Find Academic Supervisors

Diana Galvan SosaView profile

Researcher

• Natural Language Processing
• Knowledge Acquisition
• Applications of NLP in Education
• +3 more

Diana Galvan Sosa is a Researcher at the University of Cambridge, affiliated with the ALTA Institute (Automated Language Teaching and Assessment) and the NLIP group. She holds a doctoral degree in Information Science from Tohoku University (Japan). Her research focuses on Natural Language Processing (NLP), particularly knowledge acquisition and applications of NLP in education, including feedback generation systems for second language (L2) learners. She also contributes to clinical text analysis and hate speech detection research. Education: PhD in Information Science from Tohoku University, Japan. Research Interests: Grammatical Error Correction Educational Technology Clinical NLP Explainability in AI Temporal Relation Extraction Multimodal Reasoning Recent Work Trends: Her most recent publications (2023-2025) emphasize advanced NLP applications like GPT-4 analysis for error correction, hate speech detection leveraging platform guidelines, and evaluation frameworks for AI explanations. Earlier work includes foundational studies on clinical text analysis and addiction treatment populations. Labs/Groups: Active member of the ALTA Institute and NLIP group at Cambridge. Also contributes to open-source projects like the GECToR framework for grammatical error correction, reflected in her GitHub repositories.

AJung MoonView profile

Assistant Professor

• Human-Robot Interaction
• AI Ethics
• Robot Ethics
• +4 more

AJung Moon is an Assistant Professor in the Department of Electrical & Computer Engineering at McGill University, Canada. She directs the McGill Responsible Autonomy & Intelligent System Ethics (RAISE) Lab, an interdisciplinary group exploring ethical and societal implications of AI/robotics. Her affiliations include the McGill Centre for Intelligent Machines, Mila, and the Bensadoun School of Retail Management (Faculty of Management). She holds roles in global tech-policy initiatives like the IEEE Global Initiative and the International Panel on Autonomous Weapons Regulation (IPRAW). Her research focuses on human-robot interaction, AI ethics, and governance of autonomous systems. Key interests include ethical design practices, nonverbal communication in HRI, and translating ethics principles into technical workflows. She advocates for inclusive policy design through initiatives like the Open Roboethics Institute. Her recent work emphasizes systemic risk analysis for AI systems, comparative evaluations of ethics frameworks, and sociotechnical harm reduction strategies. Publications address topics ranging from ADAS liability to hesitation gestures in robot collaboration. Moon's lab actively bridges engineering and social science perspectives to advance responsible AI development. Her advisory roles include the UN Digital Cooperation Panel and Canada’s AI Advisory Council (2019-2021). She has pioneered participatory policy frameworks and demonstrated how safety engineering principles can improve AI system accountability.

Jacob K. WhiteView profile

Professor

• Computer-aided design
• CAD
• Computational prototyping
• +11 more

Jacob K. White is the Cecil H. Green Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He is a principal investigator at the Research Laboratory of Electronics (RLE) and served as its Associate Director since 2001. His academic journey includes positions as an assistant professor (1987), associate professor (1991), and full professor (1996) at MIT. Education: Bachelor's degree in Electrical Engineering and Computer Science from MIT (1980) Master's degree from the University of California, Berkeley (1983) Doctorate from UC Berkeley (1985) Research Interests: Computer-aided design and computational prototyping Numerical algorithms and simulation techniques Optimization of integrated circuits and MEMS devices BioMEMS and biomolecule design Systems biology and fluidic analysis of sensors/actuators Scientific Awards: 2008 IEEE Fellow Major Award at the 50th Design Automation Conference (DAC) Advising and Grants: No student advisees listed. Details on grants are not provided in the text. Labs/Teams: Principal Investigator at the Research Laboratory of Electronics (RLE) and leads the Computational Prototyping Group.

• Environmental Engineering
• Sustainability
• Water Treatment Technologies
• +4 more

Professor Ke Xing is a faculty member at the University of South Australia (UniSA), affiliated with the UniSA STEM school. Based at the Mawson Lakes Campus, Professor Xing is actively involved in research degree supervision and available for media commentary. Their research focuses on environmental engineering, sustainability, carbon management, and renewable energy systems. Key interests include advanced water treatment technologies (e.g., ozone nanobubble systems), industrial symbiosis, and life cycle assessment methodologies. Professor Xing has contributed to smart infrastructure solutions, such as solar-tracking systems and waste-to-energy frameworks, while exploring the intersection of ICT, construction, and carbon-neutral urban development. Education details are not explicitly provided in the text, but their work spans interdisciplinary collaborations in built environment sustainability, product-service systems, and community transformation. Research outputs emphasize innovative approaches to reducing environmental footprints in industries like construction and agriculture. No specific awards are mentioned, though their extensive publication record highlights impactful contributions to sustainable technologies and policy frameworks. Advising and grants are not detailed here, but their role as a research degree supervisor indicates involvement in mentoring graduate students. Lab or team affiliations are not explicitly stated, though collaborative projects with industry and international partners are implied through their research topics.

• Bioinformatics
• Computational Biology
• Pharmacogenomics
• +5 more

Giuseppe Agapito is a Professor at the Department of Law, Economics and Sociology (DiGES) at the University of Camerino, where he teaches courses such as Elements of Computer Science and Data Analysis. He specializes in computational biology, bioinformatics, and health informatics, focusing on genomic data analysis, machine learning applications in healthcare, and parallel computing methodologies. His research integrates multi-omics approaches, pathway enrichment analysis, and predictive modeling for drug response and disease mechanisms. Notable contributions include tools like BioPAX-Parser and cPEA, which enhance genomic data interpretation. He actively collaborates in international studies, such as the 4CE consortium analyzing SARS-CoV-2 impacts. His work addresses challenges in privacy-aware bioinformatics, high-performance computing for genomics, and AI-driven medical diagnostics. Education details are not explicitly provided in the texts, but his academic profile reflects extensive expertise in interdisciplinary fields bridging computer science and biomedical research. He maintains an active research agenda with over 50 publications since 2018, emphasizing scalable data analysis, drug biomarker discovery, and computational methods for clinical outcomes prediction. His teaching responsibilities include IT management and data analysis modules within social science curricula, reflecting a commitment to digital literacy across disciplines. Research interests span bioinformatics tool development, genomic data preprocessing, and AI applications in healthcare, with a focus on translational research. Recent articles highlight advancements in fMRI classification using graph neural networks, privacy-preserving genomic pipelines, and edge-based deep learning for medical signal analysis. Awards and grants are not explicitly listed, but his sustained contribution to international research consortia underscores his field influence. He advises students and researchers on computational methodologies and hosts weekly office hours for academic consultations.

• Management Accounting
• Organizational Behavior
• Information Systems
• +4 more

Sander van Triest is a researcher associated with the Faculty of Economics and Business at the University of Amsterdam. His work intersects Management Accounting , Organizational Behavior , and Information Systems , focusing on performance evaluation, control mechanisms, and employee dynamics in both public and private sectors. Education: Not explicitly detailed in the provided text. His research explores how subjective performance information , Robotic Process Automation (RPA) , and decentralization influence managerial decisions and organizational outcomes. His recent work (2023–2025) emphasizes remote work policies, enabling controls, and innovation in professional services. From 2023 to 2025, his publications highlight trends in employee autonomy , digital transformation , and management control systems . Earlier works (2000–2009) address cost structures, customer profitability, and accounting standards, with a focus on B2B contexts and cross-cultural management. Despite his extensive contributions, no scientific awards or honors are mentioned in the provided text. His academic collaborations and mentorship roles remain unspecified.

• Technological change and work
• Market competition and working conditions
• Precarious work
• +1 more

Professor Charles Umney is an academic specializing in International Work and Employment at the University of Leeds, specifically within the Leeds University Business School and the Work and Employment Relations Division (WERD). He has held this position since 2022, following roles at the University of Greenwich Business School and completing his PhD at WERD in 2012. Education: PhD in Work and Employment Relations, Leeds University Business School MA International Studies BA(Hons) Jazz Studies Research Interests: Technological change and its impact on warehousing jobs and the future of work. Market competition dynamics affecting healthcare, welfare-to-work services, and cultural industries. Global analysis of worker protests, particularly in the context of platform economies and precarious work. Professor Umney's recent research focuses on the intersection of digitalization, marketization, and global labor dynamics. His work examines how technological advancements and platform economies reshape labor markets, with studies on food delivery workers, healthcare, and cultural sectors. He critiques platform capitalism and explores strategies for a just transition in renewable energy sectors. His analyses highlight the persistence of industrial relations regimes and the evolving nature of labor unrest in a digital age. Scientific Awards: None explicitly mentioned in the provided texts. Advising and Grants: Current postgraduate researchers: Kiril Pasevin, Victoria Antonio, Aditya Lal, Yunus Emre Oztas. Former role as Co-Investigator on the European Research Council-funded project "The Effects of Marketization on Societies" (2012-2015). Labs/Teams: Centre for Employment Relations, Innovation and Change.

Ashirbani SahaView profile

Assistant Professor

• Biomedical Engineering
• Computer and Information Sciences
• Data Analytics
• +6 more

Ashirbani Saha is an Assistant Professor in the Department of Oncology at McMaster University, holding the inaugural BRIGHT Run Breast Cancer Learning Health System Chair. She is affiliated with the Michael G. DeGroote School of Medicine and the Escarpment Cancer Research Institute. Her research focuses on applying artificial intelligence (AI) and advanced data analytics to improve breast cancer diagnostics and patient care. She also holds associate memberships in the School of Biomedical Engineering and the Department of Computing and Software at McMaster University. Dr. Saha’s education includes a B.E. in Electronics from IIEST Shibpur (India), an M.A.Sc. and Ph.D. in Electrical and Computer Engineering from the University of Windsor, followed by postdoctoral training at Duke University and St. Michael’s Hospital. Her work spans medical imaging analysis, radiogenomics, and healthcare data science, with an emphasis on breast cancer and neurosurgery applications. Her research trends highlight AI-driven solutions for medical imaging challenges, such as tumor segmentation, radiomics analysis, and cross-dataset generalization. She has also explored work-life balance issues in healthcare professionals and ethical considerations in AI-driven healthcare research. The BRIGHT Run Chair supports her interdisciplinary initiatives to integrate AI and clinical data into a learning health system for breast cancer. Awards: BRIGHT Run Breast Cancer Learning Health System Chair (2021) Grants: $3.2M New Frontiers in Research Fund (2021) and community-endowed BRIGHT Run funding. Labs/Teams: Escarpment Cancer Research Institute, CentRE for dAta science and digiTal hEalth (CREATE).

Dennis MoekeView profile

Lecturer

• Patient Logistics
• Healthcare Logistics
• Data-driven Capacity Planning
• +4 more

Dennis Moeke is a Lecturer in Logistics and Alliances at HAN University of Applied Sciences, leading the Logistics and Alliances Lectorate. He holds a broad professional network spanning logistics, government, and academia, with a focus on healthcare logistics and sustainable urban development. His research addresses societal challenges such as affordable healthcare systems, smart logistics solutions, and livable cities through interdisciplinary collaboration. His expertise includes patient logistics, data-driven capacity planning, and optimization of healthcare processes. Key projects include the Healthy City Lab , exploring sustainable last-mile logistics in cities via two Living Labs (Campus Heijendaal and Buur & Zo), and the Living Labs Sustainable Supply Chain Management in Healthcare . He has secured significant funding, including a NWO grant (Project 439.18.457), to advance interdisciplinary research. Notable contributions include developing the Buur & Zo concept (home care concierge services) and optimizing hospital patient flows using process mining. He collaborates with organizations like Logistics Valley, Health Valley, CWZ Carinova, and Siza. His work emphasizes practical applications, translating academic insights into real-world tools for healthcare and logistics sectors. Publications span topics like home care scheduling optimization, sustainable urban logistics, and pandemic response logistics. He actively promotes the role of logistics in societal well-being through teaching and industry partnerships.

Mahya SamView profile

Teaching Professor

• Human-Robot Collaboration
• Construction Resilience
• Architectural Engineering
• +3 more

Mahya Sam, Ph.D., is an Assistant Teaching Professor at the Department of Civil Engineering, Florida International University. Her research focuses on human-robot collaboration in construction, construction resilience, architectural engineering, and sustainable construction practices. She has contributed to over a dozen peer-reviewed articles since 2020, exploring topics ranging from robotics integration in construction workflows to energy efficiency in building design. Dr. Sam earned her Ph.D. in Civil Engineering, reflecting her expertise in interdisciplinary approaches to civil engineering challenges. Her work emphasizes sustainable solutions, such as optimizing material selection for carbon footprint reduction and analyzing renewable energy trends for energy resilience. Her articles consistently highlight trends in automation adoption, construction industry strategies, and environmental sustainability. Key themes include evaluating human-robot collaboration perceptions, quantifying carbon emissions in educational buildings, and leveraging computational fluid dynamics for ventilation optimization in climate-specific designs. Despite her active research output, no scientific awards have been documented in the provided materials. Mahya Sam’s advising and grant activities remain unreported in the current data. She is affiliated with the Department of Civil Engineering and contributes to educational initiatives at FIU, though no specific labs or teams are mentioned.

Ken HylandView profile

Professor

• Academic Discourse
• Writing
• Language Education
• +7 more

Ken Hyland is an Honorary Professor at the University of East Anglia (School of Education & Lifelong Learning) and an Honorary Professor at Jilin University in China. With over 29 books and 280 papers, he is recognized as the world's leading scholar in language and linguistics by the Stanford/Elsevier Top 2% of World Scientists analysis. Former Director of the Centre for Applied English Studies at the University of Hong Kong Former Professor at UCL Institute of Education, London Foundation Fellow of the Hong Kong Academy of the Humanities His research spans academic discourse , writing pedagogy , language education , and metadiscourse analysis , with a focus on second language writing , feedback practices , and AI's role in writing . Recent projects explore ChatGPT's rhetorical strategies and digital literacy in writing evaluation. His publications demonstrate expertise in corpus linguistics , genre analysis , and cross-cultural communication . Articles show trends in analyzing AI-generated texts, medical discourse, and evolving academic writing conventions. 2022: Most Influential Scholar in Global Applied Linguistics Co-editor of Applied Linguistics and founding co-editor of Journal of English for Academic Purposes Ken has taught in Sudan, Saudi Arabia, Malaysia, Papua New Guinea, New Zealand, and Hong Kong. He contributes to projects like the LIT Educational Hub (2021–2023), enhancing digital university experiences in BRI countries.

Russ CucinaView profile

Professor

• Medical Informatics
• Genomics
• Healthcare Analytics
• +4 more

Russ Cucina, MD, MS, is a Professor of Medicine in the Division of Hospital Medicine at the University of California, San Francisco (UCSF). He holds dual roles as Vice President and Chief Health Information Officer for UCSF Health System, overseeing analytics, software infrastructure, and genetic/genomic services. His academic and professional focus integrates clinical informatics, healthcare technology, and genomic medicine to advance patient care and institutional missions. Dr. Cucina's education includes an MD from UC Davis, an MS in Biomedical Informatics from Stanford, and a BA in Molecular and Cell Biology from UC Berkeley. He is board-certified in Internal Medicine. His research interests span electronic health record (EHR) interventions, clinical decision support systems, pharmacogenomics implementation, and healthcare operations optimization. Notable achievements include the 2010 Distinguished Paper Award from the American Medical Informatics Association and the AMDIS Award for clinical informatics excellence. His work emphasizes translating data-driven solutions into actionable clinical practices, such as reducing telemetry overuse and improving discharge processes. Dr. Cucina leads UCSF's genetics/genomics laboratories and strategic partnerships in health IT. He is a vocal advocate for leveraging technology to enhance healthcare quality and accessibility, with a focus on personalized medicine and system-wide informatics advancements.

Ryan T. WhiteView profile

Associate Professor

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +11 more

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

• Artificial Intelligence
• Machine Learning
• Internet of Things (IoT)
• +5 more

G. Thippa Reddy is a prolific researcher with a focus on advanced technologies such as artificial intelligence, machine learning, and blockchain, particularly in healthcare, IoT, and cybersecurity domains. His work spans interdisciplinary areas including federated learning, edge computing, and smart city infrastructure. He has collaborated extensively with researchers like Praveen Kumar Reddy Maddikunta, Gautam Srivastava, and Mamoun Alazab, producing over 150 publications in high-impact journals like IEEE Access, IEEE Internet Things Journal, and IEEE Transactions on Industrial Informatics. His research emphasizes practical applications of AI in real-world scenarios, such as privacy-preserving medical systems, secure UAV networks, and inclusive education for individuals with disabilities. He explores cutting-edge topics like the Metaverse's role in Industry 5.0, blockchain-enhanced security frameworks, and the integration of large language models into intelligent transportation systems. Key contributions include frameworks for federated learning in healthcare, optimized routing protocols for underwater communications, and explainable AI (XAI) methods for industrial automation. His work often addresses challenges in scalability, privacy, and ethical deployment of emerging technologies.

• Machine Learning
• Image Retrieval
• Data Clustering
• +3 more

Mohammed Lamine Kherfi is a researcher affiliated with Université de Ouargla, Algeria. His work focuses on machine learning, image retrieval, and data clustering with applications in computer vision and optimization. He has collaborated extensively with researchers like Oussama Aiadi, Mebarka Allaoui, and Djemel Ziou. His research bridges theoretical advancements in machine learning with practical applications in areas such as fruit classification, semantic image retrieval, and deep learning models. Key research areas include optimization algorithms (e.g., PSO integration with t-SNE), multi-view learning, and Bayesian methods for image representation. He has contributed to improving clustering techniques, feature extraction, and the development of lightweight neural network architectures. His work often emphasizes efficient and energy-aware solutions for real-world problems. Over 30 publications span prestigious venues like Expert Systems with Applications, IEEE Access, and Multimed Tools Appl. His collaborative network includes institutions in Algeria and international partners, reflecting a global impact in computational intelligence and computer vision.