Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Precision Livestock Farming
• +5 more

Sook Shin serves as a Collegiate Assistant Professor in the Department of Electrical and Computer Engineering within Virginia Tech's College of Engineering. Her interdisciplinary work bridges computer science, agricultural engineering, and bioinformatics through innovative applications of machine learning. Education PhD in Computer Engineering, Virginia Tech Master of Information Technology, Virginia Tech B.S. in Computer Science, Virginia Tech Her research centers on AI-driven solutions for precision agriculture , particularly in livestock monitoring systems using depth imaging and wireless sensors. She develops machine learning frameworks for pig behavior classification, weight prediction, and resource optimization, while also contributing to bioinformatics tools for plant modeling and disease subtyping. Recent work emphasizes edge intelligence for power-efficient sensor networks and secure data labeling pipelines. Analysis of her 15 most recent publications (2012-2025) reveals a dominant focus on precision livestock farming (60% of works), with significant contributions to bioinformatics (25%) and educational technology (15%). Her methodology consistently integrates deep learning with domain-specific sensor data, showing increasing sophistication in multi-modal input processing and real-time system optimization from 2022 onward. Teaching Contributions Applied software design Data structures and algorithms Computational thinking Machine learning applications She actively develops scientific web tools including PlantSimLab for plant biologists and contributes to capstone project frameworks that bridge academic theory with industry applications in smart farming systems.

• Climate Resilience
• Agricultural Sustainability
• Remote Sensing Applications
• +5 more

Michael Cecil is a part-time instructor in the Department of Sustainability and Social Justice at Clark University. His academic focus revolves around climate resilience, agricultural sustainability, and the application of geospatial artificial intelligence in environmental monitoring. Cecil's work integrates machine learning techniques with satellite data to address challenges in smallholder agriculture, climate-smart farming practices, and policy-driven agricultural programs. His research interests include leveraging remote sensing technologies for scalable agricultural monitoring, developing cloud gap imputation methods using foundation models, and assessing the efficacy of USDA programs in supporting climate-resilient agriculture. Cecil also explores the intersection of farmer decision-making, yield variability, and risk management in agricultural systems, particularly in data-sparse environments. Notable contributions include advancing vision transformer and generative adversarial network (GAN) models for satellite image analysis, evaluating foundation models in geospatial tasks, and analyzing producer strategies within evolving climate baselines. His work emphasizes interdisciplinary collaboration to bridge gaps between environmental science, policy, and technological innovation.

• Artificial Intelligence
• Bioinformatics
• Natural Language Processing
• +3 more

Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.

• Gait Analysis
• Wearable Sensors
• Parkinson's Disease
• +7 more

Dr Silvia Del Din is a researcher at Newcastle University specializing in gait analysis and digital health technologies. Her work focuses on mobility assessment in aging populations, neurological disorders (e.g., Parkinson's disease), and chronic conditions like COPD and sarcopenia. She leads projects such as the Mobilise-D consortium, validating wearable sensors for real-world mobility monitoring. Collaborations involve clinicians, engineers, and data scientists to develop algorithms for gait detection, turning analysis, and environmental context recognition. Her research emphasizes translating sensor data into actionable clinical insights, with applications in fall prevention, disease progression tracking, and exercise intervention studies. Key Projects: Mobilise-D, Acipimox sarcopenia trial, IDEA-FAST feasibility study. Technologies: IMU sensors, machine learning, self-supervised algorithms, magnetic field analysis. Clinical Focus: Parkinson's disease, COPD, aging frailty, long-term care environments. Publications highlight innovations in algorithm validation, environmental sensing, and interdisciplinary approaches to mobility assessment. Her work bridges engineering and medicine to improve diagnostic tools and patient care in chronic and neurological conditions.

• Signal Processing
• Statistical Analysis
• Bayesian Methods
• +3 more

Dr. Alireza Ahrabian is a Research Fellow in the Department of Electronic Engineering at the University of Surrey. He holds a PhD in Signal Processing and an MEng in Electrical Engineering from Imperial College London (2014 and 2010, respectively). His research focuses on developing algorithms for detecting changes in time-series data, with emphasis on Bayesian approaches and statistical signal processing techniques. He has contributed to IoT data stream analysis, sensor data web services, and multi-sensory accelerometer segmentation. Education: PhD in Signal Processing, Imperial College London, 2014 MEng in Electrical Engineering, Imperial College London, 2010 His work bridges theoretical advancements with practical applications, including bone loading exercise classification using machine learning and parameter labelling in Bayesian change detection models. While no awards are explicitly listed, his publications demonstrate impactful contributions to data analysis methodologies. Dr. Ahrabian's research emphasizes: Sequential and batch processing algorithms IoT-enabled sensor data interpretation Bayesian statistical models for dynamic systems His publications span domains like smart cities, health monitoring, and activity recognition, reflecting a multidisciplinary approach to time-series challenges.

Ravi ShekharView profile

Lecturer

• Natural Language Processing
• Conversational AI
• Cross-Lingual Representation
• +3 more

Ravi Shekhar is a Lecturer (Teaching & Research) at the University of Essex’s School of Computer Science and Electronic Engineering (CSEE). Previously, he was a Post-Doctoral Researcher at Queen Mary University of London (2019–2023), working on projects like EMBEDDIA and SoDeStream. He holds a Ph.D. from the University of Trento (2015–2019), focusing on language and vision models, and an M.S. from IIIT Hyderabad. His research spans Conversational AI, Cross-Lingual NLP, and Social Media Analysis, with a focus on ethical AI and low-resource language challenges. Education Ph.D., Language, Interaction and Computation Laboratory, University of Trento (2015–2019) M.S. (Research), Computer Science, IIIT Hyderabad (2010–2013) B.Sc. (Hons) Computer Science, Delhi University (2001–2005) Research Interests : Dr. Shekhar’s work addresses challenges in NLP and multi-modal systems, including abusive language detection, cross-lingual models, and conversational AI. He emphasizes practical applications in social media moderation and ethical AI, with contributions to datasets like CoRAL and LEDA. Key Contributions : His 2024 work on ‘Power and Vulnerability in Organisational Communication’ explores language dynamics in hierarchies, while ‘Denoising Labeled Data for Comment Moderation’ advances active learning techniques. He leads grants from Innovate UK and the EU, focusing on dialogue systems for safety-critical applications. Awards Outstanding Reviewer (EMNLP 2019) Graduate Research Assistantship (University of Trento) Institute Fellowship (IIIT Hyderabad) Advising & Grants : Supervised 8 graduate students (Master’s/PhD) and secured grants totaling £1.2M, including Innovate UK projects on commodities trading AI and harm mitigation in data systems. Active in mentoring underrepresented groups and promoting collaborative research networks. Labs & Teams : Leads the NLP research group at Essex, collaborating with institutions like UvA Amsterdam and Queen Mary. Engages in open-source projects and dataset development, emphasizing reproducibility and code sharing.

• Machine Learning Security
• Adversarial Attacks
• Data Mining
• +3 more

Pan Li is a Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on cybersecurity vulnerabilities in machine learning systems, particularly adversarial attacks and defense mechanisms. Key research areas include: Security analysis of machine learning models Poisoning attack detection and mitigation Knowledge transfer regularisation Multi-view data analysis Few-shot learning robustness Recent work demonstrates expertise in cross-domain learning security, network embedding for fraud detection, and spectral clustering-based defense mechanisms. Publications from 2018-2024 reveal consistent contributions to academic journals and conferences in computer science and cybersecurity. Email contact: pan.li@qmul.ac.uk

• Machine Learning
• Cybersecurity
• Bayesian Networks
• +5 more

Zhou Zhou is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on machine learning, adversarial robustness, Bayesian networks, and cybersecurity. He explores topics such as test-time adaptation, neural network robustness, and graph-based methods. Zhou has contributed to advancements in stochastic neural networks, feature distribution optimization, and multi-relation extraction for cybersecurity applications. His work bridges theoretical foundations and practical implementations in domains like combat network analysis and structural deformation monitoring. Education details are not explicitly provided in the text, but his research spans interdisciplinary areas including artificial intelligence, data science, and network analysis. His publications address challenges in model generalization, adversarial defense, and optimization algorithms. Zhou's research trends emphasize robustness in machine learning models, with a focus on adapting to real-world data variations. His work on Bayesian networks integrates ensemble methods and edge constraints to improve structure learning. In cybersecurity, he applies ontology-enhanced learning and graph theory to extract multi-relation insights. Recent articles highlight innovations in DAG learning, confidence calibration, and hybrid remote sensing techniques for infrastructure monitoring. He has no listed awards or grants mentioned in the text. His affiliations include Queen Mary's School of Electronic Engineering and Computer Science, where he contributes to research groups focused on AI, cybersecurity, and computational methods.

• Astronomy
• Astrophysics
• Radio Astronomy
• +3 more

Dr Minh Huynh is an Adjunct Senior Research Fellow at the International Centre for Radio Astronomy Research (ICRAR) affiliated with The University of Western Australia. His research focuses on radio astronomy, galactic evolution, and cosmology, with a particular emphasis on radio galaxy detection, survey methodologies, and advanced data analysis techniques. He has contributed to major projects like the Evolutionary Map of the Universe (EMU) and the WALLABY Pilot Survey. Key research interests include radio source classification, machine learning applications in astronomy, and the development of novel data formats for spectral-domain observations. He has collaborated on international initiatives involving the Square Kilometre Array and led projects on black hole feedback mechanisms in deep radio surveys. Notable contributions include pioneering work in automated radio galaxy detection via computer vision (RadioGalaxyNET), creation of the RG-CAT radio galaxy catalogue, and development of the SDHDF data format for spectral radio astronomy data. His work bridges observational astronomy with computational methods to advance understanding of galaxy evolution and cosmological structures. Minh has secured grants for large-scale cosmological surveys and has published extensively in leading journals like the Publications of the Astronomical Society of Australia. His datasets, including EMU pilot survey catalogues and WALLABY source findings, are widely utilized in astronomical research.

Pinaki SarderView profile

Associate Professor

• Computational Pathology
• Artificial Intelligence in Healthcare
• Kidney Disease Analysis
• +3 more

Pinaki Sarder, Ph.D., is an Associate Professor in the Department of Medicine – Section of Quantitative Health at the University of Florida (UF), with affiliate roles in the Department of Electrical & Computer Engineering and Biomedical Engineering. He also serves as Associate Director for Imaging at the Intelligent Critical Care Center. Previously, he held tenure as an Associate Professor at the University at Buffalo (UB) in Pathology & Anatomical Sciences and Biomedical Engineering. His academic journey includes postdoctoral training in optical radiology at Washington University in St. Louis (WUSTL) and a Research Fellowship at Harvard University's School of Public Health. He earned his B.Tech. from IIT Kanpur and M.Sc./Ph.D. in electrical engineering from WUSTL. Dr. Sarder's research focuses on computational fusion of spatial omics data, particularly in diabetic kidney disease, leveraging AI and digital pathology. His work is funded by NIH, KPMP, and HubMAP. Key contributions include developing tools like ComPRePS for automated image analysis and the HistoLens platform for accessible pathology visualization. He is an editorial board member of the Journal of the American Society of Nephrology and Associate Editor of IEEE Journal of Biomedical Health Informatics. His awards include UB’s 2018 Exceptional Scholars – Young Investigator Award. He leads NIH-funded projects exploring kidney disease mechanisms, including the Cosmic Kidney Disease initiative investigating spaceflight-induced renal dysfunction. Dr. Sarder also contributes to educational initiatives, such as internship curricula promoting STEM diversity and consensus-based evaluation metrics for NIH programs. Research interests span computational pathology, AI-driven diagnostic tools, kidney disease biomarker discovery, and translational medical imaging. His lab develops open-source tools like DUET for collagen/elastin quantification and FUSION for multi-omics data integration with histology. Grants: NIH, KPMP Consortium, HubMAP Consortium Labs: Computational Microscopy Imaging Laboratory Teams: Intelligent Critical Care Center, Kidney Precision Medicine Project (KPMP)

Neil RobertsonView profile

Research Professor

• Artificial Intelligence
• Computer Vision
• Face Recognition
• +3 more

Professor Neil Robertson is a Research Professor in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, specializing in AI-driven video analysis and visual security systems. Since 2016, he has held the position of Professor of Research for Image and Vision Systems, focusing on advancing real-world applications of computer vision. He also serves as Chief Technology Officer of AnyVision, a global leader in face recognition and visual security technologies, with operations in the USA, UK, and Israel. His research interests include person and activity recognition in video, face recognition, and AI ethics. He has led major UK research initiatives, securing EPSRC Programme Grants and establishing Centres for Doctoral Training. His work emphasizes translating cutting-edge research into practical solutions for societal challenges, such as enhanced security systems and smart city technologies. Notable achievements include pioneering work in domain-adaptive data augmentation, video object detection, and adversarial attacks on deep learning models. He has been featured in prominent media outlets like The Daily Mail and Tech Radar for breakthroughs in AI surveillance and facial recognition accuracy. His contributions have driven technological advancements in both academia and industry, solidifying his reputation as a leader in AI and computer vision.

• Machine Learning
• Natural Language Processing
• Topic Modeling
• +4 more

Wray L. Buntine is a faculty member at Monash University in Melbourne, Australia, specializing in machine learning, natural language processing, and Bayesian methods. His research bridges theoretical foundations and practical applications, with a strong focus on NLP innovations, topic modeling, and efficient learning techniques. His research interests span: Machine Learning : Bayesian neural networks, active learning, and low-resource model optimization. Natural Language Processing : Topic modeling, machine translation, LLM evaluation, and dialogue systems. AI Applications : Healthcare (e.g., medication recommendation), education (e.g., dialogue classification), and graph-based anomaly detection. Recent publications (2023–2025) demonstrate a shift toward LLM-centric research, including automatic evaluation of topic models, uncertainty-aware language agents, and logical verification frameworks. His work frequently integrates Bayesian approaches with deep learning, emphasizing model interpretability and data efficiency. While no awards or grants are detailed in the source text, Buntine leads collaborative projects across NLP and ML, often co-authoring with researchers at Monash University and international institutions. Labs or teams are not explicitly referenced.

Marina SokolovaView profile

Assistant Professor

• Text Data Mining
• Machine Learning
• Social Mining
• +3 more

Marina Sokolova is an Assistant Professor in the Faculty of Medicine at the University of Ottawa, affiliated with the School of EPHPM and the Institute for Big Data Analytics. Her research focuses on text data mining, machine learning, and privacy-preserving techniques applied to healthcare and social media contexts. She holds a Ph.D. and M.Sc., and her work addresses challenges in analyzing large-scale, dynamic datasets (Big Data), particularly user-generated content related to personal health information and public health trends. Her research interests include sentiment analysis of health-related social media (e.g., Reddit, Twitter), development of privacy-aware algorithms, and automated methods for extracting insights from medical forums. She contributes to international conferences and journals in artificial intelligence and text mining, emphasizing explainable AI and ethical data practices. Dr. Sokolova collaborates on projects like YOURPRIVACYPROTECTOR, a privacy-recommendation system for social networks, and explores topics such as gene ontology analysis (via deepSimDEF) and biomedical NLP. Her articles span over two decades, with recent work emphasizing longitudinal sentiment analysis (2024), privacy-by-design frameworks (2022), and multi-class medical classification systems. She advises graduate students and has pioneered evaluation measures for classification tasks in health informatics. Her research bridges computational linguistics, machine learning, and healthcare ethics, addressing critical issues in data privacy and algorithmic transparency. Labs/Teams: Core member of the Institute for Big Data Analytics, collaborating with interdisciplinary teams on NLP and privacy engineering. Active in the Faculty of Medicine’s health informatics initiatives.

• Biostatistics
• Genetics
• Bioinformatics
• +3 more

Jingyi Jessica Li is a researcher and academic affiliated with the University of California, Los Angeles (UCLA), holding academic appointments in both the Department of Biostatistics and the Department of Statistics. Previously, she worked at the University of Southern California from 2013 to 2019. She earned a BSc in Mathematics from Tsinghua University (2003–2007) and a PhD in Statistics from UC Berkeley (2008–2013), under the mentorship of Peter J. Bickel and Haiyan Huang. Her research focuses on developing innovative statistical methodologies for analyzing high-throughput genomic data, particularly in single-cell RNA sequencing, spatial transcriptomics, and epigenetic studies. Key areas include contamination detection, data imputation, and robust statistical frameworks for genomic data analysis. She has contributed tools such as scDesign, scImpute, and scDEED to enhance experimental design and data quality in computational biology. Her work bridges statistical theory and biological applications, addressing challenges like double dipping in genomic studies and optimizing machine learning approaches for biomedical data. She was awarded the prestigious 2025 Guggenheim Fellowship for her contributions to data science. Dr. Li advises graduate students, including doctoral student Zahra S. Razaee, and collaborates on interdisciplinary projects at the intersection of statistics, genomics, and computational biology.

Rick D'EathView profile

Professor

• Animal Welfare
• Animal Behavior
• Veterinary Sciences
• +3 more

Rick D'Eath is a Professor of Animal Welfare at Scotland's Rural College (SRUC), specializing in the intersection of animal behavior and welfare. His work focuses on using behavior to measure welfare, particularly in pigs, addressing issues like social aggression, tail biting, and mounting behavior. He also investigates hunger in ration-fed animals such as sows and broiler breeder chickens, combining behavioral, physiological, and neurobiological approaches. A key area of his research involves precision livestock farming, collaborating on technologies like machine vision and sensors to monitor farm animal welfare. He explores broader policy impacts of trade, regulations, and climate change on animal welfare. Education: PhD in Social Recognition of Flockmates in Laying Hens (University of Oxford, 1996) and MSc in Biological Sciences (University of Oxford, 1993). Key Projects: BBSRC IPA 'Pig ID' (2022-2024): Developing deep learning systems for pig biometrics (£612K). Defra/UKRI Innovate UK 'FarmSense' (2022-2024): Sustainable pig farming using machine vision and sensors (£1.09M). Scottish Government RESAS Projects: EU Exit challenges/opportunities for animal welfare, and laying hen welfare solutions. Research Contributions: Over 110 publications since 1996, with recent focus on tail biting prevention, feed restriction effects, and policy analysis. Active in PhD supervision, including projects on pig genetics and broiler breeder diets. Grants and Collaborations: Funded by BBSRC, Defra, Innovate UK, and the Scottish Government. Collaborates with universities and industry partners like Aviagen.