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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

Jan AlberView profile

Professor

• Narrative Theory
• Cognitive Literary Studies
• Postcolonial Literature
• +3 more

Jan Alber is a Professor of Modern English and American Literature at the Department of English and American Literature and Culture, Justus Liebig University Giessen . He previously held positions at RWTH Aachen, Aarhus University, TU Darmstadt, and the University of Freiburg. His work bridges narrative theory, cognitive studies, and postcolonial literature, with a focus on unnatural narratives and empirical approaches to literary analysis. Fields of Interest: Unnatural Narratology, Cognitive Literary Studies, Climate Fiction, Postcolonial Literature Key Themes: Storyworlds, Ideology, Empirical Methods, Narrative Beginnings Alber's recent publications explore narrative theory's intersections with cognition and ideology, including analyses of digital post-postmodernist novels and climate change narratives. His empirical studies examine how readers process experimental narratives, particularly unnatural narrators and metaleptic structures. He has received prestigious awards such as the Habilitation Prize from the German Association of Anglists (2013) and the FRIAS junior group competition (2008). His research is funded by the AHRC, DFG, and Alexander von Humboldt Foundation, notably supporting interdisciplinary work on post-truth political narratives and Ukrainian scholars. Alber co-founded the Aachen Center for Cognitive and Empirical Literary Studies and serves as President of the International Society for the Study of Narrative. He organizes international workshops on cognitive narratology and teaches courses spanning literary history, theory, and postmodern fiction.

• Arithmetic Geometry
• Algebraic Geometry
• Representation Theory
• +4 more

Prof. Peter Scholze is a leading mathematician at the University of Bonn and the Max Planck Institute for Mathematics (MPIM). He holds a professorship in the Mathematical Institute at the University of Bonn and is affiliated with MPIM as a principal investigator. His research focuses on arithmetic geometry, representation theory, and p-adic geometry, with notable contributions to perfectoid spaces, prismatic cohomology, and the geometrization of the local Langlands correspondence. Scholze has organized several advanced seminars (ARGOS) and taught courses on topics like condensed mathematics, p-adic geometry, and algebraic geometry. He collaborates widely with researchers such as Laurent Fargues and Bhargav Bhatt. His recent work includes groundbreaking papers on wild Betti sheaves, motivic geometrization of the local Langlands correspondence, and the Habiro ring of number fields. Scholze has pioneered the use of prismatic cohomology to unify p-adic Hodge theories and has developed foundational frameworks in derived algebraic geometry. His research also intersects with representation theory and topological cyclic homology. Affiliations: University of Bonn (Mathematical Institute), Max Planck Institute for Mathematics (MPIM) Research Groups: Arbeitsgruppe Arithmetische Geometrie und Darstellungstheorie Teaching: Recent courses include 'Selected Topics in Algebraic Geometry' and 'Six-Functor Formalisms'. His work frequently explores connections between algebraic geometry and number theory, with a focus on geometric approaches to arithmetic problems. Scholze is a key figure in modern arithmetic geometry, influencing both foundational theory and applied areas like the Langlands program.

Niki KilbertusView profile

Assistant Professor

• Machine Learning
• Causal Inference
• Ethics in AI
• +5 more

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

Bernd MöbiusView profile

Professor

• Speech Technology
• Phonetics
• Phonology
• +8 more

Professor Bernd Möbius is a leading academic in Phonetics and Phonology at the Department of Language Science and Technology, Saarland University. His research bridges phonetic theory with speech technology applications, focusing on text-to-speech systems, prosody modeling, and computational simulations of speech processes. Current research projects: DFG SFB 1102, C1: Information density and phonetic structure predictability DFG SFB 1102, C4: Slavic intercomprehension and surprisal theory (INCOMSLAV) Research Themes: Key areas include text-to-speech synthesis, speech prosody analysis, experimental methods in speech production/perception, information density in phonetics, and cross-linguistic studies of Slavic-Germanic languages. Scientific Contributions: Recent work explores Parkinson-induced dysarthria detection, breath noise acoustics, surprisal-driven speech behaviors, multilingual BERT models for idiomaticity, and perceptual consequences of acoustic adjustments.

Kai S. ExnerView profile

Professor

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +7 more

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.

Michael CarbinView profile

Assistant Professor

• Programming Systems
• Approximate Computing
• Probabilistic Programming
• +4 more

Michael Carbin is the Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and leads the MIT Programming Systems Group. His research focuses on programming systems that address system uncertainty to enhance performance, energy efficiency, and resilience, particularly in environments involving neural networks , approximate computing , and unreliable hardware . His work spans probabilistic programming , quantum computing , and machine learning systems . Articles highlight contributions in pruning neural networks , quantum data structures , and compiler optimization , reflecting trends in deep learning , formal verification , and language-driven systems . Scientific Awards : MIT Frank E. Perkins Award (2020) Sloan Research Fellowship (2020) Facebook Research Award (2019) NSF CAREER Award (2018) Best Paper Awards at OOPSLA (2013, 2014) He has advised numerous graduate students and postdocs including Eric Atkinson, Cambridge Yang, and Charles Yuan, and served on program committees for conferences like POPL, OOPSLA, and ICLR. His group collaborates with institutions such as MIT CSAIL and explores applications in quantum algorithms and probabilistic inference .

• Nanotechnology
• Materials Science
• Photovoltaics
• +4 more

Professor Thomas Bein is affiliated with the Department of Chemistry at Ludwig-Maximilians-Universität München (LMU) , where he leads the Functional Nanosystems research group. His work focuses on synthesizing and characterizing nanostructured materials with applications in energy, catalysis, and biomedical delivery. Mesoporous nanoparticles for drug delivery Semiconductor nano-morphologies for photovoltaics Photoelectrochemical water splitting Metal-organic frameworks (MOFs) Electroactive networks His research emphasizes atomic-scale control of material architectures using self-assembly, hydrogen bonding, and covalent interactions, enabling precise tuning of electronic, optical, and catalytic properties. A review of his recent publications reveals cutting-edge investigations into covalent organic frameworks (COFs), perovskite-inspired solar materials, and functional nanoparticle systems. Key trends include optimizing energy conversion efficiency, enhancing stability in optoelectronic devices, and exploring bio-compatible nanocarriers for targeted therapies. Professor Bein’s group actively contributes to interdisciplinary projects at the intersection of chemistry, physics, and biomedical engineering, with ongoing collaborations in solar energy, sustainable materials, and nanomedicine.

Lutz SchubertView profile

Researcher

• Operating Systems
• Distributed Computing
• Parallel Processing
• +3 more

Lutz Schubert is a researcher at the Institute of Computer Science , University of Cologne. He focuses on efficient distributed and parallel execution environments for heterogeneous systems, notably the MyThOS operating system in collaboration with Brandenburg University of Technology, Cottbus-Senftenberg. His interdisciplinary work bridges computer science and digital archaeology , addressing modeling of non-deterministic events from sparse excavation data and exploring human behavior constraints in archaeological contexts. Research Interests Design of modular, scalable operating systems for distributed systems Optimization of execution environments for heterogeneous hardware Application of complex systems modeling to digital archaeology Statistical and probabilistic methods for archaeological interpretation Autonomic resource distribution and adaptation in computing Publication Trends : His work spans operating systems , parallel computing , and digital humanities , with recent emphasis on probabilistic reasoning in archaeology and adaptive OS design for multicore architectures. Labs & Collaborations : He collaborates with Brandenburg University of Technology on MyThOS and leads research in computational archaeology as chair of Computer Applications and Quantitative Methods in Archaeology (CAA) , Germany.

Matthias NießnerView profile

Professor

• 3D Digitization
• Computer Graphics
• Computer Vision
• +7 more

Prof. Matthias Nießner is a Professor at the Technical University of Munich, leading the Visual Computing Lab. His research intersects computer graphics, vision, and AI, focusing on 3D reconstruction, semantic understanding, and AI-driven video synthesis. He holds a PhD from the University of Erlangen-Nuremberg (2013) and was a Visiting Assistant Professor at Stanford University (2013–2017). Notable awards include the ERC Starting Grant (2018), Nvidia Professorship Award, and Eurographics Young Researcher Award (2019). His work has been featured in mainstream media and led to startups like Synthesia Inc. Research spans Gaussian splatting, neural radiance fields, and generative AI for 3D avatars. Over 150 publications include SIGGRAPH, CVPR, and ECCV, with best paper awards. Projects like Face2Face and ScanNet have driven innovation in facial reenactment and 3D scene datasets. Education: PhD in Computer Science, University of Erlangen-Nuremberg (2013) Diploma in Computer Science, University of Erlangen-Nuremberg (2010) Research Interests: 3D digitization, neural rendering, generative AI, non-rigid reconstruction, and applications in AR/VR. Awards: ERC Starting Grant (2018) Nvidia Professorship Award (2018) Google Faculty Award (2018) SIGGRAPH Best Emerging Tech Award (2016) Grants: Over €1.5M from ERC and industry partnerships. Labs/Teams: Visual Computing Lab at TUM and Synthesia Inc. (co-founder). Key projects include ScanNet (large 3D indoor dataset), Face2Face (real-time facial reenactment), and Gaussian-based 3D avatars. Current work focuses on diffusion models, neural radiance fields, and AI-generated media detection.

Stefan LeuteneggerView profile

Assistant Professor

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +7 more

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Quantum Gravity
• Quantum Field Theory
• String Theory
• +3 more

Douglas Stanford is an American theoretical physicist and Associate Professor of Physics at the Stanford Institute for Theoretical Physics, Stanford University. His work focuses on the intersection of quantum mechanics, gravity, and black hole physics. Dr. Stanford's educational background includes: B.S. in Physics and Mathematics from Stanford University (2009) M.S. in Mathematics from the University of Cambridge (2010), where he was a Marshall Scholar Ph.D. in Physics from Stanford University (2014), supervised by Leonard Susskind Stanford's research primarily explores the connections between quantum gravity, quantum field theory, and string theory. His groundbreaking work has focused on understanding the quantum mechanics of black holes through the lens of chaos theory, particularly examining the butterfly effect in black hole systems. He has made significant contributions to the ER=EPR conjecture, which proposes a deep connection between quantum entanglement (EPR) and wormholes (ER bridges), potentially resolving the black hole information paradox. His research often bridges theoretical physics with concepts from information theory and quantum computing. Stanford's publications reveal a strong focus on quantum chaos, black hole physics, and the connections between quantum mechanics and gravity. His work frequently examines the Sachdev-Ye-Kitaev model, traversable wormholes, and the mathematical structures underlying quantum gravity. The progression of his research shows an evolution from foundational work on black hole chaos to more complex explorations of quantum information in gravitational systems. Dr. Stanford has received several prestigious awards for his contributions to theoretical physics: Blavatnik Awards for Young Scientists (2017) for work in quantum gravity and condensed matter physics New Horizons in Physics Prize (2018) for improving understanding of quantum mechanics of black holes via chaos theory Gribov Medal (2019) for work on quantum chaos and its relation to near-horizon dynamics of black holes After completing his Ph.D. under Leonard Susskind at Stanford, Stanford conducted postdoctoral research at the Institute for Advanced Study in Princeton from 2014 to 2019. During this time, he collaborated extensively with leading physicists including Juan Maldacena and Edward Witten. He joined Stanford University as an assistant professor in 2019 and was promoted to associate professor by 2020. While specific grant information isn't detailed in the provided text, his prestigious awards suggest significant research funding support for his work in quantum gravity and black hole physics. Stanford is affiliated with the Stanford Institute for Theoretical Physics, where he continues his research on quantum gravity, black holes, and quantum information. His work is deeply connected to the broader theoretical physics community at Stanford, which has a strong tradition in string theory and quantum gravity research.

• 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.

• Machine Learning
• Explainable AI
• Neural Networks
• +3 more

Jonas Fischer is the head of the Explainable Machine Learning group at the Max Planck Institute for Informatics, Department of Computer Vision and Machine Learning. His research focuses on interpreting complex machine learning models, particularly in genomics and healthcare, aiming to enhance robustness and alignment with human decision-making. Prior to his role at MPI, he was a postdoctoral fellow at Harvard University's Department of Biostatistics, where he worked on interpretable models for gene regulatory systems in cancer. Education: PhD in Computer Science from Saarland University (2022), with a thesis titled More than the sum of its parts , exploring the intersection of pattern mining and deep learning. He has contributed to advancing methods in neural network pruning, federated learning, and low-dimensional embeddings (e.g., dtSNE, Mercat). His work bridges computational biology, data mining, and machine learning, with applications in DNA methylation analysis, graph-based differential networks, and biomedical informatics. Key research areas include: (1) Explainable AI and neural network interpretability, (2) Biomedical applications of machine learning (e.g., gene regulatory networks, cancer genomics), (3) Low-dimensional embeddings and visualization techniques, (4) Federated learning for privacy-preserving collaborative models, and (5) Pattern mining for error analysis in NLP and classification tasks. Publications span top venues like NeurIPS, ICLR, Bioinformatics, and Genome Biology. His group develops tools such as BONOBO for omics data integration and node2vec2rank for scalable graph analysis. He actively collaborates with biomedical researchers to address challenges in data-driven healthcare and precision medicine.