Find Academic Supervisors

• Image Processing
• Artificial Intelligence
• Machine Learning
• +6 more

Doç. Dr. Emre Sümer, currently serving as an Associate Professor at the Department of Computer Engineering within Baskent University , focuses on image processing, deep learning, and their applications in medical diagnostics and computer vision. His work bridges artificial intelligence with healthcare and geographic information systems. PhD (2011) : Geodesy and Geographic Information Technologies, Middle East Technical University MS (2004) : Geodesy and Geographic Information Technologies, Middle East Technical University BS (2001) : Computer Engineering, Baskent University His research spans medical imaging (e.g., Parkinson's and Alzheimer's detection), steganography for secure data transmission, and machine learning applications in healthcare and urban planning. Recent publications highlight CNN-based diagnostics, sign language recognition, and innovative advertising systems. Scientific awards include the First Oral Presentation Award at the International Congress on Artificial Intelligence in Health (2020). He has supervised multiple PhD/MSc theses on topics like zero-shot sign language recognition and steganography methods. Email: esumer@baskent.edu.tr

• Artificial Intelligence
• Image Processing
• Signal Processing

İclal Çetin Taş is an Assistant Professor in the Computer Engineering department at Başkent University. She holds a PhD (2019), Master's (2012), and Bachelor's (2009) in Electrical and Electronics Engineering from Çukurova University. Her research spans three core domains: Artificial Intelligence : Applications in predictive modeling, classification, and deep learning systems. Image Processing : Focus on medical imaging (brain tumors, dental X-rays), forgery detection, and aerial image analysis. Signal Processing : Techniques for noise reduction and signal decomposition in medical and technical contexts. Recent publications (2022–2025) demonstrate a strong emphasis on deep learning applied to interdisciplinary challenges. Key themes include healthcare (cancer diagnosis, dementia classification), sustainability (battery life estimation), agriculture (crop detection), and digital security (image forgery). Her work frequently employs convolutional neural networks (CNNs) and regression models. She advises Master's students on topics like deep learning, image analysis, and software quality, though specific student names are not listed.

Auxiliadora Sarmiento VegaView profile

Professor

• Audio Source Separation
• Biomedical Signal Processing
• Machine Learning
• +5 more

Auxiliadora Sarmiento Vega is a full professor at the University of Seville 's School of Engineering within the Department of Signal Theory and Communications . With over two decades of research experience, her work bridges audio signal processing and biomedical applications, focusing on blind source separation, entropy-based methods, and machine learning for healthcare diagnostics. Research Pillars : Audio source separation, biomedical signal/image analysis, and virtual reality integration Key Projects : ACACIA (Signal Analysis), NEUBIAS (Bioimage Analysts Network), and multiple NIH-funded biomedical imaging initiatives Academic Contributions span 15+ years, with groundbreaking work in: Alpha-Beta divergence clustering algorithms EEG processing for motor imagery BCI systems Automated breast cancer grading from histological images Glaucoma and diabetic retinopathy diagnostics via retinal image analysis Virtual reality platforms for emotion analysis research She actively collaborates with institutions like the IEEE Women in Engineering (Spanish section secretary) and NEUBIAS network , while mentoring through outreach programs like g4g Day that empower young women in STEM.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +1 more

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

Mary Lauren BentonView profile

Assistant Professor

• Computational Biology
• Gene Regulation
• Biomedical Informatics
• +3 more

Dr. Mary Lauren Benton is an Assistant Professor in the Department of Computer Science at Baylor University's College of Engineering and Computer Science. She holds a Ph.D. and M.S. in Biomedical Informatics from Vanderbilt University (2020, 2018) and a B.S.I. in Bioinformatics from Baylor University (2015). Education: Ph.D. (Vanderbilt, 2020), M.S. (Vanderbilt, 2018), B.S.I. (Baylor, 2015) Her research focuses on computational biology and gene regulation, particularly how DNA sequences influence genome function and human disease risk through integrated datasets. Recent work explores enhancer-gene interactions, epigenetic mechanisms, and the application of graph neural networks to genomic problems. Selected publications highlight interdisciplinary trends across genomics (e.g., Cis-regulatory landscapes , CTCF motif accessibility ) and computer science (e.g., Dynamic graph attention , Node classification ).

• Computer Engineering
• IoT Systems
• Machine Learning
• +4 more

Dijana Stojić is an Assistant at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac. She has held this position since January 2018 while pursuing her doctoral studies in Electrical and Computer Engineering with a specialization in Computer Engineering, which she began in 2021. Her academic journey began with excellent results at the 'Vuk Karadžić' elementary school in Čačak, followed by the Technical School in Čačak where she majored in computer electrical engineering. She earned her Bachelor's degree in Computer Engineering in 2011 (average grade 9.37) and Master's degree in 2012 (perfect average grade 10.00) from the Faculty of Technology in Čačak. Her Master's thesis, 'Software Support for Active RFID Localization,' was supervised by Prof. Dr. Siniša Ranđić. Dijana's research spans multiple domains within computer engineering, with recent publications focusing on practical applications of technology in diverse fields. Her work demonstrates strong expertise in IoT systems, machine learning applications, blockchain technology, and human-computer interaction. She has published extensively on topics including earthquake detection using mobile phones, predictive models for identifying students with difficulties in online learning, medical applications like ECG monitoring with Arduino, agricultural engineering applications, and infrastructure monitoring systems. Award for best first-year student at Faculty of Technology in Čačak Award for best second-year student at Faculty of Technology in Čačak Her professional activities include serving as Student Vice-Dean at the Technical Faculty in Čačak from December 2010 to September 2012, participating in the TEMPUS SIGMUS project, and completing an internship at D.O.O. DOCUS from June 2013 to June 2014. She was a scholarship holder of the Ministry of Education, Science and Technological Development from April 2013 to March 2017 and has been involved in significant research projects including 'Development and modeling of energy efficient, adaptable, multiprocessor and multisensory electronic low-power systems' (TR 32043) and the 'German-Serbian Collocation Dictionary for German Language and Teaching' (DeSKoll).

Stephen McKennaView profile

Professor

• Computer Vision
• Biomedical Image Analysis
• Pattern Recognition
• +3 more

Stephen McKenna is a Professor of Computer Vision at the School of Computing, University of Dundee, UK. His research spans computer vision, biomedical image analysis, and machine learning, with a focus on medical diagnostics and assistive technologies. Doctor of Philosophy, Automated Analysis of Papanicolaou Smears, University of Dundee (1994) Bachelor of Science, University of Edinburgh (1990) McKenna's work integrates artificial intelligence with biomedical applications, including skin lesion classification, gland segmentation, and multimodal risk stratification for diabetes patients. His projects often bridge computer science with healthcare and social care challenges. His recent publications highlight advancements in deep learning for medical diagnostics, such as automated lipoprotein profiling and porcine pathology inspection. Collaborations with institutions like the University of Cambridge and funding from the Engineering and Physical Sciences Research Council underscore his interdisciplinary impact. McKenna actively contributes to academic service through editorial roles in journals like Scientific Data and Machine Vision and Applications. He has supervised research teams and datasets, including the Multimodal Focused Interaction Dataset and QMB Morning Dataset.

Po YangView profile

Professor

• Machine Learning
• Pervasive Computing
• Smart Sensing
• +4 more

Professor Po Yang is a distinguished academic at the School of Computer Science , University of Sheffield , where he holds the title of Professor of Pervasive Intelligence . Previously, he led research at Liverpool John Moores University (2015-2019) before joining Sheffield. His educational background spans Wuhan University (BSc), University of Bristol (MSc), and Staffordshire University (PhD). Research Interests include Pervasive and Mobile Intelligence Machine Learning for Healthcare Smart Sensing Systems Data Science Applications in Precision Agriculture Image Analysis for Medical and Industrial Uses Publications (>100 journals, >80 conferences) demonstrate expertise in AI-driven solutions for Parkinson’s disease , Alzheimer’s progression , and agricultural pest management . His work integrates IoT , tensor multi-task learning , and federated learning to address real-world challenges. Grants totaling over £1.1M as Principal Investigator from Innovate UK , BBSRC , EPSRC , and other bodies focus on AI-enabled Sustainable Agriculture Mobile Intelligence for Pest Management Federated Learning Privacy Solutions IoT-based Healthcare Systems Labs & Teams include leadership of the Pervasive Computing Research Group and collaborative projects with institutions like MRC, Worldwide Universities Network, and industrial partners.

• Machine Learning
• Deep Learning
• Signal and Image Processing
• +12 more

BOHI Amine is a researcher at CESI (School of Engineering and Digital Tools, Department of Computer Science), with a PhD in Computer Science from the University of Toulon (2017). His academic profile spans disciplines including Machine Learning, Signal and Image Processing, and Biomedical Engineering, with a focus on applications in Digital Health and Human-Robot Interaction. Education : PhD in Computer Science (University of Toulon, 2013-2017); Master 2 in Computer Science (University of Fes, Morocco, 2009-2011); Bachelor’s in Mathematical and Computer Sciences (University of Fes, 2006-2009). His research interests include: Machine Learning and Deep Learning Signal and Image Processing 3D Shape Analysis Computer Vision Digital Health Biomimetic Feature Design BOHI’s publications reflect expertise in facial emotion recognition for elderly care, cortical folding modeling, and soft tissue organ deformation analysis using MRI. He supervises diverse research internships, mentoring students from institutions like Sorbonne Paris Nord, Université de Technologie King Mongkut, and INP Grenoble. Current projects involve developing intelligent solutions for emotional interaction with elderly individuals suffering from neurodegenerative disorders, in collaboration with VyV3 Bourgogne. He also contributes to the design of multimodal datasets and wearable health monitoring systems. BOHI’s technical work includes contributions to maritime surveillance systems (PARE project) and semantic information platforms, demonstrating a capacity to bridge theoretical research with practical applications across domains.

• Biomedical Image Processing
• Mathematical Analysis
• Computational Geometry
• +3 more

Giacomo Nardi is an Associate Professor (Maître de Conférences) at INSA Rouen starting September 2025, holding a PhD in Mathematics from Sorbonne University. His research bridges mathematical analysis and biomedical image processing through extensive collaborations with the Institut Pasteur in Paris, focusing on developing advanced mathematical frameworks for biological imaging applications. His educational background includes: PhD in Mathematics, Sorbonne University (Pierre and Marie Curie), thesis: "On a characterization of the relaxation of a generalized Willmore functional" under supervisors S. Masnou and E. Mascolo. Nardi's work spans Biomedical Image Processing and Mathematical Analysis , evolving from foundational contributions in calculus of variations (Willmore functionals, BV functions) to cutting-edge biomedical applications. His current research employs topological data analysis and geometric methods to solve complex biological imaging problems including 3D segmentation of hematopoietic stem cells, receptor dynamics analysis, and retinal degeneration quantification. This interdisciplinary approach integrates deep mathematical theory with practical biological challenges through close partnerships with biologists at the Institut Pasteur. Analysis of his publication trajectory reveals a decisive shift toward biomedical applications since 2016, with recent work (2022-2025) dominating in high-impact imaging journals and conferences. His research consistently applies geometric measure theory and functional analysis to innovate in cellular dynamics modeling and 3D image segmentation, demonstrating exceptional versatility across pure mathematics and translational biomedical research. Nardi actively mentors the next generation of researchers: Postdoctoral supervision: Somoballi Ghoshal (2024-present, Institut Pasteur) on persistent homology-based 3D cell segmentation in cardiac tissue. PhD supervision: Benjamin Dorra (defense March 5, 2025, Medetia/Institut Pasteur) on AI-driven histopathology for ciliopathy models, resulting in ISBI 2024 award-winning publications. He co-develops the open-source Fshapestk (Functional shapes toolkit) and collaborates with J.-C. Olivo-Marin's Quantitative Image Analysis Unit at the Institut Pasteur, maintaining strong ties between theoretical mathematics and experimental biology through ongoing joint projects in cellular dynamics and tissue imaging.

• Predictive Analytics
• Health Informatics
• Online Digital Platforms
• +2 more

Da Xu is an Assistant Professor of Management Information Systems at the School of Business Administration, University of Mississippi. He previously served as an Assistant Professor at California State University, Long Beach. His academic journey includes a Ph.D. in Business Administration from the University of Utah (2020), an M.S. in Management Science from University of Science and Technology of China (2014), and a B.S. in Management Information Systems from Zhejiang University of Finance & Economics (2011). Ph.D. Business Administration - University of Utah (2020) M.S. Management Science - University of Science and Technology of China (2014) B.S. Management Information Systems - Zhejiang University of Finance & Economics (2011) His research focuses on predictive analytics and deep learning applications in health informatics , with particular emphasis on online digital platforms and data mining for business intelligence . His work addresses critical challenges in patient management, disease progression prediction, and data quality improvement across healthcare and business domains. Recent publications demonstrate expertise in chronic disease modeling , including hepatitis B deterioration paths , hip fracture outcomes , and cardiovascular patient records . He employs advanced techniques like attention-based deep learning , graph neural networks , and hierarchical multilabel classification to solve complex healthcare analytics problems. He actively contributes to academic discourse through presentations at premier conferences including Conference on Information Systems and Technology (CIST) , Workshop on Information Technologies and Systems (WITS) , and INFORMS Workshop on Data Science . His research appears in top journals such as Journal of Management Information Systems and IEEE Journal of Biomedical and Health Informatics .

Hoda AllahbakhshiView profile

Researcher

• GIScience
• Spatio-temporal data analysis
• Sensor-based human mobility analysis

Hoda Allahbakhshi is a Researcher at the Digital Society Initiative of the University of Zurich , affiliated with the Faculty of Science and Department of Geography . Her work focuses on GIScience , spatio-temporal data analysis , and sensor-based human mobility analysis , particularly addressing accessibility challenges for mobility-restricted individuals. Research Themes include: Development of spatial accessibility analytics frameworks Integration of multi-sensor tracking data (GPS, accelerometers) Age-specific physical activity detection modeling Participatory approaches to urban accessibility mapping Her scientific contributions (2017-2024) analyze mobility patterns through big data and urban infrastructure interactions , with recent emphasis on microscale street elements and inclusive urban environments . Current affiliations: Department of Geography , Digital Society Initiative (University of Zurich).

Ihor SmalView profile

Assistant Professor

• Image Analysis
• Smart Microscopy
• Artificial Intelligence
• +7 more

Ihor Smal is an Assistant Professor at the Department of Biology, Faculty of Science, Utrecht University. He has held previous positions at Erasmus MC (2009-2018) and TU Delft (2019-2020). His research bridges statistical image processing, smart microscopy, and explainable AI for biological motion analysis, with applications in automated real-time imaging systems. Education: M.Sc. in Physics and Electrical Engineering, Ivan Franko National University of Lviv (1999, cum laude) Professional Doctorate in Engineering (PDEng), Eindhoven University of Technology (2005) PhD in Biomedical Imaging, Erasmus University/MC (2009) His work focuses on developing AI-driven solutions for motion registration, particle tracking, and image understanding in biological systems. He leads the NWO IMAGINE! program's Tech Work Package 2, aiming to automate imaging experiments using explainable AI. The 15 most recent publications highlight his contributions to deep learning for CT segmentation, particle tracking, diffusive state modeling, and real-time imaging optimization. Key subfields include neural networks, biomedical signal processing, and automated microscopy. Scientific Awards: VENI grant Erasmus MC Fellowship NWO BBoL grant Students: Vincent Hellebrekers Daan te Rietmole His lab at Utrecht University, accessible via www.smal.ws, collaborates with institutions like Erasmus MC and TU Delft, leveraging facilities such as the Utrecht Biology Imaging Center.

• Mathematical Biology
• Computational Biology
• Ecotoxicology
• +4 more

Karen Watanabe is an Associate Professor in the School of Mathematical and Natural Sciences at Arizona State University (ASU). She is also affiliated with the School of Medicine and Advanced Medical Engineering and holds the title of Senior Global Futures Scholar within the Global Futures Scientists and Scholars program. Her work integrates mathematical modeling with biological systems to address environmental and reproductive health challenges. Education: Ph.D. in Mechanical Engineering (Bioengineering focus) – University of California, Berkeley M.S. in Mechanical Engineering – University of California, Berkeley B.S. in Chemical Engineering (Biomedical Engineering) – Northwestern University Research Focus: Dr. Watanabe develops computational models to understand how environmental chemicals affect living organisms, particularly focusing on reproductive health. Her current projects include modeling ovarian development in mice and the hypothalamic-pituitary-gonadal axis in fathead minnows. She also works on dynamic energy budget models for fish and quantitative adverse outcome pathway (AOP) models for predictive toxicology. Recent Research Trends: Her recent publications emphasize the integration of computational tools with biological data to predict reproductive outcomes from chemical exposure. This includes building databases like MOTHER-DB for ovarian histology and developing AOPs for endocrine disruptors. Awards and Recognition: Scientific and Technological Achievement Awards – U.S. Environmental Protection Agency Grants and Projects: Dr. Watanabe is the Principal Investigator on several active and completed grants, including: NIH-funded project on in vitro to in vivo extrapolation of toxicant effects on ovarian function NSF-funded MOTHER project for multispecies ovary tissue histology Workforce training and environmental health education initiatives Research Website and Profiles: Visit her research website and ORCID profile for more details.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +6 more

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.