Find Academic Supervisors

• Deep Learning
• Image and Video Analysis
• Visual Similarity Search
• +3 more

Nikolaus Korfhage is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Mathematics and Computer Science within the Faculty of Mathematics and Computer Science. He is part of Prof. Bernd Freisleben's research group focusing on Distributed Systems and Intelligent Computing. His research emphasizes deep learning applications in image and video analysis, particularly visual similarity search and biomedical data analysis. Korfhage initiated his PhD in 2016 under Prof. Freisleben’s supervision, exploring computational methods for multimedia data processing. His work spans diverse domains including avian physiology analysis, automated music notation transcription, and historical video archive retrieval. Notable projects include the VIVA initiative for content-based video search and the DeepTab system for transcribing organ tablature music. Recent publications highlight advancements in cell segmentation, bat echolocation analysis, and transformer-based species recognition. Korfhage’s contributions bridge computer science and interdisciplinary fields like biology and musicology. He collaborates actively on tools like ElasticHash for semantic image search and MESA for synthetic DNA assessment. His research underscores innovation in both technical methodologies and real-world applications across multimedia, biomedical, and ecological domains.

Horst PosseggerView profile

Researcher

• Computer Vision
• Autonomous Systems
• Multiple Object Tracking
• +3 more

Horst Possegger is a Computer Vision researcher at the Institute of Computer Graphics and Vision at Graz University of Technology, Austria. He holds a BSc and MSc in Software Development and Business Management (2011, 2013) and a PhD in Computer Science (2018), all from Graz University of Technology. His research focuses on multiple object tracking/detection, human behavior analysis, and video analysis with applications in autonomous systems, LiDAR data processing, and robotics. Key projects include trajectory prediction, 3D object detection, and domain adaptation techniques for real-world scenarios. Recent work emphasizes cross-modal learning (e.g., vision-language models for LiDAR data), robust localization in challenging environments, and lightweight motion prediction models. He contributes to the Learning, Recognition & Surveillance (LRS) group and has published extensively in top-tier venues like IEEE/CVF CVPR and IROS. Publications span autonomous driving benchmarks, UWB-based localization, and warehouse automation. His work often bridges theoretical advancements with practical implementations in robotics and surveillance systems.

Mateusz MalinowskiView profile

Researcher

• Computer Vision
• Natural Language Understanding
• Visual Question Answering
• +4 more

Mateusz Malinowski is a researcher at the Max Planck Institute for Informatics, part of the Saarland Informatics Campus, working in the Computer Vision and Machine Learning department. His work focuses on the intersection of visual understanding and language processing, with particular emphasis on developing systems that can answer questions about visual content. He received his Master's Degree with Honors in Computer Science from Saarland University in Germany, and completed his undergraduate studies in Computer Science at the University of Wrocław in Poland. His doctoral research at Universität des Saarlandes (completed in 2017) laid foundational work for visual question answering systems. Malinowski's research interests center around the synergy between machine vision and natural language understanding, with specific focus on visual question answering, text-to-image retrieval, and spatial reasoning. His work has pioneered approaches to understanding real-world images through questioning, developing the DAQUAR dataset (the first question-answering dataset about real-world images) and neural architectures that can interpret both visual and linguistic inputs. His research bridges computer vision, natural language processing, and cognitive science to create more holistic AI systems. His publication record shows a clear progression in visual question answering research, evolving from foundational work on spatial relations and pooling methods (2013-2014) to sophisticated neural architectures for visual question answering (2015-2017), and further to more complex tasks like visual what-if questioning and boundary prediction (2018-2019). The research consistently demonstrates innovation in multimodal learning, with increasing sophistication in handling the interplay between visual and linguistic information. Malinowski has served as a reviewer for top-tier conferences including NIPS, CVPR, ECCV, and journals such as TPAMI and IJCV, demonstrating his standing in the computer vision and machine learning communities. He has advised Master's students including Ashkan Mokarian (2016) on deep learning for image captioning and Sreyasi Nag Chowdhury (2015) on contextual media retrieval. He has also contributed to teaching as a teaching assistant for Deep Learning Seminar (2015) and Probabilistic Graphical Models (2013) courses. His work is associated with the Scalable Learning and Perception group at the Max Planck Institute, where he collaborates with researchers including Mario Fritz, Marcus Rohrbach, and Bernt Schiele on advancing the state of the art in computer vision and multimodal learning.

Frank FerraroView profile

Associate Professor

• Natural Language Processing
• Probabilistic Modeling
• Unsupervised Learning
• +5 more

Frank Ferraro is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). His research focuses on natural language processing, probabilistic modeling, and multimodal systems. He holds a PhD in Computer Science from Johns Hopkins University (2017), an MSE from the same institution (2013), and dual BS degrees in Computer Science and Mathematics from the University of Rochester (2011). His research interests include event modeling, unsupervised learning strategies, and the application of large language models (LLMs) to cybersecurity, robotics, and knowledge graph consistency. Recent work explores malware detection via Yara rules, ethical AI in robotics, and interactive dashboards for multimodal situation awareness. Ferraro’s contributions span foundational NLP techniques and applied systems like the PASTA dataset for narrative analysis and the MUMOSA interactive visualization tool. He has published extensively on topics ranging from grounded language learning to distributed classification algorithms. His work bridges theoretical advancements and practical applications, often emphasizing scalability and explainability. Ferraro’s lab develops systems for human-robot interaction, virtual environments, and collaborative AI frameworks. For more details, visit his website .

• Robotics
• Artificial Intelligence
• Reinforcement Learning
• +5 more

Abdeslam Boularias is an Associate Professor in the Department of Computer Science at Rutgers, The State University of New Jersey. His research focuses on robotics, artificial intelligence, and reinforcement learning, with a strong emphasis on robotic manipulation, model-based control, and perception in cluttered environments. He leads research groups in Artificial Intelligence, Intelligent Systems, and Robotics. Key contributions include advancing techniques for dynamic object tracking, sensorimotor learning in unstructured settings, and integrating physics-based reasoning with deep learning. His work has been recognized with prestigious grants such as the NSF CAREER Award and collaborative grants with institutions like Yale University. Grants: NSF NRI Grant (2022), NSF SA&S Grant (2021), NSF CAREER Award (2024) Research Themes: Robotics in clutter, reinforcement learning for manipulation, physics-aware perception, and end-to-end systems for autonomous robots His recent publications emphasize scalable manipulation learning, one-shot imitation, and diffusion models for affordance prediction. He actively contributes to advancing robotic systems that operate reliably in complex real-world scenarios.

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

Ning Zhang is a researcher affiliated with Tsinghua University , Department of Electronic Engineering, focusing on interdisciplinary applications of computer science and artificial intelligence in domains such as medical imaging, fault diagnosis, and multimodal systems. Their work bridges theoretical advancements with practical implementations in engineering, agriculture, and finance. Research Interests : Machine learning, signal processing, graph neural networks, and neuromorphic computing. Key Contributions : Recent publications highlight innovations in event-based tracking, digital twin systems, and reinforcement learning integration with vision-language models. Technological Focus : Applications include fault diagnosis in rotating machinery, medical image segmentation (CT-Net), and AI-driven urban risk assessment. The 2024-2025 publication trends reveal a strong emphasis on deep learning for remote sensing, reinforcement learning security, and stochastic control systems. Collaborations span institutions like the University of Manchester, Peking University, and Washington University in St. Louis.

Monica S. LamView profile

Professor

• Virtual Assistants
• Natural Language Processing
• Machine Learning
• +6 more

Monica S. Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the Computer Science Department at Stanford University's School of Engineering, where she has been a faculty member since 1988. She serves as Faculty Director of the Open Virtual Assistant Lab (OVAL) and holds a courtesy appointment in the Electrical Engineering Department. Lam received her B.Sc. from the University of British Columbia in 1980 and her Ph.D. in Computer Science from Carnegie Mellon University in 1987. Her research spans multiple domains with a focus on creating trustworthy virtual assistants based on Large Language Models. She is particularly renowned for her work on the Almond virtual assistant, which emphasizes privacy preservation and open-source development. Her research interests include virtual assistants, natural language programming, machine learning, distributed mobile computing, and compiler technologies. Lam's recent publications reveal a strong focus on addressing key challenges in virtual assistant technology, particularly around hallucination reduction in LLMs, multilingual dialogue systems, semantic parsing, and multimodal interactions. Her work consistently emphasizes privacy preservation, open-source development, and user control over data. Member of the National Academy of Engineering ACM Fellow NSF Young Investigator Award (1992) ACM Most Influential Programming Language Design and Implementation Paper Award (2001) Wikimedia Foundation's Research of the Year Award (2024) for eliminating LLM hallucinations Popular Science's Best of What's New Award in Security (2019) for Almond virtual assistant Lam has advised numerous PhD students who have gone on to prominent positions at institutions like MIT, Harvard, Google, and Amazon. Her research has been supported by significant funding including NSF grants and industry partnerships. She leads the Open Virtual Assistant Lab (OVAL), which focuses on developing open, privacy-preserving virtual assistant technologies that give users control over their data while maintaining functionality.

• Optimizing compilers
• High performance computing
• Embedded systems
• +7 more

Jagannathan Ramanujam serves as the John E. and Beatrice L. Ritter Distinguished Professor in the Division of Electrical & Computer Engineering at Louisiana State University's School of Electrical Engineering and Computer Science. With a Ph.D. from The Ohio State University (1990), his academic career spans over three decades of research and teaching in computer science and engineering disciplines. Dr. Ramanujam's research trajectory demonstrates a significant evolution from foundational computer science to cutting-edge biomedical applications. His early work focused on optimizing compilers, high-performance computing, embedded systems, and computer architecture. Recent publications reveal a strategic pivot toward applying computational techniques to biological problems, with emphasis on drug synergy prediction, cancer therapeutics, protein-ligand interactions, and multi-omics data integration. This transition showcases his ability to adapt computational methods to address pressing challenges in biomedicine, particularly through graph neural networks and advanced machine learning approaches. The analysis of his 15 most recent publications indicates a strong focus on AI-driven solutions for drug discovery and cancer treatment. His research group has developed innovative tools like SynerGNet for predicting anticancer drug synergy, CancerOmicsNet for multi-omics drug profiling, and Graphsite for ligand binding site classification. These contributions represent significant advancements at the intersection of computer science and biomedical research, with potential clinical applications in personalized cancer therapy. While specific awards beyond his distinguished professorship aren't detailed in the available information, his extensive publication record spanning multiple high-impact domains demonstrates scholarly impact. His research likely involves active mentorship of graduate students and successful acquisition of research funding to support interdisciplinary collaborations between computer science and biomedical domains. Dr. Ramanujam's laboratory work appears focused on computational approaches to biomedical problems, likely involving high-performance computing infrastructure and collaborations with domain experts in pharmacology and oncology to validate computational findings. His research program exemplifies the growing trend of applying advanced computational techniques to solve complex biological challenges.

Ardhendu BeheraView profile

Professor

• Computer Vision
• Deep Learning
• Multimodal AI
• +4 more

Ardhendu Behera is a Professor of Computer Vision & AI at Edge Hill University and co-director of the Intelligent Visual Computing Research Centre. He holds a post-doctoral background from the University of Leeds and has contributed to interdisciplinary research in AI-driven healthcare, autonomous systems, and defense applications. His work bridges computer vision with multimodal AI, emphasizing real-world impact through collaborations with institutions like the University of Bristol, IISc Bangalore, and UCL. Prof Behera’s research spans deep learning, healthcare diagnostics, and autonomous vehicle systems. He has pioneered AI tools for dementia monitoring (via social robotics and multimodal data) and developed algorithms for traffic scene analysis, driver behavior recognition, and medical imaging. His recent projects include breast cancer detection networks and plant disease recognition systems using transfer learning. Key Awards: Fellow of the UK Higher Education Academy (HEA) Editorial Roles: Associate Editor for IEEE Transactions on Image Processing Outreach: Media coverage on BBC News, ITV, and The Times for dementia research; keynote speaker at NCVPRIPG 2019 and ICIoT 2019 His research emphasizes interdisciplinary collaboration, with over 100 peer-reviewed publications in top-tier venues like CVPR, ECCV, and IEEE journals. He actively mentors students in PhD/MRes programs focusing on AI and computer vision applications.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Qian WANG is a Lecturer at the Faculty of Law of Université de Montréal, contributing to both teaching and interdisciplinary research. Her work bridges legal education with advanced computational methods, though her recent publications primarily focus on artificial intelligence and computer science. Research interests span a broad spectrum including artificial intelligence, machine learning, natural language processing, and computer vision. Her work often intersects with systems-level challenges such as operating system control and cognitive modeling, demonstrating a unique interdisciplinary approach. Publications reflect a trajectory from foundational contributions in graph theory and saliency detection (2020-2021) to cutting-edge advancements in large language models and reasoning frameworks (2023-2025). Key themes include improving AI capabilities through dynamic reasoning mechanisms and exploring the ethical dimensions of autonomous systems. No academic awards or grants are explicitly noted in the provided materials, though her prolific publication record indicates active research engagement. She currently holds no listed advisees or doctoral supervision roles. Labs or collaborative teams associated with her research are not detailed in the available information.

Georg von WichertView profile

Researcher

• Cognitive Systems
• Computer Vision
• Robotics
• +3 more

Georg von Wichert is a Rudolf Diesel Industry Fellow at the Technical University of Munich (TUM) since 2009, affiliated with Siemens Corporate Technology. He holds a Diploma (M.Sc.) in electrical and control engineering from Darmstadt University of Technology (1992) and a Ph.D. in electrical engineering (1998). His research focuses on cognitive systems, including computer vision, robotics, and sensor fusion, with applications in autonomous systems and human-robot interaction. Affiliations: Siemens AG (Corporate Technology) & TUM Institute for Advanced Study (Focus Group: Cognitive Technology) Education: Darmstadt University of Technology (Diploma 1992, Ph.D. 1998) His work spans probabilistic modeling for multi-agent systems, semantic scene understanding, and robotic navigation. Notable contributions include table-top scene analysis, semantic indoor mapping, and robotic systems for everyday environments. He has led national and European research projects and contributed to initiatives like the Robotic Bar demonstration of human-robot collaboration. Scientific Awards: Rudolf Diesel Industry Fellowship (2009). Research Highlights: Developed algorithms for 6-DOF pose estimation, robotic exploration in dynamic environments, and context-aware robotic assistants. His publications address challenges in simultaneous segmentation, object recognition, and 3D localization using stereo vision.

Christoffer HeckmanView profile

Associate Professor

• Autonomy
• Robotics
• Perception
• +6 more

Christoffer Heckman is an Associate Professor in the Department of Computer Science at the University of Colorado at Boulder and holds the Jacques I. Pankove Faculty Fellow position in the College of Engineering & Applied Science. He earned his BS in Mechanical Engineering from UC Berkeley (2008) and PhD in Theoretical and Applied Mechanics from Cornell University (2012), where he was an NSF Graduate Research Fellow. His career includes postdoctoral roles at the Naval Research Laboratory and CU Boulder before joining the faculty in 2016. He also worked at Amazon as a Visiting Academic (2021–2023) and is currently a Senior Staff Software Engineer at Zoox. His research focuses on autonomy, perception, field robotics, machine learning, and artificial intelligence. He directs the Autonomous Robotics and Perception Group, aiming to develop explainable techniques in probabilistic AI. His work spans applications in intelligence, defense, environmental monitoring, and autonomous systems like vehicles and agricultural platforms. Notable projects include radar-based navigation and the Scout program for sidewalk delivery robots. Key achievements include the NSF CAREER Award and leadership in subterranean robotics challenges. His research integrates computer vision, sensor fusion, and neural dynamics to advance practical robotic solutions for medicine, search & rescue, and automation. Awards: NSF CAREER Award, Jacques I. Pankove Faculty Fellow Labs/Teams: Autonomous Robotics and Perception Group Grants: CAREER: Radar-based Perception and Navigation in Visually Degraded Environments (2024)

• Augmented Reality
• Game Technology
• Medical Imaging
• +4 more

Dr. Fabio Zünd is a Lecturer at the Department of Computer Science, ETH Zurich, affiliated with the Game Technology Center (GTC). He is based in Zurich, Switzerland, and can be reached at fabio.zund@inf.ethz.ch . His work focuses on innovative applications of Augmented Reality (AR), Game Technology, and Human-Computer Interaction (HCI), with notable contributions to medical AR integration, facial image anonymization, and interactive storytelling systems. Research interests include AR-driven medical education, personalized clinical imaging tools, and the development of immersive technologies that bridge real and virtual worlds. His projects often address challenges in user-centered design, such as scent-based gaming interfaces and emotion-aware interaction techniques. Recent work highlights include the VerA system for clinical facial image anonymization, scent prediction in video games, and latent diffusion models for art generation. He explores interdisciplinary applications in healthcare, education, and entertainment through collaborations with institutions like the ETH Library and industry partners. No scientific awards are explicitly mentioned in the provided information. His advising record remains unspecified, though he contributes to ETH’s educational programs through courses like the Seminar on Game Technology . Ongoing projects involve the Game Technology Center’s exploration of tangible interaction systems and wearable input devices such as the DigiGlo glove technology.

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

Wenwen Zhang is a Professor at the Department of Electronic Engineering, College of Information Science and Electronic Engineering, Zhejiang University. With an extensive publication record spanning from 2016 to 2025, Dr. Zhang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer vision, machine learning, and sensor systems. Her work demonstrates significant contributions to medical imaging, sensor array systems, wireless communications, and AI-assisted applications. Dr. Zhang's research interests encompass a wide range of topics including medical image analysis, sensor array systems, wireless communications, and AI-assisted applications. Her work demonstrates particular expertise in developing innovative deep learning architectures for medical imaging tasks such as cardiac segmentation and nuclei detection, as well as creating sophisticated models for gas sensing and wireless communication systems. She has made significant contributions to the fields of one-shot object detection, medical image segmentation, and sensor fusion techniques, with her research often bridging theoretical advancements with practical applications in healthcare and engineering. Analysis of Dr. Zhang's recent publications reveals a strong focus on cutting-edge deep learning approaches applied to medical imaging and sensor systems. Her work shows increasing sophistication in model architectures, moving from traditional CNNs to more complex transformer-based and hybrid models. There's a clear trajectory toward more explainable and clinically relevant AI systems, particularly in medical applications. Her research also demonstrates growing interest in multimodal approaches, combining different types of data and sensors to improve system performance. Dr. Zhang maintains active collaborations with researchers at Zhejiang University, particularly with Yuanjin Zheng and Zhiping Lin in the field of electronic engineering and sensor systems. She also collaborates extensively with Fei-Yue Wang from the University of Chinese Academy of Sciences, evidenced by multiple publications on parallel vision frameworks. Her international collaborations include work with researchers from institutions in Canada on intelligent knee sleeves and other biomedical applications. Her publication record shows consistent productivity with 12 publications in 2025 (as of this writing), 25 in 2024, and 26 in 2023, indicating an active and growing research program across multiple high-impact journals and conferences.

• Cross-Modal Retrieval
• Image-Text Matching
• Agricultural Robotics
• +4 more

Mingyong Li is a Professor in the Department of Agricultural Engineering at Huazhong Agricultural University's College of Engineering, specializing in agricultural robotics, computer vision, and cross-modal retrieval systems. His research bridges agricultural engineering with advanced machine learning techniques, focusing particularly on rice transplanting automation and precision agriculture systems. His primary research interests include Cross-Modal Retrieval, Image-Text Matching, Agricultural Robotics, Computer Vision, Machine Learning, Semantic Analysis, and Emotion Recognition. Li's work demonstrates strong integration between theoretical machine learning advancements and practical agricultural applications, with particular emphasis on developing vision-based systems for crop monitoring and robotic manipulation in farming environments. Analysis of his recent publications (2023-2025) reveals a strong trend toward multi-modal learning systems that integrate visual perception with textual understanding, particularly applied to agricultural robotics. His work shows increasing sophistication in handling ambiguity in image-text matching while maintaining practical applications in agricultural machinery automation. The research spans both theoretical machine learning advancements and their concrete implementations in agricultural equipment. Professor Li has mentored several researchers who have become frequent collaborators, including Junyu Chen, Yewen Li, and Mingyuan Ge. His work demonstrates consistent funding support through numerous collaborative projects focused on agricultural automation and intelligent systems. His laboratory appears to focus on agricultural robotics systems, with particular emphasis on seedling transplantation machinery, rice farming automation, and sensor-based monitoring systems for crop quality assessment. The research integrates mechanical engineering, computer vision, and machine learning to create practical agricultural solutions.