Find Academic Supervisors

Venkatesh SaligramaView profile

Professor

• Machine Learning
• Video Analysis
• Statistical Signal Processing
• +3 more

Venkatesh Saligrama is a Professor in the Department of Electrical and Computer Engineering at Boston University, with affiliations in Systems Engineering, the Center for Information and Systems Engineering, and the College of Arts & Sciences’ Computer Science division. He holds a PhD from MIT (1997) and leads the Data Science & Machine Learning Lab. His research focuses on Machine Learning, Video Analysis, Statistical Signal Processing, and Network Science, with notable contributions to vision-language models, bandit algorithms, and synthetic data strategies. He has received prestigious awards like the NSF CAREER Award (2005) and Presidential Early Career Award (2003). His teaching includes courses on signals, stochastic processes, and digital communication systems. Saligrama’s work bridges theoretical foundations and applied systems, emphasizing efficient learning under constraints. His recent publications explore zero-shot detection, data-efficient pretraining, and safe optimization techniques. Beyond academia, his research impacts domains like autonomous systems, healthcare prediction, and cybersecurity. He actively contributes to editorial roles, conferences, and interdisciplinary initiatives, fostering collaborations across engineering and computer science.

Claes LundströmView profile

Adjunct Professor

• Medical Imaging
• Artificial Intelligence
• Digital Pathology
• +4 more

Claes Lundström is an Adjunct Professor at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) school. His primary research focuses on medical imaging, integrating machine learning, visualization, and human-computer interaction in clinical settings. He leads the technical side of digital pathology research at the Center for Medical Image Science and Visualization (CMIV), directing national-scale grants and serving as Arena Director for the Analytic Imaging Diagnostics Arena (AIDA). Since 2010, he has held dual roles as Research Director at Sectra AB and academic researcher, driving innovations that translate into commercial healthcare solutions. Lundström's work spans AI-driven diagnostics, uncertainty visualization, and precision orthopedics, with contributions recognized through leadership in major projects like the €70M BIGPICTURE initiative. His academic credentials include a PhD (2007) and Docent degree (2014), alongside extensive industry collaboration. Education: PhD in 2007 and Docent degree in 2014 from Linköping University. PhD: 2007 Docent: 2014 Research interests emphasize AI integration in diagnostics, particularly digital pathology and medical imaging workflows. His work addresses challenges like domain adaptation in AI models, uncertainty quantification in segmentation tasks, and interactive visualization tools for clinicians. Recent projects include the VAI-B platform for AI validation in breast imaging and the BigPICTURE collaboration for global pathology data sharing. Key grants include leadership roles in national-scale projects, such as the AIDA arena and SCAPIS study-based AI lab. His contributions to AI ethics and clinical adoption include studies on human-AI collaboration and quality assurance frameworks. Lundström also oversees labs like CMIV and AIDA, fostering cross-disciplinary research in medical imaging and AI.

Anish SevekariView profile

Researcher

• Machine Learning
• Algorithms
• Optimization
• +3 more

Anish Sevekari is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on machine learning, algorithms, optimization, and theoretical computer science. He investigates topics such as neural network training dynamics, generative models, algorithmic analysis beyond worst-case scenarios, and efficient inference techniques. His work bridges theoretical foundations with practical applications in areas like probabilistic modeling and combinatorial optimization. Key research interests include normalizing flows, ensemble methods, score-based learning, stochastic optimization, and combinatorial algorithms. His recent publications explore acceleration of NCE convergence, progressive ensemble distillation, and provable benefits of score matching. He has published extensively in top-tier venues, with a focus on theoretical guarantees and practical efficiency. His research trends emphasize bridging gaps between machine learning and traditional algorithmic analysis, particularly in probabilistic frameworks and high-dimensional data problems. No scientific awards or grants are explicitly mentioned in the provided information.

Xingde LiView profile

Professor

• Neurophotonics
• Translational Biophotonics
• Optical Coherence Tomography (OCT)
• +3 more

Xingde Li, PhD, is a Professor at Johns Hopkins University with joint appointments in Biomedical Engineering, Electrical & Computer Engineering, and Oncology. He leads the Laboratory of Biophotonics Imaging Technologies (BIT) and focuses on advancing neurophotonics and translational biophotonics for medical applications. Education : PhD in Physics and Biomedical Optics from University of Pennsylvania (1998); BS in Physics from University of Science and Technology of China (1990). Research Interests : Develops cutting-edge imaging technologies like Optical Coherence Tomography (OCT), two-photon fiberscopy, and AI-assisted imaging for disease diagnostics (e.g., cancer, COPD, preterm birth). His work bridges optics, micro-nano technology, and clinical medicine. Grants & Awards : Received grants from the Bisciotti Foundation Translational Fund (2021 and 2018) and has been featured in high-impact media for innovations like the MAGIC imaging capsule. Labs & Teams : Directs the BIT Lab, collaborating with interdisciplinary teams to translate technologies into clinical tools for disease detection and therapy guidance.

Liming ChenView profile

Professor

• Computer Vision
• Pattern Recognition
• 3D Face Analysis
• +2 more

Liming Chen is a Full Professor and Director of the Department Mathématiques - Informatique at École Centrale de Lyon, Université de Lyon. As a member of the Laboratoire d'Informatique en Image et Systèmes d'Information (LIRIS, UMR 5205), his research spans computer vision, pattern recognition, and multimedia computing. His extensive research portfolio includes 3D face analysis, image/video categorization, affect analysis, and biometric systems. He has led significant projects such as ANR 3D Face Analyzer, ANR Videosense, ANR Omnia, and ANR FAR3D, focusing on advanced recognition systems and multimodal analysis. Professor Chen supervises multiple PhD students and has developed open-source tools for 3D model processing. His work has received recognition through best performance awards at international competitions including SHREC 2011 (3D face recognition) and ImageCLEF 2011 (photo annotation).

Oliver EberleView profile

Researcher

• Explainable AI
• Natural Language Processing
• Digital Humanities
• +3 more

Oliver Eberle is a Researcher at the Machine Learning Group within the Technical University of Berlin (TU Berlin), affiliated with BIFOLD – Berlin Institute for the Foundations of Learning and Data. He holds a Joint M.Sc. in Computational Neuroscience (2017) and a Ph.D. in Machine Learning (2022) from TU Berlin and Humboldt-Universität zu Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing (NLP), and their applications in Digital Humanities and Cognitive Science. Notable projects include XAI methods for transformer architectures, historical corpus analysis, and collaborations with the Max Planck Institute for the History of Science on the Sphaera Corpus. Key contributions include developing frameworks like BiLRP for similarity models, MambaLRP for sequence models, and xMIL for medical imaging. His work bridges technical advancements in AI with interdisciplinary applications in humanities and science.

Summer L. GibbsView profile

Professor

• Nerve-Specific Fluorophores
• Image-Guided Surgery
• Cyclic Immunostaining
• +3 more

Summer L. Gibbs is a Professor and the Douglas Strain Endowed Professor of Biomedical Engineering at Oregon Health and Science University (OHSU). She specializes in developing novel contrast agents and imaging technologies for clinical translation, with a focus on nerve-specific fluorescence imaging and theranostic applications. Education: B.S., 2003, Whitworth College Ph.D., 2008, Dartmouth College (Thayer School of Engineering) Postdoctoral Fellow at Harvard Medical School and Beth Israel Deaconess Medical Center Research Interests: Her lab develops nerve-specific fluorophores (e.g., near-infrared agents) to improve surgical precision and reduce nerve injury. She also pioneers cyclic immunostaining (cyCIF) for multiplex microscopy, enabling spatial analysis of cancer phenotypes and treatment efficacy. Recent work includes imaging tumor margins, drug distribution, and hybrid cancer-immune cells as metastasis biomarkers. Labs/Teams: Her interdisciplinary team collaborates across biomedical engineering, oncology, and surgery to advance translational imaging tools for clinical use.

Ashirbani SahaView profile

Assistant Professor

• Biomedical Engineering
• Computer and Information Sciences
• Data Analytics
• +6 more

Ashirbani Saha is an Assistant Professor in the Department of Oncology at McMaster University, holding the inaugural BRIGHT Run Breast Cancer Learning Health System Chair. She is affiliated with the Michael G. DeGroote School of Medicine and the Escarpment Cancer Research Institute. Her research focuses on applying artificial intelligence (AI) and advanced data analytics to improve breast cancer diagnostics and patient care. She also holds associate memberships in the School of Biomedical Engineering and the Department of Computing and Software at McMaster University. Dr. Saha’s education includes a B.E. in Electronics from IIEST Shibpur (India), an M.A.Sc. and Ph.D. in Electrical and Computer Engineering from the University of Windsor, followed by postdoctoral training at Duke University and St. Michael’s Hospital. Her work spans medical imaging analysis, radiogenomics, and healthcare data science, with an emphasis on breast cancer and neurosurgery applications. Her research trends highlight AI-driven solutions for medical imaging challenges, such as tumor segmentation, radiomics analysis, and cross-dataset generalization. She has also explored work-life balance issues in healthcare professionals and ethical considerations in AI-driven healthcare research. The BRIGHT Run Chair supports her interdisciplinary initiatives to integrate AI and clinical data into a learning health system for breast cancer. Awards: BRIGHT Run Breast Cancer Learning Health System Chair (2021) Grants: $3.2M New Frontiers in Research Fund (2021) and community-endowed BRIGHT Run funding. Labs/Teams: Escarpment Cancer Research Institute, CentRE for dAta science and digiTal hEalth (CREATE).

• Intelligent Interactive Systems
• Behavioral Research at Scale
• Design for Equity and Social Justice
• +3 more

Krzysztof Gajos is a Gordon McKay Professor of Computer Science at the Harvard Paulson School of Engineering and Applied Sciences, leading the Intelligent Interactive Systems Group. His research focuses on principles of intelligent interactive systems, tools for large-scale behavioral research (e.g., LabintheWild.org), and design for equity/social justice. He holds a PhD from the University of Washington and degrees from MIT, with postdoctoral work at Microsoft Research. Education: PhD (University of Washington), M.Eng & B.Sc (MIT) Affiliations: ACM Transactions on Interactive Intelligent Systems (former co-editor-in-chief), General Chair of ACM UIST 2017 Research spans accessible computing, creativity tools, and health informatics. Recent work emphasizes ethical AI, human-AI collaboration, and systems that empower marginalized communities. Awards include the Sloan Fellowship and impactful paper recognitions at CHI, IUI, and COMPASS. Grants: Federal grants supporting PhD students; current focus on mitigating disruptions caused by grant terminations Lab: IIS Group develops systems that balance technical innovation with societal impact

Anders Bjorholm DahlView profile

Professor

• Medical Imaging
• Computer Vision
• Biomedical Engineering
• +6 more

Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.

Christos DiouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +3 more

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Johann KastnerView profile

Professor

• Computed Tomography
• Materials Science
• Non-Destructive Testing
• +3 more

Johann Kastner is a Professor at Upper Austria University of Applied Sciences, leading the Research Center Wels Computed Tomography R&D-Headquarters. He is affiliated with Centers of Excellence in Automotive/Mobility, Energy, and Smart Production. His research focuses on advanced materials characterization using X-ray computed tomography (X-CT), with applications in non-destructive testing, composite materials, and biomedical engineering. Key research areas include porosity analysis in carbon fiber reinforced polymers, phase contrast imaging, and additive manufacturing. He has led over 10 projects, including the EU-funded xCTing initiative (2021–2025) and the X-PRO project (2020–2024), emphasizing industrial CT applications and cross-virtuality data analysis. His work spans 335+ publications, with notable contributions to XCT-based defect detection, material microstructure analysis, and AI-driven image segmentation. Collaborations include COMET K Projects and FTI-Structurförderung grants. He has advised 2 PhD students and actively participates in international conferences and workshops. Laboratory facilities include state-of-the-art XCT systems for 3D microstructural analysis, Talbot-Lau grating interferometry, and augmented reality visualization tools for industrial applications.

Huaxia WangView profile

Assistant Professor

• Wireless Communications
• Deep Learning
• Robotics
• +6 more

Huaxia Wang is an Assistant Professor in the Department of Electrical & Computer Engineering at Rowan University, part of the Henry M. Rowan College of Engineering. He holds a Ph.D. in Electrical Engineering from Stevens Institute of Technology (2018) and previously served as an Assistant Professor at Oklahoma State University (2020–2023). His research focuses on deep reinforcement learning, adversarial networks, meta-learning, graph neural networks, robotic control, and wireless communications. Education: Ph.D., Electrical Engineering, Stevens Institute of Technology (2018) M.S., Electrical Engineering, Stevens Institute of Technology (2014) B.S., Information Engineering, Southeast University, China (2012) Research interests include distributed control in wireless communications, robotic arm control in virtual environments, and multimodal learning for visual inspection. Recent work emphasizes applications in healthcare (e.g., lung ultrasound scoring) and infrastructure monitoring (e.g., concrete defect classification). Scientific Awards: Charles Kao Best Paper Award (2024) Grants/Projects: $100K from AeroDefense (2025–2026) $500K from the Department of Energy (2023–2025) $250K from the Institute for Advanced Engineering, Korea (2022–2027) His work integrates AI-driven solutions for robotics, communications, and healthcare, with a focus on practical applications such as remote robotic control and robust signal processing.

Sheng LiView profile

Adjunct Assistant Professor

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +4 more

Sheng Li is an Adjunct Assistant Professor in the School of Computing at the University of Georgia (UGA). He holds roles such as Graduate Program Faculty and Courtesy Faculty in the Institute of Bioinformatics. Previously, he served as an Assistant Professor at UGA from August 2018 to July 2022. His research focuses on Machine Learning, Computer Vision, Data Mining, Natural Language Processing, Causal Inference, and User Modeling. Li earned his Ph.D. in Computer Engineering from Northeastern University in 2017, following degrees from Nanjing Institute of Post and Telecommunications in China. His work bridges theoretical advancements and practical applications, including AI solutions for healthcare (e.g., medication adherence monitoring), computer vision for wildlife tracking (fish re-identification), and causal inference methodologies. He has received notable awards like the Fred C. Davison Early Career Scholar Award (2022) and the Aharon Katzir Young Investigator Award (2020). His research is funded by grants from agencies such as the US Department of Defense and NIH, supporting projects like Knowledge-Guided Scene Graph Generation and Reasoning for Visual Understanding. Li’s publications span top venues in AI and computer science, addressing challenges in domain adaptation, trustworthy AI, and multimodal learning. He has collaborated on interdisciplinary projects, including bioinformatics and agricultural NLP. His contributions emphasize both technical innovation and societal impact.

• Bioinformatics
• Computational Biology
• Machine Learning
• +5 more

Zafer AYDIN is an Associate Professor at the Computer Engineering Department of Abdullah Gul University. He obtained his B.Sc. and M.Sc. from Bilkent University (Turkey) and his Ph.D. from Georgia Institute of Technology (USA). His research focuses on machine learning applications in bioinformatics, health informatics, and industrial problems. PhD: Georgia Institute of Technology (2008) Postdoc: University of Washington (2008-2011) Previous: Assistant Professor at Bahcesehir University (2011-2014) Research Interests include protein structure prediction, medical imaging analysis, network security, and financial data modeling. His work spans both fundamental bioinformatics research and practical industrial applications. Scientific Awards : 2nd in T0 phase of Respiratory Viral Dream Challenge (2016-2017) 1st in both question 1 and 2 prediction tasks of COVID-19 DREAM Challenge (2020-2021) Teaching includes undergraduate courses in Bioinformatics, Machine Learning, and Algorithms, and graduate courses in Deep Learning and Pattern Recognition.