Find Academic Supervisors

Lin Dong is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on advanced energy harvesting technologies, particularly in piezoelectric materials, flexible electronics, and their biomedical applications. He leads a federally funded project (NSF grant) titled Membranous Energy Harvester with Tuning Capability for Flexible Electronics , which explores energy harvesting systems for wearable and implantable devices. His research interests include the design of self-powered sensors, piezoelectric hydrogels, and nature-inspired nanofibers for health monitoring. Notable contributions include amphibious energy generators, helical piezoelectric hydrogels for human-machine interfaces, and flexible sensors for cardiac monitoring. His work bridges materials science, biomedical engineering, and mechanical systems, with applications in smart health and wearable technology. Dr. Dong’s publications (29+ articles) emphasize energy harvesting innovations, with 16+ citations. His articles highlight trends in flexible, bio-integrated energy systems and multimodal sensing. He collaborates on projects involving body sensor networks and tunable energy harvesters for flexible electronics. He has secured a 2021 NSF grant (PI) and generated media attention for advancements in flexible energy harvesting and nanofiber research. His lab develops cutting-edge materials and devices for sustainable energy solutions and healthcare applications.

• Energy Harvesting
• Piezoelectric Materials
• Biomedical Engineering
• +3 more

Dr. Wael El-Dakhakhni is a Professor of Civil Engineering at McMaster University, holding the Martini, Mascarin and George Endowed Chair in Masonry Design. He serves as Director of the INTERFACE Institute and NSERC CaNRisk-CREATE program, focusing on systemic risk and resilience in complex systems. His research spans interdependent networks, data-driven modeling, and infrastructure resilience under climate and disaster scenarios. He leads the INViSiONLab, advancing AI-driven solutions for urban resilience through digital twins. El-Dakhakhni is a Fellow of the American Society of Civil Engineers and a Member of the Royal Society of Canada, with notable awards including the NSERC Discovery Accelerator Supplement (twice), Ontario Early Researcher Award, and John B. Scalzi Research Award. His work bridges academia and industry, contributing to codes, standards, and real-world applications in structural engineering and disaster response. Education: BSc (Ain Shams University, Egypt), MSc and PhD (Drexel University, USA). Research Interests: Complex systems simulation, systemic risk quantification, resilient infrastructure design, AI applications in urban planning, and climate resilience frameworks. His work integrates advanced machine learning, network theory, and physics-informed models to address multi-hazard challenges in energy, transportation, and urban systems. Key Projects: CITYDNA, McMasterDNA, and infrastructure digital twins for pandemic and climate crisis decision-support systems. He collaborates with governments and organizations to enhance infrastructure resilience through predictive analytics and adaptive strategies. Labs/Teams: INViSiONLab, McMaster INTERFACE Institute, NSERC-CaNRisk-CREATE program, and the Centre for Effective Design of Structures. His research has informed policy and standards in North America, emphasizing interdisciplinary solutions for systemic risk mitigation.

• Resilient Infrastructure Systems
• Systemic Risk Management
• Data-Driven Modeling
• +4 more

Nadjib Brahimi is an Associate Professor at Rennes School of Business, specializing in Supply Chain Management and Operations Research. He holds a PhD in Production and Logistics Systems from Université de Nantes and has extensive academic experience, including roles as Academic Head of the Supply Chain Management & Information Systems department (2019–2020) and Programme Manager of the MSc in Data and Business Analytics. His expertise spans Production Planning, Supply Chain Optimization, and Reconfigurable Manufacturing Systems. Education: PhD in Production and Logistics Systems, Université de Nantes, 2004 M.Sc. in Automatic Control and Applied Computing, Ecole Centrale de Nantes, 2001 Engineering Degree in Electronics and Electrical Engineering, National Institute of Electronics and Electrical Engineering, Boumerdes, Algeria, 1999 Research Focus: His work emphasizes optimization of production systems, reconfigurable manufacturing, and sustainable supply chains. Key areas include task reassignment in assembly lines, biomass supply chain design, and disruption resilience strategies. Key Contributions: Over 40 peer-reviewed articles in journals like International Journal of Production Economics and European Journal of Operational Research . Developed robust optimization models for supplier selection and production routing. Advocated for circular economy principles in hybrid manufacturing systems. Labs/Teams: He collaborates with research groups on reconfigurable manufacturing systems and bioenergy supply chain design, contributing to projects in France, UAE, and Algeria.

• Supply Chain Management
• Production Planning and Scheduling
• Operations Research
• +4 more

Professor Zoheir Sabeur is Professor of Data Science and Artificial Intelligence at Bournemouth University (2019–present) and Head of the Processes and Behaviour Understanding (PRO_BU) Research Group. He concurrently serves as Visiting Professor of Data Science at Colorado School of Mines (2017–present) and held the position of Science Director at the IT Innovation Centre, University of Southampton (2009–2019). Over three decades he has led more than 30 large-scale projects as Principal Investigator, securing over £12 million of funding from the European Commission, UKRI, DSTL, NERC, EPSRC and industry. Education PhD in Theoretical Physics, University of Glasgow (1990) MSc in Theoretical Physics, University of Glasgow (1986) BSc First-Class Honours in Physics and Applied Mathematics, Université d'Oran (1984) Advanced Leadership Programme, Ashridge Business School (2011) Research Interests Professor Sabeur’s research focuses on the fundamental theory and application of data science and artificial intelligence to understand complex human, natural and industrial processes and behaviours. His work spans multi-modal sensing, big-data analytics and machine-learning algorithms that extract actionable knowledge from large heterogeneous datasets. Application domains include: Healthcare: AI-driven diagnostics and prognostics for chronic diseases such as COPD, asthma and cancers through omics and phenotypic data integration. Environmental & Climate: Earth-observation analytics for wildlife migration and climate-change impact assessment using satellite data and global grid systems. Maritime & Cyber-Physical Security: Real-time risk assessment for shipping in extreme environments, smart-city safety and critical-infrastructure protection using computer vision and sensor fusion. Recent research has produced novel AI classifiers that analyse lung-auscultation audio signals to grade COPD severity, as well as digital-twin frameworks for detecting malicious behaviour in urban spaces. Scientific Awards & Recognition Fellow of the British Computer Society (FBCS) Fellow of the Institute of Marine Engineering, Science & Technology (FIMarEST) Chartered Engineer (CEng) and Chartered Physicist (CPhys) Multiple ORS Awards (1987, 1988, 1989) Grants & Doctoral Supervision Professor Sabeur has secured and led more than 40 funded projects since 1996, including recent grants such as INSIGHT (NIHR, 2024) and S4AllCities (H2020, 2020). He currently supervises three ongoing PhD students at Bournemouth University and has successfully graduated three others, covering topics from computational hydrodynamics to AI-based respiratory-disease analytics. He welcomes enquiries from prospective postgraduate researchers interested in data science, AI and interdisciplinary applications under schemes such as UKRI and Horizon Europe.

• Data Science
• Artificial Intelligence
• Machine Learning
• +10 more

Lesia Semenova is an incoming Assistant Professor in the Department of Computer Science at Rutgers University, set to join in Fall 2025. She currently serves as a postdoctoral researcher at Microsoft Research, NYC. Her research focuses on machine learning, responsible AI, interpretability, and healthcare applications. She holds a PhD from Duke University's Computer Science Department and prior degrees from Taras Shevchenko National University of Kyiv. Education Background: Ph.D., Computer Science, Duke University M.S. and B.S., Applied Mathematics, Taras Shevchenko National University of Kyiv Research Interests: Machine Learning Interpretability Responsible AI Frameworks Healthcare Applications of AI Reinforcement Learning Human-Centered Design Selected Awards and Recognition: 2024 Rising Stars in Computational and Data Sciences PhD Dissertation Award from Duke University Advising and Professional Activities: Semenova advises students such as Harry Chen and Dennis Tang. She has organized workshops at NeurIPS and presented at conferences like the International Joint Conference CFE-CMStatistics. Her work bridges theoretical machine learning with practical healthcare and policy applications. Professional Engagement: She is affiliated with the Artificial Intelligence and Machine Learning research groups at Rutgers, and has collaborated with institutions like the Johns Hopkins Applied Physics Lab.

• Machine Learning
• Responsible and Trustworthy AI
• Interpretability
• +4 more

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Raquel Iniesta is a Reader in Statistical Learning for Precision Medicine at King's College London, leading the Fair Modelling and TDA lab within the Department of Biostatistics & Health Informatics. Her expertise spans mathematics, statistics, and machine learning applied to precision medicine, with a focus on ethical AI integration in healthcare. She teaches advanced machine learning and statistical modeling courses and actively engages in scientific communication through workshops and media outreach. Her research emphasizes developing transparent AI models for personalized medicine, particularly in depression, hypertension, and neurodegenerative diseases. Notable contributions include studies on fasciculation analysis in ALS and ethical frameworks for healthcare AI. Publications highlight interdisciplinary approaches, combining machine learning with clinical and genetic data to improve predictive models. Dr. Iniesta leads educational initiatives, including the Machine Learning module and Introduction to Statistics programs, and contributes to public engagement by designing digital content and managing social media for research dissemination.

• Machine Learning for Precision Medicine
• Ethics of AI
• Topological Data Analysis
• +3 more

Roles & Affiliations: Duen Horng (Polo) Chau is a Professor in the School of Computational Science and Engineering at Georgia Tech. He co-directs the MS Analytics program and leads industry relations for The Institute for Data Engineering and Science (IDEaS) and corporate relations for The Center for Machine Learning. He teaches Data & Visual Analytics (CSE6242/CX4242) to over 1,000 students annually. His affiliations include the GVU Center, Institute for People and Technology (IPaT), and ML@GT. Education: PhD in Machine Learning (Carnegie Mellon University, 2012), MS in Machine Learning (CMU), MA in Human-Computer Interaction (CMU), B.Eng. in Information Engineering (The Chinese University of Hong Kong). Research: Focuses on human-centered AI, interpretable machine learning, adversarial robustness, graph visualization/mining, and social good applications (e.g., healthcare, anti-human trafficking). His lab develops tools like ActiVis (for neural network exploration), Diffusion Explainer (for text-to-image models), and TrafficVis (to combat trafficking). Research is funded by NSF, NIH, DARPA, NASA, and industry partners (Google, Intel, Meta). Awards: 17+ best paper awards, Google/Intel/Meta Faculty Awards, Outstanding Undergraduate Research Mentor (2023), Outstanding Mid-Career Faculty (2022), and the Carnegie Mellon Dissertation Award (2012). Grants & Labs: Leads projects on AI safety, robust speech recognition, and graph vulnerability. Collaborates with Children’s Healthcare of Atlanta on surgical planning via AR. His work influences industry platforms (e.g., Meta’s ML tools used by 25% engineers).

• AI Security
• Explainable AI
• Visual Analytics
• +6 more

Samir Elhedhli is a Professor in the Department of Management Sciences at the University of Waterloo, within the Faculty of Engineering. His research focuses on Large-scale Optimization, Logistics, Supply Chain Design, Healthcare Operations, Airline Scheduling, and Data Analytics. He has held grants from NSERC, CFI, OCE, and MITACS, collaborating with industries in aircraft manufacturing, airline scheduling, and warehouse management. Education: PhD in Management Science, McGill University (2001) Master's in Industrial Engineering, Bilkent University (1996) Bachelor's in Industrial Engineering, Bilkent University (1994) Research Interests: Data Analytics & Data Science Large-scale Optimization (Interior-point methods, decomposition, column generation) Supply-chain Analytics (Logistics, warehousing, routing, scheduling) Environmental Sustainability in Supply Chains Key Awards: CORS Service Award (2013) University of Waterloo Distinguished and Outstanding Performance Awards (2005–2019) Grants & Advising: Active grants from NSERC, CFI, OCE, and MITACS Currently accepting graduate student applications Administration & Service: Chair, Department of Management Sciences (2014–2018) President, Canadian Operational Research Society (2011–2012) Co-Editor-in-Chief, INFOR Journal (2014–present) Labs & Teams: Leads the WanOpt research group focused on optimization methodologies and applications.

• Large-scale Optimization
• Logistics
• Supply Chain Management
• +11 more

VENKATESH Mani is an Associate Professor at Montpellier Business School, specializing in strategic supply chain management and operations. His research bridges digital transformation with social sustainability in emerging economies. University: Montpellier Business School Department: Operations, Information, & Decisions Academic Rank: Associate Professor Research Interests include: Social sustainability in supply chains Industry 4.0 digital capabilities Agri-food supply chain resilience Blockchain and digital twin applications Carbon neutrality strategies Recent Publications span topics from pandemic-related supply chain disruptions to data-driven sustainability frameworks, with a focus on empirical studies in Indian and cross-sector contexts. Scientific Awards : Erasmus scholarship (European Union) Teaching Areas cover logistics, operations management, analytics, and research methodology, reflecting his expertise in modern supply chain dynamics.

• Supply Chain Management
• Social Sustainability
• Digital Transformation
• +4 more

Prof. Dick den Hertog is a Full Professor at Tilburg University's Department of Econometrics and Operations Research, part of the Tilburg School of Economics and Management (TiSEM). His research focuses on operations research methodologies with applications in humanitarian logistics, supply chain optimization, and robust decision-making under uncertainty. He collaborates with organizations like the UN World Food Programme to enhance operational efficiency in complex environments. Key research areas include robust optimization techniques, supply chain management in developing regions, and the integration of satellite data with machine learning for infrastructure analysis. His work addresses challenges such as food aid distribution, disaster response logistics, and predictive modeling for transportation systems in data-scarce areas. Notable projects include developing analytical tools for the WFP's supply chain planning and creating algorithms for weather-informed road speed prediction. He is affiliated with the Tilburg Sustainability Center and the Operations Research research group, contributing to both academic advancements and real-world impact through optimization solutions.

• Operations Research
• Supply Chain Management
• Humanitarian Logistics
• +3 more

Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.

• Machine Learning
• Artificial Intelligence
• Virtual Reality
• +3 more

Professor Rachel Harrison is a Professor in Computer Science at the School of Engineering, Computing and Mathematics, Oxford Brookes University. Her research focuses on software metrics, machine learning, and requirements engineering with emphasis on empirical and automated software engineering solutions. She has over 160 publications and extensive industry collaborations with organizations like IBM and Philips Research Labs. Her work has been recognized through roles as Editor-in-Chief of the Software Quality Journal and leadership in conferences such as ICSE and ESEM. She leads the Dependable System Engineering Centre (DSERC) and is part of the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and the Applied Software Engineering and Data Analytics (ASEDA) Group. Her research projects include AI applications for big data analysis (AIMi), automated review classification (ReClass), and software quality improvement (SEQUIN). Professor Harrison has served on over 50 international program committees and initiated workshops like RAISE and AIRE. Her teaching includes advanced computer science modules and leadership in courses like Essential Maths for University Study and Advanced Software Development . Her work bridges academic research and practical applications, particularly in healthcare technology (e.g., diabetes management systems) and mobile application usability. She advocates for rigorous software quality practices and has contributed to frameworks for requirements validation and risk assessment in software projects.

• Software Engineering
• Machine Learning
• Requirements Engineering
• +3 more

Robert S. Laramee is a Professor at the University of Nottingham (previously at Swansea University), specializing in visualization research. His work focuses on data visualization, scientific visualization, and computational fluid dynamics. He has authored over 170 publications in top journals like IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and IEEE Computer Graphics and Applications. Research Interests: His research spans information visualization, flow visualization, visual literacy, and educational aspects of visualization. He emphasizes practical applications in fields like healthcare, digital humanities, and computational science. Recent Trends: Recent work includes studies on treemap literacy, educational frameworks for visualization, and interactive systems for clinical data. He has also contributed to visualization resources and surveys, aiming to bridge academic and industry needs. Grants & Collaborations: Collaborations include projects on visualization for smart cities, protein-lipid interactions, and quantum chromodynamics data analysis. No specific grant details are provided in the text. Labs & Teams: Affiliated with visualization research groups at Nottingham and Swansea, though specific lab names are not mentioned.

• Computer Science
• Data Visualization
• Scientific Visualization
• +3 more

Andrew Ho is the Charles William Eliot Professor of Education at Harvard University's Graduate School of Education (HGSE). He holds a Ph.D. in Educational Psychology and an M.S. in Statistics from Stanford University. His research focuses on improving educational assessment design, particularly in measuring educational progress and inequality. He developed the Stanford Education Data Archive (SEDA), a national repository of student achievement data, and advocates for low-stakes assessment use in policy. Ho has held leadership roles including Immediate Past President of the National Council on Measurement in Education and trustee of the Carnegie Foundation. He advises seven U.S. states' testing programs and teaches graduate courses in statistics and psychometrics at HGSE. His work emphasizes the importance of accurate assessment during crises like the pandemic, advocating for standardized testing as part of a multi-measure 'census' approach to identify learning disparities and allocate resources effectively. Education: Ph.D. in Educational Psychology (Stanford, 2005), M.S. in Statistics (Stanford) Affiliations: Harvard Graduate School of Education, Technical Advisory Committees for 7 states Key Projects: SEDA, Assessment Literacy initiatives, pandemic-era testing advocacy His research bridges psychometrics with policy, emphasizing equitable assessment practices. Notable contributions include frameworks for interpreting test scores in multi-measure systems and critiques of 'learning loss' terminology favoring actionable 'learning lag' perspectives. Ho's recent work addresses pandemic-era education challenges through rigorous data analysis and policy recommendations.

• Educational Assessment
• Diversity and Inclusion in Education
• Educational Inequality
• +3 more