Find Academic Supervisors

Yang ShiView profile

Assistant Professor

• Educational Data Mining
• Learning Analytics
• AI in Education
• +4 more

Yang Shi is an Assistant Professor of Computer Science at Utah State University, specializing in educational data mining, learning analytics, and AI-driven approaches to enhance computing education. He focuses on developing data-driven methods to improve intelligent tutoring systems and student modeling in programming education. His research spans programming language processing, software analysis, and deep learning applications in education. Shi actively contributes to interdisciplinary conferences and workshops, including EDM, LAK, and SIGCSE, and has organized the CSEDM workshop. His work addresses challenges in scaling educational tools, detecting academic integrity issues in code submissions, and leveraging large language models for programming instruction. He explores novel pedagogical strategies, such as comic-based learning toolkits and the impact of on-demand code examples on novice programmers. Shi's research integrates machine learning and human-AI collaboration to address barriers in computing education, such as anomaly detection in programming datasets and improving code tracing question generation. He investigates how AI feedback systems and generative models can enhance student engagement and learning outcomes. His multi-disciplinary approach bridges computer science and educational theory, emphasizing practical applications to improve STEM education accessibility and effectiveness.

• Machine Learning
• Network Security
• Protocol Analysis
• +4 more

Dr. Sen Zhao is a Professor at Chongqing University of Posts and Telecommunications, School of Computer Science, Department of Network Engineering. His research spans machine learning applications for network security, protocol analysis, and computational fluid dynamics with biomedical applications. His primary research interests include Machine Learning, Network Security, Protocol Analysis, Computational Fluid Dynamics, Fault Diagnosis, Optimization Algorithms, and Biomedical Engineering. Dr. Zhao has developed innovative approaches for binary protocol reversing using deep learning with knowledge-driven augmentation, multi-modal contrastive learning for vulnerability code representation, and reputation incentive schemes for edge tampering detection. Analysis of his recent publications (2021-2025) reveals a strong focus on applying deep learning techniques to network security challenges, with particular emphasis on protocol analysis, device fingerprinting, and secure query processing. His work bridges theoretical machine learning advances with practical security applications in networking and IoT environments. The interdisciplinary nature of his research is evident in his computational fluid dynamics work applied to biomedical problems. Dr. Zhao has mentored numerous students including Jiayuan Li, Zhen Wang, Bo Sun, Yi Zhao, and Haoyu Bin, who appear as first authors on significant publications. His collaborative network includes prominent researchers like Hongsong Zhu, Limin Sun, and Maya R. Gupta across multiple institutions.

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +3 more

Pietro Lovato serves as a Temporary Professor in the Department of Computer Science within the School of Science and Engineering at the University of Verona. His academic work focuses on Information Processing Systems (ING-INF/05), with active teaching responsibilities across multiple degree programs including Human Centered Medical System Engineering, Computer Science and Engineering, and Bioinformatics. Dr. Lovato's research centers on advancing beyond traditional text representation models, particularly through his BeBoW (Beyond the Bag of Words) project that examines structural and statistical perspectives in information processing. His work spans artificial intelligence, machine learning, pattern recognition, and information retrieval systems with applications in bioinformatics and medical text analysis. His research demonstrates a consistent focus on developing more sophisticated models that capture contextual and structural information beyond simple word frequency approaches. His publication record from 2010-2019 shows a progression from traditional information retrieval techniques toward more complex neural network architectures and structural analysis methods. This evolution reflects broader trends in the field moving from statistical models to deep learning approaches while maintaining a focus on practical applications in specialized domains. Dr. Lovato contributes to several research laboratories at the university including the Networked Embedded Systems (NES) Laboratory and the ALTAIR Laboratory, where his work intersects with electronic systems design and parallel computing. His teaching portfolio includes both theoretical coursework and laboratory components, indicating a hands-on approach to student education in artificial intelligence and machine learning.

Bruce ChorpitaView profile

Professor

• Evidence-Based Practice
• Modular Psychotherapy
• Measurement Feedback Systems
• +2 more

Bruce Chorpita is Professor of Psychology at UCLA, specializing in evidence-based practice technologies for child and adolescent mental health. His research develops modular treatment designs, measurement feedback systems, and knowledge management frameworks that have doubled clinical improvement rates in systems like Hawaii's Child and Adolescent Mental Health Division. Chorpita's Child FIRST lab innovations include the Distillation and Matching Model for treatment components, the Revised Child Anxiety and Depression Scale (translated into 25+ languages), and coordinated knowledge systems integrating research into clinical decision-making. His randomized trials demonstrate superior outcomes for modular approaches over standard protocols in community settings.

• Materials Engineering
• Mechanical Engineering
• Civil Engineering
• +3 more

Dr. Frederic Cegla is an Associate Professor and Reader in Mechanical Engineering at Imperial College London, part of the Faculty of Engineering. He leads the Non-Destructive Evaluation (NDE) research group and contributes to the ORCA hub for robotics and AI in harsh environments. His affiliations include the Robotic Additive Manufacturing Lab and the Applied Mechanics division. Dr. Cegla chairs the Physical Acoustics Group (PAG) under the Institute of Physics (IOP), advancing knowledge in physical acoustics. Education: Research experience at Imperial College London and the University of Queensland, Brisbane, Australia. His research focuses on solving industrial challenges through scientific innovation, including defect monitoring in engineering components and corrosion detection systems. A key achievement is founding Permasense Ltd, a global leader in ultrasonic corrosion monitoring for the oil and gas industry. His work has earned multiple awards and industry recognition. Dr. Cegla collaborates with the UK Research Centre for Non-Destructive Evaluation (RCNDE), linking academia with industry. His research integrates materials, mechanical, and civil engineering principles alongside electrical and electronic engineering applications.

Shiyu ZhouView profile

Professor

• Industrial Engineering
• Systems Engineering
• Data-Driven Modeling
• +3 more

Shiyu Zhou is the David H. Gustafson Department Chair and Vilas Distinguished Achievement Professor in Industrial and Systems Engineering at the University of Wisconsin-Madison. His research develops data-driven methodologies for monitoring, diagnosing, and prognosing engineering systems with applications in manufacturing, IoT, and service systems. Zhou has pioneered methods for variation reduction in manufacturing, control of nano-manufacturing processes, and IoT-enabled system modeling. His honors include the NSF CAREER Award, multiple Best Paper Awards, and fellowship status in IISE, ASME, and SME. Recent publications focus on maintenance optimization for complex systems, anomaly detection in manufacturing, and advanced sensor technologies. His work demonstrates consistent innovation in integrating statistical modeling, optimization, and machine learning for industrial system improvement.

Dejene DedefaView profile

Assistant Professor

• Computer Networking
• Wireless Networks
• Information Privacy
• +3 more

Dejene Dedefa is the Chair and Assistant Professor of the Computer Science Department at Lincoln University. His research focuses on wireless networking protocols, information privacy in low-income regions, and context-aware systems design. He has developed novel localization algorithms for wireless networks and contributed to energy-efficient routing protocols for MANET environments. His work intersects computer science with socio-technical challenges, particularly in African contexts. Key contributions include frameworks for cross-platform UI delegation, models analyzing privacy concerns in social networks, and data-driven approaches to road safety improvement through accident pattern analysis. Dr. Dedefa's publications span 2006-2015, emphasizing interdisciplinary solutions in pervasive computing, data mining, and network optimization. His research methodologies combine algorithmic innovation with cultural and behavioral analysis to address real-world challenges in technology deployment.

• Artificial Intelligence
• Blockchain Technology
• Machine Learning
• +7 more

Prof Jianming Yong is a Professor of Information Systems at the University of Southern Queensland's School of Business. His research focuses on AI-driven systems, blockchain technology, cybersecurity in telecommunications, and healthcare informatics. He holds an MEng from South East University and a PhD from Swinburne University of Technology. Research interests include: Artificial Intelligence applications in healthcare and finance Blockchain protocols for cross-industry solutions Data privacy in cloud computing and 5G networks Machine learning for medical diagnostics and treatment optimization Recent work explores causal learning with graph neural networks, UAV-enabled transportation systems, and ethical AI frameworks. His publications address challenges in decentralized finance (DeFi), multimodal logistics sustainability, and pandemic response systems integrating IoT/5G/AI. No scientific awards have been listed. His work spans theoretical advancements in cybersecurity and practical implementations in healthcare IT systems. Ongoing projects involve developing adaptive multi-agent systems for real-time healthcare interventions and secure blockchain applications in e-commerce.

James O'reillyView profile

Senior Lecturer

• Human Nutrition
• Polyphenolic Components
• Oxidative Stress
• +4 more

James O'Reilly is a Senior Lecturer in Human Nutrition and Programme Leader for the BSc Human Nutrition programme accredited by the Association for Nutrition since 2011. He holds faculty positions at the University of Chester's Faculty of Health, Medicine and Society within the Division of Allied Health. His academic roles include teaching undergraduate and postgraduate modules on micronutrients, dietary supplements, and metabolic regulation. PhD in Human Nutrition from King's College London (1995-1999) PG Diploma in Nutrition Science from University of Chester (2008-2010) BSc in Biochemistry from Newcastle University (1987-1990) Research focuses on polyphenolic components in fruits/vegetables, particularly their antioxidant roles in preventing lipid peroxidation and DNA damage. Key studies include investigations into quercetin, soy isoflavones, and tea polyphenols. Over 15 peer-reviewed articles span topics like sports nutrition knowledge in athletes, flavonoid bioactivity, and phytoestrogen cardiovascular effects. Active in postgraduate student supervision and has certifications as a Fellow of the Higher Education Academy and Registered Nutritionist (AfN). Research has implications for dietary interventions in chronic disease prevention.

Mosbah AlownView profile

Lecturer

• Cryptography
• Cryptographic Protocols
• Privacy
• +6 more

Mosbah Alown is a part-time Lecturer in Cybersecurity at De Montfort University (DMU), affiliated with the School of Computer Science and Informatics. He is currently pursuing a Ph.D. in Electronic Voting at DMU, focusing on transformative technologies in democratic processes. His educational background includes a B.Sc. (2015) and M.Sc. (2019) in Computer Science from Al Albayet University. Education: Bachelor’s in Computer Science, Al Albayet University, 2015 Master’s in Computer Science, Al Albayet University, 2019 His research interests span Cryptography , Electronic Voting , and Blockchain , with notable work on privacy-preserving protocols and handwritten Arabic text recognition. His 2019 paper combined PCA and SVM for text analysis, while his recent 2025 work explores blockchain’s role in modern voting systems. Affiliated with the Cyber Technology Institute (CTI) , he teaches courses like Cryptography, Cyber Security Foundations, and Data Analytics. No formal advising or grant information is disclosed.

Kemper E. LewisView profile

Professor

• Large-Scale Systems Design
• Design Analytics
• Collaborative Design Methods
• +6 more

Kemper E. Lewis is the Dean of the School of Engineering and Applied Sciences and Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. He directs the Sustainable Manufacturing and Advanced Robotic Technologies (SMART) Community of Excellence, focusing on advanced manufacturing and design processes. With over 200 publications and $33 million in grant funding, his research spans large-scale systems design, decision theory, additive manufacturing, and machine learning applications in engineering. Education includes: PhD, Mechanical Engineering, Georgia Institute of Technology, 1996 MBA, University at Buffalo, 2003 MS, Mechanical Engineering, Georgia Institute of Technology, 1994 BS, Mechanical Engineering, Duke University, 1992 Research interests center on design optimization and manufacturing innovation, with recent publications exploring machine learning for additive manufacturing, blockchain in supply chains, and sustainable systems. His articles demonstrate consistent focus on AI-driven solutions for industrial challenges. Awards include: ASME Donald N. Zwiep Innovation in Education Award (2019) ASME Design Automation Award (2017) Sustained Achievement Award, Exceptional Scholar (2017) As Director of the Design of Open Engineering Systems Lab, he oversees projects integrating autonomy, intelligence, and materials technologies for next-generation manufacturing.

• Power Systems Engineering
• Climate Risk Management
• Nuclear Energy Safety
• +3 more

Dr. Andrej Stankovski is a researcher at the Reliability and Risk Engineering department of ETH Zürich. His work focuses on assessing risks and reliability in critical infrastructure systems, particularly power grids and nuclear energy facilities. Key research areas include climate risk quantification, cascading failure analysis, and socio-technical vulnerabilities in energy systems. He has developed novel methodologies for blackout event analysis and maintains a curated database of nuclear safety incidents. His research integrates multi-disciplinary approaches combining probabilistic modeling, data analytics, and socio-technical systems thinking. Notable contributions include frameworks for energy transition impact assessment, cross-sector climate risk comparisons, and multi-hazard security evaluations of transmission systems. Dr. Stankovski collaborates with industry partners and policymakers to translate technical insights into actionable resilience strategies. Publications span high-impact journals and conference proceedings, with a focus on empirical analyses of blackout events, nuclear fuel cycle optimization, and infrastructure vulnerability mapping. His work frequently addresses the intersection of technical systems and societal impacts, emphasizing equitable energy access and disaster resilience for vulnerable populations.

Ian BreakspearView profile

Senior Lecturer

• Herbal Medicine
• Naturopathy
• Phytochemistry
• +4 more

Ian Breakspear is a Senior Lecturer in Herbal and Naturopathic Medicine at Torrens University Australia, with over 30 years of clinical and academic experience. He holds leadership roles including Chair of the NHAA’s Board Member Advisory Committee and membership in the Naturopathic Editorial Board of Empowered by Evidence. His research focuses on herbal medicine efficacy (e.g., olive leaf extract in diabetes), safety, and regulatory standards. Breakspear has pioneered studies like phytoequivalence analyses and safety evaluations of medicinal plants, contributing to evidence-based curricula. He received the 2021 Bioceuticals Integrative Medicine Award and 2020 AJHNM Article of the Year Award, among others. Education: Master of Herbal Medicines (University of Sydney, 2005); Diplomas in Naturopathy, Botanical Medicine, and Remedial Massage (Australasian College of Natural Therapies, 1989–1992). Research interests include clinical trial design for herbal interventions, herbal product quality, and integrating traditional knowledge with modern practices. He leads projects such as the ESOLED trial assessing olive leaf extract’s role in Type 2 diabetes. Breakspear has authored over 30 peer-reviewed articles and actively contributes to professional organizations like NHAA and the Olive Wellness Institute. Grants and awards include the 2023 Life Membership from NHAA, recognizing his contributions to naturopathic education and research. His work bridges clinical practice, academia, and policy, emphasizing safety, standardization, and global challenges like climate change’s impact on natural medicine.

Falk ScholerView profile

Professor

• Information Access and Retrieval
• Fairness and Ethics of AI Systems
• Search Engine Evaluation
• +5 more

Professor Falk Scholer is a Professor of Information Access and Retrieval Technologies in the School of Computing Technologies at RMIT University, Melbourne, Australia. He leads the RMIT University Centre for Human-AI Information Environments (CHAI). His research focuses on understanding how search engines and recommender systems assist users in resolving information needs and measuring their effectiveness. His work emphasizes fairness, accountability, and human-centered computing. Research interests include interactive information access, evaluation methodologies, misinformation, and the ethical implications of AI systems. His projects span topics like bias mitigation in conversational search, physiological data analysis for user behavior, and generative AI adaptation for diverse scenarios. He collaborates on industry projects, such as enhancing job search systems with SEEK, improving query performance prediction, and developing fact-checking tools. His teaching interests include Data Science, Web Programming, and Research Methods. He actively supervises PhD and Master’s students in areas like fairness-aware systems and complex answer retrieval. Professor Scholer serves on committees including RMIT’s Academic Board and Human Research Ethics Committee. His research has been recognized through high-impact publications and partnerships addressing real-world challenges in search and information systems.

• Nuclear Engineering
• Thermal-hydraulics
• Severe Accident Analysis
• +4 more

Walter Villanueva is a Reader in Nuclear Engineering at the School of Computer Science and Engineering, Bangor University. As a senior academic researcher, he leads investigations in thermal-hydraulics, severe accident analysis, and computational methods for nuclear safety. His research focuses on fundamental and applied aspects of nuclear engineering including: Experimental and computational analysis of melt infiltration and solidification phenomena Thermal-hydraulic behavior of reactor components under severe accident conditions Development of advanced CFD and machine learning methodologies for safety assessments Material interactions and degradation mechanisms in nuclear environments Recent publications demonstrate strong focus on: Validation and benchmarking of computational methods for molten pool convection Experimental characterization of erosion-corrosion in lead-cooled systems Machine learning applications for nuclear accident scenario analysis Multiphase flow phenomena in debris beds and particulate systems He currently leads multiple research projects including: Experimental Investigation of Singular Two-phase Pressure Losses (2024-2029) Innovative Structural Materials for Fission and Fusion - INNUMAT (2022-2026) Experiments on MHD affected forced convection in fusion-relevant systems (2023-2026) PhD Student Placement: Fibre Optic Measurement Capabilities (2024)