Find Academic Supervisors

• Artificial Intelligence
• Neuroscience
• Biomechanics
• +3 more

Dr. Yonatan Hutabarat is a Professor at Universität Bonn, holding the Hertz-Chair for Artificial Intelligence and Neuroscience. He is affiliated with the Center for Artificial Intelligence and Neuroscience (CAIAN) and focuses on interdisciplinary research at the intersection of AI, neuroscience, and biomechanics. His work emphasizes wearable sensor technology and machine learning applications in gait analysis, posture coordination, and human movement modeling. Research interests include neural network-based gait phase estimation, biomechanical modeling under cognitive loads, and sensor integration for healthcare applications. His research has explored temporal convolutional networks, LSTM architectures, and reinforcement learning for prosthetic control. He has published extensively on quantitative gait assessment using minimal IMU sensors, virtual reality experiments, and multimodal data fusion techniques. Affiliations include CAIAN's interdisciplinary team and active participation in collaborative projects involving neuroscience and clinical applications. His laboratory work involves developing AI-driven solutions for movement disorders and wearable sensor systems. No formal awards or grants are explicitly listed, though his research indicates substantial collaborative activity. Current address: Raum 4.06, Am Propsthof 49, 53121 Bonn. Contact: y.hutabarat@uni-bonn.de

Farzad SabzikarView profile

Associate Professor

• Stochastic Processes
• Fractional Models
• Optimization Algorithms
• +2 more

Farzad Sabzikar is an Associate Professor in the Department of Statistics at Iowa State University, specializing in stochastic processes, fractional models, and optimization algorithms. He integrates mathematical theory with applications in machine learning and time series analysis. Education: PhD in Statistics (Michigan State University, 2014), MS in Mathematics (Sharif University, 2009), BS in Mathematics (Isfahan University of Technology, 2006) His research bridges fractional calculus and statistical modeling, focusing on tempered processes and their applications in turbulence analysis, geophysical flows, and high-frequency data. He employs wavelet methods and asymptotic theory to study heavy-tailed phenomena and long-range dependencies. Recent publications emphasize tempered fractional Brownian motion, stable noise modeling, and functional data analysis. Key trends include transient anomalous diffusion, machine learning for cognitive decline classification, and optimized signal processing techniques. Scientific Awards: None listed His work has implications for machine learning, geophysics, and astrophysics, though no formal advising, grant, or lab affiliations are detailed in available sources.

Sathyanarayanan N. AakurView profile

Assistant Professor

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +11 more

Sathyanarayanan N. Aakur is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. Previously, he was an Assistant Professor in the Department of Computer Science at Oklahoma State University. He is an IEEE Senior Member and has received the prestigious NSF CAREER award for his research on multi-modal event understanding. Dr. Aakur received his PhD from the University of South Florida, where he worked with Dr. Sudeep Sarkar in the Computer Vision and Pattern Recognition Group. He also holds a Master's degree in Management Information Systems from the Muma College of Business at the University of South Florida and an undergraduate degree in Electronics and Communication Engineering from Velammal Engineering College, Anna University, India. His research focuses on the intersection of computer vision, natural language processing, and psychology, with the goal of building intelligent agents that understand the visual world beyond simple recognition or captioning. His work encompasses self-supervised predictive learning for video event segmentation, commonsense reasoning to ground perception and prior knowledge, and generative modeling for building knowledge systems. Much of his group's current work focuses on analyzing, modeling, and synthesizing complex video scenes, with applications in agriculture and animal diagnostics. His recent publications demonstrate a strong focus on open-world visual understanding, neurosymbolic reasoning, and multimodal learning. His work spans from fundamental computer vision problems like egocentric action recognition and scene graph generation to applied research in agricultural technology and biomedical informatics. He has successfully published at top-tier conferences including CVPR, ICCV, ECCV, and WACV, as well as in high-impact journals like IEEE TPAMI. NSF CAREER Award (2022) IEEE Senior Member (2024) Dr. Aakur serves as Area Chair for major conferences including CVPR, WACV, ICML, and NeurIPS, and as Associate Editor for Pattern Recognition journal. He has successfully mentored numerous students who have published at top venues in computer vision and machine learning. His research group has received funding from sources including the NSF and USDA for projects related to multimodal time series classification and stress detection in precision agriculture. The lab maintains active collaborations with institutions including the University of South Florida and Florida State University.

Jingchao NiView profile

Assistant Professor

• Machine Learning
• Time Series Analysis
• Cross-Modal Learning
• +9 more

Jingchao Ni is an Assistant Professor in the Department of Computer Science at the University of Houston. He previously worked as a researcher at NEC Labs America (2018-2022) and AWS AI Labs (2022-2024). He earned his Ph.D. in Computer Science from The Pennsylvania State University's College of Information Sciences and Technology in 2018 under Prof. Xiang Zhang. Research Interests: Machine Learning, Time Series Analysis (Cross-Modal/Multimodal Integration, LLM Reasoning), Graph Learning, Anomaly Detection, Generative Models, and applications in Healthcare (personalized systems, Cyber-Physical Systems, AIOps). His recent publications focus on multimodal time series analysis, vision models for temporal data, and interpretable graph neural networks, with deployments in AWS cloud systems. He has advised students on projects involving LLM agents, causal discovery, and robust forecasting. Awards include a AAAI 2019 Most Influential Paper (PaperDigest) and an ICLR 2022 Spotlight Presentation. He leads the Data-Driven Intelligence (D2I) Group and has contributed to tutorials at KDD 2025 and IJCAI 2025.

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Sameer DeshpandeView profile

Assistant Professor

• Bayesian hierarchical modeling
• Bayesian regression trees
• Causal inference
• +3 more

Sameer Deshpande is an Assistant Professor in the Department of Statistics at the University of Wisconsin–Madison. His research bridges Bayesian methodology development with applications in public health and sports analytics. Prior to joining UW–Madison, he completed a postdoctoral fellowship with Professor Tamara Broderick at MIT and earned his Ph.D. in Statistics from the Wharton School under Professors Ed George and Veronika Rockova. His educational background includes undergraduate studies in mathematics at MIT and a year at Jesus College, Cambridge through the Cambridge-MIT Exchange program. His research focuses on advancing Bayesian hierarchical modeling, treed regression, and causal inference techniques, with particular emphasis on flexible tree-based methods like BART variants for complex data structures. Deshpande's recent publications reveal a strong trend toward developing scalable Bayesian methods for high-dimensional data while maintaining rigorous uncertainty quantification. His work frequently applies these techniques to sports analytics (particularly baseball and football) and public health studies examining long-term effects of adolescent sports participation. The consistent focus on methodological innovation paired with substantive applications demonstrates his dual commitment to statistical theory and real-world impact. He actively mentors graduate students at UW–Madison, requiring STAT 775 as preparation for research collaboration. His Deshpande Lab focuses on Bayesian computation and causal inference, though specific grant details are not publicly listed. Notable projects include the NFL Big Data Bowl submission analyzing quarterback decision-making using Expected Hypothetical Completion Probability. Outside academia, Deshpande maintains interests in cooking, cocktail making, and photography, while remaining a devoted fan of Dallas sports teams – often seen wearing a Texas belt buckle.

Ross A WoodsView profile

Professor

• Hydrology
• Water Resources
• Hydroclimatology
• +5 more

Ross A Woods is a Reader in Water & Environmental Engineering at the School of Civil, Aerospace and Design Engineering, University of Bristol , and an active member of the Cabot Institute for the Environment . His research focuses on large-sample hydrology, flood and drought risk, climate-water interactions, and catchment classification. Keywords : Hydrology, Water Resources, Hazards, Hydroclimatology Research Themes : Flood Estimation in Ungauged Catchments, Climate Change Impacts on Hydrology, Snow-Rain Transition Effects, Hydrological Modeling with Simple Models Scientific Trends His recent work emphasizes groundwater-surface water interactions (e.g., DECIPHeR-GW, 2025), snow hydrology under climate change (e.g., Northern Hemisphere SWE datasets, 2025), and catchment-scale water balance analysis (e.g., 2024 studies on soil drainage and streamflow seasonality). Key disciplines include hydrology, climate science, and environmental engineering. Scientific Awards 2022 STAHY Best Paper Award (awarded 2023) Adjunct Faculty Member (2006) Award for Outstanding Achievement (2010) He has contributed to hydrology through 671 catchment studies (CAMELS-GB dataset) and model development (e.g., TOSSH toolbox, 2021). His work bridges theoretical hydrology with applied water management in regions like New Zealand, Brazil, and the contiguous USA.

Mark SteelView profile

Professor

• Bayesian Statistics
• Econometrics
• Statistical Theory
• +4 more

Mark Steel is a Professor of Statistics at the University of Warwick, Department of Statistics. He previously held Chairs in Economics at the University of Edinburgh (1998-2000) and in Statistics at the University of Kent (2000-2003). His research focuses on Bayesian statistics, including theoretical and applied work in distribution theory, Bayesian model averaging, spatial statistics, survival analysis, and stochastic volatility models. He has extensive editorial experience, serving as Editor-in-Chief of Bayesian Analysis (2022-2025) and as Head of the Statistics Department at Warwick (2014-2018). He is currently Chair-Elect of the Objective Bayes section of the International Society for Bayesian Analysis (ISBA). His research interests span Bayesian methodology, econometrics, and statistical theory, with applications to economics, survival analysis, and spatial data. He is affiliated with the Centre for Research in Statistical Methodology at Warwick and actively contributes to academic conferences. His work has been recognized through highly cited publications, including influential papers on Bayesian model averaging and non-Gaussian statistical models. Professional activities include editorial roles in top journals such as the Journal of Econometrics , Journal of the Royal Statistical Society , and Journal of Productivity Analysis . He collaborates with researchers globally and maintains an active profile on academic platforms like Google Scholar and ResearchGate.

Dimitris KarlisView profile

Professor

• Computational Statistics
• Mixture Models
• EM Algorithm
• +12 more

Dimitris Karlis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), within the School of Information Sciences and Technology. He has been a key academic figure since earning his BSc and PhD in Statistics from AUEB in 1992 and 1999, respectively, and was promoted to Associate Professor in 2012 before advancing to full Professor. Education: BSc in Statistics, AUEB (1992) PhD in Applied Statistics, AUEB (1999) His research spans computational statistics, mixture models, EM algorithms, copulas, multivariate discrete data, and applications in sports, insurance, and seismicity. He has published extensively in top-tier statistical journals such as the Journal of the Royal Statistical Society and Statistics in Medicine . The 15 most recent publications reveal a strong focus on multivariate count data, integer-valued time series, model-based clustering using copulas, and applications in actuarial science and health. His work frequently involves mixture models, Bayesian inference, and innovative extensions of Poisson-based frameworks. Scientific Service and Recognition: Associate Editor: Metron, Communications in Statistics, IMA Journal of Management Mathematics, Stochastic Environmental Research and Risk Assessment Editor: Biometrics Bulletin of IBS Member: American Statistical Society, International Statistical Institute, International Association of Statistical Computing, Hellenic Statistical Institute Publicity Officer: Eastern Mediterranean Region, International Biometric Society Advising and Grants: He has supervised 4 completed PhDs and 18 Master’s theses, with several more in progress. He has led and participated in research projects funded by the European Union and EUROSTAT, particularly in official statistics. His advising spans methodological and applied topics in statistics. Labs and Teams: While no formal lab is named, he collaborates extensively with researchers in actuarial science, transportation, biostatistics, and environmental risk, often through joint projects and publications.

• Machine Learning
• Natural Language Processing
• Predictive Maintenance
• +4 more

Tony Lindgren is an Associate Professor at the Department of Computer and Systems Science, Stockholm University, affiliated with the Data Science Research Group and Natural Language Processing Research Group. His work bridges data science and NLP , focusing on interpretable models, constraint programming, and predictive maintenance systems. Research interests include: Machine Learning for explainability and fairness Constraint Programming in maintenance optimization Natural Language Processing for risk analytics and troubleshooting Recent publications demonstrate trends in multi-objective optimization (2025 satellite scheduling), conformal prediction (2024 CoPAL), and fault detection (2024 Automotive Nowcasting). His work often integrates domain-specific constraints with scalable algorithms across applications like food safety and autonomous vehicles. Software tools developed by Lindgren include: Example-based Feature Tweaking Rule Indexing Frameworks His research groups focus on AI-driven decision support for high-stakes domains, combining technical innovation with societal impact considerations.

Miao XuView profile

Research Fellow

• Data Science
• Machine Learning
• Time Series Analysis
• +4 more

Dr Miao Xu is a Research Fellow at the University of Queensland (UQ), affiliated with the School of Electrical Engineering and Computer Science within the Faculty of Engineering, Architecture and Information Technology. She holds an Australian Research Council DECRA Fellowship (ARC DECRA), recognizing her early-career research excellence. Her research focuses on machine learning, data science, and time series analysis, with applications in healthcare, materials science, and algorithmic fairness. Dr Xu's work addresses challenges in noisy label handling, unlearning mechanisms, and adaptive modeling for irregular data. Education: She earned a Doctor of Philosophy (PhD) from Nanjing University. She is actively involved in supervising research and contributes to the Centre for Enterprise AI at UQ. Research Interests: Dr Xu’s expertise spans machine learning , time series analysis , deep learning , and unsupervised learning . Her recent work emphasizes robust learning with noisy or incomplete labels, model unlearning, and applications in alloy design and medical informatics. She explores methods like instance-attention GNNs for irregular time series and confidence-guided techniques for adversarial attack detection. Publications: Her recent work includes advancements in GNN-based time series modeling, bias mitigation in text classification, and active learning for alloy design. Key themes include improving generalization, reducing algorithmic bias, and enhancing model transparency. Awards: Her ARC DECRA fellowship (202X–202X) supports her research on data-driven methodologies. Supervision & Grants: Available for PhD supervision in machine learning and data science. Her grants include funding for projects in unlearning mechanisms and spatiotemporal modeling. Labs/Teams: Affiliated with the Centre for Enterprise AI at UQ, collaborating on enterprise-scale AI applications and interdisciplinary research.

Mattias VillaniView profile

Professor

• Bayesian Statistics
• Machine Learning
• Computational Methods
• +3 more

Mattias Villani is Professor of Statistics at Stockholm University, specializing in Bayesian statistics and machine learning. He obtained his PhD in Statistics from Stockholm University in 2000 and has held positions at Sveriges Riksbank and Linköping University. Villani develops computationally efficient Bayesian methods for inference, prediction and decision-making with flexible probabilistic models. Research Interests: His work spans Bayesian computation (MCMC, HMC, variational inference), machine learning (Gaussian processes, mixture models), and applications in neuroimaging, transportation, and econometrics. Research focuses on scalable Bayesian methods for large datasets and complex models. Publication Focus: Recent articles concentrate on Bayesian neuroimaging analysis, transportation network modeling, and efficient MCMC algorithms. Methodological innovations in subsampling techniques for large-scale Bayesian computation represent a significant research trend. Student Advising: Supervises PhD students in statistical methodology development and applications. Current research groups focus on spatiotemporal modeling, locally stationary processes, and neuroimaging statistics.

Steve MarronView profile

Professor

• Statistics
• Data Science
• Machine Learning
• +4 more

Steve Marron is the Amos Hawley Distinguished Professor of Statistics and Operations Research at the University of North Carolina at Chapel Hill (UNC-CH). He holds a joint appointment in the School of Data Science and Society and is a professor in the Department of Biostatistics at the Gillings School of Global Public Health. Additionally, he serves as an adjunct professor in the Department of Computer Science within the College of Arts & Sciences. His research focuses on statistics, data science, and machine learning, with a particular emphasis on integrating diverse data types such as genomics, imaging, and demographic data. Education: Marron earned an AA from Orange Coast College (1974), BS from University of California, Davis (1977), and PhD from UCLA (1982). He has held faculty positions at UNC-CH since 1982 and Cornell University (2001-2002). His honors include Fellowships from the Institute of Mathematical Statistics and American Statistical Association, and he is a top-cited mathematician (1991-2001). Research interests include object-oriented data analysis, high-dimensional data methods (HDLSS), visualization techniques like SiZer, and statistical methodology for imaging and genomics. Notable contributions include Distance-Weighted Discrimination (DWD), Principal Nested Spheres, and JIVE for data integration. His work has applications in cancer genomics, medical imaging, and bioinformatics. Awards and recognitions include the Amos Hawley Professorship, S. N. Roy Memorial Lectureship, and Saw Swee Hock Visiting Professorship. Marron has advised numerous students and collaborated on NIH-funded grants, including studies on cancer genomics and imaging. His lab focuses on developing statistical tools for complex, multi-source data analysis.

• Forecasting
• Time Series Analysis
• Inventory Management
• +5 more

Nikolaos Kourentzes is a Professor of Informatics at the University of Skövde , specializing in forecasting and operations research. His work bridges theoretical advancements in time series analysis with practical applications in supply chain management, tourism demand, and renewable energy forecasting. Academic Rank: Professor Department: Department of Information Technology Research Interests: His research focuses on hierarchical and temporal forecasting methodologies, integrating macroeconomic indicators into demand planning, inventory optimization, and machine learning applications. He explores forecast reconciliation, shrinkage estimators, and the role of expert judgment in predictive analytics. Recent Publications: Highlights include advances in hierarchical forecasting with leading indicators, probabilistic forecasts during crises like the pandemic, and complex smoothing techniques. His work spans journals such as Omega , International Journal of Forecasting , and European Journal of Operational Research . Collaborations: Kourentzes collaborates with researchers globally, including George Athanasopoulos, Rob Hyndman, and Robert Fildes, across domains like tourism analytics, tire industry forecasting, and public health modeling.

Mahsa SalehiView profile

Senior Lecturer

• Data Mining
• Machine Learning
• Time Series Analysis
• +5 more

Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.