Find Academic Supervisors

• Biostatistics
• Genomic Data Analysis
• Statistical Methodologies
• +7 more

Dr. Zhide Fang is a Full Professor of Biostatistics at the LSU School of Public Health and holds a conjoint appointment in the Department of Genetics, School of Medicine at LSU. He is also a statistician at the Louisiana Clinical & Translational Science Center and a member of the Southeast Biomedical Informatics Consortium. His work focuses on developing statistical methodologies for genomic data analysis, experimental design optimization, and big data algorithms. Education: PhD in Statistics, University of Alberta (1999) MS in Statistics, Huazhong Normal University (1989) BS in Mathematics, Huazhong Normal University (1986) Research Interests: Dr. Fang specializes in genomic data analysis (including microarrays, NGS, and metagenomics), statistical modeling for public health and biomedical research, optimal experimental design theory, and algorithms for big data. His collaborative work spans oncology, ophthalmology, and pediatric medicine. Publications: Recent work includes studies on ethnic disparities in diabetes management, AI-driven surgical metrics, prostate cancer genetics, and optimal design methodologies. His articles reflect interdisciplinary collaboration in biostatistics, computational biology, and clinical research. Labs/Teams: Active in translational science through the Louisiana Clinical & Translational Science Center and the Southeast Biomedical Informatics Consortium. Engaged in collaborative projects at LSU Health's School of Medicine and School of Public Health.

• Healthcare Management Science
• Simulation Modeling
• Breast Cancer Research
• +3 more

Mehmet Ali Ergün is an Assistant Professor in the Department of Industrial Engineering at Istanbul Technical University. Previously, he served as a Doctor Lecturer at Istanbul City University (2018-2020) before joining Istanbul Technical University in 2020. His academic journey includes an Integrated Doctorate from the University of Wisconsin-Madison (2012-2018) and a Licence (Bachelor's) in Computer Engineering from Bogazici University (2004-2009). Dr. Ergün's research focuses on healthcare management science, particularly in breast cancer screening and mortality modeling, organ transplantation optimization, and applying machine learning to healthcare decision making. His work combines operations research methodologies with real-world healthcare challenges to develop evidence-based decision support tools. His research fingerprint shows strong emphasis on Breast Cancer (100%), Cancer Mortality (58%), Breast Cancer Screening (51%), and Simulation Modeling (39%). His publication record demonstrates consistent scholarly output since 2014, with 18 research outputs including journal articles, book chapters, and conference contributions. His most impactful work includes the 2021 paper on the Impact of the COVID-19 Pandemic on Breast Cancer Mortality, which has garnered 123 Scopus citations and significant policy attention. Dr. Ergün has received notable recognition for his research, with his work being Picked up by 87 news outlets Blogged about by 3 sources Referenced in 1 policy source Shared by numerous social media users Read by hundreds of researchers on Mendeley Currently, Dr. Ergün serves as Principal Investigator for an active BAP project titled "An alternative scoring system for determining donor kidney quality" (2025-2026), demonstrating his ongoing commitment to addressing critical healthcare system challenges through operations research methodologies.

• Applied Mathematics
• Approximation Theory
• Orthogonal Polynomials
• +5 more

Darío Ramos López is a Professor of Applied Mathematics at the Department of Mathematics, Universidad de Almería (UAL). He coordinates the Double Degree in Economics and Mathematics and teaches subjects such as Mathematics II and Numerical Methods and Optimization . His research focuses on Approximation Theory, Orthogonal Polynomials, Bayesian Networks, and Machine Learning applications in biomedical optics and data streams. He leads the research group on Approximation Theory and Orthogonal Polynomials (FQM229). His work integrates mathematical modeling with real-world applications, including corneal data analysis (via the rPACI R package) and environmental conservation projects. He developed the AMIDST toolbox , a Java library for scalable probabilistic machine learning, supporting analysis of massive data streams in finance and other domains. He has authored over 30 publications since 2010, with recent contributions to nonstationary data stream inference and keratoconus detection using Bayesian methods. Tutoring sessions are held in-person or online at CITE III INFORMATICA-MATEMAT (Office 1-400), with flexible scheduling via email appointment.

Xiang ChangView profile

Lecturer

• Robotics
• Control Systems
• Neural Networks
• +3 more

Xiang Chang is a Lecturer in the Engineering Unit at Aberystwyth University, within the Faculty of Business and Physical Sciences. His research focuses on robotics, control systems, and neural networks. He holds a Doctor of Philosophy (PhD) from Aberystwyth University, completing his thesis titled 'Robotic Imitation Learning from Videos: Boosting Autonomy and Transferability' in 2024. Research Interests: Robotics and Aerial Manipulation Control Systems and Nonlinear Dynamics Neural Networks and Deep Learning Image Processing and Computer Vision Reinforcement Learning and Optimization His recent work emphasizes advancements in unmanned aerial systems, neural network compression, and improving training efficiency in diffusion models. Key contributions include studies on convergence time control for aerial manipulators and techniques for long-tailed recognition in deep learning. Grants & Supervision: While no grants are explicitly listed, his thesis supervision indicates involvement in guiding research in robotic imitation learning and autonomous systems. He collaborates internationally, with research outputs often involving interdisciplinary teams. Labs/Teams: Active in the Engineering Unit's robotics and control systems research groups at Aberystwyth University.

Jungong HanView profile

Academic

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +3 more

Roles and Affiliations: Jungong Han is an Honorary Professor at Aberystwyth University's Department of Computer Science, where he also serves as Director of Research. He holds an Honorary Professorship at the University of Warwick. His research focuses on Computer Vision, Artificial Intelligence, and Machine Learning. He has authored/co-authored over 200 papers in top-tier journals/conferences and serves as Associate Editor-in-Chief of Elsevier Neurocomputing. Research Interests: Prof. Han’s work spans Computer Vision (e.g., saliency detection, video analysis), Machine Learning (neural networks, optimization), and interdisciplinary applications in medical imaging and fuzzy systems. His research addresses challenges like long-tailed classification, few-shot learning, and anomaly detection. Recent Trends in Publications (2023-2025): His recent work emphasizes few-shot learning, medical image analysis, and vision-language models. Notable contributions include adversarial diffusion for anomaly detection, expert-constrained ensembles for imbalanced data, and connectome-based predictive modeling in neurology. Awards: IAPR Fellowship (2023). Grants & Projects: Led the Royal Society-funded project Multi Modality Medical Image Segmentation - International Exchange (2023-2025). Collaborates extensively with researchers globally on topics like brain connectivity and deep learning.

Natthakan Iam-OnView profile

Lecturer

• Machine Learning
• Data Clustering
• Computer Vision
• +4 more

Natthakan Iam-On is a Lecturer in the Department of Computer Science at Aberystwyth University. She specializes in machine learning, data clustering, and their applications in cybersecurity, astronomical data analysis, and healthcare informatics. Her research focuses on developing advanced algorithms for fraud detection, transient event detection in astronomy, and improving data imputation techniques. Her work spans interdisciplinary collaborations, including projects on privacy-preserving data fusion for social media analysis and cultural heritage documentation using convolutional neural networks. She also explores cybersecurity through fuzzy logic-based attack tree analysis and has contributed to enhancing AI-driven solutions for food safety in UK-Vietnam collaborations. Recent publications highlight her focus on applying machine learning to solve complex problems such as imbalance data classification, transient event detection in sky surveys, and optimizing clustering algorithms for gene expression analysis. Her research trends emphasize interdisciplinary applications, including medical decision support systems and improving fraud detection in banking sectors. She has been a Co-Investigator in a British Council-funded project (2025–2026) to strengthen UK-Vietnam food safety research through AI and data science. Her collaborative networks include Trenten Roberts and Teerapong Boongoen, extending to international partnerships in computational intelligence and data-driven solutions.

• Machine Learning
• Data Mining
• Big Data
• +6 more

Mehrdad Almasi is affiliated with the University of Luxembourg and Tarbiat Modares University, Tehran, Iran. His research focuses on Machine Learning , Data Mining , and Big Data applications. Key Research Areas : Associative classification, sentiment analysis, and scalable data processing. Technical Domains : Pattern recognition, distributed computing, and Android malware detection. Published Work : Collaborated on graphical models for database joins, large-scale classification systems, and multi-objective evolutionary algorithms.

Xi TongView profile

Research Fellow

• Relativity and Gravitation
• Machine Learning Applications
• Theoretical Physics
• +3 more

Dr. Xi Tong is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the University of Cambridge's Faculty of Mathematics. His research focuses on two primary areas: Relativity and Gravitation, with contributions to black hole dynamics and cosmological observables, and Machine Learning Applications, particularly in healthcare diagnostics and urban infrastructure analysis. He is affiliated with the Relativity and Gravitation research group at DAMTP. Dr. Tong's work bridges theoretical physics and applied machine learning. In theoretical physics, he explores phenomena like black hole superradiance termination and cosmological parameter mapping. In machine learning, he develops uncertainty-aware models for healthcare diagnostics, federated learning frameworks for imbalanced data, and geospatial analysis tools for urban infrastructure. HIGHLIGHTED RESEARCH THEMES: Cosmological simulations and gravitational wave dynamics Audio-based disease detection (e.g., respiratory and cardiac health) Federated learning for privacy-preserving healthcare analytics Urban mobility modeling through spatiotemporal data PUBLICATIONS HIGHLIGHTS: Pioneering work on black hole superradiance modulation (2024-2025) Development of RespLLM for respiratory health prediction (2024) FLea framework for federated learning optimization (2024) GUI dataset for global urban infrastructure analysis (2024) LAB AFFILIATION: Active contributor to DAMTP's Relativity and Gravitation group, collaborating on gravitational physics and astrophysical simulations.

Eric BrownView profile

Academic

• Data Science
• Operations Research
• Power Systems Optimization
• +3 more

Eric Brown is an Assistant Professor of Practice and Director of the Undergraduate Program in the Department of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University. His academic roles include teaching undergraduate and graduate courses in data science, information systems, and operations management. Dr. Brown holds a PhD in Industrial Engineering from Texas A&M University (2020), where his dissertation focused on efficient algorithms for contingency response in electric power systems. He also earned an MEng in Industrial Engineering from Texas A&M (2011) and a BS in Mathematics from Abilene Christian University (2008). His research interests are centered on the intersection of optimization and machine learning, with applications in power systems and medical treatment. Specifically, he focuses on: Data mining theory, particularly imbalanced data classification and medical applications Mathematical modeling of linear and nonlinear methods in power systems optimization Development of algorithms for discrete nonlinear optimization His recent publications (2018-2023) emphasize advancements in power systems optimization and medical data science. Key contributions include developing heuristic methods for post-contingency power flow violations and undersampling techniques for imbalanced medical datasets. He has received the DHS HS-STEM Career Development Grant Fellowship (2010-2011), supporting his research in Homeland Security-related fields. In teaching, he contributes to courses in data science and operations research. His research has been supported by grants such as the DHS fellowship. He collaborates with institutions like the National Security Agency and Teledyne Brown Engineering through past professional roles.

Marcos ZampieriView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing (NLP)
• Machine Learning
• +7 more

Marcos Zampieri is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's School of Computing. He holds a PhD from Saarland University, where he was affiliated with the German Research Center for Artificial Intelligence (DFKI). His research focuses on computational linguistics and natural language processing (NLP), emphasizing robust NLP system development through large-scale text analysis from diverse sources like social media and news. Key research areas include multilingual benchmarking for code generation (e.g., mHumanEval), offensive language detection across languages, and lexical simplification (ALEXSIS-PT). He has co-edited influential volumes such as Similar Languages, Varieties, and Dialects: A Computational Perspective . Over 100 peer-reviewed publications and multiple conference proceedings underscore his prolific output. Recent work explores ethical implications of AI in education, cross-lingual NLP challenges, and leveraging large language models (LLMs) for tasks like mental health analysis and educational tools. Collaborations include projects on code-mixed emotion detection (EmoMix-3L) and privacy-preserving offensive language identification via federated learning. Zampieri advises on shared tasks like the BEA 2024 Lexical Simplification Pipeline and leads workshops such as VarDial. His datasets (e.g., Sold for Sinhala offensive language) and benchmark frameworks have become industry standards. Despite no explicitly listed grants or labs, his work spans interdisciplinary collaborations in computing, linguistics, and healthcare.

Bernardo Canovas SeguraView profile

Researcher

• Artificial Intelligence
• Medical Informatics
• Data Modeling
• +4 more

Bernardo Canovas Segura is a Researcher at the Department of Computer Science and Systems Engineering, Faculty of Informatics, University of Murcia. He holds a PhD in Computer Science from the same institution (2019), focusing on medical informatics approaches for decision support in antimicrobial stewardship, supervised by Dr. Manuel Campos Martínez and Dr. Jose M. Juarez. His research group, AIKE: Artificial Intelligence and Knowledge Engineering, focuses on AI applications in healthcare, particularly in infection control and antimicrobial resistance management. Key areas of research include spatial-temporal modeling for epidemiological data, clinical decision support systems, and visual analytics for hospital infection outbreaks. He contributed to developing tools like OBViz (a visualization tool for infection analysis) and GRALENIA (an AI-driven system for antimicrobial resistance surveillance). His work integrates machine learning, graph databases, and subgroup discovery for patient phenotyping and predictive modeling. Notable projects include modeling Clostridioides difficile spread in hospitals, analyzing seasonality impacts on infection predictions, and fusing AI with semantic web technologies for antibiotic stewardship. He has collaborated on agent-based and epidemiological hybrid models for infection spread simulation. His research emphasizes interpretable models for high-dimensional clinical data and interdisciplinary approaches to healthcare challenges. Labs/Teams: Member of AIKE research group and previously affiliated with Applied Engineering group. Active in developing visualization tools and decision support frameworks for hospitals. Future directions include advancing real-time outbreak detection systems and AI-driven antimicrobial management strategies.

Antonio Guillen TeruelView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Data Analysis
• +3 more

Antonio Guillen Teruel is a Researcher at the University of Murcia , affiliated with the Department of Information and Communication Engineering within the Faculty of Informatics . His research focuses on Artificial Intelligence , Machine Learning , and their applications in Environmental Modeling and Health Informatics . He is part of the Intelligent Systems and Telematics research group. Key research interests include AI-driven solutions for streamflow prediction, long-term health outcomes analysis (e.g., long COVID), and addressing imbalanced datasets in machine learning. Recent work highlights include developing frameworks for imbalanced learning and predictive models for public health crises like the pandemic. No scientific awards or grants are explicitly listed. He has no recorded advisees. His work is anchored in interdisciplinary collaborations, leveraging telematics and data science for real-world challenges.

• Computer Vision
• Emotion Recognition
• Paralinguistic Information
• +2 more

Kavisha Jayathunge is a Lecturer at Bournemouth University, teaching in the MSc Animation Software Engineering unit. His Ph.D. research focuses on unsupervised emotion categorization from audiovisual inputs using computer vision. He investigates transformer-based architectures to mitigate biases in emotion detection datasets. Holds an M.Eng. in Electronics and Software Engineering from the University of Glasgow. Research addresses multimodal information fusion and scalable emotion classification systems for public service applications.

Farhana Ferdousi LizaView profile

Lecturer

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

Dr. Farhana Ferdousi Liza is a Lecturer in Computing Sciences at the University of East Anglia (UEA), affiliated with the School of Computing Sciences. She holds positions as an Associate Member of Cyber Intelligence and Networks and a member of Data Science and AI groups. Her research focuses on Trustworthy Artificial Intelligence, Machine Learning, Natural Language Processing, and Data Science applications in healthcare, public sector, and social media. She leads the national AI project working group under Research Libraries UK and chairs UEA’s Disability Liaison Officer role for inclusive pedagogy. Education: PhD in Computing Sciences from the University of Kent (2020), funded by JILP Endowment Scholarship. Prior roles include research at the ESRC Business and Local Government Data Research Centre (University of Essex) and an internship at The Alan Turing Institute. She has taught in the UK, USA, and Bangladesh, with expertise in courses like Information Retrieval and Database Systems. Key projects include BRA(AI)N (resilience in AI navigation), MHR-DAIDAI (mental health AI analytics), and ASSIST (AI in mental health triage). Awards include the Wellcome Mental Health Data Prize. Her work spans AI ethics, systemic safety, adversarial robustness, and NLP for public sector decision-making. Recent activities include peer reviews for journals like Expert Systems with Applications and participation in conferences such as UEA Strategy 2030. She advocates for AI security, workforce well-being, and interdisciplinary collaborations in computational social science.

Victor KebandeView profile

Assistant Professor

• Cybersecurity
• Digital Forensics
• IoT/IIoT Security
• +5 more

Dr. Victor Kebande is an Assistant Professor of IT Security in the Secure Distributed Systems (SDS) group at Blekinge Institute of Technology's Department of Computer Science (DIDA). He holds a PhD in Cyber and Information Security from 2018 and has held postdoctoral and visiting researcher roles at institutions like Luleå University of Technology and Colorado State University. His research focuses on cybersecurity, digital forensics, IoT/IIoT security, blockchain, and critical infrastructure protection. He is a member of IEEE, ACM, and IITPSA, and serves on the editorial board of Forensic Science International: Reports . Education: PhD in Computer Science (Cyber and Information Security & Digital Forensics) - 2018 Research Interests: Cybersecurity, digital forensics, IoT/IIoT security, cloud security, blockchain, threat modeling, privacy-preserving techniques, and AI integration with immersive realities. Projects: dAIEDGE: Horizon Europe-funded Network for trustworthy AI at the Edge DISS: Threat detection in IoT systems Symphony: Robust distributed service exposure Awards: None explicitly mentioned, but he holds editorial and reviewer roles in top journals. Grants & Advising: Supervised numerous theses on topics like IoT security, blockchain forensics, and cloud forensics. Active in funding projects like HINTS and Eclipse ArrowHead. Labs/Teams: Part of SDS group, ATLAS Institute (CU Boulder), and collaborations with industry partners like Axis Communications.