جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Causal Modeling
• Statistical Learning Theory
• +۹ مورد دیگر

Christian Reimers is a Project Group Leader and PostDoc at the Department Biogeochemical Integration (BGI) of the Max Planck Institute for Biogeochemistry in Jena, Germany. He leads the Project Group Adapting Machine Learning for Earth Systems and contributes to the Research group Atmosphere-Biosphere Coupling, Climate and Causality , focusing on integrating machine learning with Earth systems research. Education : M.Sc. and B.Sc. in Mathematics from Georg-August-University, Göttingen (2013-2017, 2011-2013) His research lies at the intersection of machine learning , causal modeling , and Earth system science . Recent work explores hybrid physics-AI models for evapotranspiration and carbon cycle dynamics, spatiotemporal deep learning for climate teleconnections, and bias mitigation in neural network classifiers. Publications span climate informatics , phenology modeling , and neural network interpretability . Key trends in his 15 most recent articles (2018-2025) include: deep learning architectures for Earth observation; causal inference in climate-vegetation interactions; hybrid modeling combining physics and data-driven approaches; and bias detection in medical imaging applications. His work often addresses interpretability challenges in black-box models. Current affiliations include: Max Planck Institute for Biogeochemistry (since 2021) Project Group EarthNet Research group Atmosphere-Biosphere Coupling, Climate and Causality

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).

• Large Language Models
• Formal Language
• Mathematical Formalization
• +۳ مورد دیگر

Lan Zhang holds a Doctor of Philosophy in the Department of Computer Science. His research focuses on formalization of mathematical statements using large language models (LLMs), syntactic analysis, and information retrieval systems. He has contributed to constructing mathematical libraries through consistent autoformalization techniques and explored epistemic ensemble methods for formal mathematical reasoning. His work bridges AI with symbolic mathematics, emphasizing automated theorem proving and latent space modeling. Research interests include large language models, formal verification, and mathematical logic. His recent studies address challenges in de-noising formal languages and evaluating unsupervised text representations using synthetic datasets. Zhang's publications span topics from latent variable analysis to ensemble methods in mathematical reasoning systems. His research outputs demonstrate expertise in applying LLMs to formalize complex mathematical definitions and derive multi-operational mathematical processes in latent spaces. While no awards or grants are explicitly mentioned, his active contributions to mathematical formalization and VAE-based text representation indicate significant academic engagement.

Wolfgang Utschickمشاهده پروفایل

استاد

• Signal Processing
• Wireless Communications
• Radar Technology
• +۳ مورد دیگر

Prof. Wolfgang Utschick is a full Professor at the Technische Universität München (TUM), holding the professorship for Methods of Signal Processing since 2002. He is affiliated with the TUM School of Computation, Information and Technology and the Department of Electrical Engineering and Information Technology (EI). Since 2017, he has served as Dean of the EI Department, overseeing academic and research activities. His research integrates applied mathematics into signal processing applications across wireless communications, radar technology, vehicle safety, and machine learning. Prof. Utschick earned his doctorate after industry experience and completed postdoctoral studies at TUM and ETH Zurich. He has led numerous industrial and DFG-funded projects, resulting in patents in signal processing and over 150 peer-reviewed publications. Notable contributions include advancements in channel estimation, MIMO systems, and quantum radar technology. His research interests emphasize the intersection of mathematical theory and practical engineering, with a focus on optimizing communication systems and safety-critical applications. Awards include IEEE Fellow (2021), TUM Sabbatical for Teaching Excellence (2014), and multiple IEEE publication awards. Prof. Utschick’s work bridges academia and industry, addressing challenges in 5G/6G networks, radar innovation, and machine learning-driven signal processing. He remains active in curriculum development and departmental leadership at TUM.

• Computational Materials Science
• Defect Properties
• Mechanical Behavior of Materials
• +۴ مورد دیگر

Dallas R. Trinkle is an Ivan Racheff Professor and Associate Head of the Department of Materials Science and Engineering at the University of Illinois, Urbana-Champaign. He holds a Ph.D. in Physics from Ohio State University (2003) and joined UIUC's faculty in 2006 after postdoctoral research at the Air Force Research Laboratory. His research focuses on computational methods to study defects in materials at the atomic scale, including density-functional theory and machine learning applications in diffusion and mechanical behavior. Education : Ph.D. in Physics, Ohio State University, 2003 Postdoctoral Researcher, National Research Council (Air Force Research Laboratory), 2003–2006 Research Interests : Trinkle’s work centers on defects in materials, including dislocations, point defects, and interfaces, with applications to structural metals like magnesium, titanium, and palladium. Key areas include: Computational materials science using atomistic methods (DFT, tight-binding) Mechanical behavior of alloys and phase transformations Mass transport mechanisms, including diffusion of interstitials and solutes Machine learning for materials discovery and predictive modeling Awards : TMS Young Leader International Scholar (2008) NSF CAREER Award (2009) Xerox Award for Faculty Research (2011) TMS Brimacombe Medal (2019) Advising & Grants : Trinkle mentors graduate and postdoctoral researchers in computational materials science. His research is supported by NSF, Boeing, and the Department of Energy. He has developed educational modules integrating computation into undergraduate curricula, emphasizing digital materials and atomic-scale simulations. Labs & Groups : His research group maintains a Trinkle Group page with resources on computational tools (e.g., Mg solute database on GitHub) and ongoing projects.

• Machine Learning
• Computational Biology
• Molecular Biology
• +۵ مورد دیگر

Michael Wiliam Robben is an Assistant Professor at the University of Illinois at Urbana-Champaign with dual appointments in the Department of Animal Sciences and Department of Nutritional Sciences. He is also affiliated with the Carl R. Woese Institute for Genomic Biology. He holds a Ph.D. in Molecular Biology from South Dakota State University (2022) and a B.Sc. in Biology from Salisbury University (2016). Research Focus: Dr. Robben integrates machine learning and artificial intelligence with multi-omic approaches for biomedical and agricultural research. His lab focuses on: T-cell dynamics in inflammatory diabetes Spatial analysis of tumor microenvironments Computer vision for biomedical imaging Multi-omic data integration techniques Publication Trends: His recent work (2023-2025) demonstrates strong emphasis on computational biology methods, including deep learning architectures (GANs, VAEs) for medical imaging and single-cell RNA sequencing applications. Research spans immunology, cancer biology, and genomic regulation with publications in high-impact journals. Awards: No scientific awards mentioned in source materials. Lab & Teams: Leads research group focusing on computational immunology and machine learning applications. Collaborates with researchers across institutions as evidenced by multi-author publications.

Armeen Taebمشاهده پروفایل

استادیار

• Selective Inference
• Multiple Testing
• Graphical Models
• +۷ مورد دیگر

Armeen Taeb is an Assistant Professor in the Department of Statistics at the University of Washington . Previously, he was a postdoctoral fellow at ETH Zürich under the ETH Foundations of Data Science, mentored by Peter Bühlmann. He earned his PhD in Electrical Engineering at Caltech under Venkat Chandrasekaran's supervision. Research Interests : His work bridges optimization and statistics , focusing on Graphical and latent-variable modeling Provably optimal causal model learning False positive error control in non-traditional settings Domain adaptation Applications in physical sciences Article Trends : His publications span causal inference (2022-2025), graphical models (2017-2025), convex optimization (2018-2025), and statistical robustness (2020). Recent work (2025) addresses extremal graphical modeling and selective inference challenges. Scientific Awards : ETH Zürich Foundations of Data Science Postdoctoral Fellowship (2019-2021) Caltech Resnick Institute Fellowship (2016-2018) W. P. Carey & Co. Prize for Applied Mathematics (2020) Caltech Graduate Fellowship (2013-2014) Grants : National Science Foundation DMS-2413074 (PI), University of Washington Royalty Research Fund (PI). Service : President of the Institute of Mathematical Statistics New Researcher Group; co-organized IMS New Researchers Conferences (2024-2025).

Hans Julius Skaugمشاهده پروفایل

استاد

• Biostatistics
• Marine Ecology
• Computational Statistics
• +۴ مورد دیگر

Hans Julius Skaug is a Professor in the Department of Mathematics at the University of Bergen (UiB), Norway, with a distinguished career spanning several decades focused on statistical methodology and its applications to biological and ecological problems. Dr. Skaug's research expertise encompasses several interconnected domains: Biostatistics, particularly applications of statistics and probability to marine ecology Computational statistics, with pioneering work on Automatic Differentiation and Laplace approximation for complex model fitting Artificial Intelligence, where he has recently focused on variational autoencoders and diffusion models Development of innovative methods for line transect surveys and close-kin mark recapture (CKMR) His publication record shows a clear progression from foundational statistical methodology to increasingly sophisticated applications. The 2016 paper with Bravington and Anderson on 'Close-kin mark-recapture' in Statistical Science established him as a leader in population estimation methods. Recent publications (2023-2025) demonstrate continued innovation in refining CKMR techniques, applying TMB to diverse fields like insurance claims analysis, exploring sparse Bayesian learning, and addressing measurement errors in marine mammal surveys. His work consistently bridges theoretical statistical development with practical applications to real-world conservation and management problems. Dr. Skaug served as co-Editor in chief of the Scandinavian Journal of Statistics from 2018 to 2021, demonstrating his significant standing in the international statistical community. He teaches STAT110 Basic course in statistics at UiB during both spring and fall semesters and has conducted specialized workshops on CKMR and TMB at institutions including Dalhousie University in Halifax and the Institute of Marine Research in Bergen. He is actively involved in software development for statistical modeling through the TMB project (https://github.com/kaskr/adcomp) and ADMB (http://admb-project.org/), which implement his theoretical work on combining Automatic Differentiation with statistical modeling. His current research trajectory shows increasing integration of AI techniques with traditional statistical approaches, reflecting his observation that backpropagation in deep learning is fundamentally the same computational technique as Automatic Differentiation, which he has worked with for over 20 years.

• Deep Learning
• Variational Autoencoders
• Generative Models
• +۳ مورد دیگر

Gü cin Baykal Can serves as a Research Fellow within the Department of Mathematics and Computer Science at the University of Southern Denmark. Her academic work centers on data science with specialized focus on advanced deep learning architectures and generative modeling techniques. Her research spans Deep Learning, Variational Autoencoders, and Generative Models as core specialties, extending to Machine Learning, Artificial Intelligence, and Computer Science. She develops novel methodologies to overcome critical challenges like codebook collapse in discrete variational autoencoders, significantly advancing unsupervised representation learning systems and neural network efficiency. Her publication profile features a 2024 Pattern Recognition article introducing EdVAE, an evidential discrete variational autoencoder framework. This work exemplifies her contributions to Computer Science and Artificial Intelligence, particularly in Neural Networks, Representation Learning, and Unsupervised Learning domains, demonstrating technical innovation in generative model stability. No scientific awards or honors were documented in the available information. There is no available information regarding graduate student supervision, research grant funding, or academic advising activities. Details about laboratory affiliations, research team memberships, or collaborative projects were not specified in the source material.

Rachael A Mansbachمشاهده پروفایل

استادیار

• Computational Biophysics
• Statistical Physics
• Deep Learning
• +۵ مورد دیگر

Dr. Rachael Mansbach is an Assistant Professor in the Department of Physics at Concordia University, Montreal, and holds a Tier II Canada Research Chair in Computational Biophysics. She specializes in interdisciplinary research combining computational biophysics, machine learning, and molecular dynamics to address challenges in antimicrobial resistance and therapeutic design. Research Highlights: Developing interpretable machine learning models for antimicrobial peptide design Investigating structural ensembles of disulfide-rich toxins via computational modeling Multi-scale modeling of mucin-like proteins for biomimetic material design Applying network theory to analyze SARS-CoV-2 Spike protein dynamics and deep learning architectures Quantum and molecular dynamics simulations of photosynthetic proteins Collaborations: Partnered with the Chakraborty Lab (Northeastern), Zazubovits Lab (Concordia), and Gauthier Lab (Concordia) on hydrogel design, photosynthesis, and long COVID imaging applications. Scientific Awards: Computational Science and Engineering Fellow (UIUC) Building Future Faculty Fellow (NCSU) Mavis Future Faculty Fellow (UIUC) APS Forum on Graduate Student Affairs Travel Award UIUC Physics Graduate Travel Award

• Logic
• Artificial Intelligence
• Cognitive Science
• +۳ مورد دیگر

Dr. Ian Pratt-Hartmann is a Senior Lecturer at the School of Computer Science, specializing in Formal Methods. He received his PhD in Philosophy from Princeton University (1987) and previously studied Mathematics and Philosophy at Brasenose College, Oxford. His research spans logic, artificial intelligence, and cognitive science, focusing on intersections between logic and complexity theory, logic and geometry, and logic and natural language. He has supervised 11 PhD students, including Dominik Schoop, Nick Player, and Yegor Guskov, with topics ranging from mereotopology to temporal logics.

• Artificial Intelligence
• Medical Image Computing
• Computer Vision
• +۹ مورد دیگر

Gözde Ünal is a Professor at Istanbul Technical University (ITU) in the Department of Artificial Intelligence and Data Engineering, under the School of Computer and Informatics. She has held leadership roles including Deputy Dean and former Director of the Application and Research Center. She previously served as Associate and Assistant Professor at Sabancı University and taught at Georgia Institute of Technology. Education: Ph.D., Electrical and Computer Engineering, North Carolina State University (1998–2002) M.Sc., Electrical and Electronics Engineering, Bilkent University (1996–1998) B.Sc., Electrical and Electronics Engineering, Middle East Technical University (1992–1996) Her research focuses on medical image computing, computer vision, deep learning, augmented and virtual reality, and AI for healthcare . She specializes in image segmentation, point cloud processing, and surgical scene analysis. Her work bridges computer science with biomedical applications, particularly in MRI, ultrasound, and dermoscopic imaging. She leads interdisciplinary research in both medical and cultural heritage domains. The recent publications (2021–2025) highlight a strong trend in deep learning for medical image segmentation , adversarial attacks on point clouds , AI in surgical skill assessment , and cultural heritage digitization using 3D point clouds . Her work increasingly employs transformers, self-supervised learning, and multimodal fusion, with a focus on robustness and real-world deployment. Scientific Awards: Fakülte Yılın Seçkin Eğitimcisi Ödülü, ITU, 2018 Marie Curie Alumni Association Career Award, 2017 GENÇ BİLİM KADINI, L’Oréal Turkey, 2010 GEBİP, Turkish Academy of Sciences, 2010 She has supervised numerous students and research projects, securing grants from EU and TTO (Technology Transfer Office). Her projects include AI-based optimization in banking, AI for assisted reproduction, deep learning for quality control, and normalization of multimodal brain networks. She leads a dynamic research group working on cutting-edge AI applications. She is actively involved in professional societies: President of the Marie Curie Alumni Association Turkish Chapter, member of MICCAI, IEEE, and TMRD. Her lab focuses on AI-driven solutions in medical imaging and heritage preservation, with strong international collaborations, particularly in Europe.

• Machine Learning
• Natural Language Processing
• Language Modelling
• +۷ مورد دیگر

Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.

Rebecca Davisمشاهده پروفایل

دانشیار

• Asymmetric Organocatalysis
• Computational Chemistry
• Physical Organic Chemistry
• +۶ مورد دیگر

Rebecca Davis is an Associate Professor in the Department of Chemistry at the University of Manitoba , Faculty of Science. Her research integrates computational, kinetic, and synthetic methods to address challenges in asymmetric organocatalysis and antibiotic discovery. Academic Rank: Associate Professor Institution: University of Manitoba Contact: Rebecca.Davis@umanitoba.ca Research Interests: Davis specializes in asymmetric organocatalytic reactions, focusing on mechanisms and catalyst design. She employs computational modeling and machine learning to develop molecular feature libraries for antibiotic discovery, particularly targeting Gram-negative bacteria. Her work also includes photo-aminocatalysis, remote functionalization, and cheminformatics applications. Publication Trends: Recent articles highlight her interdisciplinary approach, combining machine learning with synthetic chemistry for drug discovery, mechanistic studies in organocatalysis, and photophysical characterization of excited-state intermediates. Scientific Awards: Davis served as Past Chair of the CSC Organic Division and is a member of the Digital Research Alliance of Canada’s Researcher Council. Advising: She supervises graduate and undergraduate researchers in projects spanning asymmetric synthesis, computational modeling, and antibiotic development. Labs & Collaborations: The Davis Research Group collaborates with academic and industrial partners on predictive models for synthetic methods and data generation for machine learning in drug discovery.

Nikos Komodakisمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Nikos Komodakis is a Professor in the Computer Science Department at the University of Crete, Greece, where he develops efficient, scalable and mathematically well-grounded algorithms for analyzing visual data including static natural images, video, and medical image data. His research spans deep learning, computer vision, machine learning, and artificial intelligence with significant contributions to self-supervised learning, few-shot learning, and knowledge distillation techniques. His work demonstrates a strong theoretical foundation combined with practical applications, particularly in medical imaging. Komodakis has published extensively in top-tier computer vision venues including CVPR, ICCV, ECCV, and IEEE Transactions on Image Processing. His recent publications (2022-2025) show a growing emphasis on medical image analysis applications while maintaining strong contributions to fundamental computer vision problems. Notable contributions include novel approaches for unsupervised representation learning that surpass state-of-the-art methods, effective techniques for knowledge distillation (such as the QUEST framework), and innovative frameworks for few-shot visual learning. Komodakis serves on the editorial boards of prestigious journals including the International Journal of Computer Vision, Computer Vision and Image Understanding Journal, and Computational Intelligence Journal. He has been a frequent area chair for major computer vision conferences including CVPR, ICCV, ECCV, and BMVC. Spyros Gidaris received the Ponts Foundation Best Thesis Prize and the University Paris-Est Best Thesis prize under Komodakis' supervision Sergey Zagoruyko received the AFRIF 2018 Thesis Prize for his PhD work supervised by Komodakis His research group has developed influential techniques including Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning, which surpassed previous state-of-the-art methods. The group maintains active GitHub repositories for many of their publications, demonstrating commitment to reproducible research. Current research directions include advancing medical image analysis through deep learning, improving self-supervised learning frameworks, and developing more efficient neural network architectures.