Find Academic Supervisors

• Artificial Intelligence in Medicine
• Orthopedics
• Bone Tumors
• +5 more

Claus Wimmer is a researcher at the Chair of Thermodynamics within the Faculty of Mechanical Engineering at the Technical University of Munich (TUM), holding the position of Researcher. His work bridges mechanical engineering principles with clinical medicine, specifically focusing on orthopedic applications through interdisciplinary collaboration with medical institutions. His primary research interests include: Artificial Intelligence in Medical Imaging and Diagnosis Orthopedic Surgery and Arthroplasty Optimization Bone Tumor Classification and Analysis Wearable Sensor Technology for Gait Assessment Machine Learning for Surgical Outcome Prediction Digital Health Implementation in Clinical Settings Analysis of Wimmer's recent publications (2024-2025) reveals a concentrated research trajectory in developing AI-driven solutions for orthopedic challenges. Key trends include deep learning applications for bone tumor segmentation in radiological images, predictive modeling of arthroplasty failures using national registry data, and systematic evaluations of wearable technology for postoperative rehabilitation. His work consistently emphasizes practical clinical translation, with frequent collaborations leveraging German healthcare datasets and addressing real-world implementation barriers in medical AI. No scientific awards were documented in the available information. Details regarding student supervision, research grants, laboratory infrastructure, or collaborative research teams were not provided in the source material, though his publication patterns suggest active engagement with clinical partners in orthopedic departments and medical technology development.

Amanda BergView profile

Adjunct Associate Professor

• Computer Vision
• Thermal Imaging
• Machine Learning
• +5 more

Amanda Berg is an Adjunct Associate Professor at the Department of Electrical Engineering, Linköping University, affiliated with the Computer Vision Laboratory (CVL). Her research focuses on thermal imaging, computer vision, and machine learning applications in environmental monitoring and security systems. Key research areas include: Thermal-Visible Spectrum Correlation 3D Reconstruction from Satellite Data Anomaly Detection in Wild Environments Multispectral Video Analysis Wildlife Management via UAV Imagery Her work spans computer vision, anomaly detection, and thermal imaging, with recent publications on neural radiance fields, wildfire detection benchmarks, and multispectral tracking algorithms. The 15 most recent articles demonstrate a strong focus on thermal infrared applications and AI-driven image analysis.

• Machine Learning
• Deep Learning
• Cognitive AI
• +11 more

Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005

Maria E. LopezView profile

Adjunct Assistant Professor

• Computer Science
• Machine Learning
• Data Mining
• +3 more

Maria E. Lopez is an Adjunct Assistant Professor in the Department of Foreign Language at Virginia Commonwealth University. While her institutional affiliation is in linguistics, her research focuses on advanced computational methods in machine learning and distributed systems.

• Machine Learning
• Computer Science
• Artificial Intelligence
• +3 more

Dr. Petra Bevandic is a researcher at the Faculty of Engineering at Universität Bielefeld within the Machine Learning Group . Her work spans key areas in computer vision and machine learning. Primary Affiliation: Faculty of Engineering, Machine Learning Group, Universität Bielefeld Research Interests: Specializes in semantic segmentation and anomaly detection Focus on open-set recognition and domain adaptation Active in diffusion models and garment reconstruction Scientific Contributions: Pioneering work on virtual try-on/try-off systems Developing robust methods for out-of-distribution detection Advancing multi-domain image segmentation techniques

Thomas LiebigView profile

Assistant Professor

• Distributed Data Mining
• Reinforcement Learning
• Multi-Agent Learning
• +11 more

Thomas Liebig is an Assistant Professor of Smart City Science at TU Dortmund University's Artificial Intelligence Unit and a Principal Investigator at the Lamarr Institute for Machine Learning and Artificial Intelligence. He also serves as a senior AI architect supporting Materna SE in integrating artificial intelligence into the public sector and industry, and previously held an Adjunct Professor position in Data Privacy and Ethics at the University of Nicosia. His research focuses on distributed data mining, reinforcement learning, multi-agent systems, privacy-preserving learning, and spatio-temporal modeling. Liebig has made significant contributions to graph neural networks, probabilistic modeling with sum-product networks, and traffic flow prediction systems. His work bridges theoretical machine learning with practical applications in smart cities, transportation, and healthcare. Liebig's recent publications demonstrate a strong trend toward privacy-preserving machine learning techniques, particularly differential privacy applied to distributed settings. His work on sum-product networks has advanced interpretable probabilistic modeling, while his research on graph neural networks has explored novel connections with classical algorithms like PageRank. His practical applications span transportation systems, healthcare, and smart city infrastructure. Liebig has supervised numerous graduate students working on topics including blockchain-based data analysis, traffic prediction systems, and privacy-preserving machine learning. His research has been supported by projects including SFB876 (Providing Information by Resource-Constrained Data Analysis) and collaborations with industry partners. He is actively involved in the academic community, having co-organized workshops such as Mining Urban Data at ICML and Computational Transportation Sciences. His work appears in top venues including IEEE International Conference on Knowledge Graph, ECML/PKDD, and IEEE Transactions on Intelligent Transportation Systems.

• Machine Learning
• Computer Vision
• Bioinformatics
• +4 more

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.

• Machine Learning
• Data Analysis
• Energy Efficiency
• +3 more

Mengjie Han serves as Associate Professor in Microdata Analysis and Senior Lecturer in Data and Information Management within the Department of Information and Technology at Dalarna University. Her academic profile demonstrates a strong interdisciplinary focus connecting computational methods with sustainability applications. Dr. Han's research interests center on applying machine learning and artificial intelligence techniques to solve complex sustainability challenges, particularly in urban environments and energy systems. Her work spans multiple domains including positive energy districts characterization, human mobility prediction, building energy optimization, and advanced classification methods. She has developed expertise in integrating fuzzy logic, genetic algorithms, and natural language processing with practical engineering applications to improve energy efficiency in buildings and transportation systems. Analysis of her recent publications reveals a clear trajectory toward increasingly sophisticated applications of AI in sustainability contexts, with notable emphasis on positive energy districts research. Her 2024-2025 publications demonstrate methodological innovation in multi-label classification, fuzzy decision systems, and optimization algorithms specifically tailored for energy applications. The interdisciplinary nature of her work bridges computer science, urban planning, and environmental engineering. Dr. Han teaches advanced courses that reflect her research expertise, including Research Methodology (GIK34Y), Complexity and operations analysis methods (AMI23C), and Applied Big Data and Cloud Computing (GIK2Q3). These courses provide students with both theoretical foundations and practical applications of data science methods.

Guan XuView profile

Assistant Professor

• Biomedical Imaging
• Photoacoustic Imaging
• Biomechanics
• +4 more

Guan Xu, PhD (Assistant Professor at University of Michigan Medical School), holds dual appointments in the Department of Ophthalmology and Visual Sciences and Biomedical Engineering. His expertise lies in biomedical imaging technologies, particularly photoacoustic imaging and biomechanical analysis for disease diagnosis. Predoctoral Award: Congressionally Directed Medical Research Programs Postdoctoral Fellowship: American Heart Association Career Development Award: American Gastroenterology Association Senior Research Award: Crohn’s and Colitis Foundation R37 MERIT Award: National Cancer Institute Dr. Xu's research focuses on advancing photoacoustic imaging for clinical applications, including: Ocular tumor diagnosis and heterogeneity measurement Glaucoma-related biomechanical analysis of aqueous veins and sclera Prostate cancer aggressiveness assessment via endoscopic probes Inflammatory arthritis evaluation using LED-based systems Intestinal fibrosis quantification through spectroscopic imaging Biomechanical modeling for disease progression His 2024-2025 publications highlight advancements in: Multi-modal imaging platforms for clinical translation Deep learning integration in 3D ultrasound/photoacoustic analysis Bioengineering of ocular tissue models Spectral analysis for cancer grading Biomechanical markers in inflammatory diseases Finite element analysis of ocular structures Dr. Xu leads the Biomedical Imaging and Biomechanics Lab at Kellogg Eye Center, focusing on: Developing novel imaging systems for disease characterization Quantifying tissue deformation under mechanical variables Creating translational tools for real-time clinical diagnostics Collaborating across disciplines for technology validation

Agata FilipowskaView profile

Assistant Professor

• search systems
• profiling
• information extraction
• +9 more

Agata Filipowska is an Assistant Professor at the Department of Information Systems, Institute of Informatics and Quantitative Economics, Poznań University of Economics and Business. Her work bridges computer science, semantic web technologies, and management studies, with a focus on data quality, profiling, and business process management. PhD in Computer Science (2010) and Economics (2009) Active in semantic technologies, machine learning, and art market analytics Key projects include FP7 LOD2 and USE-ME.GOV . Research Interests: Search systems, profiling, information extraction, business process management, and semantic web applications. Her interdisciplinary approach addresses challenges in data quality, user behavior, and art market analysis. Article Trends: Recent work emphasizes knowledge graph ensembles (2024), art market data enrichment, and mobility-based behavioral biometrics. Earlier studies focus on BPM, social telco applications, and Polish language NLP. Activities: She contributes to semantic interoperability, microgrid energy forecasting, and telecom data analysis. Her collaborations span 50 co-authors, including Anna Fensel, Dominik Filipiak, and Piotr Kałużny.

Hyun Oh SongView profile

Associate Professor

• Machine Learning
• Combinatorial Optimization
• Algorithms
• +7 more

Hyun Oh Song is an Associate Professor in the Department of Computer Science and Engineering at Seoul National University , focusing on machine learning, combinatorial optimization, and algorithms. Previously, he was a Research Scientist at Google Research and a Postdoctoral Fellow at Stanford University . Education : Ph.D. in Computer Science (2014) from UC Berkeley , B.S. from Hanyang University Research Interests : Solving combinatorial problems in AI, with applications in neural network compression, adversarial robustness, and reinforcement learning. Teaching : Courses include Deep Learning, Machine Learning, and Probability & Computing at SNU. His startup DeepMetrics raised $2.2M in combined VC and government funding. Scientific Awards : Samsung Lee Kun Hee Scholarship Foundation (5-year Ph.D. fellowship) Publications : 15 recent works span efficient CNN compression (ICML2022), adversarial attack mitigation (ICML2022), neural relation graphs for label noise detection (NeurIPS2023), and Co-Mixup data augmentation (ICLR2021).

Prateek MittalView profile

Professor

• Privacy-preserving systems
• Secure systems
• Privacy enhancing technologies
• +12 more

Prateek Mittal is a Professor in the Department of Electrical and Computer Engineering at Princeton University, with associated faculty appointments in the Department of Computer Science and the Center for Information Technology Policy. His leadership roles include Associate Chair of the ECE Department (2025) and Director of Undergraduate Studies (2025), demonstrating his significant institutional impact. Mittal's research focuses on privacy-preserving and secure systems, with particular expertise in privacy enhancing technologies (including anonymous communications and statistical data privacy), adversarial machine learning, and Internet/network security. His methodological approach draws on data science, network science, distributed systems, and applied cryptography. He has made foundational contributions to website fingerprinting research, developing precision optimizers that revolutionized open-world traffic analysis attacks. His recent publications reveal a strategic shift toward examining security and privacy challenges in large language models and AI systems, with research on context manipulation attacks, privacy auditing frameworks, and robust defenses against adversarial inputs. This represents a natural evolution of his work from traditional network security to the frontier of AI security. Outstanding Paper Award and Honorable Mention, ICLR 2025 ACM Distinguished Member (2024) Distinguished Alumni Awards from IIT Guwahati and UIUC (2024) ACM Grace Murray Hopper Award (2023) Multiple Caspar Bowden Award Runner Up recognitions (2020-2022) National Science Foundation CAREER Award (2016) Professor Mittal has received consistent recognition for teaching excellence through Princeton Engineering's Commendation List in multiple years. His research program has been supported by prestigious funding from ARO, ONR, NSF, and industry partners including Google, Facebook, IBM, Intel, and Cisco. He serves in significant leadership roles including Deputy Chair of the ACM Grace Murray Hopper Award Committee (2025-2026) and Editorial Board member for Privacy Enhancing Technologies.

Mikhail ShapiroView profile

Professor

• Biomedical Engineering
• Synthetic Biology
• Neuroscience
• +3 more

Mikhail Shapiro is the Max Delbrück Professor of Chemical Engineering and Medical Engineering at the California Institute of Technology , and an Howard Hughes Medical Institute Investigator since 2021. His research laboratory develops acoustic and magnetic biomolecular tools for imaging and controlling cellular function in vivo. Chemical Engineering Faculty Medical Engineering Affiliation Howard Hughes Medical Institute Investigator Research focuses on noninvasive control of biological systems through: Acoustic reporter genes (ARG 2.0) for real-time tumor monitoring Ultrasound neuroimaging via acoustically transparent cranial windows Gas vesicle engineering for cellular manipulation Microrobotics with acoustic hydrogels His 2024-2025 publications demonstrate breakthroughs in: Nonlinear ultrasound for deep-tissue visualization Acoustic percolation switches for targeted drug delivery SEMPER systems for multi-gene expression control Scientific recognition includes: HHMI Investigator (2021-) Vilcek Foundation Prize in Biomedical Innovation Roger Tsien Award for Chemical Biology Packard Fellowship in Science and Engineering

Stergios ChristodoulidisView profile

Researcher

• Artificial Intelligence
• Medical Imaging
• Earth Sciences
• +7 more

Stergios Christodoulidis is a researcher specializing in artificial intelligence and its applications in medical imaging and earth sciences. His work spans deep learning methodologies for image analysis, including multiple instance learning, dosiomics, and registration techniques. He contributes to interdisciplinary research in computational biology, digital pathology, and radiomics. Core research in AI-driven medical image analysis Contributions to earth sciences through remote sensing and satellite imagery Focus on low-data regimes and uncertainty quantification in AI models His recent publications highlight innovative approaches in: Visually grounded bias discovery in clinical AI Conformal prediction adaptation for vision-language models Self-supervised learning for histopathology and radiology applications Integration of biological constraints in machine learning workflows

Halil KilicogluView profile

Associate Professor

• Biomedical Informatics
• Natural Language Processing
• Computational Semantics
• +4 more

Halil Kilicoglu is an Associate Professor at the School of Information Sciences (iSchool), University of Illinois at Urbana-Champaign. He holds affiliate appointments at the National Center for Supercomputing Applications , Division of Nutritional Sciences , Personalized Nutrition Initiative , and Center for Health Informatics . His research bridges Natural Language Processing , Biomedical Informatics , and Scientific Reproducibility . Education: PhD in Computer Science (2012), Concordia University Previous Role: Staff Scientist, U.S. National Library of Medicine (NIH) Research Focus: Kilicoglu develops advanced NLP and machine learning techniques to extract and organize knowledge from biomedical texts. His work enhances clinical trial transparency , drug repurposing , literature-based discovery , and scientific communication . Current projects include automated assessment of randomized controlled trials and knowledge graph construction for biomedical domains. Recent Article Trends: His publications emphasize transformer models , retrieval-augmented generation , and multi-label classification applied to citation integrity , diet-microbiome associations , and clinical trial reporting . Scientific Awards: TrustNLP 2023 Best Paper SemEval 2021 Best System Paper IMIA Yearbook Best Paper (2020, 2017) AMIA Distinguished Paper (2016, 2007) Students: Mentors PhD candidates in information science and informatics , including Janina Sarol, Lan Jiang, Mengfei Lan, Shufan Ming, Gibong Hong, Evan Guerra, and Joe Menke. Labs & Teams: Leads a lab at the iSchool focused on biomedical text mining , collaborating with institutions like NIH and SpringerNature. Projects include SemRep extension , MENAGERIE tool , and COMBINI initiative .