TensorFlow Lite Professors and Supervisors

Duncan WilsonView profile

Professor

University College LondonUnited Kingdom

Duncan Wilson is a Professor of Connected Environments at the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London. His work bridges academia and industry, focusing on IoT, AI, and spatial analysis to enhance understanding of built and natural environments. Current role: Professor of Connected Environments at UCL Education: PhD in Artificial Intelligence and Machine Vision (UCL, 1997), BEng (Hons) in Electrical Engineering (Loughborough University, 1993) Research interests include: Cognitive computing at the network edge Extraordinary sensory systems for data capture Spatial reasoning and digital twins IoT for healthcare and biodiversity Edge AI and TinyML Recent articles span digital twin development , IoT for biodiversity monitoring , and smart healthcare infrastructure . He has received recognition for collaborative R&D approaches during his directorship at Intel's Sustainable Connected Cities institute. Teaching: Leads MSc Connected Environments and modules on IoT ethics, AI on microcontrollers, and sensor network deployment Projects: IoT Living Lab at UCL, Project Hercules for eye clinic analytics, and Shazam for Bats environmental monitoring Professional activities: Former Director of Intel Collaborative Research Institute (2012-2018), ex-member of Smart London Board (2017-2022)

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Vimal Kumar KumarView profile

Research Fellow

University of LimerickIreland

Dr. Vimal Kumar Kumar serves as a Research Fellow at the University of Limerick's Department of Computer Science and Information Systems, affiliated with Lero – the Irish Software Research Centre. His academic career includes prior faculty positions as Assistant Professor at Jaypee University of Information Technology (2009-2022) and Lecturer at SRM Institute of Science and Technology (2007-2009), followed by a Postdoctoral Researcher role at University College Dublin (2022-2023). His educational background includes: PhD in A Novel sense based Hindi to Tamil machine translation system from Jaypee Institute of Information Technology (2019) Masters from Anna University Chennai (2005-2007) Bachelor from PSG College of Technology (2000-2004) Dr. Kumar's research centers on Natural Language Processing with specialized focus on machine translation for Indian languages (Hindi, Tamil), text summarization, and sentiment analysis. He extends this work into computer vision applications for medical imaging and activity recognition, plus deep learning implementations for IoT-based agricultural monitoring. His methodology frequently addresses challenges in low-resource language processing and develops optimized neural network architectures for mobile deployment. Analysis of his publication trends reveals consistent interdisciplinary innovation, particularly in healthcare applications (retinal disease detection), agricultural technology (crop yield prediction), and social media analytics. His work demonstrates strong emphasis on practical AI solutions for resource-constrained environments and underrepresented linguistic communities, often leveraging generative models and contextual embeddings. Dr. Kumar actively contributes to Lero's research initiatives, aligning his technical work with UN Sustainable Development Goals through software solutions addressing global challenges in health, food security, and digital inclusion.

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Marko MartinovićView profile

Senior Lecturer

University of Slavonski BrodCroatia

Marko Martinović is a Senior Lecturer at the University of Slavonski Brod, affiliated with the College of Technical Sciences, Department of Informatics and Information Technology. He holds a PhD in Economics from the University of Osijek (2022) and a master's degree in Computing from the University of Zagreb (2006). His career spans academia and industry, with prior roles at the Polytechnic of Slavonski Brod and private sector experience in IT. PhD in Economics, University of Osijek, 2022 MSc in Computing, University of Zagreb, 2006 His research is centered on Artificial Intelligence , Machine Learning applications , and Financial Engineering . He explores AI in diverse domains including financial markets, renewable energy, sports technology, and ethical computing. His work often bridges technical computing with economic and social implications. The recent publications highlight a strong trend in applied AI , particularly using neural networks for forecasting (financial indices, currency, demand), optimization (edge AI, microcontrollers), and societal analysis (search engine bias). His work combines theoretical models with real-world datasets, emphasizing practical implementation and performance evaluation. Marko actively mentors students, supervising numerous undergraduate theses in computing and informatics. He is involved in institutional research projects such as the MOD-INO project (2019–2022), contributing to curriculum and research development. He has not received any explicitly mentioned scientific awards or major grants in the provided text. He teaches core courses including Basics of Artificial Intelligence , Python Programming , Neural Networks and Deep Learning , and Basics of Informatics . His technical expertise is complemented by a CCNA certification, reflecting a strong foundation in networking.

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Veysel Harun ŞAHİNView profile

Associate Professor

Sakarya UniversityTurkey

Veysel Harun ŞAHİN is an Associate Professor at the Department of Software Engineering , Faculty of Computer and Information Sciences , Sakarya University. He has been affiliated with Sakarya University since 2007, initially serving as a Research Assistant (2007–2014) and later as a Lecturer (2014–present) before advancing to his current role. Education: Doctorate in Computer and Information Engineering (2005–2013), Sakarya University, thesis: 'Switched approach to allocating memory for large objects in real-time Java' Master’s in Electrical and Electronics Engineering (2002–2005), Sakarya University, thesis: 'Generalized scattering matrix technique and macsyma' Bachelor’s in Electronics Engineering (1994–2001), Ankara University Research interests span software testing, mobile applications (Android, smartphones), artificial intelligence (deep learning, neural networks, TensorFlow Lite), control flow graph reconstruction, and RISC-V ISA. His work also intersects with medical imaging (skin lesions) and emerging fields like monkeypox-related computational approaches. Contact: vsahin@sakarya.edu.tr

Official University Profile

Gözde YOLCU ÖZTELView profile

Assistant Professor

Sakarya UniversityTurkey

Gözde YOLCU ÖZTEL serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences. With a Ph.D. in Computer and Information Engineering completed in 2019, she has established herself as an active researcher in artificial intelligence with significant contributions to computer vision applications. Her educational journey includes: Ph.D. in Computer and Information Engineering (2014-2019) with thesis on deep learning-based interest detection through facial analysis M.Sc. in Computer and Information Engineering (2012-2014) focusing on Kinect-based virtual mirror design B.Sc. in Computer Engineering (2007-2011) from Sakarya University Dr. YOLCU ÖZTEL's research bridges deep learning with practical healthcare and public safety applications. Her work demonstrates exceptional adaptability to emerging challenges, particularly in developing AI systems for pandemic response (masked face detection, social distancing monitoring) and medical diagnostics (monkeypox/skin lesion classification). She specializes in creating efficient mobile-compatible deep learning models that maintain accuracy while operating on resource-constrained devices. Analysis of her 15 most recent publications reveals a clear trajectory toward healthcare-oriented computer vision, with increasing focus on mobile medical applications since 2020. Her research consistently combines novel deep learning architectures with real-world implementation constraints, particularly smartphone deployment. The integration of traditional computer vision techniques (like LBP) with modern deep networks represents a distinctive methodological signature across her work. She actively contributes to research through TÜBİTAK 2209 projects as student advisor and leads institutional research initiatives including SAYZEK-ATP (2024) and MAG Engineering Research Support Group. Her project portfolio spans from mathematical assistant software development to emergency drone-based object detection and MRI-based brain tumor classification. Dr. YOLCU ÖZTEL teaches core software engineering courses including Database Management Systems, Artificial Intelligence Fundamentals, and Data Mining while maintaining active research output. She previously organized the 2017 Facial Expression Recognition workshop, demonstrating ongoing commitment to academic community building.

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