcher at Archimedes/Athena Research Center. Her work focuses on incentive-aware machine learning, strategic interactions in algorithms, and the societal impacts of AI systems. Prior to MIT, she was a FODSI Postdoctoral Fellow at UC Berkeley and earned her PhD from Harvard University under Yiling Chen’s supervision. Her research is supported by grants from Amazon, Google, MacArthur Foundation, and MIT’s GenAI Consortium. Education: PhD in Computer Science (Harvard University, 2020), Diploma in Electrical & Computer Engineering (National Technical University of Athens, Greece). Key research areas include algorithmic economics, strategic learning, and mechanism design for online platforms. She has advised over 10 students across PhD, MBA, and undergraduate programs, emphasizing ethical and societal implications in her mentorship. Research Themes: Incentive-aware learning, strategic user interactions, fairness in algorithms, AI policy, and recommendation systems Awards: Amazon Research Award (2023), Google Faculty Award (2025), NeurIPS Spotlight Recognition (2023), AAAI Best Paper Shortlist (2019) Her recent work explores how machine learning systems can be designed to account for human adaptation, including studies on recommendation systems, revenue management fairness, and causal mechanisms in strategic classification. She teaches courses on data modeling (15.060) and operations research (15.090) at MIT, blending theoretical rigor with practical industry applications. Podimata’s interdisciplinary approach bridges computer science, economics, and management science, with publications in top venues like NeurIPS, EC, COLT, and ICML. She actively engages in policy discussions around AI governance through her research on algorithmic transparency and societal impacts.

Xin Xiaمشاهده پروفایل

پژوهشگر ارشد

• Recommender Systems
• Machine Learning
• Graph Neural Networks
• +۳ مورد دیگر

Dr. Xin Xia is a Postdoctoral Research Fellow at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing machine learning techniques, particularly in the domain of recommender systems, with an emphasis on on-device deployment, graph-based learning, and self-supervised methods. He has contributed to optimizing recommendation systems for edge computing environments and improving their efficiency in resource-constrained settings. Key research areas include: On-device recommendation systems and edge AI Graph neural networks for recommendation Self-supervised learning and contrastive techniques Efficient model optimization and knowledge distillation His publications analyze trends such as transitioning cloud-based systems to on-device applications, reducing communication overhead in distributed models, and simplifying graph contrastive learning frameworks. These contributions highlight his expertise in bridging theoretical advancements with practical, deployable solutions. Dr. Xia’s work has been presented at top-tier conferences such as SIGIR, CIKM, and AAAI, reflecting the academic rigor and industry relevance of his research.

Jing Zhangمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

Philip Naumannمشاهده پروفایل

پژوهشگر

• Optimal Transport
• Explainable AI
• Evolutionary Algorithms
• +۳ مورد دیگر

Philip Naumann is a PhD student and research associate at the Machine Learning group of Technische Universität Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on optimal transport, explainable AI, evolutionary algorithms, and counterfactual explanations. He holds a Master's degree in Computer Science from Leibniz Universität Hannover (2020), with a semester abroad at The Chinese University of Hong Kong in 2019. Previously, he worked as a research associate at L3S Research Center and Freie Universität Berlin, focusing on evolutionary algorithms and continual learning. Education: MSc in Computer Science, Leibniz Universität Hannover, 2020 Research Interests: Philip's work bridges theoretical foundations of machine learning with practical applications in explainability and algorithmic transparency. He explores optimal transport theory to understand dataset shifts and develops consequence-aware counterfactual methods for ethical AI systems. His earlier research at L3S/FU Berlin emphasized evolutionary algorithms for adaptive learning systems. Publications: Recent work includes advancements in XAI for optimal transport frameworks and consequence-aware sequential decision-making models. His research trends reflect a strong focus on bridging mathematical rigor with real-world AI transparency challenges. Advising/Grants: No specific grants or advisory roles mentioned in the provided texts. Currently focused on doctoral research. Labs/Teams: Part of TU Berlin's Machine Learning group and affiliated with BIFOLD, collaborating on foundational AI research initiatives.

Lichao Sunمشاهده پروفایل

استادیار

• Artificial Intelligence Security
• AI Privacy
• Machine Learning
• +۷ مورد دیگر

Lichao Sun is an Assistant Professor of Computer Science and Engineering at Lehigh University. He holds a Ph.D. in Computer Science from the University of Illinois at Chicago (2020), and bachelor's and master's degrees in Computer Science and Engineering from the University of Nebraska-Lincoln. His research focuses on AI security and privacy, addressing vulnerabilities in AI systems, particularly in large language models and multimodal systems. He also explores data mining, federated learning, and reinforcement learning applications. His educational background includes: Ph.D., Computer Science, University of Illinois at Chicago (2020) M.S., Computer Science, University of Nebraska-Lincoln B.A., Computer Science and Mathematics, University of Nebraska-Lincoln Research interests emphasize mitigating security threats in AI, including adversarial attacks (backdoors, token-level vulnerabilities), robust watermarking, and privacy-preserving techniques in federated learning. His work integrates vision-language models, multimodal reasoning, and ethical AI safety. Recent studies highlight innovations in secure embedding aggregation, generative AI defenses, and strategic reasoning in LLMs. Publications (2025) reflect a focus on AI security, multimodal systems, and reinforcement learning applications, with contributions to conferences like CCS, NeurIPS, and AAAI. His research bridges theoretical advancements with practical cybersecurity solutions for modern AI architectures. No scientific awards explicitly mentioned in the provided text. His advising and grants details are not detailed here, though his prolific publication record suggests active research funding. No lab/teams explicitly referenced in the input data.

Joaquin Aguadoمشاهده پروفایل

مدرس

• Computer Science
• Synchronous Programming
• Concurrency
• +۱۱ مورد دیگر

Joaquin Aguado is a Lecturer in the Department of Computer Science at Constructor University Bremen, Germany. He is affiliated with the School of Computer Science & Engineering and contributes to academic programs such as Computer Science, Software Data and Technology, and Robotics and Intelligent Systems. His research focuses on synchronous programming, deterministic concurrency, and formal semantics. Key areas of interest include compositional timing analysis, SCCharts for safety-critical applications, and constructive scheduling frameworks. His work bridges theoretical computer science with practical applications in embedded systems and real-time computing. Selected publications highlight his contributions to synchronous reactive multithreading, dataflow programming strategies, and conformance testing of distributed systems. These include titles like "Synchronised Shared Memory and Model Checking" and "SCCharts: Sequentially Constructive Statecharts for Safety-Critical Applications" .

• Economic Theory
• Behavioral Economics
• Political Economy
• +۳ مورد دیگر

Professor Nejat Anbarci is a distinguished academic in the Business School at Durham University, specializing in Economic Theory, Behavioral Economics, and Political Economy. He earned his PhD in Economics from the University of Iowa in 1988, followed by teaching roles at U.S. universities until 2008, then in Australia, before joining Durham in 2018. His research focuses on game theory, bargaining solutions, institutional economics, and experimental economics. Notable contributions include analyses of arbitration mechanisms, evolutionary game models, and the impact of natural disasters on political systems. His work bridges theoretical and applied economics, with publications in top journals like the Quarterly Journal of Economics , Management Science , and Journal of Economic Theory . Key themes include dispute resolution efficiency, AI-driven decision-making in games, and the interplay between environmental shocks and governance. Anbarci’s research often employs experimental methods and natural experiments to explore real-world economic behaviors and policy implications. His articles span topics ranging from strategic bargaining to climate change effects on institutions, demonstrating a commitment to both foundational theory and policy-relevant insights. While no scientific awards are explicitly listed, his prolific publication record reflects sustained academic excellence. Advising and grants details are not provided in the text, but his global academic journey underscores active collaboration across institutions.

Yangming Liمشاهده پروفایل

پژوهشگر ارشد

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Indranil Mukhopadhyayمشاهده پروفایل

استاد

• Biostatistics
• Statistical Genetics
• Genomics
• +۳ مورد دیگر

Indranil Mukhopadhyay serves as a Professor in the Department of Statistics at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of statistics, genomics, and public health. His work bridges theoretical statistical methodology with urgent real-world health challenges, particularly in infectious disease modeling and genomic data analysis. His primary research interests encompass biostatistical methodology development for multi-omics integration, epidemiological modeling of infectious diseases (notably HIV/AIDS and COVID-19), and statistical genetics for complex trait analysis. He has pioneered robust computational frameworks for handling high-dimensional biological data, with emphasis on isoform quantification, gene expression clustering, and genetic association studies in diverse populations. Analysis of his recent publications reveals a strong trajectory toward public health applications , where statistical models directly inform pandemic response strategies and disease control policies. His work consistently demonstrates innovation in adapting mathematical frameworks (e.g., critical community size theory) to contemporary health crises, while maintaining rigorous methodological foundations in sequential estimation and robust statistics. No scientific awards were documented in the provided materials. Information regarding student advising, research grants, or laboratory infrastructure was not available in the source text. His publication record suggests active collaboration with medical researchers, particularly in studies involving Indian population cohorts and rare genetic disorders. No details about specific research laboratories or collaborative teams were provided in the available documentation.

Aline Parreauمشاهده پروفایل

پژوهشگر

• Discrete Mathematics
• Combinatorial Optimization
• Combinatorial Game Theory
• +۳ مورد دیگر

Aline Parreau is a junior researcher at CNRS, affiliated with the GOAL team of the LIRIS laboratory at Université Claude Bernard Lyon 1. Her research focuses on discrete mathematics, combinatorial games, graph theory, and positional games. She earned her PhD in Discrete Mathematics from Université Joseph Fourier (Grenoble) in 2012, followed by postdoctoral research at the University of Liège. She coordinates the ANR project P-GASE, studying combinatorial and algorithmic aspects of positional games on graphs. PhD: Identification problems in graphs (2012), supervised by Sylvain Gravier. Postdoctoral work: Discrete Mathematics at University of Liège (2013–2014). Her teaching includes operational research and computer science courses at various institutions, including Lyon 1 University and Lille 1 University. She is actively involved in scientific mediation through initiatives like Maths à modeler and MATh.en.JEANS, promoting mathematical research to the public. Key activities include advising PhD and MSc students, organizing seminars, and contributing to research on graph theory, combinatorial games, and algorithmic optimization.

Thu D. Nguyenمشاهده پروفایل

استاد

• Datacenters
• Sustainability
• Computer Systems
• +۵ مورد دیگر

Thu D. Nguyen is a distinguished Professor and currently serves as the Dean of Mathematical and Physical Sciences at the School of Arts and Sciences, Rutgers University. She holds a faculty position in the Department of Computer Science where she has made significant contributions to research and education. Her research interests span multiple domains within computer science, with a particular focus on Datacenters , Sustainability , Computer Systems , Approximate Computing , and Computer Science Education . Dr. Nguyen leads the Computer and Networked Systems research group, where her team investigates innovative approaches to energy-efficient computing and data management. Analysis of her recent publications reveals a strong trend toward addressing challenges in big data management, missing data imputation, blockchain applications in education, and machine learning for educational technology. Her work bridges theoretical computer science with practical applications in educational systems and healthcare data management. Dr. Nguyen has received numerous prestigious awards recognizing her contributions to the field: Rutgers Big Ten Academic Alliance Department Executive Officers Fellow (2018) Best paper award at FAST 2016 for research on datacenter reliability IEEE Micro Top Picks selection (2014) Multiple best paper awards including at IGCC 2010 NSF CAREER award (2005-2010) Faculty of the Year Award for Excellence in Teaching (awarded in 2000 and 2002) Her research has been supported by substantial grants including a $4 million DIBBS grant, NSF CSforAll grant, Microsoft SEIF grant, and multiple NSF CRI grants. Dr. Nguyen has played a leadership role in establishing Rutgers' new Data Science major, minor, and certificate programs, reflecting her commitment to advancing computer science education.

Liyi Liمشاهده پروفایل

استادیار

• Formal Methods
• Programming Languages
• Software Engineering
• +۴ مورد دیگر

Liyi Li is an Assistant Professor in the Department of Computer Science at Iowa State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign, where his research focused on compiler verification and formal methods. After completing his postdoctoral work at the University of Maryland, he expanded his research to include quantum computing, software engineering, and compiler optimization. Education: B.S. in Computer Science, University of Illinois at Urbana-Champaign (2012) M.S. in Computer Science, University of Illinois at Urbana-Champaign (2014) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2020) Research Interests: Liyi Li’s work bridges formal methods, programming languages, and quantum computing. Key areas include quantum program verification, compiler correctness, memory-safe dialects (e.g., Checked C), and distributed quantum systems. He emphasizes applying formal techniques to ensure software reliability and security. Awards: 2020 UMD Victor Basili Postdoctoral Fellowship 2019 UIUC Spring Outstanding Teaching Assistant Award 2012 UIUC University Honor (Bronze Tablet) Grants & Advising: Co-PI for NSF Grant NQVL:QSTD (2024–2025) focusing on quantum analog pathways PI for NSF Grant CCF-2422127 (2024–2027) on Just-in-Time Verification Advises over 13 students, including PhD candidates at Iowa State, University of Maryland, and William & Mary Collaborations: Liyi Li collaborates with institutions such as the University of Maryland and works with researchers like Mingwei Zhu and Xiaodi Wu on quantum verification and compiler optimization tools.

Richard W. Neuمشاهده پروفایل

استاد

• Mechanical Behavior of Materials
• Thermomechanical Fatigue
• Creep and Fatigue Analysis
• +۷ مورد دیگر

Richard W. Neu is a Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering, with a courtesy appointment in the School of Materials Science and Engineering. He directs the Mechanical Properties Characterization Facility and leads the IMat Initiative's Materials in Extreme Environments research. His expertise spans mechanical behavior of materials under extreme conditions, including thermomechanical fatigue, creep, fretting fatigue, and additive manufacturing. He holds a Ph.D., M.S., and B.S. from the University of Illinois at Urbana-Champaign (1991, 1988, 1986). Neu's research focuses on predicting material degradation in harsh environments (e.g., aerospace, power generation, medical devices). Key areas include high-temperature materials, hydrogen energy infrastructure, and AI-driven materials modeling. His work is funded by industries and agencies like NASA and the U.S. Air Force. Scientific awards include ASTM Professor of the Year (2023), ASME Fellow (2017), and multiple best paper awards. His lab collaborators include institutions like Oak Ridge National Lab and Rolls-Royce. Current projects involve AI safety for infrastructure, high-throughput materials development, and microstructure-sensitive modeling.