جست‌وجوی استاد و پژوهشگر

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Hanti Linمشاهده پروفایل

دانشیار

• Philosophy of Science
• Formal Epistemology
• Inductive Logic
• +۴ مورد دیگر

Hanti Lin serves as Associate Professor in the Department of Philosophy at the University of California, Davis, where his research bridges formal epistemology, philosophy of science, and computational methodologies. His work addresses foundational challenges in scientific inference, particularly focusing on justifying inductive reasoning beyond traditional statistical frameworks. Education: Ph.D. in Philosophy, Carnegie Mellon University (2013) M.S. in Logic, Computation and Methodology, Carnegie Mellon University (2010) M.A. in Philosophy, University College London (2007) B.A. in Physics, National Taiwan University (2003) Research Focus: Lin's scholarship centers on resolving Hume's problem of induction through novel convergence frameworks, with significant contributions to causal inference without faithfulness assumptions and unified theories of inductive logic . His interdisciplinary approach integrates statistics , machine learning theory , and formal epistemology to justify scientific inference methods that resist conventional statistical justification. Recent work demonstrates increasing sophistication in connecting theoretical computer science with philosophical epistemology. Publication Trends: Analysis of Lin's 15 most recent publications reveals a clear trajectory toward synthesizing machine learning practice with philosophical foundations. The period 2022-2025 shows heightened focus on internalist reliabilism in statistical practice, historical-philosophical analysis of machine learning , and unified convergence frameworks spanning formal learning theory, statistical inference, and supervised learning. This evolution reflects growing recognition of philosophy's role in addressing fundamental limitations in contemporary AI methodologies. Awards: No scientific awards were documented in the provided materials. Teaching & Mentoring: Lin teaches advanced courses including "Theory of Knowledge," "Formal Epistemology," and "Logic, Probability, and Artificial Intelligence," emphasizing rational justification in scientific inquiry. While specific advisees aren't listed, his curriculum design demonstrates commitment to training next-generation researchers in interdisciplinary methodology. His postdoctoral position at Australian National University preceded his current UC Davis appointment, establishing his research trajectory.

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Fredrik Kahl is a Professor at Chalmers University of Technology, leading the Computer Vision Group under the Department of Signal Processing and Medical Technology. His research spans Computer Vision , Machine Learning , and Medical Image Analysis , with a focus on geometric deep learning and 3D reconstruction. University: Chalmers University of Technology Department: Signal Processing and Medical Technology Email: fredrik.kahl@chalmers.se His work addresses rotation equivariance , out-of-distribution detection , and privacy-preserving representations . Recent publications explore Gaussian splatting for 3D edge mapping, semi-supervised learning frameworks, and symmetry encoding in ReLU networks. Projects include collaborations with institutions like Wallenberg AI, Autonomous Systems and Software Program and grants from VINNOVA and Vetenskapsrådet (VR) .

Yi Dengمشاهده پروفایل

استاد

• Climate Variability
• Atmospheric Dynamics
• Hydroclimate
• +۷ مورد دیگر

Professor Yi Deng is affiliated with the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology . His research focuses on climate variability across multiple timescales, atmospheric dynamics, and climate modeling. University: Georgia Institute of Technology School: School of Earth and Atmospheric Sciences Email: yi.deng@eas.gatech.edu Research interests include: Hydroclimate variability at regional scales Polar-tropical interactions ENSO and Annular Modes feedbacks Probabilistic graphical models Climate networks Atmospheric scale-interactions Aerosol-circulation coupling Surface heat flux parameterization Recent publications highlight trends in climate extremes, monsoon dynamics, surface flux modeling, and multiscale interactions. Key subfields include synoptic-scale atmospheric disturbances, energy transport mechanisms, and data-driven climate analysis tools. No scientific awards or student advising information were explicitly mentioned in the provided texts.

Yoshi Gotohمشاهده پروفایل

مدرس

• Speech and Language Processing
• Video Analysis
• Information Retrieval
• +۲ مورد دیگر

Dr. Yoshi Gotoh is a Lecturer and Student Projects Officer in the Department of Computer Science at the University of Sheffield's School of Computer Science . He holds a PhD from Brown University and a first degree in Engineering from the University of Tokyo. As a member of the Speech and Hearing (SpandH) research group, his work bridges audio-visual processing and language technologies. His core research explores: Video analysis and retrieval systems Natural language generation for video content 3D visual speech animation Crowd behavior modeling through trajectory clustering Medical imaging enhancements via colorization techniques Analysis of his 15 most recent publications reveals strong emphasis on multimodal systems combining computer vision with speech/language processing. Dominant themes include egocentric video analysis, human activity recognition, and cross-modal translation between visual and textual domains. He has secured significant research funding as Co-Principal Investigator: £218,226 from Innovate UK (2021-2024) for fake imagery detection £393,115 from Innovate UK (2018-2021) for unsupervised dubbing systems £284,248 from EPSRC (2001-2005) for spoken language summarization He leads projects within the Speech and Hearing laboratory, focusing on developing computational methods for audiovisual integration and video understanding systems.

Yongxin Chenمشاهده پروفایل

دانشیار

• Control Theory
• Machine Learning
• Robotics
• +۹ مورد دیگر

Yongxin Chen is an Associate Professor in the School of Aerospace Engineering at the Georgia Institute of Technology. He received his BSc in Mechanical Engineering from Shanghai Jiao Tong University (2011) and a PhD in Mechanical Engineering from the University of Minnesota (2016). Prior to joining Georgia Tech, he held positions as a Research Fellow at Memorial Sloan Kettering Cancer Center (2016-2017) and an Assistant Professor at Iowa State University (2017-2018). His research spans control theory, machine learning, robotics, and optimal transport. Key areas include developing efficient MCMC algorithms, advancing diffusion models for generative AI, and integrating uncertainty synthesis into control systems. He leads the Foundations of Learning And Intelligent Robots (FLAIR) lab, relocated to Georgia Tech's CODA building in 2024, focusing on systems that harmonize autonomy, stochastic control, and optimization. 2023 : Best Paper at NeurIPS and CoRL, plenary talks at ACC and MTNS. 2022 : Donald P. Eckman Award, plenary talk at ACC. 2021 : Simons-Berkeley Fellowship and Balakrishnan Award. 2020 : NSF CAREER Award. His recent publications emphasize diffusion models (e.g., DEIS, gDDIM, DiffCollage) and optimal transport applications. He has graduated three PhD students and mentors active researchers in generative AI, robotics, and control theory. Collaborations include institutions like Duke, University of Minnesota, and international visitors.

Kazim Topuzمشاهده پروفایل

دانشیار

• Probabilistic Graphical Models
• Data Mining
• Explainable AI
• +۳ مورد دیگر

Kazim Topuz is an Associate Professor of Business Analytics and Operations Management at the Collins College of Business, The University of Tulsa. He serves as Program Director of the Master of Business Analytics degree and holds a Ph.D. in Industrial Engineering from Wichita State University. Education Ph.D., Industrial Engineering, Wichita State University (Dissertation: Data Mining Applications in Healthcare) Master of Engineering, Information Systems Engineering, Lehigh University Master of Science, Industrial and Systems Engineering, Rutgers University His research focuses on designing probabilistic graphical models (Bayesian Belief Networks, Markov Networks) integrated with data mining techniques for data-driven decisions. He specializes in explainable AI applications across healthcare, accident severity analysis, student retention, and mental health domains. Topuz has published in leading journals such as European Journal of Operational Research, Decision Support Systems, and Information Systems Frontiers. He has served as special issue editor for Decision Support Systems and Annals of Operations Research. Scientific Awards 2025 Mayo Teaching Excellence Award 2023 IISE Gold Award 2022 Mayo Research Excellence Award Chapman Professorship Award (2020-2022) Wichita State University Outstanding Doctoral-level Student (2016) Turkish Ministry of National Education Fellowship Rutgers University Fellowship

Sabine Barratمشاهده پروفایل

استادیار

• Image Analysis
• Image Indexing
• Image Retrieval
• +۴ مورد دیگر

Sabine Barrat is an Assistant Professor at the University of Tours, affiliated with the University Institute of Technology of Tours (IUT) and the Fundamental and Applied Computer Science Laboratory (LIFAT). She teaches courses in website design and databases. Dr. Barrat obtained her PhD in Computer Science from Nancy 2 University in 2009, focusing on probabilistic models for image recognition. She completed a JSPS Postdoctoral Fellowship at Osaka Prefecture University and served as Vice-President for Digital Systems at the University of Tours (2016-2020). Her research explores image analysis, indexing/retrieval systems, automatic annotation, and document processing. Core methodologies include Bayesian networks, feature indexing structures, and hybrid visual-semantic modeling. Recent publications emphasize scalable image retrieval systems and document classification techniques. She received the JSPS Postdoctoral Fellowship for her work on character recognition. Dr. Barrat collaborates with the RFAI research team at LIFAT laboratory, focusing on pattern recognition and intelligent indexing systems.

Ian Horswillمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Interactive Art
• Interactive Entertainment
• +۷ مورد دیگر

Ian Horswill is an Associate Professor of Computer Science at Northwestern University , with joint appointments in the Departments of Electrical Engineering/Computer Science and Radio/Television/Film. He directs the Division of Graphics and Interactive Media and the Animate Arts Program, blending AI research with interactive art and entertainment. Research Focus: Autonomous agents, emotion/personality modeling for virtual characters, procedural animation via the Twig system, and interdisciplinary education in the Animate Arts Program. Key Projects: Twig (procedural animation), Meta (Scheme-like programming language), and role-passing architectures for efficient inference in robotics. Publications span AI, robotics, and games, with a focus on real-time decision-making, believable character behaviors, and cognitive architecture. His work has been recognized with the 2001 Nils Nilsson Prize . Students: Robin Hunicke, Magy Seif El-Nasr, and Rob Zubek, all of whom contributed to systems for game difficulty adjustment, dynamic lighting design, and natural-language dialog with NPCs. Affiliations: Chair of the Doctorial Consortium for the 2009 Foundations of Digital Games conference, member of the IGDA Education Committee.

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.

Archontis Giannakidisمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Biomedical Image Analysis
• +۸ مورد دیگر

Archontis Giannakidis is a Senior Lecturer in Data Science at the School of Science and Technology , Nottingham Trent University (NTU). He leads modules in Discrete Mathematics & Computational Complexity (Year 2) and Convex Optimisation (Year 3), while supervising undergraduate and Masters dissertations. Education: PhD in Electronic Engineering (Inverse Problems), University of Surrey (2010) Research Interests focus on applying Deep Learning and Machine Learning to biomedical data processing, particularly in Biomedical Image Analysis , Convolutional Networks , and Diffusion MRI . His work emphasizes automating intellectual tasks, efficient data representation, hidden pattern discovery, and decision optimization in healthcare and environmental contexts. Recent Article Trends highlight his expertise in 2D echocardiography-based cardiac quantification , MRI-driven ACL tear diagnosis , and landslide-tsunami prediction via geometry-invariant machine learning. These studies often integrate uncertainty modeling, attention mechanisms, and lightweight architectures for clinical and environmental applications. Scientific Awards : Fellow of the Higher Education Academy (FHEA) Advisory Roles include mentoring PhD students Tuan Aqeel Bohoran (Marie Skłodowska-Curie-funded) and David Gwillym Jenkins (internal funding), alongside visiting/external PhD candidates Michael Lystbaek (Aarhus University) and Athanasios Siouras (University of Thessaly). He has received grants from HORIZON 2020 , EPSRC , and Innovate UK , and collaborates with institutions like the Archimedes Research Unit (Greece) and National Heart and Lung Institute , Imperial College London. Professional Activity includes editorial board membership for Frontiers in Physiology , peer reviewing for journals like IEEE Transactions on Medical Imaging , and external examining for University of Strathclyde’s MSc programs. He also contributes to conference organization and Portuguese grant evaluation panels.

• Bayesian Modeling
• High-Dimensional Problems
• Change-Point Problems
• +۴ مورد دیگر

Nilabja Guha is a Lecturer in Probability and Statistics at the University of Maryland Baltimore County (UMBC). Her research focuses on Bayesian modeling for complex statistical problems, including high-dimensional datasets, inverse problems, uncertainty quantification, and stochastic approximation methods. Education Master of Statistics, Indian Statistical Institute Doctor of Philosophy, University of Maryland Baltimore County Research Interests She specializes in probabilistic graphical models, change-point detection, and Bayesian computational methods. Her work addresses challenges in statistical inference for systems with inherent uncertainty and dynamic structures.

• Human-in-the-Loop
• Machine Learning
• Predictive Maintenance
• +۴ مورد دیگر

Alexander Nikitin is a Postdoctoral Researcher in the Department of Computer Science at Aalto University, working within the Probabilistic Machine Learning group under Professor P. Marttinen. His research integrates human expertise with machine learning systems to solve complex real-world problems, particularly in predictive maintenance and uncertainty quantification. He holds a Master's degree in Engineering and Technology from the State University Higher School of Economics, awarded in 2019, and completed his doctoral thesis at Aalto University in 2024. Nikitin's work centers on Human-in-the-Loop systems where human feedback enhances AI performance, predictive maintenance using graph-based models for industrial applications, and uncertainty quantification in large language models. His approach combines probabilistic methods with deep learning to address challenges in spatiotemporal data analysis, domain adaptation, and non-separable systems. Recent publications demonstrate his focus on practical implementations where machine learning directly impacts operational efficiency. Analysis of his 2021-2025 publications reveals a clear trajectory toward industrial AI applications, with increasing emphasis on human-AI collaboration frameworks. His work bridges theoretical machine learning advances (like kernel entropy methods for LLMs) with concrete use cases in workstation monitoring and synthetic time series generation, frequently appearing in top-tier venues like NeurIPS and KDD. He actively contributes to major research initiatives including the FCAI Flagship 2 project (2022-2026), which develops next-generation AI systems through collaboration between Aalto University, the University of Helsinki, and industry partners like Elisa. His earlier work on error log analysis for predictive maintenance (2020-2023) established foundations for current human-in-the-loop approaches. Nikitin operates within Aalto's Probabilistic Machine Learning ecosystem and the Finnish Center for Artificial Intelligence (FCAI), where he collaborates with leading researchers including Sami Kaski and P. Marttinen on projects spanning generative modeling, workstation maintenance, and large-scale AI deployment.

• Machine Learning
• Neuroscience
• Computer Science
• +۴ مورد دیگر

Christian Toth is a researcher active in fields including Causal Inference, Machine Learning, and Community Detection. His work involves interdisciplinary approaches at the intersection of computer science and neuroscience. Education: BSc Dipl.-Ing. Research focuses on causal modeling, probabilistic graphical models, and signal processing applications. Recent publications address Bayesian causal inference and random walk-based community detection. Collaborations include institutions like Graz University of Technology (inferred from Institute of Signal Processing and Speech Communication affiliation) and researchers in Austria.

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +۷ مورد دیگر

Nedialko Nedialkov is a Professor in the Department of Computing and Software at McMaster University, Faculty of Engineering. His research centers on differential-algebraic equations (DAEs) , interval methods , and computational science , with a focus on rigorous numerical solutions and structural analysis. Contact: nedialk@mcmaster.ca Teaching: Courses include Scientific Computing, Advanced Topics in Computing and Software, and Principles of Programming. Research Highlights: Nedialkov has developed symbolic-numeric methods for DAEs, improved verified ODE integration, and created tools like DAETS for structural analysis. His work spans applications in hybrid systems , control theory , and biomedical computing . Publications Trends: Over the past 15 years, his scholarship emphasizes DAEs, validated numerical methods, and computational modeling. Key subfields include Port-Hamiltonian systems , block triangularization , interval constraints , and automatic differentiation . Labs & Teams: Collaborates with McMaster Experts and contributes to open-source projects for validated numerical computing.