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Carl VondrickView profile

Professor

• Computer Vision
• Machine Learning
• Multimodal Perception
• +11 more

Carl Vondrick is a Professor in the Department of Computer Science at Columbia University. His research focuses on creating robust and versatile perception systems that leverage video and interaction with the natural world, with applications in 3D reconstruction, visual question answering, and robot manipulation. Former research scientist at Google Visiting researcher at Cruise Education: PhD (2017) from MIT, advised by Antonio Torralba BS (2011) from UC Irvine, advised by Deva Ramanan His research explores multimodal approaches for cross-task and cross-modal transfer, scene dynamics, audiovisual perception, interpretable models, and spatial awareness systems. The lab emphasizes zero-shot generalization and neuro-symbolic methods while addressing safety and robustness in AI systems. Key publication trends include: 2025: Video generation for robotics 2024: Differentiable rendering and cross-modal reasoning 2023: Robust perception and 3D modeling Scientific Awards: 2024 PAMI Young Researcher Award 2021 NSF CAREER Award Teaching Roles: Teaching Computer Vision II (2021-2025), Computer Vision I (2018-2019), and Representation Learning (2020-2022). Advising: Advises 8 current PhD students and has mentored 5 graduated students now at institutions like MBZUAI and UMD. The lab recruits 1-2 PhD students annually through Columbia’s PhD program. Grants and Collaborations: Funded by NSF, DARPA, Toyota Research Institute, Amazon Research, and Google.

Christian TheobaltView profile

Professor

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

• German Linguistics
• Syntax
• Formal Grammar
• +3 more

Prof. Stefan Müller is a Professor at the Institute for German Language and Linguistics , part of the Faculty of Languages and Literature at Humboldt University. His research focuses on syntax, formal grammar theories (especially Head-Driven Phrase Structure Grammar), and computational linguistics. He leads the CoreGram Project , developing cross-linguistic grammars using HPSG formalisms. His work includes analyses of German syntax, constituent order, anaphoric binding, and register phenomena. He is also involved in open-access publishing initiatives through Language Science Press . Contact: St.Mueller@hu-berlin.de . Education and academic background details are not explicitly stated in the provided text, but his professional trajectory indicates deep expertise in theoretical linguistics. His research spans multiple languages including German, Mandarin, and Persian, with a focus on grammar implementation and syntax-semantics interfaces. Key contributions include the Head-Driven Phrase Structure Grammar Handbook (2024), analyses of headless nominal structures in German (2022), and exploration of large language models' theoretical implications (2024). He actively engages in interdisciplinary projects bridging formal grammar and computational methods.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +4 more

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

Andreas BullingView profile

Professor

• Human-Computer Interaction
• Computer Vision
• Machine Learning
• +4 more

Andreas Bulling is a Professor at the Institute for Visualisation and Interactive Systems , University of Stuttgart, Germany. His research focuses on Human-Computer Interaction , Eye Tracking , and Computer Vision , with applications in Machine Learning , Virtual Reality , and Information Visualization . 2025 Publications: HOIGaze (Extended Reality), ChartQC (Data Visualization), HAIFAI (Human-AI Interaction), SummAct (Behavioral Summarization), Chartist (Chart Reading). 2024 Contributions: HumanEYEze (Multimodal AI), HOIMotion (3D Object Detection), MultiMediate'24 (Engagement Estimation), Unified Model of Saliency (Scanpath Prediction). His recent work explores gaze estimation , interactive behavior modeling , and privacy-preserving eye-tracking systems . Key subfields include Extended Reality , Neural Networks , and Behavioral Biometrics . While no explicit scientific awards are mentioned, his research has been widely cited (8,003 total citations) and downloaded (132,740 times). Andreas leads projects in Interactive Systems and collaborates with institutions such as Aalto University , KU Leuven , and National University of Singapore . His lab focuses on eye movement analysis , human motion forecasting , and task-driven input modeling .

• Machine Learning
• Adversarial Robustness
• Computer Vision
• +4 more

Matthias Hein is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Tübingen. His research focuses on Machine Learning , Adversarial Robustness , and Out-of-Distribution Detection , with applications in computer vision and medical imaging. He has received notable recognition including the Best Paper Honorable Mention Prize at ICLR 2021 and Outstanding Paper Award at CVPR 2021. His work includes developing benchmarks like RobustBench and Spurious ImageNet , and frameworks such as Sparse-RS and DIG-IN . His recent publications emphasize adversarial robustness across multiple domains (vision, text), counterfactual explanations for classifiers, and improved OOD detection methods . Collaborators include prominent researchers like Francesco Croce, Julian Bitterwolf, and Alexander Meinke. Scientific Awards : Best Paper Honorable Mention (ICLR 2021) CVPR 2021 Outstanding Paper Award Key Research Areas : Adversarial Robustness Vision-Language Models Medical Imaging AI Neural Network Calibration

• Natural Language Processing
• Machine Learning
• Multimodal Learning
• +5 more

Ekaterina Shutova is an Associate Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam. She concurrently holds a Visiting Associate Professor position in the Computer Science Department at Stanford University. She leads the Amsterdam Natural Language Understanding Lab and heads the NLP & Digital Humanities research unit at ILLC. An ELLIS Scholar, she earned her PhD from the University of Cambridge Computer Laboratory and Pembroke College. Her research has been funded by ERC, Innovate UK, British Academy, Leverhulme Trust, Google, Meta, and Deloitte. Her research spans natural language processing and machine learning, with core interests in: Few-shot learning for NLP Multilingual and cross-lingual systems Joint modeling of language and vision Cognitive processing and semantic representation Figurative language interpretation Computational social science applications Her recent publications (2024-2025) predominantly focus on multimodal learning, cultural alignment in AI, metaphor processing, and evaluation methodologies for language models. These works reflect a trend toward integrating cognitive science with multilingual systems and ethical considerations. Awards & Fellowships: ERC Consolidator Grant (2025) ELLIS Scholar Outstanding Paper Award at ACL 2023 Finalist for Outstanding Certification by TMLR Runner-up Best Paper Award at NAACL-HLT 2016 Research Leadership: She directs the Amsterdam Natural Language Understanding Lab, supervising 8 PhD students, 1 MSc student, and 34 alumni. Her projects include an ERC-funded initiative on globally accessible language technology and an AI Democratization grant for hate speech detection.

Bernt SchieleView profile

Professor

• Computer Vision
• Multimodal Sensor Processing
• Machine Learning
• +3 more

Prof. Bernt Schiele is a Max Planck Director at the Max Planck Institute for Informatics and holds a Professorship at Saarland University. His research focuses on understanding multimodal sensor data, with key areas in computer vision, 3D object recognition, and machine learning. He leads the Computer Vision and Machine Learning group, addressing challenges in sensor fusion, scene understanding, and human activity recognition. Schiele has held academic roles at TU Darmstadt, ETH Zurich, and MIT, and contributes to top journals like IEEE Transactions on PAMI and conferences like ECCV. His work emphasizes robust models, interpretability, and domain adaptation for real-world applications. Education: PhD (1997, Grenoble), MSc (1994 Karlsruhe/1993 Grenoble) Key Positions: MIT (1997-2000), ETH Zurich (1999-2004), TU Darmstadt (2004-2010) Research interests span 3D scene understanding, multimodal sensor processing, and machine learning techniques for large-scale data. His recent work advances robust object detection, explainable AI, and domain-invariant training methods. He also chairs major conferences like ECCV 2018 and co-chairs ICCV 2011. Publications highlight innovations in interpretable vision transformers, certified explanations, and test-time adaptation. Despite no listed awards, his contributions shape foundational areas of computer vision and multimodal AI.

Yin Tat LeeView profile

Associate Professor

• Convex Optimization
• Convex Geometry
• Graph Algorithms
• +2 more

Yin Tat Lee is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington, and a Senior Principal Researcher in Microsoft AI. His research spans convex optimization , convex geometry , graph algorithms , online algorithms , and differential privacy , with applications in machine learning and theoretical computer science.

• Recommender Systems
• Machine Learning
• Natural Language Processing
• +4 more

Julian McAuley is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. His research spans recommender systems, machine learning, natural language processing, music information retrieval, and multimodal learning. He maintains an active research group with numerous PhD students and postdocs working on cutting-edge AI problems. His research interests focus on developing advanced algorithms for personalized recommendation systems, with particular emphasis on sequential recommendation, multimodal learning, and integrating large language models with traditional recommendation approaches. His work bridges the gap between theoretical machine learning and practical applications across multiple domains including e-commerce, music, and healthcare. McAuley has published extensively in top-tier conferences including NeurIPS, ICML, KDD, SIGIR, and ACL, with his most recent work exploring the intersection of large language models and recommendation systems. His publications reveal a strong trend toward multimodal approaches that combine text, vision, and audio for more comprehensive understanding and recommendation. He has received significant research funding from major technology companies including Google, Amazon, Facebook, Adobe, and Samsung, as well as government agencies like the National Science Foundation and Department of Defense. His work has practical applications across multiple industries, with a focus on improving user experience through better personalization. McAuley advises numerous PhD students who have gone on to successful careers at leading technology companies and academic institutions. His former students include Wang-Cheng Kang and Jianmo Ni at Google DeepMind, Chris Donahue and Zachary Lipton as assistant professors at CMU, and Ruining He at Google Deepmind.

Zeynep AkataView profile

Professor

• Multimodal Learning
• Explainable Artificial Intelligence
• Zero-Shot Learning
• +2 more

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence . Education: PhD, University of Grenoble / INRIA Rhône-Alpes, 2014 MSc, RWTH Aachen University, 2010 BSc, Trakya University, Turkey, 2008 Research Interests: Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning . A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning . Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing. Publication Trends: Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision . Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems. Scientific Awards: Lise-Meitner Award for Excellent Women in Computer Science (2014) Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019) ERC Starting Grant, European Commission (2019) DAGM German Pattern Recognition Award (2021) ECVA Young Researcher Award (2022) Alfried Krupp Award (2023) Advising & Funding: Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.). Labs & Teams: She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.

• Natural Language Processing
• Computational Linguistics
• Event Detection and Understanding
• +4 more

Teruko Mitamura is a prominent researcher at Carnegie Mellon University with over three decades of contributions to natural language processing, computational linguistics, and artificial intelligence. Her work spans from foundational research in event representation to advanced applications in multimodal systems and question answering. Her research interests focus on event detection and understanding, question answering systems, information retrieval, and multimodal processing. She has made significant contributions to event coreference resolution, timeline construction, and cross-document event analysis, developing methodologies that have become standard in the field. Her work often bridges theoretical advances with practical applications, particularly in complex information environments requiring deep semantic understanding. Natural Language Processing : Specializing in event extraction, coreference resolution, and narrative understanding with over 179 publications Question Answering Systems : Developing advanced techniques for complex question answering, particularly through NTCIR QA Lab and PoliInfo tasks Multimodal Processing : Integrating textual, visual, and temporal information for richer understanding in systems like ProMQA Evaluation Methodologies : Creating robust frameworks for assessing NLP systems through TAC KBP Event Tracks Her recent publication trends show a strong focus on leveraging large language models for event understanding, multimodal question answering, and timeline construction. She has expanded her research into specialized domains including patent analysis and novelty examination, demonstrating the breadth of her research impact across academic and practical applications. Active participant in major NLP conferences including ACL, EMNLP, NAACL, and AAAI with consistent publications Long-standing collaborator with researchers at CMU's Language Technologies Institute including Eduard H. Hovy and Eric Nyberg Contributor to shared tasks that have shaped research directions in event processing and question answering Organizer of multiple NTCIR QA Lab tasks focused on political information question answering Dr. Mitamura has mentored numerous researchers who have gone on to make their own contributions to the field, as evidenced by her extensive co-authorship network and the progression of her former students and collaborators into faculty and research positions. Her work continues to evolve with the field while maintaining her focus on deep semantic understanding of events and narratives.

Toby Jia-Jun LiView profile

Assistant Professor

• Human-Computer Interaction
• Artificial Intelligence
• Machine Learning
• +6 more

Toby Jia-Jun Li is an Assistant Professor in the Department of Computer Science and Engineering at the University of Notre Dame, where he leads the SaNDwich Lab. He also serves as the Director of the Human-Centered Responsible AI Lab in the Lucy Family Institute for Data & Society and is a Faculty Fellow at the Institute for Educational Initiatives (IEI). Previously, he was affiliated with Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and GroupLens Research. Dr. Li's research spans the intersection of Human-Computer Interaction (HCI), End-User Software Engineering, Machine Learning (ML), and Natural Language Processing (NLP), with recent work focusing on addressing societal challenges in the future of work through human-AI collaborative approaches. His work has resulted in over 40 publications at premier venues including CHI, UIST, CSCW, ACL, and ICSE, with 8 papers winning Best Paper or Honorable Mention awards. His recent publications demonstrate a strong focus on human-AI collaboration across various domains, including code understanding, privacy, accessibility, and creative tools. The work shows a trajectory toward increasingly sophisticated integration of human-centered design with AI capabilities, particularly using large language models to enhance human productivity and address societal challenges. Google Research Scholar Award recipient Recipient of Yahoo! Fellowship ($100,000/year) Best Paper Award at UIST 2020 Best Paper Honorable Mention Award at CHI 2021 Best Paper Award at CSCW 2024 Best Paper Award at CHI 2025 Dr. Li actively mentors Ph.D. students and has established collaborations with Google, Microsoft Research, IBM Research, Adobe, Verizon, and J.P. Morgan. His research has been supported by NSF, Google Research Scholar Program, AnalytiXIN Initiative, Yahoo! InMind project, and J.P. Morgan. He is currently recruiting Ph.D. students and undergraduate researchers for his SaNDwich Lab, which focuses on developing interactive systems to empower individuals to create, configure, and extend AI-powered computing systems.

• Computer Vision
• 3D Human Modeling
• Digital Humans
• +5 more

Michael J. Black is a Professor and Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department and serves as Managing Director . He is also an Honorarprofessor at the University of Tübingen 's Faculty of Science . His career spans roles at Brown University (2000-2010), Xerox PARC, and academic-industry collaborations with Amazon and Meshcapade.

• Affective Neuroscience
• Emotion Regulation
• Schizophrenia Spectrum Disorders
• +4 more

Univ-Prof. Dr. med. Malek Bajbouj serves as Director of the Institute for Affective Neuroscience and Emotion Modulation at Charité – University Medicine Berlin's Campus Benjamin Franklin (CBF), operating within the Department of Neurology, Neurosurgery and Psychiatry (CC 15). His position integrates clinical leadership with translational neuroscience research focused on severe mental illnesses. Dr. Bajbouj's research program centers on affective neuroscience and emotion dysregulation mechanisms in psychiatric disorders, particularly schizophrenia spectrum conditions and depression. He pioneers multimodal intervention approaches combining neuromodulation (tDCS), oxytocin augmentation, mindfulness therapies, and digital health tools. His work emphasizes translational biomarker development using neuroimaging, machine learning, and physiological stress parameter analysis to personalize treatment for treatment-resistant populations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) novel treatment combinations for negative symptoms in schizophrenia (oxytocin + mindfulness, yoga therapy); (2) real-world implementation of neuromodulation (at-home tDCS protocols, technical efficacy monitoring); and (3) global mental health responses to crises (pandemic impacts on vulnerable groups, culturally adapted refugee interventions). His methodology consistently employs rigorous randomized controlled trials with embedded biomarker studies. As director of his eponymous institute, Dr. Bajbouj leads a multidisciplinary team conducting neuroimaging studies, clinical trials, and international collaborations focused on emotion modulation pathways. The institute coordinates research across CC 15's clinical infrastructure at CBF Building V, with particular emphasis on bridging laboratory neuroscience with clinical psychiatry through the DepressionDC and OXYMIND trial frameworks.