جستجوی استاد راهنمای دانشگاهی

Stefano Coniglio is an Associate Professor of Computer Science and Artificial Intelligence at the University of Bergamo (UniBg), where he joined in 2022 after earning his PhD in Information Technology from the Polytechnic University of Milan in 2011 and conducting research abroad in Germany and the United Kingdom for ten years. His research focuses on deep learning models, machine learning using neural networks, algorithm design and analysis, and mathematical optimization. With over 50 scientific publications in international journals and conference proceedings, his work spans multiple domains including energy systems, medical applications, and theoretical computer science. Coniglio has made significant contributions to graph neural networks, as evidenced by his highly cited papers on quaternion-valued Laplacians and directional hypergraph neural networks. His research trends show a strong interdisciplinary approach, with publications bridging computer science with energy systems, cardiology, and mathematical optimization. His work on S-ICD screening demonstrates practical medical applications of deep learning, while his theoretical contributions to graph neural networks have advanced the field of geometric deep learning. Since November 2023, Coniglio has been a member of the UniBg Interdepartmental Working Group on Artificial Intelligence, collaborating on the design of an interdepartmental doctoral program in AI. He serves as the unit leader for the PRIN-PNRR project HEXAGON: Highly specialized EXact Algorithms for Grid Operations at the National level since 2023. Coniglio teaches Computer Science, Artificial Intelligence, and Deep Learning courses across multiple departments at UniBg, including the Department of Economics and the Department of Letters, Philosophy, and Communication. He has collaborated with major organizations including National Grid Plc, UK MetOffice, Airbus, Babcock International, British Telecom, CESI, and CISL, demonstrating strong industry connections and practical application of his research.

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Ilias Diakonikolas is the Sheldon B. Lubar Professor in the Department of Computer Sciences at UW-Madison, with affiliations in Statistics, the Wisconsin Institute for Discovery, and the Data Science Institute. He holds a Ph.D. from Columbia University and previously held positions at USC, University of Edinburgh, and UC Berkeley. Research examines: Tradeoffs between statistical efficiency, computational efficiency, and robustness in machine learning High-dimensional robust statistics and information-computation tradeoffs Foundations of deep learning and nonparametric estimation Publications establish fundamental limits for learning algorithms and develop efficient methods for distribution testing and robust estimation. Awards include Sloan and Romnes Fellowships, NSF CAREER Award, and Best Paper at NeurIPS 2019. Advises PhD students in theory of computing and machine learning, leading the Algorithmic High-Dimensional Robust Statistics research program. Organizes workshops on computational-statistical frontiers and authored the Cambridge University Press textbook on robust statistics.

• Robust Statistics
• Machine Learning Theory
• Algorithmic Game Theory
• +۲ مورد دیگر

Prof. Frederic Fol Leymarie is a Professor in the Department of Computing at Goldsmiths, University of London. He specializes in AI, robotics, and computer graphics, with a focus on creative systems and their applications in art and biosciences. His work includes developing robots capable of artistic skills, interactive platforms like FoldSynth for molecular visualization, and projects like Mutator VR. He co-leads the MSc in Computer Games & Entertainment and teaches advanced topics in graphics and game design. His research spans shape understanding, AI-driven art, and interdisciplinary collaborations with bioscience specialists. Education: PhD in Computer Science from Brown University (2003) Key Projects: Mutator VR (2016–2020): An artistic VR project FoldSynth: Interactive tool for visualizing molecular structures Bioblox: Educational game for protein docking Research Interests: AI creativity, human-robot interaction, computer vision, and art-technology interfaces Prof. Leymarie leads London Geometry , a consulting group applying geometric algorithms to solve complex problems. His work bridges computational methods with artistic and scientific domains, emphasizing interdisciplinary innovation.

• Artificial Intelligence
• Computer Vision
• Robotics
• +۶ مورد دیگر

Professor Paul Scott is a faculty member at the University of Huddersfield, holding the Taylor Hobson Chair in Computational Geometry within the School of Computing and Engineering. He leads the Centre for Precision Technologies as Research Director and specializes in surface texture and form metrology, precision engineering, and AI-driven manufacturing solutions. His research integrates mathematical principles with advanced instrumentation, focusing on algorithm development for surface characterization and international standardization (ISO TC/213). Scott earned a PhD in Statistics from Imperial College London and a DSc from the University of Huddersfield. His career includes 26 years at Taylor Hobson Ltd, developing metrology instruments. He has pioneered de facto standard algorithms for surface analysis and contributed to 23 ISO standards. Current research spans computational geometry, AI semantic labeling, and machine-readable standards via Category Semantic Language (CSL). His work addresses Industrie 4.0 challenges, including smart algorithms for feature recognition and digital twins modeling. Collaborations span global institutions, with projects funded by EPSRC (e.g., Future Advanced Metrology Hub). Key areas include XCT-based surface measurement, additive manufacturing metrology, and precision instrumentation. Notable contributions include decomposition theory for geometrical products, filtration techniques in surface analysis, and bridging metrology with Industry 4.0 through data-driven approaches. His h-index of 33 reflects over 3,600 citations across 193+ publications.

• Computational Geometry
• Surface Texture and Form Measurement
• Precision Engineering
• +۷ مورد دیگر

Daniel Irving Bernstein is Assistant Professor in Tulane University's Department of Mathematics. His research spans combinatorics, discrete geometry, and algebraic statistics, with particular focus on matroid theory, rigidity theory, and geometric combinatorics. He teaches undergraduate and graduate courses in linear algebra, topology, and geometric combinatorics. His research explores: Combinatorial structures in algebraic statistics Geometric rigidity and reconstructibility problems Matroid representations and lifts Algebraic methods in machine learning Recent work develops connections between combinatorial geometry and statistical learning theory. His publications demonstrate: Applications of matroid theory to statistical thresholds Geometric approaches to matrix completion Combinatorial foundations of deep learning Tropical geometric methods in phylogenetics He maintains active collaboration with computational biology and machine learning researchers.

• Combinatorics
• Discrete Geometry
• Algebraic Statistics
• +۲ مورد دیگر

Zhengchao Wan is an Assistant Professor in the Department of Mathematics at the University of Missouri, part of the College of Arts and Science. His research focuses on the intersection of machine learning, topological data analysis, metric geometry, and probability theory. He explores applications of these areas in graph-based learning, optimal transport, and geometric data analysis. His work includes developing novel methods for graph neural networks, analyzing effective resistance in networks, and advancing topological techniques like persistent homology and the persistent Laplacian. He also contributes to foundational studies in metric geometry, including ultrametric spaces and Gromov-type distances. Recent publications highlight his contributions to robust graph learning algorithms, flow matching ODE dynamics, and the theoretical underpinnings of graph transformers. His research bridges abstract mathematical concepts with practical machine learning challenges, emphasizing both theoretical rigor and real-world applicability.

• Machine Learning
• Topological Data Analysis
• Metric Geometry
• +۳ مورد دیگر

Dr. Adib Zaman is an Assistant Professor in the School of Information Technology at the University of Cincinnati (UC), where he also serves as the Ohio Cyber Range Institute Coordinator. Prior to UC, he was a Postdoctoral Research Associate in the Pharmacotherapy and Translational Research Department at the University of Florida (2021–2022). He holds a PhD in Industrial Engineering from Texas Tech University (2021), an MS from Northern Illinois University (2018), and a BSc from Bangladesh University of Engineering and Technology (2015). His research focuses on the intersection of artificial intelligence, electronic health records (EHR) analytics, and healthcare informatics. Key areas include machine learning for predictive modeling in clinical outcomes, deep learning for medical imaging segmentation, and data-driven approaches to optimize healthcare decision-making. He has led grants such as the UC Office of the Provost Faculty Development Grant (2023–2024) and contributed to NIH-funded research on hypertension prediction. Dr. Zaman teaches courses like Advanced Storage Technologies and Data-Driven Cybersecurity at the graduate level. He actively serves on academic committees, including the College of Education, Criminal Justice, and Human Services RPT Committee, and advises the Bangladeshi Student Community at UC. His work bridges engineering, data science, and healthcare, aiming to advance clinical outcomes through computational innovation.

• Data Science
• Bioinformatics
• Operations Research
• +۳ مورد دیگر

Daniel Cuppoletti is a Professor at the University of Cincinnati, focusing on aeroacoustics , fluid dynamics , and propulsion systems . He serves as Principal Investigator (PI) for numerous federal and industry-sponsored grants, including projects funded by the Air Force, NASA, and General Electric Aviation. Research Interests: Aeroacoustics, supersonic jet noise reduction, detonation amplification, urban air mobility (UAM), and electric vertical takeoff and landing (eVTOL) systems. Grants: Over $1.4 million in awarded and active funding since 2019, including NASA, Air Force, and Ohio Department of Higher Education grants. Students: Mentored undergraduate and graduate scholars like Amanda Devillier, Alex Newberg, and Riley Gaetano, with a focus on propulsion and acoustics research. Labs: Affiliated with the University of Cincinnati's Engineering Research Center, conducting experiments on jet noise control and fluidic injection techniques. Article Trends: His recent work emphasizes noise reduction in supersonic and hypersonic flows , rotor-rotor interaction noise for UAM vehicles, and machine learning applications in fluid dynamics . Collaborations span institutions including NASA, Air Force Research Laboratory, and Ohio Space Grant Consortium.

• Aeroacoustics
• Fluid Dynamics
• Aerospace Engineering
• +۷ مورد دیگر

Wei Xu is a Research Professor in the Department of Computer Science at Stony Brook University and a Computational Scientist leading the Trustworthy Artificial Intelligence (TAI) group at Brookhaven National Laboratory's Artificial Intelligence Department. She holds a Ph.D. from Stony Brook University and M.S./B.S. degrees from Zhejiang University. Her research bridges artificial intelligence, high-performance computing, and scientific visualization, with current focus areas including explainable AI for scientific applications, quantum computing performance, and neural representations for ensemble simulations. Her work spans: AI/ML for Science : Developing trustworthy AI models for climate prediction, quantum computing, and medical diagnostics. Visual Analytics : Creating novel tools for multivariate volume visualization and quantum state representation. High-Performance Computing : Optimizing exascale workflows and performance analysis. Recent publication trends (2021-2025) demonstrate strong emphasis on explainable AI techniques for scientific domains, neural representations for data efficiency, and visualization methods for complex scientific data. Her papers frequently appear in top venues like TVCG, ICLR, and IEEE VIS. Awards & Honors: Women@Energy Showcase (2014) Best Paper Awards: PacificVis (2025), ISAV/SC20 (2020), Fully3D Workshop (2009) Spotlight Presentation at ICLR TCCML Workshop (2025) She secures funding through DOE BER/SciDAC programs and BNL initiatives (LDRD, NSLSII DSSI). As Trustworthy AI Group Lead, she oversees projects integrating AI with scientific infrastructure. She actively contributes to conferences as committee member (SC23/VIS24) and session chair (AAAI24, SC23).

• Artificial Intelligence for Science
• Explainable and Trustworthy AI
• Machine Learning
• +۶ مورد دیگر

Thomas G. Fevens is a Professor in the Department of Computer Science and Software Engineering at Concordia University, and an Adjunct Professor in the Department of Surgery at McGill University. He holds a PhD in Computing and Information Science from Queen's University (1999), with prior degrees in Astrophysics and Physics. His research focuses on medical imaging applications of deep learning, computational geometry, and surgical software innovation. Notable work includes computer-aided diagnosis systems for breast cancer using cytological images, 3D surgical planning algorithms, and motion tracking frameworks for ACL injury assessment. Education: PhD in Computing and Information Science, Queen's University (1999) MSc in Computing and Information Science, Queen's University (1994) MSc in Physics (General Relativity), Queen's University (1993) BSc in Astrophysics (Honours), Queen's University (1990) Research emphasizes interdisciplinary applications of AI in healthcare, including: Medical image segmentation using U-Net architectures Deep learning for tumor localization Kinect-based motion analysis systems Generative adversarial networks (GANs) for medical imaging Publications demonstrate contributions to both core computer science (mobile ad hoc networks, computational geometry) and clinical applications (surgical planning, cancer diagnosis). Active in industry collaboration through the ML-MVP lab, focusing on translating AI research into clinical tools.

• Deep Learning
• Medical Image Processing
• Computer-Aided Medical Diagnosis
• +۵ مورد دیگر

Amirouche Moktefi is a Senior Lecturer at the Ragnar Nurkse Department of Innovation and Governance, Tallinn University of Technology. His research focuses on the history of logic, logic diagrams, and the contributions of historical figures like Lewis Carroll and John Venn. He explores diagrammatic reasoning, formal logic systems, and the intersection of mathematics with philosophical thought. His work spans academic publications analyzing historical logic developments, such as Euler and Venn diagrams, and the evolution of symbolic logic. He also engages with interdisciplinary topics like ethics education and the social dimensions of logic. Notable contributions include studies on Carroll’s logical innovations and the historical context of mathematical logic in France. His articles reflect a deep engagement with both historical and methodological aspects of logic, emphasizing visual tools and their role in reasoning. He frequently publishes in journals and conference proceedings, contributing to debates in the philosophy and history of logic.

• History of Logic
• Mathematical Logic
• Logic Diagrams
• +۴ مورد دیگر

Jian Tang is an Associate Professor in the Department of Decision Sciences at HEC Montréal, holder of a Canada CIFAR Chair in Artificial Intelligence Education. He earned a Ph.D. in Computer Science from Peking University. His research focuses on machine learning, bioinformatics, and AI applications in drug discovery and protein design. He teaches courses like Machine Learning II: Deep Learning and has supervised four MSc projects in data science and AI applications. Key research interests include graph neural networks, knowledge graphs, protein structure prediction, and AI-driven scientific discovery. His recent work spans LLM reasoning, geometric diffusion models for molecular design, and multi-modal learning for biomedical applications. Notably, he received the HEC Montréal's 2022 New Researcher Award for exceptional scientific contributions. Publications highlight advancements in protein engineering, antibody design, and AI in healthcare. Collaborations include work with institutions like CDPQ on AI R&D and Bombardier on machine learning for demand forecasting. His lab explores cutting-edge AI techniques to address complex problems in medicine and computational biology.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +۵ مورد دیگر

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +۱۲ مورد دیگر

Mauricio Barahona is a Professor of Biomathematics and Director of the EPSRC Centre for Mathematics of Precision Healthcare at Imperial College London. He holds appointments in the Department of Mathematics within the Faculty of Natural Sciences. His research integrates dynamical systems, network science, and machine learning to address challenges in healthcare, biological systems, and complex networks. He obtained his PhD in Theoretical Physics from MIT, followed by postdoctoral fellowships at Stanford and Caltech. Key roles include leading the EPSRC Centre since 2016 and contributing to interdisciplinary initiatives such as the Biomathematics Group and the Artificial Intelligence Network. His work spans disease clustering analysis, healthcare system optimization, and applications of AI in precision medicine. He has pioneered methods for analyzing complex networks in biological and clinical contexts, including protein dynamics, antimicrobial resistance spread, and multimorbidity patterns. Research focuses on network-based models for healthcare integration, disease co-occurrence patterns, and machine learning techniques for biomedical data. His lab develops tools like RamanSPy for spectroscopic analysis and HCGA for network phenotyping. Collaborations involve diverse fields from physics to clinical medicine, emphasizing translational applications.

• Biomathematics
• Network Science
• Dynamical Systems
• +۷ مورد دیگر

Lian Xu is a Research Fellow in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA). She holds a PhD from UWA (2021) and specializes in computer vision, deep learning, and annotation-efficient methodologies. Her work focuses on weakly supervised learning, multi-modal data fusion, and applications in healthcare, agriculture, and maritime surveillance. Education: PhD in Computer Science, University of Western Australia (2021) Research Interests: Weakly supervised semantic segmentation Multi-modal learning with vision and language Computer vision applications in healthcare and agriculture Efficient learning strategies for limited labeled data Her research output emphasizes techniques such as auxiliary task integration, token-based transformers, and spectral analysis for hyperspectral imaging. Recent work includes advancements in small object detection for maritime surveillance and crop stress classification using hyperspectral data. Awards: Recipient of the Award for Growth in Innovation and Entrepreneurship (2021) for contributions to applied research. Teaching & Engagement: Facilitated courses on computer vision (CITS4402) and visualization (CITS2401). Co-organizer of workshops at DICTA 2024, ACCV 2022, and VCIP 2021 on small object detection and deep learning limits. Grants: Co-investigator on the Empowering Robots with 3D Vision and Reasoning for Enhanced Home Assistance project (2024–2025).

• Computer Vision
• Deep Learning
• Annotation-efficient learning
• +۵ مورد دیگر