Find Academic Supervisors

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +6 more

Seong Tae Kim is a Professor at Kyung Hee University's Department of Biomedical Engineering. He previously held affiliations at the Technical University of Munich (Germany) and KAIST's Image and Video Systems Laboratory in South Korea. His research focuses on computer vision, medical imaging, and AI applications in healthcare. Kim's work emphasizes explainable AI, neural network interpretability, and deep learning for medical diagnosis. He has contributed to advancements in knowledge graph-based reasoning, video object segmentation, and generative models for medical data synthesis. His research often bridges computer vision techniques with biomedical applications, such as cancer diagnosis and surgical phase recognition. Over 100 publications since 2003 highlight his expertise in neural networks, federated learning, and robust training methods. Notable projects include developing frameworks for COVID-19 CT analysis and generating realistic biomedical datasets. He collaborates widely with institutions like the Max Planck Institute for Informatics (via Nassir Navab's lab) and Samsung Research. Current research interests include interpretable machine learning models, longitudinal medical image analysis, and AI-driven healthcare solutions. His work frequently appears in top conferences like CVPR, MICCAI, and ECCV.

Ryan TibshiraniView profile

Professor

• High-Dimensional Statistics
• Nonparametric Estimation
• Distribution-Free Inference
• +5 more

Ryan Tibshirani is a Professor of Statistics at the University of California, Berkeley, and Principal Investigator in the Delphi group. Previously, he was faculty at Carnegie Mellon University (2011–2022). He holds a Ph.D. in Statistics (2011) and a B.S. in Mathematics (2007) from Stanford University. His research focuses on high-dimensional statistics, nonparametric methods, distribution-free inference, and machine learning, with applied work in computational epidemiology, particularly tracking and forecasting epidemics like influenza and COVID-19. He has contributed to open-source tools like the conformalInference R package for predictive inference. Education: Ph.D. in Statistics, Stanford University (2011); B.S. in Mathematics, Stanford University (2007). Professional service includes Editor-in-Chief roles for Foundations and Trends in Machine Learning and Statistics, and membership in the Institute of Mathematical Statistics Council. Research interests span theoretical and applied statistics, including convex optimization, numerical methods, and collaborative efforts in public health forecasting. His work on the Delphi group has advanced real-time epidemic monitoring via sensor fusion and probabilistic forecasting. Recent projects address challenges in estimating time-varying epidemic severity and improving predictive model calibration under distribution shifts. Key contributions include unifying conformal prediction theories, developing trend filtering methods for lattice data, and advancing understanding of cross-validation in overparameterized models. His software tools emphasize reproducibility and scalability for large-scale statistical tasks.

Jony SheyninView profile

Assistant Professor

• Computational Psychiatry
• Neuroimaging (functional MRI)
• Anxiety Disorders
• +3 more

Jony Sheynin, PhD, is a Research Assistant Professor at Texas A&M University's Department of Psychiatry and Behavioral Science within the School of Medicine. His work focuses on computational modeling, neuroimaging (fMRI), and behavioral analyses to study psychopathologies like anxiety disorders and PTSD. He holds a PhD in Biomedical Engineering from Rutgers University and advanced degrees from Ben-Gurion University of the Negev. Professional training includes postdoctoral studies at the University of Michigan's Department of Psychiatry. Research Interests: Computational Psychiatry, neuroimaging techniques (functional MRI), mechanisms of PTSD and anxiety disorders, avoidance behavior, and decision-making processes. His studies investigate neural correlates of fear extinction, trauma recovery predictors, and translational neuroengineering approaches for neurological conditions like tetraplegia. Publications Trends: Recent work emphasizes PTSD symptom trajectories using deep learning models, fMRI biomarkers for treatment response, and transcutaneous spinal stimulation for neurological assessment. His research bridges computational neuroscience with clinical applications, particularly in precision psychiatry and neurorehabilitation. Awards: 2016: Trainee Professional Development Award (Society for Neuroscience) 2015: Dean’s Award for Outstanding Academic Achievement (Rutgers) Advising & Grants: While no listed advisees or grant details are provided, his collaborative research indicates involvement in clinical trials and interdisciplinary projects. Current work extends to computational models of PTSD and neurorehabilitation technologies.

• Offshore Technology
• Marine Engineering
• Control Systems
• +5 more

Professor Vahid Hassani is affiliated with the Department of Mechanical, Electrical and Chemical Engineering at Oslo Metropolitan University's Faculty of Technology, Art and Design. His research focuses on offshore technology, marine control systems, underwater robotics, and machine learning applications in maritime engineering. Key contributions include studies on ship maneuvering dynamics, underwater glider navigation, and ROV automation. His work spans advanced topics such as data-driven control methodologies, dynamic positioning systems, and sensor fusion for autonomous underwater vehicles. Recent projects include developing optical modems for underwater communication and high-fidelity ROV simulators for pilot training. He has authored over 48 scientific publications, with a strong emphasis on naval architecture, robotics, and control systems. Research interests also include experimental validation of hydrodynamic models using facilities like the Planar Motion Mechanism (PMM). His studies frequently address challenges in adverse marine conditions, such as tunnel navigation for vessels and energy-efficient surface effect ship designs. Publications highlight interdisciplinary approaches combining control theory, machine learning, and marine engineering. Notable collaborations involve institutions like NTNU, ASME, and IEEE, with contributions to conferences like OCEANS and the International Conference on Ocean Engineering.

• Wireless Communication Security
• Internet of Things
• Machine Learning Applications
• +5 more

Wen Chen is a Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, with significant research contributions across multiple disciplines including wireless communication security, Internet of Things, and machine learning applications. Their work demonstrates strong collaborations with institutions including Chinese Academy of Sciences, Wayne State University, and various medical research centers. Research interests focus on cutting-edge applications of machine learning to solve real-world problems in cybersecurity, medical imaging, and energy systems. Recent work bridges theoretical computer science with practical implementations, particularly in federated learning approaches for resource-constrained environments and innovative security mechanisms for IoT devices. The research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and medical applications. Publication trends show increasing focus on practical implementations of theoretical concepts, with particular emphasis on security applications for wireless networks and medical diagnostics. Recent work integrates multiple machine learning paradigms to address challenges in non-IID data environments, sparse labeling scenarios, and resource-constrained hardware implementations. Current research directions include advancing secure communication protocols for IoT devices, developing efficient medical diagnostic tools using AI, and optimizing energy systems through intelligent prediction and control mechanisms. The work consistently demonstrates strong technical depth while maintaining practical applicability to real-world challenges.

Ramchandra RimalView profile

Assistant Professor

• Machine Learning
• Statistical Network Models
• Classification and Clustering
• +3 more

Dr. Ramchandra Rimal is an Assistant Professor in the Department of Mathematical Sciences at Middle Tennessee State University (MTSU), specializing in Data Science. He holds a PhD from the University of Central Florida (2020), an MS from UCF (2017), and a BS from Tribhuvan University (2009). His research focuses on Machine Learning, Statistical Network Models, and Classification/Clustering, with applications across diverse fields such as healthcare, finance, and biological sciences. He develops novel methodologies to solve real-world problems, including predictive modeling for stock markets, neurocognitive analysis, and network topology estimation. Dr. Rimal has published in top journals like the Journal of the Royal Statistical Society and Journal of Machine Learning Research , with notable contributions to stochastic block models, density deconvolution, and deep learning frameworks. His work bridges theoretical advancements with practical applications in data-driven decision making. Awards: 2019 UCF Graduate Research Excellence Award, 2018 UCF Teaching Assistant Award Teaching: Courses include Probability & Statistics, Applied Predictive Modeling, and Data Science graduate programs at MTSU. He actively presents at conferences such as the Symposium on Data Science and the Southeastern International Conference on Combinatorics. His research group collaborates on interdisciplinary projects, emphasizing both computational innovation and domain-specific relevance.

Saeed SametView profile

Professor

• Cybersecurity
• Privacy-Preserving Data Mining
• Federated Learning
• +5 more

Dr. Saeed Samet is a Professor in the School of Computer Science at the University of Windsor. His research focuses on cybersecurity, privacy-preserving data mining, federated learning, blockchain technology, and healthcare informatics. He has led projects such as the privacy-preserving personal health record system (P3HR) and the decentralized electronic health records (DEHR) model leveraging blockchain. Samet actively engages in interdisciplinary collaborations, including developing frameworks like SEERa for community prediction and RNBFT for scalable Byzantine consensus. He advises students like Saghi Khani on topics such as social isolation detection algorithms. Notably, his work addresses adversarial attacks in machine learning and federated learning security. Samet has contributed to initiatives like the International Collegiate Programming Contest and the WE-Spark Health Institute grants. Education While formal academic credentials are not explicitly detailed in the text, Samet holds a professorship, indicating advanced qualifications in computer science or related fields. His extensive publications suggest doctoral-level expertise. Research Interests Samet’s research spans cybersecurity mechanisms, privacy-preserving methodologies (e.g., homomorphic encryption, federated learning), blockchain applications in healthcare, and machine learning robustness. Recent work emphasizes scalable consensus algorithms (RNBFT), adversarial defense in federated learning, and frameworks for decentralized data evaluation. His projects often integrate real-world challenges, such as combating fake news via generative AI and enhancing pandemic response through blockchain-based patient tracking. Awards & Grants Recipient of a WE-Spark Health Institute grant for innovative research in Windsor-Essex (2021). His work on social isolation algorithms (2020) and privacy-preserving statistical analysis (2019) underscores sustained funding support for impactful projects. Advising & Grants Advises Saghi Khani on machine learning algorithms for social isolation detection. Collaborates on grants involving interdisciplinary teams, such as the $287,000 WE-Spark Health Institute award. His research often bridges academia and industry, addressing practical challenges in healthcare and cybersecurity. Labs & Teams Leads initiatives in the School of Computer Science’s research groups, focusing on privacy-preserving systems and distributed learning. Collaborates with organizations like the WE-Spark Health Institute and the University of Windsor’s interdisciplinary teams on projects such as the DEHR blockchain model and federated learning frameworks.

• Computational Neuroscience
• Machine Learning in Healthcare
• Neuroimaging Techniques
• +3 more

Dr. Jose Sanchez Bornot is a Researcher in the School of Computing, Engineering and Intelligent Systems at Ulster University. His work focuses on computational neuroscience, machine learning applications in healthcare, and neuroimaging techniques. He specializes in developing advanced algorithms for analyzing magnetoencephalography (MEG), electroencephalography (EEG), and functional MRI (fMRI) data to identify biomarkers for neurological disorders, particularly Alzheimer's disease and mild cognitive impairment. His research integrates physics-informed neural networks, graph neural networks, and state-space models to enhance diagnostic accuracy and understand neural mechanisms. Key research interests include: computational modeling of brain dynamics, biomarker detection through multimodal data fusion (MEG/MRI), functional connectivity analysis, and machine learning for medical diagnostics. Recent work emphasizes improving Alzheimer's intervention strategies via combined MEG-MRI pipelines and exploring the role of excitatory-inhibitory balance in neural disorders. Publications highlight trends in applying advanced mathematical techniques (e.g., penalized regression, autoencoders) to solve ill-posed inverse problems in neuroimaging. His methods address challenges like missing data imputation and cross-frequency interactions in complex brain networks. Contributions span theoretical developments (e.g., modified Newton-Raphson algorithms) and practical applications in clinical settings.

Subhasish DasView profile

Assistant Professor

• Bayesian Nonparametrics
• Machine Learning
• Predictive Analytics
• +3 more

Subhasish Das is an Assistant Professor of Instruction in the Department of Computer Science at the University of Texas at San Antonio (UTSA). He holds a Ph.D. in Computer Engineering from Arizona State University and a B.Tech. in Computer Science and Engineering from the West Bengal University of Technology. His research focuses on Bayesian Nonparametrics, Machine Learning, Predictive Analytics, and Time-Series Analysis, with applications spanning healthcare monitoring, planetary science, and bioinformatics. Dr. Das's work emphasizes interdisciplinary approaches, such as combining machine learning with physical systems modeling and developing multimodal reasoning frameworks. His recent publications explore topics like action recognition in visual-linguistic modalities, predictive analytics for physical systems, and image prioritization for space missions. He is actively involved in teaching and advancing computational methodologies in academia. Leveraging tools like kernel density estimation and deep learning, his research bridges theoretical foundations with practical applications in biomedicine, robotics, and space technology. His academic contributions highlight the integration of computational techniques with domain-specific challenges to drive innovation.

Alan WelshView profile

Professor

• Statistical Inference
• Statistical Modelling
• Mixed Models
• +4 more

Alan Welsh is the E.J. Hannan Professor of Statistics at the Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics. He holds prestigious fellowships from the Australian Academy of Science, Institute of Mathematical Statistics, and American Statistical Association. His research focuses on statistical inference, mixed models, robustness, nonparametric methods, ecological monitoring, and survey analysis. Education: PhD in Statistics (ANU, 1984) and BSc (Hons) in Mathematical Statistics (University of Sydney, 1981). Research interests emphasize advanced statistical methodologies with applications in ecology, environmental science, and healthcare. He has authored over 176 publications and secured funding through grants like 'Analytics for the Australian Grains Industry' (2024–2027) and 'Statistical Machine Learning' (2019–2024). His work spans high-dimensional data analysis, copula-enhanced models for medical imaging (e.g., myopia screening via fundus images), and spatial-temporal modeling of environmental data. He has also contributed to statistical software, notably the 'rpql' and 'mplot' R packages. Awards: Moran Medal (1990), Pitman Medal (2012), E.J. Hannan Medal (2019), and Honorary Life Membership (International Biometric Society, 2018). Welsh has advised numerous research students and served as Editor-in-Chief of the Australian and New Zealand Journal of Statistics (2012–2015) and Co-Editor of Biometrics (2020–2022). His current projects address challenges in big data analysis, robust estimation, and model selection for complex datasets.

Nils RütherView profile

Professor

• Hydraulic Engineering
• Sediment Transport
• Flood Modeling
• +5 more

Overview Prof. Nils Rüther is a Professor of Hydraulic Engineering at the Technical University of Munich (TUM) within the TUM School of Engineering and Design . His research focuses on sediment transport mechanics, hydraulic modeling of rivers and urban flooding, and hydropower infrastructure sustainability. He specializes in integrating advanced numerical methods with geospatial data analysis for environmental engineering applications. Research Interests His work spans experimental hydraulics, CFD modeling, and field observations. Key areas include: Braided river morphodynamics using satellite imagery Sediment transport quantification via ADCP and SfM techniques Flood risk assessment in steep mountain rivers Optimization of hydropower plant designs Climate change impacts on river systems Technical Expertise Rüther employs cutting-edge tools like OpenFOAM, TELEMAC, and machine learning models for predictive hydraulics. His experimental work uses physical scale models and advanced instrumentation (ADCPs, SfM, ultrasound). Awards & Grants While no specific awards are listed, his active participation in EU-funded projects like HYPOS and DIRT-X indicates recognition in the field.

Xingchen ZhangView profile

Lecturer

• Human Intention Prediction
• Human Pose Estimation
• Image Fusion
• +3 more

Dr. Xingchen Zhang is a Marie Skłodowska-Curie Individual Fellow at the Personal Robotics Laboratory (PRL), Imperial College London, and holds a Lecturer position in the Department of Electrical and Electronic Engineering. Previously, he served as a Teaching Fellow and Research Associate at Imperial College. He teaches Deep Learning courses and is actively involved in academic reviews for prestigious programs like UKRI Future Leaders Fellowships and top conferences/journals such as CVPR, ECCV, and IEEE TNNLS. Xingchen earned his BSc from Huazhong University of Science and Technology (2012) and PhD from Queen Mary University of London (2018). His research focuses on human motion prediction, pose estimation, image fusion techniques (visible-infrared, multi-focus), visual object tracking, and deep learning applications in computer vision. His work has led to a notable book on image fusion, awarded the National Science and Technology Academic Publications Fund in China (2019). His recent publications emphasize real-world applications such as traffic flow prediction, pedestrian privacy protection, and fusion-based object tracking. He contributes to advancing graph neural networks for mobility analysis and spatio-temporal modeling, while maintaining a strong focus on interdisciplinary robotics and AI solutions. Awards: Marie Curie Fellowship, National Science & Technology Academic Publications Fund (2019) Key Roles: Reviewer for UKRI FLF, CVPR, ECCV, IJCV Labs: Personal Robotics Lab (PRL), Imperial College London

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +3 more

Timea Bezdan is a researcher at Singidunum University, specializing in artificial intelligence, machine learning, and optimization algorithms. She holds a doctoral degree in Advanced Protection Systems (2021-2024) from Singidunum University, following master's degrees in Contemporary Information Technologies (2020-2021) and bachelor's degrees in Software and Data Engineering (2017-2020) and Marketing and Management (2013-2017). Her research focuses on applying AI to environmental science, healthcare, and computational efficiency, with notable work on air pollution modeling, disease detection, and algorithm optimization. Key research interests include neural network architecture design, hybrid metaheuristic approaches, and bias mitigation in AI systems. Recent projects address demographic bias in age estimation models and energy-efficient cloud-edge computing. Her work spans interdisciplinary collaborations, integrating machine learning with environmental monitoring and medical diagnostics. Education: Doctoral studies: Advanced Protection Systems, Singidunum University (2021–2024) Master's: Contemporary Information Technologies, Singidunum University (2020–2021) Bachelor's: Software and Data Engineering, Singidunum University (2017–2020) Bachelor's: Marketing and Management, Singidunum University (2013–2017) Publications highlight contributions to AI-driven environmental analysis, healthcare applications (e.g., Parkinson’s disease detection), and optimization techniques for neural networks. Her work often combines metaheuristic algorithms like firefly, bat, and particle swarm optimization with machine learning frameworks. Research groups and collaborations include Singidunum University’s Institute for Applied AI, with active participation in international conferences such as IEEE Symposia and Sinteza. Ongoing projects focus on explainable AI for emotion recognition and AI-based crisis analysis of environmental pollutants.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +5 more

Dr. Sriparna Saha is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Patna, India. She holds a Ph.D. from the Indian Statistical Institute Kolkata and has held leadership roles including Head of Department (2021-2023) and Associate Dean for Research and Development (2019-2021). Her research focuses on AI, machine learning, natural language processing, bioinformatics, and multiobjective optimization. She has authored over 400 publications with an h-index of 38 and received awards such as the NASI Young Scientist Platinum Jubilee Award and Google India Women in Engineering Award. Her work spans multimodal systems, medical image analysis, and computational social systems. Education: M.Tech (2005) and Ph.D. (2011) in Computer Science from Indian Statistical Institute Kolkata. Research Interests: Multimodal information processing, NLP, machine learning, bioinformatics, and optimization techniques. Awards: Includes Lt. Rashi Roy Memorial Gold Medal, BIRD Award, and multiple fellowships (Humboldt, CNRS, etc.). Administrative Roles: IEEE Student Branch Councilor, Senate Member, and Visvesvaraya Nodal Officer at IIT Patna. Her recent work includes advancements in multimodal recommendation systems, breast cancer prognosis models, and computational social systems for crisis management. She has also contributed to hate speech detection in multilingual contexts and medical imaging diagnostics.

• Artificial Intelligence
• Machine Learning
• Distributed Systems
• +3 more

Caesar Wu is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the PCOG department. His work focuses on trustworthy AI, distributed learning systems, machine learning optimization, and strategic decision-making frameworks. He holds a Postdoctoral researcher position and contributes to advancing methodologies in data-centric AI, transformer models, and cybersecurity. Research interests include: - Trustworthy AI : Developing robust frameworks for explainability, fairness, and algorithmic transparency. - Distributed Systems : Optimizing communication and fault tolerance in distributed machine learning. - Time Series Forecasting : Hyperparameter tuning and transformer-based predictive models. - Financial Applications : Strategic decision-making in credit risk assessments using ML. Recent publications (2023-2025) emphasize: Trustworthiness metrics for stochastic gradient descent in distributed environments Unified hyperparameter optimization pipelines for transformer models Survey analysis of data-centric AI in time series forecasting Ethical considerations in AI-driven strategic decisions No scientific awards were explicitly mentioned in the provided texts. Current role involves no formal advisees, but contributes to interdisciplinary research teams within SnT. His work integrates machine learning with cloud computing principles, reflecting expertise in both theoretical and applied domains.