جستجوی استاد راهنمای دانشگاهی

Zhou Zhi-Huaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
• +۶ مورد دیگر

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

Dieu Tien Buiمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence
• Machine Learning
• GIS and Geomatics
• +۶ مورد دیگر

Dieu Tien Bui is a Full Professor in the Department of Business and IT at the University of South-Eastern Norway (USN) School of Business. His research focuses on Geospatial Artificial Intelligence Machine Learning GIS and Remote Sensing Natural Hazard Modeling Environmental Problems (landslides, floods, soil salinity, biomass) . He has contributed to over 15 recent publications in journals like Science of the Total Environment , Remote Sensing , and Geomorphology , emphasizing hybrid AI models for landslide and flood susceptibility. His work spans Vietnam, India, China, and Iran with applications in climate change adaptation and disaster management. Scientific Awards: Global Highly Cited Researcher PhD Supervision: He has supervised 8 PhD students at institutions including USN, NTNU, and Vietnamese universities.

• Trustworthy machine learning
• Explainable AI
• Interpretability
• +۵ مورد دیگر

Damien Garreau is Professor for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg , Germany. Until March 2024 he served as Associate Professor in the Probability and Statistics team of the J. A. Dieudonné laboratory at Université Côte d'Azur and was a member of the Inria Maasai team in Sophia-Antipolis. Earlier positions include post-doctoral research at the Max Planck Institute for Intelligent Systems in Tübingen and PhD studies in the Inria Sierra team in Paris. Education & Career Path PhD, Inria Sierra team, Paris – advisors Sylvain Arlot & Gérard Biau Post-doc, Max Planck Institute for Intelligent Systems, Tübingen – mentor Ulrike von Luxburg Associate Professor, Université Côte d’Azur / Inria Maasai (until March 2024) Professor for Theory of Machine Learning, Julius-Maximilians-Universität Würzburg (since 2024) Research Focus Garreau’s research centers on trustworthy machine learning . He investigates how to explain, audit, and robustify modern AI systems, with particular emphasis on post-hoc interpretability , statistical guarantees of explanation methods, fairness , and causality . Representative contributions include theoretical analyses of LIME and Anchors, novel explanation methods such as SMACE and GLEAMS, and practical tools for vision and NLP that remain faithful under adversarial or out-of-distribution settings. Across computer vision, natural-language processing, and healthcare applications, his work bridges rigorous theory with impactful algorithms, advancing the societal goal of deploying AI systems whose decisions can be trusted and understood by humans. Scientific Awards & Recognition Best Paper Award , ECML 2024 Area Chair , ICML 2025 ANR JCJC Grant NIM-ML (2021–2025) Université franco-allemande support for Winter School on Causality and Explainable AI Advising, Grants & Collaborative Projects Garreau has successfully supervised or co-supervised a growing cohort of doctoral and master’s students, including Gianluigi Lopardo, Kensuke Mitsuzawa, Martin Charachon, Jonas Wacker, Samuel, Antonio, Magamed, Arthur Assad, Charbel Yahchouchi, and Mariana Chaves. He is the PI of the ANR JCJC project NIM-ML , whose goal is to develop next-generation interpretability methods endowed with statistical guarantees. He co-organizes the annual Winter School on Causality and Explainable AI , fostering Franco-German academic exchange. Labs & Teams Since 2024 he leads the Professorship for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg. Previously he was a core member of the Maasai Inria team on the Sophia-Antipolis campus, and an active collaborator of the J. A. Dieudonné mathematics laboratory. He maintains strong ties with the TML group at the Max Planck Institute for Intelligent Systems and regularly hosts joint visitors and workshops.

• Finance
• Machine Learning
• Asset Pricing
• +۶ مورد دیگر

Dacheng Xiu is a Professor at the University of Chicago Booth School of Business and affiliated with the National Bureau of Economic Research (NBER). His research spans finance, machine learning, and econometrics, focusing on asset pricing, volatility modeling, and high-frequency data analysis. His work includes developing machine learning frameworks for financial applications, such as return prediction, factor models, and text mining of market data. Recent publications emphasize leveraging large language models (e.g., BERT, GPT) and deep learning architectures (e.g., autoencoders) to address challenges in empirical asset pricing and portfolio optimization. He has collaborated extensively with scholars like Bryan T. Kelly and Stefano Giglio, contributing to high-impact journals and working papers. His research also explores the statistical limits of arbitrage and weak signal detection in financial markets, with applications to risk premium estimation and factor zoo regularization.

• Machine Learning
• Transportation Systems
• Renewable Energy
• +۳ مورد دیگر

Joe Severino serves as a Researcher III in Computational Science at the National Renewable Energy Laboratory (NREL), focusing on machine learning applications for transportation and renewable energy systems. His work addresses real-time optimization challenges in traffic coordination, power grid management, and sustainable energy integration through advanced computational methods. His research spans Machine Learning, Transportation Systems, Renewable Energy, Traffic Signal Control, Predictive Modeling, and Road Networks. He develops solutions for traffic state estimation using probe vehicle data, fairness-aware load shedding in power grids, and explainable AI for energy research, frequently employing ensemble methods and adaptive control strategies to enhance model accuracy and equity. Analysis of his 2024-2025 publications reveals a strong trend toward real-time machine learning solutions for critical infrastructure. Key themes include network-wide traffic signal coordination using adaptive control frameworks, fairness optimization in power grid operations, and interpretable AI models for renewable energy systems. His work consistently bridges theoretical machine learning with practical engineering constraints. As part of NREL's Computational Science group, Severino collaborates extensively with interdisciplinary teams to advance computational approaches for sustainable transportation and energy resilience.

• Biostatistics
• Machine Learning
• Medical Statistics
• +۱ مورد دیگر

Siyu Zhou is a Research Fellow in the Department of Biostatistics and Bioinformatics at Emory University. Their work focuses on advancing machine learning and statistical methods for healthcare applications. Zhou holds a Ph.D. from the University of Pittsburgh, an M.Phil. from The Hong Kong University of Science and Technology, and a B.S. from the same institution. Their research interests include predictive modeling in clinical settings, variable importance analysis in random forests, and healthcare data analytics. Recent articles explore topics such as glycemic control monitoring and algorithm optimization for medical decision-making.

Eric Brachmannمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Object Pose Estimation
• +۵ مورد دیگر

Dr. Eric Brachmann is a Researcher at Heidelberg University 's Visual Learning Lab (since 2017) and a Guest at Leibniz University Hannover (since 2019). He earned his Dr. rer. nat. in 2018 from TU Dresden (summa cum laude), preceded by a Diplom in media computer science (2012) and studies (2006–2012) at TU Dresden. Doctorate: TU Dresden (2018, summa cum laude) Diplom: TU Dresden (2012, passed with distinction) Education: Media and computer science (2006–2012) His research focuses on Computer Vision and Machine Learning , particularly 6D object pose estimation , camera localization , and neural-guided optimization . His work bridges classical geometric methods (e.g., RANSAC) with modern deep learning techniques, including differentiable optimization and reinforcement learning for pose estimation. His publications emphasize end-to-end learning , robust model fitting , and RGB-D image analysis , with applications in robotics and 3D scene understanding. Key contributions include DSAC, CONSAC, and neural extensions of RANSAC for efficient hypothesis sampling. 2018 : GI Dissertation Award nomination 2014 : ACCV Honorable Mention Demo Award 2012 : Enno Heidebroek Award for top graduate 2008–2012 : German National Academic Foundation scholarship 2008 : IBM Award for intermediate diploma As a co-organizer of ICCV and ECCV workshops, Eric drives collaboration in visual localization and 6D pose estimation . He has reviewed for CVPR, ICCV, NeurIPS, and TPAMI, earning recognition as an Outstanding Reviewer (CVPR 19, NeurIPS 19). He has held industry roles at IBM (2010–2011) and T-Systems (2008–2009). At TU Dresden and Heidelberg, he taught courses on computer vision and 3D world reconstruction , supervised theses, and developed practical seminars.

Konrad Abramowiczمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +۴ مورد دیگر

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.

• Functional Data Analysis
• Spatial Statistics
• Applied Statistics
• +۴ مورد دیگر

Sara Sjöstedt de Luna is a Professor in Mathematical Statistics at Umeå University's Department of Mathematics and Mathematical Statistics. She is a board member of The Wallenberg AI, Autonomous Systems & Software Program (WASP) and holds the title of Docent. Her research spans functional data analysis, spatial statistics, and their applications in climatology, forestry, and human motor control. Affiliation : Umeå University Department : Mathematics and Mathematical Statistics Research Focus : Functional data analysis, spatial statistics, and interdisciplinary applications in environmental and biomedical domains Her recent work involves developing statistical tools like the fdaMocca and fiberLD R packages, advancing methodologies for clustering functional data with covariates, and refining kriging approaches for spatiotemporal predictions. She has contributed to climate reconstruction using varved lake sediment data and biomechanical analysis of knee kinematics post-injury. Applications of her research include: Climate modeling using historical sediment records Wood fiber length determination for forestry studies Biostatistical analysis of human motor control Sjöstedt de Luna employs both parametric and nonparametric statistical techniques, with a focus on handling dependent data structures, censoring challenges, and algorithm design for complex datasets.

Robert Jakobمشاهده پروفایل

پژوهشگر

• Digital Health
• Mobile Health (mHealth)
• Machine Learning
• +۶ مورد دیگر

Robert Jakob is a postdoctoral researcher at the Chair of Information Management at ETH Zurich and Co-Director of the Agentic Systems Lab. He completed his PhD in Applied Machine Learning at the Centre for Digital Health Interventions at ETH Zurich, where his doctoral research focused on user churn prediction and prevention in digital health interventions using machine learning methods. His academic journey includes international research experiences at Harvard University's John A. Paulson School of Engineering and Applied Sciences with Prof. Susan Murphy's Statistical Reinforcement Learning Lab and at the SEC Future Health Technologies Lab at the National University of Singapore. His collaborative work extends to major public health institutions including the Federal Office of Public Health of the Swiss Confederation (BAG), the Federal Food Safety and Veterinary Office (BLV), and the Swiss Research Institute for Public Health and Addiction (ISGF), as well as private sector partners like Pathmate Technologies AG and WayBetter Inc. Robert's research interests center on digital health interventions, with a particular focus on understanding factors that influence user adherence, developing machine learning models to predict user churn, and evaluating strategies to prevent nonadherence. His work spans multiple health domains including nutrition, weight management, mental health, and chronic disease management, bridging the fields of computer science, behavioral science, and public health. His publication record demonstrates a strong emphasis on systematic reviews of factors influencing mHealth app adherence, development of predictive models for user churn, and evaluation of behavior change techniques in digital health interventions. His research has been published in high-impact journals including Journal of Medical Internet Research, Annals of Behavioral Medicine, and Computers in Human Behavior Reports, with multiple publications in 2022-2024 showing consistent research productivity. ETH Doc.Mobility Fellowship (Harvard University) 2023 KITE Award Nominee - Digital Health Project 2022 BayStartUp Award - Jury price for 'Bavaria's best founders' 2017 EXIST Business Startup Grant - German Government Stipend 2017 Volkswagen Foundation Grant for 'aspiring young academics' 2013 Robert's educational background combines technical and business perspectives, with a PhD in Applied Machine Learning from ETH Zurich, a master's degree in Technology and Management from Technical University of Munich, and a bachelor's degree in Industrial Engineering from Karlsruhe Institute of Technology. His pre-academic career included founding a mobile games startup and professional experience at major corporations including Accenture, Fortiss, Infineon, and Volkswagen, providing him with practical industry insights that inform his academic research. As Co-Director of the Agentic Systems Lab, Robert contributes to cutting-edge research at the intersection of artificial intelligence, human-computer interaction, and digital health, with a mission to develop more adaptive, effective, and engaging health interventions through data-driven approaches that can address the global challenge of noncommunicable diseases.

• Artificial Intelligence
• Machine Learning
• Constraint Optimization
• +۸ مورد دیگر

Dr. James Atlas is a Senior Lecturer in the Department of Computer Science and Software Engineering at the University of Canterbury's Faculty of Engineering, where he has been employed since July 2018. His primary affiliations include full-time academic responsibilities at this institution. His research spans: Core CS : Artificial intelligence, machine learning, constraint optimization Systems : Distributed and high-performance computing Applied domains : Medical data analysis (CT reconstruction, health prediction), earth/space exploration (flood mapping, environmental monitoring), and multi-agent systems Education : Curriculum development, threshold concept gamification Recent publication analysis (2019-2025) reveals dominant themes in deep learning applications for medical imaging (spectral CT reconstruction, bioacoustic analysis) and environmental sensing (3D tree modeling, vineyard monitoring, flood prediction), with consistent interdisciplinary collaboration. He actively supervises 10 graduate students researching: Machine learning for global flood exposure mapping CT reconstruction algorithms and artifact correction EMG-EEG hybrid prosthetics Hierarchical reinforcement learning Automated 3D digital tree modeling Bioacoustic monitoring systems

• Machine Learning
• Evolutionary Algorithms
• Swarm Intelligence
• +۵ مورد دیگر

Mehdi Neshat is a Visiting Scholar at the Data Science Institute within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He holds a PhD in Engineering from the University of Adelaide (2016-2020) and has extensive experience as a research data scientist specializing in computational optimization methods applied to complex engineering and healthcare problems. His educational background includes: PhD in Engineering, University of Adelaide (2016-2020) Neshat's research focuses on developing and applying advanced computational methods to solve real-world challenges. He specializes in evolutionary algorithms, swarm intelligence, and machine learning techniques for optimizing renewable energy systems, particularly wave and wind energy converters. His work extends to healthcare applications including genomic analysis and medical diagnostics, as well as structural engineering optimization and smart building energy management. His interdisciplinary approach bridges theoretical algorithm development with practical implementation across multiple domains, demonstrating exceptional versatility in computational problem-solving. Analysis of his recent publications reveals a strong trend toward sophisticated ensemble methods and hybrid optimization approaches that combine multiple algorithms to overcome limitations of single-method approaches. His research shows increasing sophistication in handling multi-objective optimization problems, particularly in renewable energy systems where trade-offs between power output, system stability, and cost must be balanced. The geographical focus of his energy research centers on Australian coastal regions, with practical applications for wave and wind farm deployment. Neshat has received significant recognition for his research contributions: Back-to-back Best Paper Prizes at the GECCO conference (2019 and 2020), a CORE A-ranked international optimization and machine learning conference His collaborative research spans multiple institutions and disciplines. Previously, he served as a Postdoctoral Research Associate with the Genomic analysis team at the Australian Centre for Precision Health, Cancer Research Institute, University of South Australia, and as a Senior Research Fellow at the Center for Artificial Intelligence Research and Optimization, Torrens University Australia. His work demonstrates consistent engagement with multidisciplinary teams across engineering, computer science, and healthcare domains, with a strong emphasis on practical implementation of theoretical methods. At UTS, Neshat contributes to the Data Science Institute's research agenda, focusing on applying advanced computational methods to complex real-world problems where traditional analytical approaches fall short. His current work continues to expand the boundaries of optimization techniques for renewable energy systems while exploring new applications in healthcare analytics and structural engineering.