Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +4 more

Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.

Niels OtaniView profile

Associate Professor

• Cardiac Electrophysiology
• Computational Biology
• Biomechanical Imaging
• +9 more

Niels Otani is an Associate Professor in the School of Mathematics and Statistics at the College of Science, Rochester Institute of Technology (RIT). He holds a BA from the University of Chicago and a Ph.D. from the University of California, Berkeley. His research focuses on cardiac electrophysiology, computational biology, and biomechanical imaging, with a particular emphasis on understanding and controlling cardiac arrhythmias such as ventricular fibrillation. Dr. Otani has pioneered methods for visualizing action potential propagation using ultrasound and developed novel defibrillation strategies that reduce energy requirements. He also investigates the role of ephaptic coupling in cardiac dynamics and the mechanisms underlying spiral wave formation and termination. His work bridges mathematics, physics, and cardiology, with applications in clinical therapies and biomedical engineering. Dr. Otani's research has been supported by grants such as an NSF award for developing diagnostic tools for cardiac disease. He teaches advanced courses in multivariable calculus, linear algebra, and research thesis supervision. Collaborations span interdisciplinary teams, including veterinary cardiology and biomedical imaging groups. His contributions include over 50 publications on topics ranging from action potential dynamics to computational modeling of cardiac tissue, with a focus on translating theoretical findings into clinical solutions.

• Machine Learning
• Natural Language Processing
• Computer Vision
• +3 more

Mohammad Alsharid is a Visiting Fellow at the University of Oxford's Institute of Biomedical Engineering and a postdoctoral research fellow at Khalifa University. He holds a DPhil in Engineering Science (2022) from the University of Oxford, where his thesis focused on automated textual captioning for ultrasound visuals. His research integrates machine learning, natural language processing, and computer vision to advance biomedical image analysis, particularly in fetal ultrasound. Alsharid’s work emphasizes multimodal interplay in medical imaging and automated workflow analysis. Education: DPhil in Engineering Science (2022), University of Oxford; BSc (exact discipline unspecified). Research Interests: Develop AI-driven solutions for medical imaging through multimodal learning, curriculum learning approaches for ultrasound captioning, and automated analysis of fetal echocardiography scans. His methods leverage gaze-assisted systems and self-supervised representation learning for improved diagnostic support. Publications Highlight Trends: Focus on automated captioning for medical videos, curriculum-based learning frameworks, and multimodal neural networks for biomedical applications. Lab Affiliations: Active in the Institute of Biomedical Engineering at Oxford, contributing to interdisciplinary projects in AI and healthcare.

Jane-Ling WangView profile

Professor

• Deep Learning
• Functional Data Analysis
• Longitudinal Data Analysis
• +4 more

Jane-Ling Wang is a Distinguished Professor in the Department of Statistics at the University of California, Davis. Her research focuses on advancing statistical methodologies for functional and longitudinal data analysis, deep learning applications, and survival analysis. She holds a Ph.D. from UC Berkeley and has contributed extensively to interdisciplinary fields including neuroscience, biostatistics, and machine learning. Wang has received numerous accolades, including being elected an Academician at Academia Sinica (2022), recipient of the Humboldt Research Award (2020), and the ICSA Distinguished Achievement Award (2018). Her work bridges theory and practice, addressing challenges in data sparsity, dynamic systems modeling, and high-dimensional statistical inference. Her recent publications emphasize innovative techniques such as SAND (Transformer-based data imputation) and adaptive basis layers for functional data analysis. These contributions underscore her expertise in integrating modern computational tools with classical statistical frameworks.

Kaspar RiesenView profile

Researcher

• Pattern Recognition
• Machine Learning
• Computer Vision
• +4 more

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Yongxin ZhaoView profile

Associate Professor

• Bioengineering
• Cell and Tissue Engineering
• Diagnostic and Therapeutic Methods
• +5 more

Dr. Yongxin (Leon) Zhao is an Associate Professor in Biomedical Engineering and Biological Sciences at Carnegie Mellon University's College of Engineering. He leads a research group developing tools for understanding biological systems at the biomolecular level, with a focus on expansion microscopy and optogenetic probes. His work has advanced nanoscale imaging and diagnostic methodologies, supported by grants from NIH, NSF, DoD, and others. Education: B.S., Sun Yat-Sen University (2009); Ph.D., University of Alberta (2014); Postdoctoral Research at University of Alberta (2014). Key achievements include the GECO calcium indicators and QuasAr voltage indicators, and foundational contributions to expansion microscopy (Nature Biotechnology 2017, 2023; Science 2022). Research interests span bioengineering, cell/tissue engineering, and nanotechnology-driven diagnostics. Zhao's lab pioneers applications in neuroscience, cancer, and infectious diseases. Awards include the NIH Director's New Innovator Award (2018–2023) and MIT Translational Fellowship (2015–2016). Collaborations emphasize global technology democratization, with projects ranging from nanofabrication to computational analysis using deep learning. His lab's tools are widely adopted in neurobiology and pathology labs worldwide.

Sijia YangView profile

Associate Professor

• Public Health Communication
• Science Communication
• Digital Media Effects
• +3 more

Sijia Yang is an Associate Professor at the School of Journalism and Mass Communication at the University of Wisconsin-Madison . His research focuses on message effects and persuasion in digital media, particularly in public health and science communication. He employs experimental, computational (e.g., causal machine learning, multimodal analysis), and community-engaged methods to address challenges in health communication interventions. Ph.D. in Communication: Annenberg School for Communication, University of Pennsylvania (2019) M.A. in Communication: University of Illinois at Urbana-Champaign (2012) B.A. in English Language and Literature: Renmin University of China (2010) His current research explores three key areas: (1) moralization/politicization of health issues and intervention design, (2) causal machine learning for evaluating multimodal messages, and (3) leveraging AI for pro-social messaging in underserved communities. His work bridges computational methods with societal impact, emphasizing ethical considerations and practical applications. Recent articles highlight his contributions to topics like cannabis warning labels, misinformation correction via TikTok, and AI-driven health chatbots. Despite no explicitly listed awards, his active research portfolio reflects significant scholarly engagement in health communication and digital media studies. His advising and grants focus on collaborative projects with rural communities and under-resourced populations, though specific grants are not detailed here. No lab or team affiliations are mentioned in the provided texts.

Eduardo EyrasView profile

Professor

• RNA Biology
• Cancer Research
• Long-read Sequencing Technologies
• +4 more

Eduardo Eyras is a Professor at the Australian National University (ANU) and EMBL Australia Group Leader, leading research in computational RNA biology and cancer genomics. He directs the Centre for Computational Biomedical Sciences and is part of the Shine-Dalgarno Centre for RNA Innovation. His work focuses on transcriptome and epitranscriptome analysis using long-read sequencing, machine learning, and computational methods to study cancer mechanisms. Eyras holds a PhD in Mathematics from the University of Groningen (1999) and previously led research at the Sanger Institute and Pompeu Fabra University. Affiliations: Director, Centre for Computational Biomedical Sciences Researcher, Shine-Dalgarno Centre for RNA Innovation Member, Division of Genome Sciences and Cancer Leader, The Eyras Group - Computational RNA Biology Research Interests: Development of algorithms for long-read sequencing Machine learning applications in RNA biology Epitranscriptomic modifications and cancer Therapeutic mRNA platform steering Key Projects: Novel algorithms for transcriptome variation analysis Predictive models of RNA modifications in disease Ribosomal DNA variation analysis Advisees & Grants: Supervises PhD students (e.g., Favour Oyelami, Stefan Prodic) and leads ARC-funded projects on mRNA diagnostics and epitranscriptomic therapies. Collaborates with global teams on forensic genomics, cancer drug resistance, and AI-driven translational research. Labs/Teams: Leads the Eyras Group, collaborating with the Hannan Group (Cancer Therapeutics) and Shirokikh Group (Protein Biosynthesis).

• Collaborative Research and Discovery
• IT-Enabled Business Process Innovation
• Knowledge Mining and Visualization
• +3 more

Christopher Asakiewicz is a Teaching Professor at the School of Business, Stevens Institute. His research focuses on Collaborative Research and Discovery, IT-Enabled Business Process Innovation, Knowledge Mining and Visualization, Healthcare Analytics, Cognitive Systems, and Artificial Intelligence. PhD in Information Management (2003), Stevens Institute MS in Electrical Engineering (1983), Stevens Institute BS in Electrical Engineering and Computer Science (1977), Columbia University His research explores the intersection of artificial intelligence and healthcare, particularly in accelerating translational research through knowledge mining, cognitive systems, and data integration. He has pioneered methods for leveraging AI in biobanking, collaborative discovery, and healthcare analytics, with a focus on enterprise-level applications. Recent publications highlight applications of AI and machine learning in translational research, healthcare analytics, and IT-enabled business process innovation. His work often bridges computer science, systems engineering, and life sciences. Asakiewicz contributes to institutional service through roles on the Institute Curriculum Committee and BI&A Advisory Board. He has secured significant research funding for projects involving Merck Pharmaceuticals, Genesis Research, and the Department of Defense.

Min ShinView profile

Professor

• Computer Vision
• Multiple Object Tracking
• Automated Visual Inspection
• +4 more

Min Shin is a Professor and Chair of the Computer Science Department at the University of North Carolina at Charlotte (UNC Charlotte), where he also serves as Assistant Dean for Research in the College of Computing and Informatics. He earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2001. His research focuses on Computer Vision, particularly multiple object tracking and automated visual inspection of nuclear power plants. He leads the Video and Image Analysis Lab, developing user-friendly tracking software funded by NSF, NIH, DARPA, and industry grants. Notable projects include the ABCTracker.org platform, which deploys algorithms refined over 10+ years of research. Dr. Shin’s work spans tracking applications in diverse domains such as ants, bees, cells, termites, robots, and vehicles. He has mentored Ph.D. and undergraduate students extensively, emphasizing computational methods in biology and engineering. His service includes roles as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics-B, and program committees for top conferences (ICCV, CVPR, ECCV). His grants and collaborations reflect interdisciplinary strengths in vision algorithms, usability, and real-world deployment.

Ernesto Jiménez-RuizView profile

Senior Lecturer

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +5 more

Dr. Ernesto Jiménez-Ruiz is a Senior Lecturer in Artificial Intelligence and Director of Research at City, University of London. He holds a PhD from Jaume I University, Spain (2010), and has held academic positions since 2010. His research focuses on knowledge graphs, ontology alignment, semantic web technologies, and neuro-symbolic AI. Currently, he teaches modules on semantic web technologies and research methods. He leads the Research Center affiliated with City St George's, University of London. Administrative Roles: Director of Research (2024–present), Senior Tutor for Research (2023–2024) Research Interests: AI, knowledge representation, ontology alignment, semantic integration, and biomedical informatics. His work bridges machine learning and symbolic reasoning, with contributions to ontology matching and knowledge graph embeddings. He supervises PhD students in areas like knowledge graph alignment and neuro-symbolic systems. Awards: SWSA Ten-Year Award (2021) for influential ontology alignment work. He is a Fellow of AdvanceHE (FHEA). Grants & Collaborations: Leads projects on semantic industrial data modeling and collaborates with Samsung Research UK, Bosch, and Statoil. Active in initiatives like the Turing Institute's Knowledge Graph Interest Group.

Colleen BaileyView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

Peyman BadakhshanView profile

Researcher

• Business Process Management
• Process Mining
• Industrial Engineering

Dr. Peyman Badakhshan is a Researcher at the Chair of Information Systems and Business Process Management at the University of Münster. His expertise spans Business Process Management, Process Mining, and Industrial Engineering. Educational background includes a PhD in Business/Managerial Economics from Universität Liechtenstein, an MSc in Information Systems, and an M.Eng. in Industrial Engineering. Research focuses on operationalizing process analytics across domains like supply chain, healthcare, and manufacturing. Recent work develops frameworks for agile BPM and process mining tools, with applications in medical training, emergency services, and industrial process optimization. Publications demonstrate consistent innovation in mapping business process landscapes, developing diagnostic instruments, and creating large-scale datasets for academic use. Research integrates methodologies from decision analysis, fuzzy logic, and pattern recognition to solve complex operational challenges.

Yi DuView profile

Researcher

• Infectious Disease Surveillance
• Data Science in Public Health
• COVID-19 Surveillance
• +3 more

Yi Du is a Healthcare Epidemiologist in the Department of Epidemiology at the College of Public Health, University of Nebraska Medical Center (UNMC), a position held since 2022. Previously, Du served as an Emergency Medical Services (EMS)/External Cause of Injury Code (E-code) Data Analyst Intern and State Epidemiologist Assistant at the Nebraska Department of Health and Human Services, focusing on COVID-19 surveillance. Education: PhD in Epidemiology, University of Nebraska Medical Center, 2017-2022 MS in Public Health, University of Miami, 2014-2016 MS in Developmental Neurobiology, Lanzhou University, 2011-2014 BS in Bioscience, Lanzhou University, 2007-2011 Yi Du's research focuses on infectious disease surveillance, particularly for COVID-19, including case investigation, hospitalization, death, and vaccine effectiveness. Additional areas include injury surveillance (drug overdose, suicide, EMS), data science applications in public health for data integration and visualization, and studies on nonalcoholic fatty liver disease. Du employs cross-sectional, surveillance, and survey methodologies to address public health challenges. Recent publications (2021-2022) demonstrate a strong emphasis on COVID-19 epidemiology, with multiple studies on school-based transmission, community risk assessment, and case growth rates. Other work addresses occupational health in agricultural settings (musculoskeletal discomfort, pulmonary conditions) and injury surveillance. The research integrates data science for public health applications, reflecting a multidisciplinary approach to epidemiological challenges. No scientific awards, prizes, or fellowships were mentioned. Yi Du has not listed any formal advisees or students. Additionally, no grant information was provided in the text.

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +5 more

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics