جستجوی استاد راهنمای دانشگاهی

Ziming Zhangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Ziming Zhang is an Assistant Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI) , with additional affiliations in Data Science and Robotics Engineering. He previously held research roles at Mitsubishi Electric Research Laboratories (MERL) and Boston University. PhD in Computing (2013) from Oxford Brookes University , UK MS in Computing Science (2010) from Simon Fraser University , CA BS in Computer Science and Technology (2005) from Northeastern University , China Research interests span computer vision , machine learning , and their applications in point cloud processing , medical imaging , autonomous driving , and IoT . He leads the Vision, Intelligence, and System Laboratory (VISLab) at WPI. Recent publications focus on 3D reconstruction , hyperbolic learning , and robust classifiers . Awards include the R&D100 Award 2018 and NSF funding for data-efficient deep learning. PhD Students: Yecheng Lyu (co-supervised), Guojun Wu (co-supervised), Hangrui Zhang, Xuechu Yu Master's Students: Yun Yue, Yuping Shao Visiting Scholars: Fangzhou Lin His lab partners with industry and academic institutions, focusing on autonomous systems , robotics , and scientific imaging projects.

James Saundersonمشاهده پروفایل

مدرس ارشد

• Convex Optimization
• Semidefinite Programming
• Polynomial Optimization
• +۴ مورد دیگر

Dr. James Saunderson is a Senior Lecturer and Director of Education in the Department of Electrical and Computer Systems Engineering at Monash University. He holds a PhD in Electrical Engineering and Computer Science from MIT and has held postdoctoral roles at Caltech and the University of Washington. His expertise spans convex optimization, semidefinite programming, and quantum information theory. Education : PhD in EECS, MIT (2015) MS in EECS, MIT (2011) Bachelor of Engineering (Honours) and Bachelor of Science (Honours), University of Melbourne (2008) Research Interests : Convex optimization, quantum information theory, signal processing, and algorithm design. Focuses on algebraic and geometric aspects of optimization, with applications in engineering and quantum systems. Recent Projects : Exploiting duality in quantum relative entropy optimization Hyperbolic programming and conic optimization Applications in nanotechnology and bioinformatics Teaching : Courses include Control System Design, Signals and Systems, and Optimization for Engineers. Awards : SIAM Optimization Best Paper Prize (2020) Grants and Collaborations : Australian Research Council Discovery Early-Career Research Fellow (2020–2024) Leading projects in quantum optimization and bioengineering applications.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Chandrika Sadanandمشاهده پروفایل

استادیار

• Low Dimensional Topology
• Geometry
• Hyperbolic Geometry
• +۳ مورد دیگر

Chandrika Sadanand is an Assistant Professor in the Department of Mathematics at Bowdoin College. She holds a PhD from Stony Brook University and a BS from the University of Toronto. Her research focuses on low-dimensional topology and geometry, specifically exploring curves on surfaces, hyperbolic geometry, billiards, translation surfaces, and 3-manifolds. She has held postdoctoral positions at the University of Illinois Urbana Champaign, Technion, and Hebrew University of Jerusalem. Dr. Sadanand teaches courses in mathematical reasoning, geometry, and topology. Her recent courses include MATH 2020 (Introduction to Mathematical Reasoning), MATH 2404 (Geometry), and MATH 3402 (Topology). She has also mentored undergraduate research projects on flat surfaces and participated in outreach activities through programs like the Stony Brook Math Summer Camp and WISE. Her research contributions include studies on translation surfaces of infinite type, billiards dynamics, and geometric structures. She has contributed to innovative conferences like the Nearly Carbon Neutral Geometry and Topology Conferences through video presentations. Her work bridges pure mathematics with computational methods, addressing questions in geometric topology and dynamical systems.

Carmen Galaz Garcíaمشاهده پروفایل

دانشگاهی

• Environmental Data Science
• Remote Sensing
• Machine Learning in Ecology
• +۳ مورد دیگر

Carmen Galaz García is an Assistant Teaching Professor at the Bren School of Environmental Science & Management at UC Santa Barbara. She teaches data science courses including EDS 220 and capstone projects, emphasizing accessible technical education and DEIJ initiatives. Previously, she worked at the National Center for Ecological Analysis and Synthesis (NCEAS), analyzing remote sensing data and developing educational resources. Carmen holds a Ph.D. in Mathematics (UCSB) and a B.Sc. in Mathematics from the University of Guanajuato/CIMAT, Mexico. Her research focuses on environmental data science applications such as invasive species mapping using machine learning and geospatial analysis. She actively contributes to reproducible workflows in Python and collaborates on topological data analysis in environmental contexts. Professional affiliations include NCEAS and Bren School's environmental data programs. Education: Ph.D. in Mathematics, UC Santa Barbara (2021) B.Sc. in Mathematics, Universidad de Guanajuato/CIMAT (2015) Research emphasizes interdisciplinary approaches combining mathematics, ecology, and data science to address environmental challenges. She leads capstone projects integrating real-world environmental datasets and mentors students through collaborative data science workflows.

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Mehrtash Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University. He joined Monash in 2018 after five years at Canberra Research Laboratory-NICTA working with Prof. Richard Hartley and Prof. Fatih Porikli, and earlier at Queensland Research Laboratory-NICTA with Prof. Brian Lovell. His research focuses on machine learning, computer vision, and geometric learning with applications in medical imaging and diffusion models. Recent Research Trends (2025): 3D Gaussian splatting compression, diffusion transformers for visual correspondence, hyperbolic geometry in hierarchical structures, and robust learning from noisy labels. Scientific Awards: Outstanding Reviewer, CVPR'21 Advising Highlights: Mentored students contributing to papers at ICCV'24, CVPR'25, ICLR'25, and Nature Machine Intelligence. Labs & Teams: Collaborates with Data61-CSIRO, ARC, and US Air Force Research Laboratory.

Frank NIELSENمشاهده پروفایل

استاد

• Data science
• Geometric science of information
• Information geometry
• +۴ مورد دیگر

Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.

Baris Coskunuzerمشاهده پروفایل

استاد

• Geometric Topology
• Topological Data Analysis
• Machine Learning
• +۳ مورد دیگر

Baris Coskunuzer is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), part of the School of Natural Sciences and Mathematics. He holds a PhD from Princeton University (2004) and has held academic positions at institutions including Yale University, MIT, Boston College, and Koç University. His research focuses on Geometric Topology, Topological Data Analysis (TDA), and Machine Learning, with applications in medical imaging, drug discovery, and blockchain analysis. He has led multiple NSF-funded research grants and collaborates internationally. Education: PhD in Mathematics, Princeton University, 2004 M.S. in Mathematics, Caltech, 2001 B.S. in Mathematics, Bogazici University, 1999 Research Interests: Geometric Topology Topological Data Analysis Machine Learning Medical Imaging Blockchain Analysis Data Science Grants & Awards: NSF-DMS ATD Research Grant (2023–2026) NSF-DMS AMPS Research Grant (2022–2025) Young Scientist Award, Turkish Science Academy (2016) Fulbright Scholar Award (2014) Over 60 peer-reviewed publications in journals such as Communications on Pure and Applied Mathematics and NeurIPS Advising & Labs: Supervises a research group focused on Topological Machine Learning (TML) Collaborates on projects like Topo-ML for medical diagnostics and GraphPulse for temporal graph analysis

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Chun-Liang Li is a research scientist at Apple MLR and an affiliate assistant professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington. His work bridges machine learning theory with practical applications in computer vision and natural language processing, focusing on efficient model training and representation learning. His educational background includes: Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019), supervised by Prof. Barnabás Póczos B.S. and M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013), supervised by Prof. Hsuan-Tien Lin Li's research centers on generative models and representation learning , with significant contributions to document understanding (FormNet series), multimodal systems (Pic2word), and large language model efficiency . His work consistently addresses real-world challenges like reducing training costs while maintaining performance, as seen in distillation techniques and synthetic data optimization. Analysis of his 2022-2024 publications reveals three dominant trends: (1) LLM efficiency through curriculum training and model updating, (2) structural document understanding via graph-based methods, and (3) multimodal representation learning for vision-language tasks. These reflect his cross-cutting approach to improving model scalability and applicability. His scientific recognition includes: IBM Ph.D. Fellowship (2018) Best student paper runner-up at IJCAI (2017) Double first-place wins in KDD Cup Tracks (2011, 2013) While specific grant details aren't listed, his award-winning KDD Cup performances and extensive publication record suggest strong funding support. He collaborates widely with students and researchers, though formal advisees aren't specified. His current roles at Apple MLR and UW position him at the industry-academia interface for cutting-edge AI development. At Apple, Li contributes to the Machine Learning Research group's core vision-language projects, while his UW affiliation enables academic mentorship and cross-institutional collaboration on foundational ML research.

• Recommender Systems
• Personalization
• Data Mining
• +۶ مورد دیگر

Alexander Tuzhilin is a prominent academic researcher in the fields of recommender systems, personalization, and data mining. His work spans over two decades, focusing on theoretical foundations and practical applications of context-aware recommendations, optimization in data validation, and temporal database systems. He has collaborated extensively with scholars like Gediminas Adomavicius, Konstantin Bauman, and Balaji Padmanabhan. Research Interests: Recommender systems, context-aware computing, temporal database design, and optimization techniques in data mining. Publications: 10+ peer-reviewed articles in journals such as Information Systems Research , Management Science , and INFORMS Journal on Computing , with a focus on algorithmic innovation and business impact. Collaborations: Worked with leading researchers in information systems and operations research, contributing to interdisciplinary advancements in eCRM and data-driven decision-making.

• Natural Language Processing
• Legal Informatics
• Multilingual NLP
• +۳ مورد دیگر

Ilias Chalkidis is an Assistant Professor specializing in Natural Language Processing at the Department of Computer Science, University of Copenhagen. He is actively affiliated with the Natural Language Processing research section, contributing to both theoretical and applied advancements in the field. His research spans multiple high-impact domains with particular emphasis on: Legal natural language processing and multilingual legal reasoning Large language model applications in political and social contexts Fairness-explainability trade-offs in AI systems Innovative representation learning techniques for textual data Analysis of his recent publications reveals a strong focus on bridging legal informatics with cutting-edge NLP methodologies. His work on multilingual legal corpora (including the 689GB MultiLegalPile dataset) and legal decision influence prediction demonstrates practical applications for judicial systems. Simultaneously, his investigations into LLMs as voting assistants and European political spectrum analysis showcase innovative intersections between computational social science and language technology. His technical contributions to contrastive learning and hyperbolic embeddings provide foundational advances for document representation. Chalkidis actively participates in the research community through workshop organization (Natural Legal Language Processing Workshop 2023-2024) and conference presentations. His research has been published in top-tier venues including ACL, EMNLP, and ECAI, with significant citations reflecting community impact. While specific advising relationships aren't documented in the provided materials, his collaborative work patterns suggest active mentorship within the NLP research ecosystem.

Alex Kontorovichمشاهده پروفایل

استاد

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +۵ مورد دیگر

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

Andrew Zimmerمشاهده پروفایل

دانشیار

• Several Complex Variables
• Discrete Subgroups of Lie Groups
• Geometric Structures on Manifolds
• +۳ مورد دیگر

Andrew Zimmer is an Associate Professor and Director of Undergraduate Studies in the Department of Mathematics at the University of Wisconsin-Madison. His office is located in 611 Van Vleck Hall. He is actively engaged in research and mentoring graduate and undergraduate students in the field of mathematics. Dr. Zimmer's research focuses on geometric problems in several complex variables and discrete subgroups of Lie groups. His work spans multiple areas including: Geometric structures on manifolds Several complex variables Riemannian geometry Discrete subgroups of Lie groups Geometric group theory Hyperbolic dynamics Ergodic theory His recent publications demonstrate a strong focus on the intersection of complex analysis, geometry, and group theory. Zimmer's work frequently examines geometric structures, particularly focusing on convex domains, Anosov representations, and rigidity phenomena. A significant portion of his research explores how intrinsic metrics relate to geometric properties of domains in complex spaces, with applications to understanding the behavior of automorphism groups and boundary structures. Dr. Zimmer actively mentors students through the Madison Experimental Mathematics Lab, providing undergraduate research opportunities. He currently advises two graduate students: Rou (Vicky) Wen (since 2022) and Aleksander Skenderi (since 2022). His mentoring philosophy emphasizes seeking advice from multiple sources and learning from the experiences of others who have completed PhD programs in mathematics. Dr. Zimmer maintains an active research program with numerous collaborations, particularly with researchers such as R. Canary, T. Zhang, M. Islam, and others. His work appears in top mathematics journals including Geometry and Topology, Journal of Differential Geometry, and Advances in Mathematics.

• Signal Processing
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Professor Danilo P. Mandic, affiliated with Imperial College London, UK, is a leading researcher in signal processing, machine learning, and biomedical signal analysis. His work spans quaternion algebra, tensor networks, and neural networks for real-world applications. 2025: Published 11+ works on EEG/PPG analysis, quantum learning, and tensor-based LLM compression 2024: Active in interpretable transformers, graph learning for financial data, and hearable devices Research focuses on hypercomplex signal processing, graph neural networks, and medical AI applications. Recent work explores quaternion calculus for signal processing, tensor network structures for LLMs, and hearable device optimization. Key publication trends include: 2025 emphasis on quantum-aware learning, 2024 graph-based time series clustering, and 2023 foundational work on graph CNNs and matched filtering approaches. Collaborates extensively with Dongpo Xu, Sayed Pouria Talebi, Clive Cheong Took, and Tobias Reichenbach on projects involving ear-EEG, ECG enhancement, and financial sentiment analysis.

Ryoma Hattoriمشاهده پروفایل

استادیار

• Reinforcement Learning
• Decision Making
• Number Sense
• +۴ مورد دیگر

Ryoma Hattori is an Assistant Professor at the University of Florida, based at the UF Scripps Biomedical Research campus in Jupiter, FL. His laboratory, the Hattori Lab, focuses on neural mechanisms underlying cognitive functions, learning, and their disruption in autism. Dr. Hattori received his educational degrees from prestigious institutions: Ph.D. in Molecular and Cellular Biology from Harvard University (2016) A.M. in Molecular and Cellular Biology from Harvard University (2012) B.S. in Biophysics and Biochemistry from the University of Tokyo (2010) His research interests center on decision making, reinforcement learning, and number sense, using systems and computational approaches. The lab employs techniques such as in vivo 2-photon imaging, optogenetics, virtual reality behaviors, and machine learning to investigate neural activity and plasticity dynamics in mice. A significant focus is understanding how these processes are impaired in autism spectrum disorder. Analysis of his recent publications reveals a strong emphasis on computational neuroscience and neural circuit mechanisms. His work spans from developing advanced imaging and analysis tools to uncovering fundamental principles of value coding and meta-reinforcement learning, with applications in both basic neuroscience and artificial intelligence. Dr. Hattori has received numerous scientific awards, including: Outstanding Mentor Award 2025 from Society of Research Fellows, UF Scripps SFARI Bridge-to-Independence Award 2022-Current from Simons Foundation Warren Alpert Distinguished Scholar Award 2021-2024 from Warren Alpert Foundation Postdoctoral Grant Award 2021-2022 from The KANAE Foundation And several fellowships during his postdoctoral and graduate training. As a principal investigator, Dr. Hattori leads multiple active grants, including the Shenoy Undergraduate Research Fellowship in Neuroscience (2025-2026) and a project on "Neural activity and plasticity dynamics for reinforcement learning in autism" funded by the Simons Foundation. His mentorship has been recognized with the Outstanding Mentor Award. The Hattori Lab is a dynamic research group utilizing cutting-edge technologies to explore the neural basis of cognition, with a particular interest in translational implications for autism and related disorders.