Find Academic Supervisors

• Machine Learning
• Medical Imaging
• Positron Emission Tomography (PET)
• +3 more

Samuel Kuttner is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway. He specializes in applying artificial intelligence to medical imaging challenges, with core expertise in Positron Emission Tomography (PET), deep learning algorithms, and quantitative image analysis. His work bridges machine learning with clinical applications in oncology and neurology. Research Focus: Kuttner leads innovative projects at the intersection of AI and medical physics. His primary interests include: Developing non-invasive methods for arterial input function estimation in dynamic PET imaging Implementing deep learning for tumor segmentation and recurrence prediction in glioblastoma Optimizing quantitative PET/MRI through artifact correction and reconstruction algorithms Advancing physics-informed machine learning models for biomedical applications Publication Trends: His recent articles (2020-2025) demonstrate strong focus on: AI-driven solutions for PET quantification challenges Clinical translation of radiomics and deep learning in neuro-oncology Methodological innovations for preclinical and clinical imaging workflows Interdisciplinary collaborations across machine learning, physics, and clinical medicine Research Groups: He actively contributes to the Nuclear Medicine and Radiation Biology (NMRB) group and the Machine Learning Group at UiT, fostering cross-disciplinary research in medical technology innovation.

• Machine Learning
• Artificial Intelligence
• Data Analytics
• +9 more

Dr. Mukesh Prasad is an Associate Professor at the School of Computer Science , University of Technology Sydney (UTS). With expertise in Machine Learning , Artificial Intelligence , and Computer Vision , his research addresses applications in healthcare, biomedical science, and smart infrastructure. He holds a Ph.D. in Computer Science from National Chiao Tung University, Taiwan, and an M.S. in Computer and Systems Sciences from Jawaharlal Nehru University, India. Key research areas: Machine Learning, AI, Brain-Computer Interfaces, IoT, and Evolutionary Computation Industry experience: Principal Engineer at TSMC (2016-2017), Postdoctoral Researcher at National Chiao Tung University Dr. Prasad has secured competitive grants for AI applications in disaster response, conversational agents, and medical diagnostics. His work has been published in high-impact venues like IEEE , ACM Transactions , and Springer Nature , with over 200 peer-reviewed papers. He serves on editorial boards for journals including Frontiers in Neurorobotics and ACM Computing Surveys . Scientific Awards: Vice Chancellor Teaching and Learning Citation Award (2019) Alumni Fellowship for Ph.D. (2014) Golden Bamboo NCTU Fellowship (2010) Professional Members: IEEE (2011), ACM (2019)

• Astroparticle Physics
• Instrumentation
• Cherenkov Telescopes
• +3 more

Simone Incardona serves as a Research Fellow in the Department of Physics and Chemistry 'Emilio Segrè' at the University of Palermo, where he contributes to advanced instrumentation for gamma-ray astronomy and interdisciplinary machine learning applications. His institutional affiliation centers on the Cherenkov Telescope Array (CTA) project, specifically the development of the Schwarzschild-Couder Telescope (SCT) prototype. His research spans astroparticle physics instrumentation with emphasis on silicon photomultiplier (SiPM) technology and front-end electronics for Cherenkov light detection. Key focus areas include ASIC design for telescope cameras, optical system commissioning, and neural network architectures for time series analysis. This dual expertise in hardware development and computational methods supports cutting-edge observations in high-energy astrophysics. Analysis of his 2021-2023 publications reveals concentrated work on the pSCT prototype, addressing SiPM array assembly, SMART ASIC characterization, and Crab Nebula detection. The research trajectory demonstrates progression from component-level testing (2021) to integrated system validation (2023), with consistent contributions to CTA's medium-sized telescope instrumentation. No scientific awards were documented in available sources. Student advising activities and research grant details remain unspecified in current records, though his role involves supervising technical aspects of telescope instrumentation projects. He operates within the University of Palermo's CTA collaboration team, focusing on the pSCT's camera development and optical subsystems. This group interfaces with international partners in the global Cherenkov Telescope Array Observatory, contributing to hardware validation and observational campaigns.

Robert HaaseView profile

Lecturer

• Bioimage Analysis
• Medical Informatics
• GPU-Accelerated Image Processing
• +6 more

Dr. Robert Haase is a Lecturer and Training Coordinator at the Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) under Leipzig University , with prior leadership roles at the DFG Cluster of Excellence 'Physics of Life' at TU Dresden . He specializes in Bioimage Analysis , GPU-Accelerated Image Processing , and Large Language Models (LLMs) for life sciences. His research focuses on democratizing bioimage analysis through open-source tools like CLIJ , clesperanto , and bia-bob , aiming to bridge microscopy with data science . Recent projects explore LLM-driven code generation for image analysis and interactive workflow design in platforms like napari . He leads initiatives such as the NFDI4BioImage consortium for research data management in Germany and GloBIAS , a global society for bioimage analysts. Funded by organizations including the Chan Zuckerberg Initiative (CZI) and DFG , his work emphasizes reproducibility , open science , and interdisciplinary collaboration in bioimaging. As an educator, he conducts training programs like "Large Language Models for Bioimage Analysis" and "Collaborative Working with Git" , and contributes to workshops at institutions such as EMBO , Institut Pasteur , and ScaDS.AI Summer Schools .

Raghav GnanasambandamView profile

Assistant Professor

• Scientific Machine Learning
• Surrogate Modeling
• Uncertainty Quantification
• +2 more

Raghav Gnanasambandam is an Assistant Professor in the Industrial & Manufacturing Engineering department at Florida A&M University-Florida State University College of Engineering. He holds a Ph.D. in Industrial and Systems Engineering from Virginia Tech (2024) and a dual bachelor's/master's degree in Mechanical Engineering from IIT Madras (2019). His research focuses on Scientific Machine Learning, Surrogate Modeling, Uncertainty Quantification, and Metal Additive Manufacturing. Educational Background: Ph.D., Industrial and Systems Engineering, Virginia Tech (2024) Dual Degree (B.S./M.S.), Mechanical Engineering, IIT Madras (2019) He develops physics-informed machine learning techniques for digital twins in manufacturing, with applications in laser scan path optimization, thermal modeling, and multi-physics simulations. His work combines deep learning with physical constraints to improve process efficiency and predictive accuracy. Scientific Awards: ISE Outstanding Doctoral Student, Virginia Tech, 2023 As principal investigator of the M2M Research Group, he leads projects supported by Amazon Web Services. His teaching includes courses in manufacturing systems and machine learning applications.

David TschirschwitzView profile

Lecturer

• Computer Vision
• Object Detection
• Instance Segmentation
• +3 more

David Tschirschwitz is a Lecturer at Bauhaus-Universität Weimar's Faculty of Media (Department of Media Informatics) with additional affiliations at the Faculty of Civil and Environmental Engineering's Geodesy Institute. His research spans computer vision, object detection, and document analysis, with leadership in projects like VarInsightAI. Research Focus: Specializes in human label variation, image retrieval systems, annotation noise handling, and AI evaluation methods. Key applications include construction industry datasets, historical document processing, and medical object detection. Teaching & Advising: Regularly teaches advanced courses in computer vision and deep learning. Currently advises 4 students on topics ranging from table recognition to image registration. Recent Publications: Published 6 peer-reviewed papers between 2022-2025, predominantly at top venues like WACV and GCPR. Work emphasizes practical datasets, annotation reliability, and evaluation frameworks for computer vision systems.

• Machine Learning
• Computer Vision
• Deep Learning
• +7 more

Professor Eyad Elyan is a leading academic and researcher at Robert Gordon University's School of Computing, Engineering and Technology, where he serves as a Professor in Machine Learning and Computer Vision. He is the founder and head of the Machine Vision Research Group, driving innovative research in applied computer vision and deep learning with significant industry impact. Professor Elyan's research focuses on converting complex and unstructured data into knowledge and actionable insights, with particular emphasis on learning from images, videos, and other forms of unstructured data. His work spans engineering diagrams processing, remote inspection for oil and gas installations, intelligent condition monitoring of offshore assets, predictive maintenance, biometric applications, and medical datasets analysis. His expertise in ensemble-based learning and learning from unstructured and imbalanced datasets has been successfully implemented in various real-world applications. Professor Elyan was awarded the UK Knowledge Transfer Partnership Academic of the Year Award in 2023 for his transformative work in developing pioneering AI solutions for the oil and gas sector, and was a finalist for the Scottish Knowledge Exchange Award in 2024. These recognitions highlight his exceptional ability to bridge academic research with practical industry applications. His research has been supported by various public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), NetZero Technology Centre (NTZ), and Historic Environment Scotland. Professor Elyan has supervised twelve PhD students to completion and examined more than fifteen others. He plays an active role in the academic community as a Fellow of the British Higher Education Academy and The International Neural Network Society, and serves as the Scotland Data Lab Innovation Centre Ambassador. Under Professor Elyan's leadership, the Machine Vision Research Group has developed innovative solutions including an end-to-end system for processing Piping and Instrumentation Diagrams (P&ID), AI-driven inspection systems for oil and gas assets, and defect recognition technologies. His work demonstrates a consistent commitment to translating cutting-edge research into practical tools that address real-world challenges, particularly in the energy sector.

MÜCAHİT ÇalışanView profile

Lecturer

• Image Processing
• Machine Learning
• Biomedical Engineering
• +2 more

Dr. MÜCAHİT Çalışan serves as a Lecturer at Bingöl University with dual appointments in the Computer Engineering Department (Faculty of Engineering and Architecture) and the Distance Education Application and Research Center. Holding a PhD from İnönü University (2022), he contributes to both teaching and research in computer engineering. His academic journey includes: Bachelor's Degree: Electronics and Computer Education, Fırat University (2003-2007) Master's Degree: Electronics and Computer Education, Fırat University (2011-2013) PhD: Computer Engineering, İnönü University (2016-2022) Dr. Çalışan's research centers on thermal imaging applications and machine learning techniques, with notable work in biomedical engineering such as skull thickness calculation using thermal analysis. His methodology combines finite element methods with image processing algorithms, extending to dimension reduction techniques and autoencoder optimization for coding performance. The evolution from thermal camera fundamentals (2011) toward biomedical applications (2021) demonstrates growing specialization. His publication portfolio shows consistent output with international reach, highlighted by the 2021 Applied Sciences article featuring cross-border collaboration. While specific grant details aren't public, the research trajectory suggests active projects in thermal imaging and machine learning applications. Teaching core courses including Database and Operating Systems, he bridges theoretical knowledge with practical implementation. Though no dedicated laboratory is documented, his computational research likely utilizes university resources for thermal analysis and machine learning experiments.

Archie Singh KhumanView profile

Senior Lecturer

• Artificial Intelligence
• Computational Intelligence
• Machine Learning
• +5 more

Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.

Timothy BeckerView profile

Assistant Professor

• Genomics
• Deep Learning
• Generative Modeling
• +8 more

Timothy Becker serves as the John D. MacArthur Assistant Professor of Computer Science at Connecticut College, where he joined in 2023. His research focuses on transforming complex datasets into functional instruments through advanced computational techniques. His academic background includes: B.M. in Music Production from The Hartt School of Music B.S. in Computer Science from the University of Hartford Ph.D. in Computer Science and Engineering from the University of Connecticut Becker specializes in genomics applications but maintains active interdisciplinary collaborations in ecology and transportation. His core methodology integrates deep learning, generative modeling, and data visualization to develop open-source software tools. He emphasizes practical implementations for real-world data analysis and creates interactive tutorials to foster persistent student learning. His publication record from 2018-2025 reveals consistent contributions to bioinformatics and environmental data science, particularly in structural variation analysis and river connectivity modeling. Key trends include developing neural network frameworks for genomic data integration and creating visualization methods for multi-omic datasets. Becker actively seeks undergraduate research collaborators, encouraging students with domain-specific data or research questions to initiate projects. His interdisciplinary approach connects computer science with life sciences and environmental studies through numerous cross-institutional partnerships.

• Food Science
• Food Analytics
• Biotechnology
• +6 more

Birthe P Møller Jespersen is an Associate Professor and External Researcher in the Department of Food Science, specializing in Food Analytics and Biotechnology at the University of Copenhagen. Her research focuses on applying advanced analytical techniques to food science problems, particularly in cereal analysis and functional foods. Dr. Jespersen's research interests include: Food Analytics using hyperspectral imaging and near-infrared spectroscopy Barley and cereal analysis for food and brewing applications Functional foods development Application of machine learning and deep learning in food analysis Protein analysis in plant-based foods Food processing and quality assessment Her recent publications demonstrate a strong trend toward integrating advanced computational methods with traditional food analysis techniques. She has been actively working on applying hyperspectral imaging combined with machine learning algorithms to analyze grain quality, protein content, and food processing parameters. Her research bridges the gap between traditional food science and modern data science approaches. Dr. Jespersen has contributed to the scientific community through numerous publications and media appearances discussing food science topics. Her media contributions include discussions on converting animal feed to human food and the importance of grain fibers in healthy diets. Her professional activities include: Research in food analytics and biotechnology Development of analytical methods for food quality assessment Application of advanced imaging and spectroscopy techniques Integration of machine learning with food analysis

• Data visualisation
• Visual Analytics
• Information Visualisation
• +4 more

Christy Jie Liang is an Associate Professor at the School of Computer Science, University of Technology Sydney (UTS), where she leads the Data Visualisation Research Lab in the Visualisation Institute. With extensive experience in both academic and industry settings, including appointments at IBM and Peking University, she has established herself as a leading researcher in data visualization and visual analytics. Dr. Liang earned her PhD in Data Visual Analytics from UTS, where she was awarded the University Medal with First Class Honours. Her educational background includes a Bachelor of Information Technology (First Class Honours) also from UTS. Professor Liang's research focuses on data visualization and visual analytics, with particular emphasis on information visualization, narrative visualization, and the application of these techniques to real-world problems. Her work spans multiple domains including finance, food safety, biomedical applications, smart cities, and social media. She has developed novel visualization techniques and owns five intellectual properties in this field. Her recent publications demonstrate a clear trajectory toward more sophisticated visualization techniques that integrate machine learning, with increasing focus on narrative visualization, user engagement across demographics, and practical applications in domains such as public health and education. The interdisciplinary nature of her work is evident in collaborations across computer science, behavioral science, and domain-specific applications. Dr. Liang has received significant recognition for her work, including: University Medal with First Class Honours from UTS Capital Markets CRC Honours scholarship Australian Postgraduate Awards As an educator, Professor Liang coordinates core subjects for Bachelor of Information Technology, Bachelor of Computer Science with Honours, Master of Interaction Design, and Master of Business Analytics programs. She has recently developed enterprise learning courses including short courses and micro-credentials in data visualization education. Her leadership extends to service roles as associate editor for JVLC and Journal Visual Informatics, program committee member for numerous conferences, and advisory board member for the Australian Computer Society and Peking University Medical Visualization Centre. Professor Liang leads the Data Visualisation Research Lab, which focuses on developing innovative visualization techniques and applying them to real-world problems. The lab maintains strong industry connections, with collaborations spanning government agencies, academic institutions, and commercial enterprises across multiple continents.