Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Energy Systems Optimization
• +4 more

Dr. Harsha Kumara Moraliyage is a Lecturer in Business Information Systems at La Trobe University, specializing in AI and machine learning applications. He holds a PhD in Artificial Intelligence and Data Science from La Trobe and has over a decade of software development experience. His research focuses on AI-driven solutions for energy systems optimization, cybersecurity, healthcare informatics, and sustainable development. He leads the award-winning La Trobe Energy Analytics Platform (LEAP), contributing to the university's net-zero emissions target by 2029. Education: PhD in Artificial Intelligence and Data Science (La Trobe University), certifications in Deep Learning, TensorFlow, and AI from Coursera. Research Interests: Adversarial machine learning, natural language processing, time series forecasting, energy analytics, MLOps, AutoML, and AI ethics. Current projects include developing smart energy platforms, cybersecurity frameworks for energy systems, and empathetic AI tools for healthcare. Teaching: Teaches Master of Business Analytics courses such as Data Wrangling (BUS5DWR), Principles of Business Analytics (BUS5PB), and Artificial Intelligence and Hyperautomation (BUS5PR1). Focuses on designing curriculum to enhance AI literacy. Grants & Labs: Leads the LEAP project, a smart energy analytics platform, and collaborates on AI-driven solutions for net-zero carbon emissions. Active in interdisciplinary teams involving energy infrastructure, robotics, and telehealth. Future Work: Expanding research in generative AI for sustainable cities, explainable AI frameworks, and ethical AI integration in education and healthcare sectors.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Professor Ian Harris is a faculty member in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences. His research focuses on secure hardware/software systems, natural language processing for security, and AI-driven solutions for cybersecurity challenges. He holds a B.S. (1990), M.S. (1992), and Ph.D. (1997) in Computer Science from the Massachusetts Institute of Technology and UC San Diego, respectively. His work spans AI/ML, natural language understanding, cybersecurity, and privacy. Key projects include developing cyber test ranges for IoT security, detecting social engineering attacks via scam signatures, and creating empathy-driven conversational models for mental health support. Notable achievements include UCI's fourth-place finish in the eCTF competition (2022) and the Embedded Security Team's 11th global ranking (2024). He also contributes to diversity initiatives in tech, emphasizing equitable access to STEM fields. Recent publications highlight advancements in adversarial AI defense (e.g., jailbreak attack detection) and distributed LLM inference systems for mobile devices. His research bridges theoretical computer science with practical applications in embedded systems security and healthcare technology.

Jordan ViceView profile

Research Fellow

• Artificial Intelligence
• Machine Learning
• Explainable AI
• +7 more

Jordan Vice is a Research Fellow at The University of Western Australia's School of Physics, Maths and Computing, specializing in Computer Science and Software Engineering. They hold a Ph.D. in Mechatronic Engineering from Curtin University (2023) and a 1st Class Honours degree in the same field (2020). Their research focuses on AI transparency, fairness, security, and reliability, with expertise in generative models, explainable AI, and robotics. Education: Ph.D. in Mechatronic Engineering (2020-2023), Curtin University, Thesis: Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module Bachelor of Mechatronic Engineering (2015-2019), Curtin University, Thesis: Bi-modal Affect-Based Authentication Machines Research Interests: AI ethics, generative models, cybersecurity in AI systems, facial expression analysis, and AI applications in healthcare. They advocate for transparent and accountable AI deployment while addressing security and privacy challenges. Recent work explores bias quantification in text-to-image models and backdoor attacks in generative systems. Key Trends in Publications: Focus on adversarial machine learning, generative model vulnerabilities, and ethical AI frameworks. Notable contributions include frameworks for real-time affect assessment and methodologies to quantify bias in text-to-image systems. Awards: Chancellor's Commendation (2023) Proxima Consulting Prize (2020) RTP Scholarship (2019) Advising & Grants: No explicitly listed advisees or grants, though their work suggests involvement in collaborative research projects. Labs/Teams: Affiliated with the School of Computer Science and Software Engineering at UWA, contributing to interdisciplinary AI research groups.

Jiefei WeiView profile

Research Fellow

• Safe AI
• Explainable AI
• Computer Vision
• +3 more

Jiefei Wei is a Postdoc Research Fellow in the Department of Statistics at the University of Warwick. They collaborate with Professor Giovanni Montana on a CRUK-funded project developing AI and computer vision techniques for breast cancer diagnosis and risk prediction. Their research focuses on Safe AI, Explainable AI, Computer Vision, Medical Imaging, and Machine Learning applications in healthcare. Research interests include adversarial machine learning robustness, medical image analysis, and AI-driven diagnostic systems. Their publications span topics like emotion recognition in video ads, adversarial attack defense, and autonomous vehicle decision-making. No scientific awards or advising roles are listed. Their work integrates statistical methodologies with cutting-edge AI, addressing both technical security challenges and clinical healthcare applications.

• Learning and Behavior Sciences
• Affect and Cognition in Learning
• Neurodiversity
• +2 more

Ruth Wertz serves as Assistant Professor of Engineering Practice within Purdue University's School of Engineering Education, conducting research from her laboratory in WANG 3521A with office space in Armstrong Hall (ARMS 1347). Her academic foundation includes a Ph.D. in Engineering Education and graduate degrees in Civil Engineering from Purdue. Educational Background: Ph.D. in Engineering Education, Purdue University M.S. in Civil Engineering, Purdue University B.S. in Civil Engineering, Trine University (formerly Tri-State University) Research Focus: Dr. Wertz investigates learning and behavior sciences through interdisciplinary lenses including affect/cognition dynamics , neurodiversity , identity formation , and play-based learning in engineering contexts. Her work pioneers measurement frameworks for cognitive and emotional dimensions of educational experiences, emphasizing inclusive pedagogical approaches that accommodate diverse learner profiles. Publication Trends: Her 2013-2022 publications reveal consistent investigation into measurement methodologies across engineering education domains—from information literacy in design tasks to data science competency frameworks. Recurring themes include cognitive load analysis in statics problem-solving, affective interventions in programming courses, and community-based inquiry models in online learning environments. Scientific Recognition: No scientific awards or fellowships are documented in the provided materials Academic Contributions: While specific advising relationships remain unlisted, her collaborative publications with engineering education researchers indicate active mentorship within research teams. No grant funding details appear in the source text. Research Infrastructure: She directs experimental work through her WANG 3521A laboratory, focusing on empirical studies of learning behaviors and cognitive processes in engineered educational environments.

• Music and Emotions
• Music Perception
• Music Cognition
• +3 more

Professor Tuomas Eerola is a prominent academic in music cognition at Durham University, holding the title of Professor of Music Cognition in the Department of Music. He has also served as Director of Research (2013-2015, 2021-), Head of Department (2018-2020), and Associate Fellow at the Institute of Advanced Study. His career includes a PhD from the University of Jyväskylä (2003) and postdoctoral research in Europe and the U.S., later becoming a professor there until 2013. His research focuses on music and emotion, perception of musical structure, computational modeling, and open science practices. Notable projects include the Sweet Sorrow (Academy of Finland), Interpersonal Entrainment in Music Performance (AHRC), and collaborations on EU-funded initiatives. He leads the Music & Science Lab and co-directs the MA in Music Psychology program. Eerola has authored over 100 publications, including a 2024 textbook Music and Science: A Guide to Empirical Research . His work emphasizes transparency in research, open data sharing, and reproducibility. He has mentored many academics, including Kai Tuuri and Jonna Vuoskoski, and serves on editorial boards for journals like Psychology of Music .

Saman Priyantha KumarawaduView profile

Teaching Professor

• Artificial Intelligence
• Deep Learning
• Internet of Things (IoT) Applications
• +6 more

Saman Priyantha Kumarawadu is an Associate Teaching Professor in the Department of Computer Science at Syracuse University's College of Engineering and Computer Science. His work bridges academic research and real-world applications. Research Focus: Pioneering deep learning methodologies for real-time decision making in health, agriculture, and education. Securing IoT infrastructures across smart homes, health, and wearables. Exploring AI's socioeconomic and cultural impacts. Key Publications: Span AI applications in emotion detection, sign language recognition, medical diagnostics, and IoT security. His work emphasizes practical implementations of deep learning and machine learning algorithms.

Wen HuaView profile

Research Fellow

• Data Science
• Machine Learning
• Data Mining
• +5 more

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

• Neuroscience
• Sleep Research
• Memory Consolidation
• +4 more

Gina Rochelle Poe is a Professor at UCLA's College of Letters and Science with joint appointments in Integrative Biology and Physiology, Psychiatry and Biobehavioral Sciences, and Neurobiology. She previously held the Eleanor Leslie Chair in Innovative Brain Research (2022-2023). Her research program examines neural mechanisms of sleep, memory consolidation, and emotional processing, with particular focus on locus coeruleus function and hippocampal-neocortical interactions during sleep states. Professor Poe's research investigates how sleep architecture supports memory optimization, neural plasticity, and emotional adaptation. Her work spans: Noradrenergic regulation of sleep-dependent memory processing Sex differences in sleep physiology and hormonal influences Stress-induced sleep alterations in PTSD models Hippocampal place cell dynamics during REM sleep Development of automated sleep monitoring technologies Her publication trends reveal consistent focus on sleep neurophysiology (85% of articles), with increasing emphasis on translational applications in stress disorders since 2015. Recent work explores interspecies comparisons of noradrenergic systems (2024) and overnight neural plasticity mechanisms (2024). Honors: Eleanor Leslie Chair in Innovative Brain Research (2022-2023) Professor Poe leads multiple NIH-funded initiatives including: R01MH060670: REM Sleep and Memory (2000-2024) R25NS089462: Neuroscience Scholars Program (2014-2024) R21MH119020: Enhancing Non-REM Sleep Dynamics (2018-2021) She mentors through UCLA's MARC U*STAR Program and directs a laboratory investigating sleep circuit functions.

Sabrina CaldwellView profile

Senior Lecturer

• Human centred computing
• human-AI teaming
• digital image authentication
• +6 more

Sabrina Caldwell serves as a Senior Lecturer and Researcher at The Australian National University's School of Computing. As Co-chair of JPEG Trust within the ISO/IEC JTC 1/SC 29/WG 1 working group, she has led the development of international standard ISO/IEC 21617-1 for media trustworthiness assessment, scheduled for publication in October 2024. Her work bridges academic research with global standardization efforts in digital media security. Her research interests focus on two interconnected domains: digital image authentication (particularly distinguishing authentic photographs from AI-manipulated content) and physiological signal processing using neural networks. Within human-AI teaming, she investigates deception detection through multimodal analysis of nonverbal behavior and develops frameworks for assessing the credibility of visual media in the age of generative AI. Her fingerprint reveals strong concentrations in neural networks (100%), physiological signal processing (85%), and affective computing (30%). Analysis of her 29 research outputs shows increasing emphasis on multimodal deception detection and media trustworthiness standards since 2020. Her work demonstrates consistent collaboration with international researchers like Temmermans, Bhowmik, and Kyburz, with notable projects including MEC23: Advanced Biometrics and C2 Sociotechnical Experimentation. The publications reveal a strategic shift toward practical standardization frameworks after 2022, coinciding with her JPEG Trust leadership role. Caldwell holds active research supervision registration and leads projects including the 2023-2026 Australian government travel grant for international standards participation and the 2024 Human-machine aesthetics seed grant. Her projects consistently address real-world applications of computing for social good, particularly in media trust and human-centered biometrics.

Nicholas CostenView profile

Professor

• Computer Vision
• Digital Phenotyping
• Health Informatics
• +3 more

Dr. Nicholas Costen is a Professor in Computer Vision at the Manchester Metropolitan University, affiliated with the Department of Computing and Mathematics and the School of Computing, Mathematics and Digital Technology. He holds a PhD in Mathematics and Psychology from the University of Aberdeen and has held academic roles since 1991, including Senior Lecturer and Reader. His research focuses on Dynamic Digital Phenotyping, leveraging smartphone sensor data to analyze human behavior, particularly in health contexts like smoking cessation and MS management. He is also actively involved in teaching scientific methods and project planning. Education: Postgraduate Certificate in Education (Manchester Metropolitan University), PhD in Mathematics and Psychology (Aberdeen), M.Sc. in Research Methods in Psychology (Reading), M.A. in Experimental Psychology (Oxford). He is a Fellow of the Higher Education Academy and British Computer Society, and a member of IEEE and APA. Research interests span computer vision, medical imaging, behavioral analysis, and machine learning. Recent projects include Zero Trust access control models and predictive smoking behavior analysis via smartphone data. His work bridges computational methods with healthcare applications, emphasizing ethical and practical outcomes. Scientific contributions include over 50 publications, with notable works on biometric identification, embryo imaging, and facial recognition. He is open to supervising PhD students in his research areas.

Michael CarlView profile

Professor

• Machine Translation
• Computational Linguistics
• Translation Studies
• +6 more

Dr. Michael Carl is a Professor at Kent State University's Department of Modern & Classical Language Studies and Director of the Center for Research and Innovation in Translation and Translation Technology (CRITT). He specializes in Machine Translation, Computational Linguistics, and Translation Process Research, with a focus on analyzing behavioral translation data through the CRITT TPR-DB. Over 25 years, his work has explored translation dynamics, cognitive effort, and the integration of AI into translation workflows. His research emphasizes empirical methods, including eye-tracking and keystroke logging, to study translation processes. Recent work addresses post-editing strategies, syntactic complexity impacts, and modeling human translation using active inference agents. His CRITT TPR-DB contains extensive behavioral datasets, enabling cross-linguistic and cross-modal analysis of translation behaviors. Publications span theoretical contributions to applied studies, often bridging computational linguistics and cognitive science. He has organized global conferences and workshops, advancing interdisciplinary collaboration in translation studies. No scientific awards are explicitly listed, but his sustained contributions reflect a leadership role in the field.

Jannik WolffView profile

Researcher

• Generative Models
• Multimodal Learning
• Probabilistic Machine Learning
• +6 more

Jannik Wolff is a Researcher and Ph.D. Candidate at TU Berlin's Machine Learning Group, advised by Prof. Klaus-Robert Müller and Shinichi Nakajima. He is also a Research Associate at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). Previously, he worked at SAP AI Research. His research focuses on generative models for multimodal data, probabilistic modeling, and applications in computer vision and inverse problems. He holds a Master’s degree from TU Berlin and a patent for graph-based priors in zero-shot learning. Key research interests include generative models (e.g., diffusion models, transformers), probabilistic ML, and multimodal learning. He has published peer-reviewed work on hierarchical VAEs, mixture-of-experts models, and low-shot learning techniques. Notable contributions include a patent on graph-based priors for image classification and a drone delivery system optimized via hybrid behavior planning. His work on Bayesian deep learning for optimization and emotion recognition using EEG data further highlights his interdisciplinary expertise.

• Systems Engineering
• Requirements Analysis
• Action Research
• +3 more

Daune West is a Senior Lecturer in the School of Computing, Engineering and Physical Sciences. Her research focuses on systems inquiry, requirements engineering, and action research, with emphasis on human-technology interaction. She leads the Educational Research Group, exploring teaching methodologies in computing and requirements analysis. Current projects involve analyzing human-horse interactions using physiological signals to study equine-assisted therapy's psychological benefits. She has collaborated with signal processing colleagues to explore emotion detection through brain signals in equine contexts. Her work contributes to UN Sustainable Development Goals related to mental health and well-being. Major awards include the Sir Geoffrey Vickers Memorial Award (1990). She has organized CPD courses on systems thinking and agile methodologies, and reviewed for journals like the International Journal of Systems and Society. Research activities span over three decades with publications in action research, systems theory, and interdisciplinary applications.

Chuan LuView profile

Senior Lecturer

• Bioinformatics
• Medical Imaging
• Artificial Intelligence
• +4 more

Chuan Lu is a Senior Lecturer in Bioinformatics at the Department of Computer Science, Aberystwyth University. He holds a BEng from Tianjin University (China), an MSc from KU Leuven (Belgium), and a PhD from KU Leuven. His research focuses on applying artificial intelligence and computational methods to address challenges in medical imaging, reproductive health, and plant science. He has led multiple projects funded by organizations such as the Engineering and Physical Sciences Research Council and the Welsh European Funding Office. Research interests include AI-driven phenotyping in plants (e.g., Arabidopsis), diagnostic modeling for endometriosis and ectopic pregnancies, and healthcare data integration through knowledge graphs. His work bridges disciplines like bioinformatics, computer science, and medicine, with contributions to clinical decision support systems and open-source biobank management frameworks. Recent publications highlight advancements in EEG signal analysis, AI applications in agricultural phenotyping, and ultrasound-based predictive models for surgical planning. Projects include the 'Miscanthus AI' initiative for sustainable plant breeding and the 'LImBuS' open-source biobank system.