Find Academic Supervisors

• Signal Processing
• Probability
• Inverse Problems
• +7 more

Christian Heinrich is a Professor at the University of Strasbourg, affiliated with the ICube - IMAGeS team (Images, Modeling, Learning, Geometry and Statistics). His research focuses on signal processing, inverse problems, machine learning, and optical imaging, particularly polarimetric imaging and biological tissue analysis. He collaborates closely with Jihad Zallat (ICube-TRIO) and has contributed to the development of methodologies for medical imaging and computational neuroscience. Education: Electrical Engineering (Supélec, 1990), Master's in Signal Processing (Université Paris-Sud/Supélec, 1994), PhD in Signal Processing (Université Paris-Sud/Supélec, 1997), Aggregation in Applied Physics (1998), Habilitation (University of Strasbourg, 2008) His research spans polarimetric imaging techniques for tumor monitoring, brain connectivity analysis through multimodal MRI and fMRI, and advanced image processing methods like non-local means filtering and spatial mixture models. Recent work explores cryo-EM reconstruction, topological preservation in surgical planning, and uncertainty quantification in medical imaging. Christian Heinrich co-leads the IRIV master’s degree (Imaging, Robotics, and Engineering for the Living) and the Franco-German DaSAI double degree program in Data Science and Artificial Intelligence. He has published extensively in journals like Biomedical Optics Express , IEEE Transactions on Biomedical Engineering , and Medical Image Analysis .

Albert GüvenişView profile

Professor

• Biomedical Engineering
• Molecular Imaging
• Radiomics
• +5 more

Professor Albert Güveniş is a distinguished faculty member in the Department of Biomedical Engineering at Boğaziçi University's Faculty of Engineering. Holding a PhD from Drexel University, he previously served as an Assistant Professor at the University of Pennsylvania's Radiology Department specializing in Positron Emission Tomography. His current research focuses on high-efficacy biomedical system design and cancer theranostics through molecular imaging and radiomics. His primary research interests span Molecular Imaging , Radiomics , Cancer Theranostics , and Clinical Engineering , with emphasis on improving diagnostic/therapeutic systems for cancer and Alzheimer's disease. His group develops computational frameworks for tumor delineation, radiotherapy planning optimization, and early cancer detection using PET/CT and SPECT-CT technologies. His recent publications (2019-2023) demonstrate strong focus on radiomic biomarkers for cancer staging, treatment response prediction, and tumor characterization across multiple disease sites including lung, renal, and oropharyngeal cancers. The work integrates machine learning , Monte Carlo simulations , and statistical decision theory to solve clinical challenges in precision medicine. Research Fellowship from Drexel University BUVAK Publication Award for Class A publication Best Oral Presentation Award at Budapest conference (2017) ISO Innovation Award among 5,000+ projects Reviewer Appreciation Certificate (2019) Professor Güveniş actively mentors PhD and MSc students, with recent graduates including Ayşegül Oral and Alpaslan Koç. His research receives international collaboration support, particularly with US-based cancer research laboratories. He leads projects funded by major grants including 1/2 M USD from EU educational programs. His core team includes Didar Talat, Alpaslan Koc, and Niyazi Erturk, with frequent international researcher exchanges.

• Ecoacoustics
• Biodiversity Monitoring
• Machine Learning Applications
• +5 more

Sarab Sethi is a Lecturer in the Department of Life Sciences at Imperial College London, where he develops computational approaches for biodiversity monitoring through ecoacoustics. His work integrates machine learning, sensor technology, and ecological fieldwork to address conservation challenges, particularly in tropical ecosystems. His research focuses on advancing soundscape ecology through innovative methodologies including random forest analysis of avian migration patterns, development of low-power acoustic monitoring systems, and standardization of acoustic indices. Key contributions involve demonstrating how soundscapes predict species occurrence in tropical forests and creating open-source tools like the SAFE Acoustics network for real-time ecosystem monitoring. Recent publications reveal a strong trend toward scalable biodiversity assessment solutions, with increasing emphasis on robotics-assisted surveys, virtual acoustics validation, and robust machine learning models that function effectively under noisy field conditions. His work consistently bridges theoretical ecology with practical conservation applications. Dr. Sethi actively contributes to scientific discourse through peer review for high-impact journals including Nature , Nature Reviews Biodiversity , and Proceedings of the National Academy of Sciences , demonstrating significant scholarly engagement within the ecological research community. He leads the development of critical infrastructure for ecoacoustic research, including the Acoustic Index User's Guide and validation frameworks for ecoacoustic metrics, which enable standardized biodiversity assessment across global ecosystems. His fieldwork in Malaysian Borneo provides foundational data for tropical conservation applications.

Jeong-Woo ChoiView profile

Professor

• Artificial Intelligence
• Audio Signal Processing
• Machine Learning
• +3 more

Professor Jeong-Woo Choi is a faculty member in the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), specializing in cutting-edge audio artificial intelligence research. His work bridges electrical engineering and machine learning with applications in industrial monitoring and immersive media technologies. His primary research focuses on Audio Signal Processing and Sound Source Separation , developing next-generation AI systems capable of isolating and classifying complex soundscapes. Key areas include spatial semantic segmentation of acoustic scenes, multichannel signal analysis, and transformer-based neural architectures for abnormal sound detection in drones, factory pipes, and border surveillance systems. His team pioneers techniques mimicking human auditory processing through waveform analysis and directional cues. Professor Choi's team secured first place in the 2025 IEEE DCASE Challenge against 86 global competitors, achieving unprecedented 11 dB performance in sound separation metrics. This breakthrough enables advanced applications in AR/VR spatial audio editing and industrial anomaly detection. His scientific recognition includes: First place in 'Spatial Semantic Segmentation of Sound Scenes' at IEEE DCASE Challenge 2025 He actively mentors graduate researchers including PhD candidate Kwon Young-hoo and Master's student Kim Do-hwan. His lab operates under significant funding from South Korea's National Research Foundation (Mid-career Researcher Support Project), Ministry of Education (STEAM Research Project), and Defense Acquisition Program Administration (Future Defense Research Center). The research team maintains KAIST's leadership in audio AI through their specialized laboratory within the Electrical Engineering department, recently developing the world's highest-performance sound separation model combining Transformer and Mamba architectures.