Find Academic Supervisors

Khalil EsperView profile

Researcher

• Verification and Model Checking
• Energy Optimization
• Real-time Systems
• +4 more

Khalil Esper is a Researcher at the Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), where he works at the Chair of Computer Science 12 (Hardware-Software Co-Design). His research focuses on verification, energy optimization, and runtime requirement enforcement in embedded systems and MPSoCs. His educational background includes: Informatics Engineering from Aleppo University, Syria (2010-2015) European Master in Embedded Computing Systems (EMECS) from Rhineland-Palatinate University of Technology Kaiserslautern-Landau (Germany) and Norwegian University of Science and Technology (Norway) (2017-2019) Esper's research interests center around verification and model checking, energy optimization on MPSoC, real-time systems and embedded systems, and autonomic computing. His work particularly focuses on runtime requirement enforcement mechanisms for non-functional properties in multi-processor systems-on-chip, with applications extending to medical devices and human-robot interaction systems. He has developed approaches using finite state machines, reinforcement learning, and evolutionary algorithms to ensure system properties are maintained during execution. His publication record shows a strong trend toward applying formal methods and runtime enforcement techniques to increasingly complex systems, with recent work expanding into safety-critical applications like orthoses and human-robot interaction. The interdisciplinary nature of his research bridges computer science, embedded systems engineering, and biomedical applications. Esper has supervised multiple theses including: Sascha H.: Runtime Requirement Enforcement of Non-Functional Requirements on MPSoCs Using Fuzzy Logic (2022) Iana S.: Feedback-Based Control of Non-functional Program Execution Properties on Linux (2023) Philipp L.: Runtime Requirement Enforcement of Functional and Non-Functional Requirements of a Knee Orthosis Based on a Digital Twin (2024) Avinash N.: Runtime Requirement Enforcement of Safety Properties of an Ankle Orthosis Based on a Digital Twin (2024) Zhiyi T.: Generation of Environment FSMs Using Machine Learning Techniques (2025) Moustafa A.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction Based on a Digital Twin (2025) Florian K.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction (2025) He has been actively teaching courses on Approximate Computing and Embedded Systems since the 2021/2022 academic year, demonstrating his commitment to academic instruction alongside his research activities. Esper is involved in the InvasIC research project, part of the DFG Transregional Collaborative Research Center 89 on Invasive Computing, which explores novel approaches to resource management in parallel computing systems.

• Internet of Things (IoT)
• Machine Learning
• Smart Agriculture
• +9 more

Laura Belli is a Researcher at the Department of Engineering and Architecture , University of Parma, with a focus on interdisciplinary projects bridging IoT, Machine Learning, and Smart Systems . Her work spans smart agriculture, vehicular networks, and urban mobility. Research Interests: Internet of Things (IoT) in agriculture and transportation Machine Learning for predictive modeling and data analysis Edge Computing and network optimization Blockchain applications in data integrity Driver health and stress monitoring Recent Publications highlight her contributions to privacy-preserving vehicular systems , smart farming datasets , and adaptive IoT protocols . She collaborates on projects like OPEVA and DistriMuse , emphasizing data-driven innovation.

• Human-Computer Interaction
• Digital Transformation
• User Experience Design
• +6 more

Professor Maurizio Caon is a faculty member at HES-SO (University of Applied Sciences and Arts Western Switzerland). His primary research areas include Human-Computer Interaction, Digital Transformation, and User Experience Design, with a focus on aging populations and health technology. He leads the HumanTech Institute and contributes to projects like NESTORE COACH and PEGASO, which develop virtual coaching systems for elderly well-being and adolescent health behavior change. His research explores tangible interfaces for e-coaching, gestural interaction design for professional cyclists, and data privacy mechanisms using pictograms. While his publications span topics from AI implementation in HRM to posthumanist perspectives on enhancement, his core work centers on applying digital technologies to improve health outcomes across age groups. He collaborates with institutions like Politecnico di Milano and contributes to European Union-funded initiatives. Professor Caon's recent work examines conversational agents for life-course health interventions, virtual reality exergame design, and wearables' impact on body awareness. His projects integrate behavioral change theories with IoT architectures and human-centered design principles. Despite active research engagement, no scientific awards are explicitly mentioned in the provided texts.

David B. LarsonView profile

Professor

• Radiology
• Artificial Intelligence in Medicine
• Quality Improvement in Healthcare
• +4 more

David B. Larson is Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine and Director of the Stanford Radiology AI Development and Evaluation (AIDE) Lab. He is also Associate Chief Quality Officer for Improvement at Stanford Health Care and co-founder of the American College of Radiology's Learning Network. MD and MBA from Yale University (2002) Pediatric internship and radiology residency/fellowship at University of Colorado Health Sciences Center (2003-2008) Board-certified in Pediatric Radiology and Diagnostic Radiology His research focuses on sociotechnical healthcare systems , radiology quality improvement , and AI implementation . Key projects include optimizing CT radiation dose, developing EMM frameworks for AI monitoring, and establishing ACR accreditation standards for radiology AI. Recent publications analyze diagnostic certainty frameworks , clinical history completeness , and radiation dose harmonization . His work emphasizes interdisciplinary collaboration through programs like the Radiology Improvement Summit and the Stanford Medicine Center for Improvement. He actively mentors postdoctoral researchers and leads initiatives in pediatric imaging , clinical decision support systems , and medical AI ethics . Current teaching includes graduate medical education courses in radiology and AI applications.

Christos OuzounisView profile

Professor

• Bioinformatics
• Digital Biology
• Biological Computation
• +5 more

Christos Ouzounis is a Professor of Bioinformatics at the Department of Informatics, Aristotle University of Thessaloniki , with a career spanning institutions including the European Bioinformatics Institute , King's College London , and University of Toronto . His work bridges Computational Biology , Digital Biology , and Metagenomics , focusing on large-scale data analysis, machine learning applications, and functional annotation of proteins. Education : BSc in Biological Sciences (1986), MSc in Biological Computation (1987), and DPhil in Computational Chemistry (1993) Key Roles : Director of the Bioinformatics Centre at King's College London (2007-2010), Research Director at IDEP-EKETA (2014-2020) His research interests include low-complexity protein sequences , Covid-19 seasonality patterns linked to UV radiation, and metagenomic analysis of urban microbiomes in cultural heritage sites. Current projects involve machine learning models for microbial coexistence networks, ontological classification of biomedical literature, and bioinformatics tool development . Publications highlight trends in archaeal genomics , functional dark matter in metagenomics, and epidemiological modelling . Notable collaborations include work on BioTextQuest v2.0 for concept discovery and MjCyc for metabolic pathway analysis.

Daniel TwardView profile

Assistant Professor

• Neuroimaging
• Computational Neuroscience
• Machine Learning
• +4 more

Dr. Daniel Tward is an Assistant Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Department of Neurology and the Department of Computational Medicine. He earned his Ph.D. in Biomedical Engineering from Johns Hopkins University and completed postdoctoral training at the Kavli Neuroscience Discovery Institute. His research integrates neuroimaging, machine learning, and differential geometry to analyze brain structure changes in neurodegenerative diseases like Alzheimer's, with a focus on bridging molecular pathology and clinical imaging. Research Interests: Dr. Tward's work addresses challenges in neuroimaging data complexity, developing computational tools to map brain anatomy across scales (from centimeters to microns). Key areas include neurodegeneration in the medial temporal lobe, multi-modal image registration, and spatial transcriptomics. His lab emphasizes high-dimensional statistics and geometry-driven analysis to improve diagnostic accuracy and clinical trial design. Grants & Projects: Secured NIH funding for: A 3D multimodal human brain atlas integrating MRI and histology. CloudReg—a distributed framework for massive neuroimage registration. Contributions to the BRAIN Initiative Cell Census Network (BICCN) for mouse/rat brain atlases. Students & Training: Mentors undergraduate researchers via the BIG Summer program, with projects on neural networks, spatial transcriptomics, and MRI analysis. No PhD/Master's advisees listed.

Bena BrownView profile

Academic

• Aboriginal and Torres Strait Islander health
• Oncology
• Cancer survivorship
• +4 more

Dr Bena Brown is a clinician/researcher and Adjunct Senior Fellow at the UQ Poche Centre for Indigenous Health , affiliated with the Faculty of Health and Behavioural Sciences at the University of Queensland. She is also an Advanced Speech Pathologist at Brisbane's Princess Alexandra Hospital and supervises higher-degree students in the School of Health and Rehabilitation Sciences . Her work bridges clinical practice and research to enhance cancer care for Aboriginal and Torres Strait Islander peoples . Research Interests : Cancer Care, Survivorship, Novel Service Delivery Models, Telehealth, Patient-Centered Care. Grants : Over $3.6 million in research funding, including projects like Yarning About Cultural Safety and PROMISE . Committee Memberships : Human Research and Ethics Committees at Metro South Health, Queensland Collaborative for Cancer Survivorship, COSA Patient-Reported Outcome Working Group. Publications : Over 60 peer-reviewed articles focusing on head and neck cancer outcomes , lymphoedema , dysphagia , and culturally responsive care .

Yuanfang GuanView profile

Professor

• Bioinformatics
• Computational Biology
• Machine Learning in Medicine
• +5 more

Professor Yuanfang Guan is affiliated with the University of Michigan in the Department of Computational Medicine and Bioinformatics. Their research focuses on bioinformatics and computational biology applications in medical research. Key research areas include: Computational oncology with emphasis on tumor subclonal reconstruction AI applications in clinical pharmacology and drug response prediction Machine learning for neurogenetic disorders like SCA3 Development of digital health measures for neurological diseases Single-cell sequencing data analysis and quality control Epigenomic data imputation and genomic feature analysis Recent publications highlight collaborative projects on LSD1 inhibitors for sickle cell disease, long COVID prediction models, and optimization of genomic deep learning. Their work involves algorithm development for cancer evolution analysis, disease biomarker identification, and AI-driven biomedical applications.

• Medical Statistics
• Epidemiology
• Health Services Research
• +12 more

Dr. Matthew Barclay is a Principal Research Fellow (Statistician) in Cancer Healthcare Epidemiology at University College London's Behavioural Science and Health department. With expertise spanning medical statistics, epidemiology, and health services research, his work focuses on analyzing cancer registry data, primary care electronic health records, and healthcare claims data to improve understanding of cancer diagnosis and treatment pathways. Dr. Barclay's educational background includes: PhD in "The design of composite indicators of healthcare quality: a multi-method analysis" from University of Cambridge (2021) MSc in Statistics with Medical Applications from University of Sheffield (2015) MMath in Mathematics from Durham University (2011) Dr. Barclay's research spans the entire cancer diagnostic and management pathway, with particular focus on risk of cancer in primary care patients, cancer characteristics at diagnosis (including socio-demographic variations in staging), treatment patterns, and short-term outcomes. His methodological expertise lies in applied statistics, particularly in cohort design within electronic health record datasets and developing consistent data resources for research. His work frequently employs cancer registry data, primary care records, and healthcare claims to address critical questions in cancer epidemiology and healthcare quality. Analysis of Dr. Barclay's recent publications reveals a strong focus on cancer diagnosis pathways, particularly how symptoms present in primary care lead to cancer diagnosis. His work spans multiple cancer types but has particular emphasis on lung, colorectal, and breast cancers. Methodologically, his research combines epidemiological approaches with advanced statistical techniques applied to large-scale datasets including UK Biobank, cancer registries, and primary care records. International comparative studies through the International Cancer Benchmarking Partnership represent another significant strand of his recent work. Dr. Barclay advises postgraduate and PhD students on topics related to his research interests in cancer epidemiology and health services research. His collaborative network spans multiple institutions and countries, reflecting the international nature of his research on cancer diagnosis and treatment pathways.

Ajitha RajanView profile

Professor

• Software Testing
• Software Verification
• Automated Testing
• +12 more

Ajitha Rajan is a Professor (Personal Chair of Software Testing and Verification) at the School of Informatics, University of Edinburgh. Previously, she was a post-doctoral researcher at Oxford University's Computer Science Department and Laboratoire d'Informatique de Grenoble (LIG) in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under the supervision of Prof. Mats Heimdahl. Her research spans two main directions: Automated Software Testing Techniques covering test input generation, test oracles, and coverage metrics; and Biomedical Artificial Intelligence focusing on cancer survival models, interpretability for biological sequences, and medical images. Her work bridges software engineering and biomedical applications, particularly in the development of trustworthy AI systems for healthcare. Professor Rajan leads several significant research projects including a Royal Society Industry Fellowship (2022-2025) on AutoTest for autonomous vehicle perception safety, the H2020 European Project KATY (2021-2025) on AI for genomics and personalized medicine where she serves as Edinburgh Lead PI, and an EPSRC Trustworthy Autonomous Systems Node project (2020-2024). Her recent publications demonstrate strong activity across software testing, explainable AI, and biomedical applications, with numerous papers accepted to top conferences in 2025 including ML for Healthcare, IJCAI, and ESEM. Among her scientific recognitions, she received the Best Reviewer Award at ISSTA'25. Her work has been consistently published in leading venues including ICSE, ICASSP, and Communications Biology. Professor Rajan actively mentors PhD students working on diverse topics from automated testing of speech recognition systems to explainable AI for medical image analysis and cancer immunotherapy. She teaches undergraduate courses in Software Testing, Computer Programming, and Embedded Systems, and has been instrumental in establishing several fully funded PhD positions through Centres for Doctoral Training at the University of Edinburgh.

• Human-Computer Interaction
• Universal Design of ICT
• Pattern Recognition
• +4 more

Frode Eika Sandnes is a Professor at the Department of Information Technology , Faculty of Technology, Art and Design , Oslo Metropolitan University , focusing on Human-Computer Interaction and Universal Design of ICT . His research spans innovative interaction techniques, skill reuse, pattern recognition, image analysis, and intelligent systems. Current research trends include accessibility in digital education, lightweight deep learning models, and physical interface usability Recent publications analyze color picker efficiency, 3D-printed prosthetics, and authentication technologies His work integrates interdisciplinary approaches to enhance usability and inclusivity in technology. Notable projects involve automated readability assessments and AI applications in diverse domains like aquaculture and medical diagnostics.

Gal ElidanView profile

Associate Professor

• Probabilistic Graphical Models
• Machine Learning
• Copula Models
• +3 more

Gal Elidan is an associate professor in the Department of Statistics at the Hebrew University of Jerusalem, Faculty of Social Sciences. His work bridges statistical theory with practical applications in machine learning and artificial intelligence. Dr. Elidan's research focuses on Probabilistic Graphical Models , with emphasis on fundamental representations and methods for inference and learning in large-scale domains. His work addresses structure learning, discovery of hidden variables, transfer of knowledge between related tasks, and nonlinear high-dimensional representations of continuous or hybrid distributions. He also applies these techniques to real-life applications including computational biology, machine vision, and medical informatics, with recent focus on principled techniques for medical diagnosis. His publication record shows consistent contributions to top machine learning venues with a recent emphasis on copula models for high-dimensional data. His work demonstrates a progression from foundational graphical model research to specialized applications in medical informatics and time series analysis. Scientific recognition includes: Runner-up for Best Student Paper Award at UAI 2004 Best Paper Award at ISMB 2001 Dr. Elidan serves on the editorial board of the Journal of Artificial Intelligence Research (JAIR) and has contributed to the development of important software tools including FastInf, a library for large-scale inference of graphical models, and LibB, a package for Bayesian network inference and learning. He has organized significant academic events including the NIPS 2011 Workshop on Copulas in Machine Learning and co-organized the PASCAL2 Probabilistic Inference Challenge and UAI 2010 Approximate Inference Challenge.

William E. JanesView profile

Assistant Professor

• Assistive Technology
• Rehabilitation Engineering
• Amyotrophic Lateral Sclerosis (ALS)
• +4 more

William E. Janes is an Assistant Professor and Academic Fieldwork Coordinator in the Department of Occupational Therapy at the University of Missouri . Holding a PhD in Health and Rehabilitation Science, his work focuses on creating accessible assistive technologies through the AT-AT Lab to enhance independence for individuals with disabilities. Occupational Therapy Doctorate (OTD) – Washington University School of Medicine Master of Science in Clinical Investigation (MSCI) – Washington University School of Medicine Bachelor of Science in Psychology and Sociology – Southern Illinois University – Edwardsville His research spans ALS intervention , pediatric handwriting assessment , and 3D-printed assistive technologies , with recent publications addressing technology-driven solutions for neurodegenerative conditions and innovative educational practices. Janes has secured significant grants including a $760,000 award to advance ALS research and a $400,000 ALS Association grant for caregiver alert systems. Recognized as a 2025 AOTA Fellow , he leads initiatives in fieldwork educator competency and has contributed to professional standards through the University of Missouri Faculty Scholars Program (2017–2018). His teaching includes courses in Bioengineering Design and Evidence-Based Practice , while maintaining active memberships in the American Occupational Therapy Association and RESNA . As director of the AT-AT Lab , Janes champions low-cost technology adaptation , including recent projects making gaming more accessible for users with disabilities. His work bridges clinical practice , educational innovation , and community-focused technological development .

Emmanuel MorinView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Bilingual Terminology Extraction
• +5 more

Emmanuel Morin is a Full Professor in Computer Science at the University of Nantes, France. He is affiliated with the Computer Science Department of Nantes Institute of Technology (IUT) and leads the Natural Language Processing (TALN) team at the Digital Sciences Laboratory of Nantes (LS2N UMR CNRS 6004). His research focuses on computational linguistics with particular emphasis on multilingualism and multimodality. His academic leadership includes serving as Head of the NLP team (2017-present), Co-responsible for the ATAL (Machine Learning and Natural Language Processing) option of the Computer Science Master (2017-present), and Director of the Nantes computer science training department (2014-present). He also co-edits the Traitement Automatique des Langues (TAL) journal and serves on the steering committee of ATALA since 2004. Current projects: Atlantic 2020, ALALA Project (2019-present), ANR ADDICTE (2017-present), and Atlanstic 2020 RAPACE Project (2016-present) Past projects: Labex CominLabs LIMAH (2014-2019), ANR CRISTAL (2012-2016), and European FP7-ICT TTC project (2010-2012) Professor Morin's research spans natural language processing with a focus on bilingual terminology extraction and comparable corpora analysis. His recent work (2020-2022) shows strategic expansion into medical informatics applications like FrenchMedMCQA, while maintaining his foundational work on domain adaptation of language models using graph-based networks. His publications demonstrate a consistent trajectory from early terminology extraction methods to contemporary applications in specialized language processing. He has supervised numerous PhD students, with current advisees including Kévin Espasa, Martin Laville, Merieme Bouhandi, and Antoine Caubrière. His past students have made significant contributions to the field of natural language processing, continuing his research legacy in academia and industry.

• Ubiquitous computing
• Mobile health
• Wearable sensing
• +3 more

Parker Ruth is a PhD student in the Department of Computer Science at Stanford University ’s School of Engineering . He holds dual B.S. degrees in Computer Engineering and Bioengineering from the University of Washington (2021). His research lies at the intersection of ubiquitous computing , mobile health , and wearable sensing , focusing on sensor design and biophysical prior integration into time series algorithms. Parker’s work bridges computer science and biomedical engineering , with applications in neuromuscular disease monitoring , cardiac auscultation , and public health surveillance . NSF Graduate Research Fellowship (2021) Tau Beta Pi Fellowship (2021) Barry Goldwater Scholarship (2020) College of Engineering Dean’s Medal for Academic Excellence (2021) His publications highlight wearable health technology , mobile diagnostics , and RNA biosurveillance in public spaces. Parker’s academic trajectory reflects a commitment to interdisciplinary research and global health innovation .