جست‌وجوی استاد و پژوهشگر

Nadine von Frankenbergمشاهده پروفایل

عضو هیئت علمی

• Ubiquitous computing
• Smart buildings
• Indoor Environmental Quality (IEQ)
• +۳ مورد دیگر

Nadine von Frankenberg is a Teaching Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. She holds a PhD from the Technical University of Munich, Germany. Her research focuses on ubiquitous computing, smart buildings, indoor environmental quality (IEQ), human-in-the-loop control systems, digital health, and educational technology. She has contributed to projects like LATEST (a machine learning-based thermal control system) and OccuTherm (occupant thermal comfort inference using body shape data). Her work bridges computational methods with real-world applications in both technical and educational domains. Her research interests include developing human-centric smart building systems that address thermal comfort conflicts in shared spaces, leveraging machine learning for stress prediction in extreme environments, and advancing interactive learning methodologies in software engineering education. Notable contributions include the design of active learning strategies for large classes and tools like Stager for automated programming exercise assessment. Her publications span ACM conferences such as BuildSys, CHI, and ICSE, reflecting her interdisciplinary approach combining computing with environmental and educational challenges. Though no specific awards are listed, her work demonstrates significant impact in both technical and pedagogical innovation. She advises no listed students but has collaborated on multiple research projects with graduate and undergraduate teams.

• Rheumatology
• Autoimmune Diseases
• Primary Sjögren's Syndrome
• +۷ مورد دیگر

Steven Young-Min is a researcher specializing in rheumatology, with a focus on autoimmune conditions such as primary Sjögren's syndrome and rheumatoid arthritis. His work involves biomarker discovery, genetic associations, disease progression, and patient-reported outcomes, often in collaboration with large UK-based research consortia like BRAGGSS. Although his current institutional affiliation is not explicitly stated, his long-standing contributions to clinical and translational research in rheumatology suggest a strong academic presence. PhD in Rheumatology or related field (inferred from research depth) Extensive collaboration with UK rheumatology research networks Active in clinical and genetic studies from 2001–2017 His research interests center on understanding the mechanisms of fatigue, dryness symptoms, radiographic progression, and treatment response in autoimmune diseases. He has investigated the role of cytokines, matrix metalloproteinases, and genetic polymorphisms in disease activity and outcomes. His work bridges clinical observation with molecular and genetic analysis to improve patient stratification and therapeutic efficacy. The 15 most recent publications reflect a consistent focus on primary Sjögren's syndrome and rheumatoid arthritis, with increasing emphasis on genetic and transcriptional biomarkers, patient-reported outcomes, and treatment monitoring. Key themes include the discordance between subjective and objective disease measures, the utility of biomarkers in predicting progression, and the impact of psychological and autonomic factors on symptom burden. No scientific awards are mentioned in the provided text. There is no information available about student advising or research grants. However, his participation in large collaborative studies such as BRAGGSS indicates involvement in funded research programs. His work on genetic associations and treatment response suggests leadership or significant contribution to multicenter projects. While no specific lab or research team is named, his repeated collaborations with groups studying rheumatoid arthritis genetics (e.g., BRAGGSS) and Sjögren's syndrome registries imply active membership in established research networks focused on autoimmune disease mechanisms and outcomes.

Mahalakshmi Sabanayagamمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +۳ مورد دیگر

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

Xiao Zhangمشاهده پروفایل

استادیار

• Mobile Computing
• AIoT
• Cyber Physical Systems
• +۹ مورد دیگر

Dr. Xiao Zhang is an Assistant Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn's College of Engineering and Computer Science. He leads the Trustworthy AIoT Lab (TAI Lab) and advises the Immersive Computing Club. Previously, he was a Postdoctoral Associate at Duke University and holds a Ph.D. from Michigan State University, M.S. from Northwestern Polytechnical University, and B.E. from Taiyuan University of Technology. Education: Ph.D. in Computer Science and Engineering - Michigan State University M.S. - Northwestern Polytechnical University B.E. with Honors - Taiyuan University of Technology Dr. Zhang's research focuses on next-generation wireless systems with emphasis on mobile computing, AIoT, cyber-physical systems, and AI-assisted sensing/localization. His work explores spatial-temporal diversities in Optical Wireless Communication (OWC) to enable secure, location-aware communication for IoT and human-centered computing. Applications include LiFi, V2X networks, underwater navigation, digital health, smart cities, HCI, and AR/VR. Recent publications demonstrate a strong focus on optical communication systems (OWC, LiFi), drone technology, IoT security, and AI-assisted sensing. Trends include innovations in 3D optical connections, adversarial defense for audio systems, radar-based point cloud generation, and federated learning solutions for heterogeneous IoT environments. Awards & Grants: 2025: IHP Research Engagement, OE Review/Creation Grants, Creative Teaching Fund 2024: NSF I-Corps, RAG Grant 2023: Dissertation Fellowship (MSU) 2022: Travel Fellowship (MSU) 2019-2023: Multiple Graduate Fellowships 2016-2018: Postgraduate Scholarships 2013-2015: National/University Scholarships Dr. Zhang advises 10+ students across multiple levels including PhD candidate Deniz Acikbas, MS students Ashwin Sarvadey, Jaskirat Sudan, Rohit Raval, and Nishaant Madhankumar, and undergraduate researchers including NSF STEM Scholars Christian Nwobu, Fatima Qasem, and Fatima Mohammed. His TAI Lab focuses on trustworthy AI-driven IoT systems.

Veronika Rockovaمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• High-dimensional Data Analysis
• +۳ مورد دیگر

Veronika Rockova is the Bruce Lindsay Professor of Econometrics and Statistics in the Wallman Society of Fellows at the University of Chicago Booth School of Business. She joined Booth after postdoctoral training at the Wharton School and has been internationally recognized for her work at the intersection of statistics and machine learning. Her research focuses on developing decision-centric statistical tools for large datasets, specializing in Bayesian computation Variable selection High-dimensional decision theory Hierarchical modeling Uncertainty quantification for generative AI Recent publications highlight trends in Bayesian CART mixing rates Generative posterior sampling Deep learning integration with Bayesian frameworks Tree-based bandit approaches for ABC Quantile methods for credible sets Scientific recognition includes COPSS President's Award (2024) COPSS Emerging Leader Award (2023) NSF CAREER Award (2020) She currently serves on editorial boards for Annals of Statistics Journal of the American Statistical Association Journal of the Royal Statistical Society (Series B) and mentors PhD students in econometrics and statistics.

• Computer Vision
• Machine Learning
• Object Recognition
• +۶ مورد دیگر

Prof. Bastian Leibe serves as a University Professor at RWTH Aachen University, leading the Computer Vision Group within the Chair of Computer Sciences 8 (Computer Graphics, Computer Vision, and Multimedia). His research focuses on developing computer vision applications for mobile devices, robotic systems, and autonomous vehicles, with strong institutional ties to the Cluster of Excellence "UMIC - Ultra High-Speed Mobile Information and Communication". His core research spans visual object recognition, tracking, self-localization, and 3D reconstruction, with increasing emphasis on integrated solutions for real-world deployment. Recent work demonstrates deep specialization in autonomous driving perception systems, human-robot interaction interfaces, and foundational computer vision methodologies that bridge theoretical advances with practical engineering constraints. Analysis of recent publications reveals dominant trends in LiDAR-based anomaly detection for autonomous systems, efficient 3D scene understanding frameworks, and novel applications of foundation models in robotics. The group consistently contributes to top-tier conferences with innovations in diffusion models, vision transformers, and interactive segmentation techniques that push the boundaries of real-time mobile vision. The Computer Vision Group maintains active educational engagement through specialized lectures and seminars in computer vision and machine learning, while operating from the UMIC Research Centre facility in Aachen with direct industry and academic collaborations in mobile information systems.

Eirik Myrvoll-Nilsenمشاهده پروفایل

پژوهشگر ارشد

• Climate Statistics
• Statistical Climatology
• Bayesian Inference
• +۳ مورد دیگر

Eirik Myrvoll-Nilsen serves as a Postdoctoral Fellow at the Department of Mathematics and Statistics, UiT The Arctic University of Norway, where he bridges statistical methodology with climate science and environmental monitoring. Based in Tromsø, he actively contributes to interdisciplinary research through collaborations within UiT's Complex Systems Modeling, Statistics and Data Analysis, and Machine Learning research groups, focusing on computationally intensive approaches to earth system challenges. His research expertise spans Climate Statistics, Statistical Climatology, Bayesian Inference, Long-range Dependence, Machine Learning for Environmental Science, and Paleoclimatology. He develops advanced statistical models for climate data analysis—including ice core chronology and global temperature response systems—while pioneering deep learning applications for automated classification of microfossils and marine fauna detection in underwater imagery, emphasizing methodological rigor and practical environmental solutions. Analysis of his recent publications reveals a cohesive research trajectory centered on uncertainty quantification in paleoclimate reconstructions and machine learning for environmental monitoring. Key methodological themes include Bayesian modeling of long-memory climate processes, statistical synchronization of ice core records, and deep learning frameworks for foraminifera classification—all demonstrating innovative integration of computational statistics with domain-specific climate and marine science challenges. Scientific awards: No scientific awards or honors were documented in the provided information. Advising and grants: The available text contains no details regarding doctoral/master's students supervised by Dr. Myrvoll-Nilsen or specific research grants secured. Labs and teams: He is an active member of UiT's Complex Systems Modeling (CoSMo), Statistics and Data Analysis, and Machine Learning research groups. Additionally, he contributes to the project 'Transforming ocean surveying by the power of DL and statistical methods,' which develops deep learning pipelines for automated analysis of marine environmental video data from Norwegian fjords and coastal waters.

Kristian Saboمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Mathematics
• Data Cluster Analysis
• +۷ مورد دیگر

Dr. Kristian Sabo is a Full Professor at the School of Applied Mathematics and Informatics , Josip Juraj Strossmayer University of Osijek, Croatia. His research focuses on Applied and Numerical Mathematics with applications in Agriculture , Medicine , and Seismology . He has developed innovative clustering algorithms like adaptive Mahalanobis fuzzy clustering and hybrid RANSAC-DBSCAN methods. PhD (2007) and MSc (2003) in Mathematics, University of Zagreb BSc (1999) in Mathematics and Computer Science, University of Osijek Research Highlights : Created Minimal Distance Index for optimal cluster validation in complex datasets Developed DIRECT algorithm adaptations for global optimization in multi-dimensional partitions Applied clustering methods to earthquake analysis and medical imaging Software Development : Co-developed LeArEst R package for noisy image analysis Created Mathematica modules for pandemic forecasting models

Jaakko Pehkonenمشاهده پروفایل

استاد

• Labor Economics
• Health Economics
• Economics of Education
• +۲ مورد دیگر

Jaakko Pehkonen serves as a Professor at the School of Business and Economics, University of Jyväskylä, where he is based in room Ag D313.4. His research is anchored within the Empirical Microeconomics (EME) research group, which specializes in labor economics, health economics, regional economics, and economics of education using micro-level data and advanced econometric techniques. Contactable via jaakko.k.pehkonen@jyu.fi or +358503732133, his work addresses real-world economic policy questions through rigorous empirical analysis. His research portfolio demonstrates exceptional depth in health-labor market intersections, including analyses of sickness absence policies in healthcare, tobacco smoking's impact on early-career trajectories, and weight-depression relationships using genetic instruments. Additional expertise spans family economics (parental responses to children's job loss), retirement coordination among couples, digital healthcare adoption, and Nordic economic convergence. The EME group's methodological rigor—evident in quasi-experimental designs and Finnish register data utilization—characterizes his contributions to causal inference in applied economics. Recent publications reveal a strategic focus on policy-relevant microeconometric studies, particularly Finnish register-based analyses of labor market dynamics and health interventions. His work consistently employs difference-in-differences, instrumental variables, and observational study designs to evaluate policy impacts across healthcare, retirement, and unemployment domains. This concentration on causal identification in real-world settings distinguishes his empirical approach within European labor economics. As a core member of the Empirical Microeconomics research group, Professor Pehkonen contributes to a collaborative ecosystem investigating labor market frictions, health determinants, regional disparities, and educational outcomes. The group's interdisciplinary framework enables comprehensive analysis of socioeconomic phenomena through microdata, fostering evidence-based policy recommendations for Nordic and international contexts.

David Pageمشاهده پروفایل

استاد

• Machine Learning
• Biostatistics
• Bioinformatics
• +۵ مورد دیگر

David Page is the Duke Health Distinguished Professor of Biostatistics & Bioinformatics and Computer Science at Duke University, where he chairs the Biostatistics & Bioinformatics Division. His research focuses on developing machine learning algorithms for biomedical data, particularly de-identified electronic health records (EHR) and high-throughput molecular data. Ph.D. in Computer Science from University of Illinois (1993) His work emphasizes causal inference , multi-relational data analysis , and interpretable machine learning , with applications in genomics, pharmacovigilance, and clinical decision support. Recent projects include EHR-based risk surveillance for autism/ADHD, sepsis subtyping, and deprescribing optimization. His publications highlight temporal modeling ( Soft Phenotyping ), differential privacy in multi-site studies, and neural network interpretability as infinite tree models. Key collaborations span urology, ophthalmology, and oncology. National Institutes of Health National Heart, Lung, and Blood Institute Department of Defense National Institute of Mental Health Page serves as Master of Biostatistics and instructor in Duke's Department of Biostatistics & Bioinformatics, with secondary appointments in Computer Science.

Yulong Luمشاهده پروفایل

استادیار

• Mathematical foundations of machine learning and data sciences
• Applied probability and stochastic dynamics
• Applied analysis and PDEs
• +۲ مورد دیگر

Yulong Lu serves as an Assistant Professor at the School of Mathematics, University of Minnesota, and holds affiliated faculty status with the Data Science Initiative in the College of Science and Engineering. His research bridges theoretical mathematics and practical applications in data science, with active recruitment of undergraduate and graduate researchers. Lu earned his Ph.D. in Mathematics and Statistics from the University of Warwick under Andrew Stuart and Hendrik Weber. His academic trajectory includes an Assistant Professorship at the University of Massachusetts Amherst (2020-2023) and a Phillip Griffiths Research Assistant Professorship at Duke University (2017-2020) mentored by Jonathan Mattingly and Jianfeng Lu. His research spans mathematical foundations of machine learning , applied probability , stochastic dynamics , applied analysis , PDEs , Bayesian statistics , and uncertainty quantification . Recent work demonstrates deep integration of diffusion models, transformers, and operator learning to solve complex physical systems while establishing theoretical convergence guarantees. Analysis of his 15 most recent publications reveals dominant trends in physics-informed generative modeling for PDEs, in-context learning for dynamical systems, and theoretical analysis of deep learning approximations. His work consistently connects abstract mathematical frameworks with concrete scientific computing applications across fluid dynamics, quantum mechanics, and optimization. No scientific awards were documented in the provided materials. Lu actively mentors researchers through open positions for Ph.D. students (Fall 2026 intake), postdocs via Mathjobs, and UMN internships focused on deep learning theory and scientific applications. His group emphasizes self-motivated collaboration on theoretical and applied challenges in machine learning. He leads a research group developing theory for deep learning applications in scientific computing, with current projects including diffusion-based PDE solvers, transformer architectures for dynamical systems, and uncertainty quantification for inverse problems.

• Quantum Computing
• Machine Learning
• Computational Neuroscience
• +۳ مورد دیگر

Johnson Thomas is a Professor in the Department of Computer Science at Oklahoma State University, with affiliations in both the Stillwater and Tulsa campuses. His research focuses on Quantum Computing, Machine Learning, and Computational Neuroscience, with notable work on quantum circuit optimization and spiking neural networks. He holds degrees from the University of Reading (PhD, 1995), University of Edinburgh (MSc, 1983), and University of Wales (BSc, 1982). His teaching spans advanced topics in databases, quantum computing, and programming languages. Recent courses include *Quantum Computing*, *Advanced Topics in Information Systems*, and *Discrete Mathematics for Computer Science*. He has also advised doctoral students and led research in distributed systems and security. Research interests include quantum algorithms, neurocomputational models, and causal inference. His work bridges theoretical foundations with practical applications, such as ROS security frameworks and NIRS-based forage quality prediction. Over 20 funded projects highlight his expertise in big data, autonomous systems, and sensor networks. Publications emphasize quantum circuit design, medical ML interpretation, and computational neuroscience. Collaborations include interdisciplinary projects with healthcare and agricultural sectors. His contributions advance both theoretical computer science and applied technologies.

David Kristjanson Duvenaudمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• AGI Governance
• +۳ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto, affiliated with the Faculty of Arts and Science. He is a founding member of the Vector Institute and holds a Schwartz Reisman Chair in Technology and Society. His research focuses on AGI governance, existential risk mitigation, and probabilistic machine learning. Previously, he conducted postdoctoral research at Harvard and earned his Ph.D. from the University of Cambridge under advisors Carl Rasmussen and Zoubin Ghahramani. Education: Ph.D. (University of Cambridge), M.Sc. (University of British Columbia) Research Interests: Neural ODEs, AI ethics, probabilistic modeling, and scalable generative systems. His work bridges foundational machine learning theory with real-world applications, including chemical design and alignment science. Key contributions include LLM Processes, sabotage evaluations for frontier models, and neural ODE frameworks. He teaches advanced machine learning courses (e.g., STA414, CSC2547) and advises students like James Requeima and Raymond Douglas. His awards include a Sloan Fellowship and distinguished paper recognitions. Current projects address systemic risks posed by incremental AI progress, emphasizing long-term societal impacts and governance mechanisms.

• Intelligent Agents
• Probabilistic Graphical Models
• Deep Learning
• +۳ مورد دیگر

Prof. Yifeng Zeng is a Professor and Head of Research & Knowledge Exchange&UoA 11 Lead in Northumbria University's Department of Computer and Information Sciences. He leads the Autonomous Systems research group, focusing on intelligent agents, deep learning, computational intelligence, and digital education. With a PhD in Engineering (2005), he has secured the EPSRC New Investigator Award (2019) and led Innovate UK projects. His work spans multiagent systems, healthcare AI applications, and data-driven decision models. Affiliations: Northumbria University, Autonomous Systems Group Research Interests: His primary areas include probabilistic graphical models, reinforcement learning in multiagent systems, and AI-driven healthcare solutions like optical coherence tomography (OCT) for cancer diagnosis. He explores ethical AI through policy legibility and interpretable decision-making frameworks. Publications: Recent work emphasizes multiagent learning strategies, spiking neural networks, and deep learning applications in medical imaging. His 2025 articles highlight advancements in legible agent policies and cooperative exploration methods. Awards: EPSRC New Investigator Award (2019) Grants & Supervision: Co-Investigator on the Turing Network Development Award (2022). Supervises 2 researchers, focusing on autonomous systems and AI ethics. Leads projects integrating machine learning with healthcare diagnostics and educational technology. Labs & Teams: Directs the Autonomous Systems group, collaborating on typhoon response systems and medical imaging AI. Active in interdisciplinary projects blending robotics, computer vision, and decision theory.

Yi-Zhe Songمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Professor Yi-Zhe Song is a Professor of Computer Vision and Machine Learning at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP), one of Europe's largest AI research centers. He leads the SketchX Lab, focusing on understanding human sketching to advance computer vision and cognitive science. His roles include Co-Director of the Surrey Institute for People-Centred AI and Programme Lead of Surrey’s MSc in Artificial Intelligence. He holds a PhD (2008) and MSc (2004, Best Dissertation Award) from the University of Cambridge and Bath. Research interests span sketch-based systems, fine-grained visual classification, domain adaptation, and generative models. His lab has produced over 67 publications in top-tier conferences (CVPR, ICCV, ECCV) and journals, including a Best Paper Award at BMVC 2015. He is an Associate Editor for IEEE TPAMI and Frontiers in Computer Science, and has served as Programme Chair for BMVC 2021. Recent trends in his work emphasize explainable AI (SketchXAI), 3D reconstruction from sketches, and practical applications like virtual try-on and fashion-focused V+L models. His MSc AI programme integrates technical, ethical, and business aspects, preparing students for diverse AI roles. Awards: Best Paper Award (BMVC 2015), Senior Member of IEEE, Fellow of the Higher Education Academy. Lab Leadership: 3 academics, 2 postdocs, 14 PhD students in the SketchX Lab. Grants & Partnerships: Collaborations with industry and international funding bodies (e.g., EPSRC, São Paulo Research Foundation). Future Work: Expanding AI’s societal impact through inclusive education, human-centric AI, and democratizing generative AI tools.