جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.

• Embedded Systems
• Microprocessor Technology
• Autonomous Driving
• +۵ مورد دیگر

Jakob Czekansky is a Lecturer for Special Tasks at the Technical University of Central Hesse (THM) , affiliated with the Department of Mathematics, Natural Sciences and Computer Science (MNI) and the Institute of Technology and Computer Science . He holds an M.Sc. in Engineering and Computer Science (2020) and a B.Sc. in Computer Science Engineering (2018), both from THM. His research focuses on embedded systems , autonomous driving , and remote laboratory technologies . He leads the MICRO project—a virtual microprocessor lab—and has collaborated on industrial projects like a ZIM-funded wood analysis system with Rinntech-Metriwerk GmbH. Key interests include: Microcontroller development Edge computing for autonomous vehicles Hybrid lab infrastructure Computer vision applications He supervises student theses in embedded systems, distributed software, and quantum computing visualization. As a member of the International Association of Online Engineering (IAOE) and Global Online Laboratory Consortium , he promotes remote-access education tools.

Bodo Rosenhahnمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Big Data Analysis
• +۷ مورد دیگر

Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.

• Machine Learning
• Perovskite Solar Cells
• Materials Science
• +۵ مورد دیگر

Milan Harth serves as a Lecturer at the Technical University of Munich within the Simulation of Nanosystems for Energy Conversion group, part of the TUM School of Computation, Information and Technology. His work focuses on the intersection of computational methods and energy materials research. His research interests span machine learning applications for materials science, particularly in perovskite solar cells and nanoelectronics. He specializes in combining computational modeling with experimental approaches to optimize materials for energy conversion applications, with particular expertise in machine vision techniques for material characterization and quantum dot synthesis. His publication record shows a consistent focus on applying machine learning to materials discovery and characterization, with recent work emphasizing data-efficient optimization methods and computer vision techniques for analyzing perovskite materials. The research demonstrates strong interdisciplinary connections between computational physics, materials engineering, and artificial intelligence. Teaching responsibilities include Computational Methods for Nanoelectronics, PhD seminars in SNE (Simulation of Nanosystems for Energy Conversion), and the Seminar on Quantum Engineering and Machine Learning, indicating his role in training the next generation of computational materials scientists.

Kai Zhouمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Heavy Ion Physics
• +۴ مورد دیگر

Dr. Kai Zhou is a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) in the Theoretical Sciences division, where he leads the 'Deepthinkers' research group. Born in China in 1987, he completed his B.Sc. in Physics from Xi'an Jiaotong University in 2009 and earned his PhD with 'Wu You Xun' Honors from Tsinghua University in 2014. After postdoctoral research at Goethe University Frankfurt's Institute for Theoretical Physics, he joined FIAS in August 2017 as a Research Fellow focusing on Deep Learning applications in physics. Frankfurt Institute for Advanced Studies (FIAS), Theoretical Sciences (2017-present) Goethe University Frankfurt, Institute for Theoretical Physics (Postdoc) Tsinghua University, Physics (PhD, 2014) Xi'an Jiaotong University, Physics (B.Sc., 2009) Dr. Zhou's research bridges artificial intelligence and theoretical physics, with a focus on applying machine learning techniques to complex physical systems. His work spans heavy-ion collisions, lattice quantum field theory, seismology, and renewable energy systems. He has developed innovative deep learning approaches to extract physical insights from complex data, including constructing an Equation-Of-State meter for heavy ion collisions. His research demonstrates how physics can inform AI development while AI enhances our understanding of physical phenomena. Analysis of Dr. Zhou's recent publications reveals a strong trend toward integrating physics principles with machine learning architectures. His work increasingly focuses on Bayesian inference methods applied to QCD phase transitions, physics-informed neural networks for solving inverse problems in nuclear physics, and developing specialized architectures that preserve physical symmetries. The interdisciplinary nature of his research is evident in applications spanning from heavy-ion collisions to neutron star physics and industrial process optimization. Wu You Xun Honors (PhD) Third party funding through Samson AG: AI for science BMBF funding within ErUM data program: Deep Learning for CBM computing DAAD exchange program Xidian-FIAS International Joint Research Center: AI for science BMWI: AI for energy Nvidia: GPU Grant Dr. Zhou actively mentors doctoral and master's students, currently advising seven graduate students across multiple institutions. His research group 'Deepthinkers' has secured significant third-party funding from diverse sources including industrial partners (Samson AG), government agencies (BMBF, BMWI), and international collaborations (DAAD, Xidian University). His approach combines theoretical physics with cutting-edge AI techniques to address complex problems in both fundamental science and industrial applications. The 'Deepthinkers' research group operates at the intersection of AI and physics, developing novel methodologies that leverage physical principles to enhance machine learning and vice versa. Their work on applying deep learning to heavy-ion collisions represents a significant advancement in extracting meaningful physical insights from complex collision data. The group maintains strong international collaborations, particularly with Chinese institutions through the Xidian-FIAS International Joint Research Center.

Alexander Braunمشاهده پروفایل

استاد

• Physics
• Optical Measurement Technology
• Camera Systems for Autonomous Driving
• +۵ مورد دیگر

Prof. Dr. Alexander Braun serves as Professor of Physics at Düsseldorf University of Applied Sciences within the Faculty of Electrical Engineering & Information Technology. His office is located in Building 5, Room 05.2.054, and he can be reached at alexander.braun@hs-duesseldorf.de. His teaching responsibilities include physics courses for engineering students, with a focus on Newtonian mechanics as evidenced by his "Physics for KIT" course (WS 15/16). His academic background includes: Education at Hackettstown High School in New Jersey, USA Physics studies in Göttingen with specialization in Optics and Lasers Research stay at École Supérieure de Physique et Chimie Industrielle in Paris Diploma thesis at Laser-Laboratorium Göttingen on "Laser spectroscopic measurement methods for temperature determination in sprays" (collaboration with Bosch and VW) Doctoral work at Institute for Laser Physics in Hamburg on quantum computing, completed at University of Siegen Prof. Braun's research focuses on physically-realistic optical simulation for autonomous driving systems. Unlike conventional photorealistic approaches, his work emphasizes physically accurate modeling of optical properties, particularly point spread functions (PSF). His research group employs linear system theory combined with artificial neural networks to model measured PSFs, enabling accurate simulation of real-world optical effects on camera systems. This approach allows for creating convolution kernels that transform synthetic scenes into images matching specific camera system characteristics. His publication record shows a clear progression from quantum computing research to applied optics for automotive applications. The 15 most recent publications demonstrate significant focus on how optical aberrations impact AI performance in vision systems, with particular attention to automotive applications. His work bridges traditional optical engineering with modern AI, developing metrics and simulation tools that account for real-world optical imperfections. Key contributions include SOLAS (Superpositioning an Optical Lens in Automotive Simulation) and research on how windshield quality affects AI algorithm performance. Prof. Braun has been active in IEEE P2020 Automotive Image Quality standardization efforts, presenting at conferences like Automotive Glazing 2019. His research has direct practical implications for automotive camera system validation, production quality control, and development of more robust vision systems that can handle real-world optical conditions. His work on quantum annealing and digital annealing also shows continued interest in computational optimization methods, with potential applications in traffic flow optimization and logistics.

Ajey Jacobمشاهده پروفایل

استاد پژوهشی

• Silicon photonics
• Non-volatile memory
• Neuromorphic computing
• +۵ مورد دیگر

Ajey Jacob is the Director of the Application Specific Intelligent Computing (ASIC) Lab at the University of Southern California's Information Sciences Institute (ISI). With 16 years of industry experience from Intel Corporation and GlobalFoundries, Dr. Jacob brings deep expertise in semiconductor research, development, and manufacturing to his academic role. His professional journey includes founding and leading GlobalFoundries' Differentiating Technology Research Group and managing strategic research consortia at Intel. Dr. Jacob's research focuses on advanced semiconductor technologies with particular emphasis on silicon photonics , non-volatile memory (MRAM, FeRAM, RRAM), photonic SRAM , in-memory computing , and processing-in-pixel architectures . His work bridges materials, devices, integration, and fabrication aspects of next-generation computing systems. Recent publications demonstrate a strong trajectory toward photonic computing for AI acceleration and energy-efficient edge intelligence systems. Dr. Jacob's publication record is exceptional with over 300 worldwide patents (including more than 220 USPTO issued patents), four book chapters, and more than 100 journal and conference papers. His most recent work (2023-2025) shows continued innovation in photonic memory systems, neuromorphic computing architectures, and optical interconnects for high-performance computing. Master Inventor at GlobalFoundries (2017-2020) Semiconductor Research Corporation (SRC) Mahboob Khan Outstanding Industry Liaison Award (2013, 2016) Dr. Jacob actively contributes to the research community as a committee member for IEEE Electronics Components and Technology Conference (ECTC) Silicon Photonics Session, has served as chair and co-chair for the International Conference on Frontiers of Characterization and Metrology for Nanoelectronics (FCMN), and is co-chair for the MIT/AIM Photonics monolithic integration session of the Integrated Photonics Systems Roadmap (IPSR). His industry-academia bridge provides unique opportunities for students interested in both fundamental research and practical semiconductor technology development. The ASIC Lab under Dr. Jacob's direction represents a convergence point for photonics, memory systems, and AI acceleration research, with strong connections to semiconductor industry challenges and opportunities. His work on photonic SRAM and in-memory computing addresses critical bottlenecks in modern computing architectures while maintaining practical considerations from his extensive industry experience.

Hanno Scharrمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Hanno Scharr serves as a Researcher within the Data Analytics and Machine Learning group (IAS-8) at the Institute for Advanced Simulation, Forschungszentrum Jülich—a leading German national research center. His work integrates advanced computational methodologies with scientific problem-solving across interdisciplinary domains. His research specializes in Machine Learning and Data Analytics , with significant contributions to Artificial Intelligence and Computer Vision frameworks. This focus enables robust analysis of complex scientific datasets, particularly through pattern recognition algorithms and high-performance computing applications. As part of IAS-8, Dr. Scharr collaborates within Forschungszentrum Jülich's ecosystem of 20+ specialized institutes, leveraging resources like the Jülich Supercomputing Centre. His group's work directly supports the center's strategic missions in information science and energy research through data-driven simulation approaches. No scientific awards were documented in the source materials. While student advising details remain unspecified, his role involves mentoring within Forschungszentrum Jülich's graduate programs and potential collaborations with affiliated universities like RWTH Aachen. Grant activities likely align with Helmholtz Association funding priorities in data science infrastructure. Dr. Scharr operates within the Data Analytics and Machine Learning team (IAS-8), which develops scalable AI solutions for Forschungszentrum Jülich's research divisions. This group maintains critical partnerships with the Peter Grünberg Institute for quantum computing applications and the Institute of Neuroscience for medical imaging analytics.

• Quantum Thermodynamics
• Quantum Information Theory
• Quantum Computing
• +۶ مورد دیگر

Nicole Yunger Halpern is a theoretical physicist affiliated with the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland. Her research focuses on quantum thermodynamics, quantum information theory, and the interdisciplinary concept of 'quantum steampunk,' which merges 19th-century thermodynamics with modern quantum science. She earned her PhD at Caltech under John Preskill, with whom she later collaborated on foundational quantum physics topics. Her work bridges theoretical challenges in quantum systems with practical applications in autonomous quantum machines, lattice gauge theories, and strong-coupling thermodynamics. Halpern's research explores the tradeoff between quantum complexity and thermodynamic work, particularly in quantum computation contexts like qubit reset and data compression. She has co-authored studies on autonomous quantum refrigerators, leveraging superconducting qubits for practical quantum thermodynamic devices. Her collaborations span institutions such as Chalmers University of Technology and the University of Toronto, reflecting her interdisciplinary approach. She also co-supervised Shayan Majidy, a PhD student at the Institute for Quantum Computing, during his transition into quantum thermodynamics. Halpern emphasizes storytelling in research communication, advocating for coherent narratives in academic applications. Her popular science book, Quantum Steampunk: The Physics of Yesterday’s Tomorrow , synthesizes her research vision and has influenced her theoretical work on lattice gauge theories and phase transitions. She has contributed to frameworks like strong-coupling thermodynamics and DiVincenzo-inspired criteria for autonomous quantum machines, aiming to reduce reliance on classical control systems in quantum computing. Her work intersects with nuclear physics, condensed matter, and experimental quantum thermodynamics, positioning her as a bridge-builder between foundational theory and emerging technologies.

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۵ مورد دیگر

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

Christian Bauckhageمشاهده پروفایل

استاد

• Machine Learning
• Pattern Recognition
• Quantum Computing
• +۲ مورد دیگر

Christian Bauckhage is Professor of Computer Science specializing in Pattern Recognition at the University of Bonn and Lead Scientist for Machine Learning at Fraunhofer IAIS. He serves as co-director of The Lamarr Institute for Machine Learning and Artificial Intelligence. With over 20 years of experience in AI, his current research focuses on quantum computing applications in machine learning. His research spans quantum machine learning, explainable AI, financial document analysis, and medical imaging. Recent publications demonstrate strong focus on quantum computing applications, large language models in finance, and explainability techniques. Bauckhage has published over 200 papers on pattern recognition, machine learning, and quantum computing applications. His research group at Fraunhofer IAIS focuses on industrial applications of AI in finance, healthcare, and agriculture.

René Glebkeمشاهده پروفایل

پژوهشگر

• Cyber-Physical Systems
• Industrial Control Systems
• Data Communication
• +۳ مورد دیگر

René Glebke is a Researcher at RWTH Aachen University's Communication Systems Group (COMSYS), focusing on Cyber-Physical Systems (CPS). His work emphasizes reliable data communication for industrial control, critical infrastructure, and low-level packet processing. He is affiliated with projects like QuGrids (Quantum-based Energy Grids) and the Cluster of Excellence 2023 Internet of Production. Previously, he contributed to DFG SPP 1914 projects (e.g., REFLEXES, REDeFiNE). Teaching: Organized Practical Internet eXperience (PIX) Lab and seminars on Advanced Internet Technology. Awards: Best Paper (2012), Best Student Paper (2012), and a Best Paper Nomination (2021). Research Trends: Recent articles address energy grids, NILM domain bias, and in-network computing for industrial systems. Advising: Supervised 33 students across Bachelor’s/Master’s theses and 34 seminar topics. Active in lab organization (e.g., Research Focus Class on In-Network Processing). Labs/Teams: Core member of COMSYS, involved in the Internet of Production initiative.

• Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Werner Martin is a Professor of Big Geospatial Data Management at the Technical University of Munich (TUM), affiliated with the School of Engineering and Design and the Department of Aerospace and Geodesy. His research focuses on georeferenced data processing, distributed computing, quantum algorithms, and machine learning applications in geospatial contexts. Prof. Martin holds a doctorate from LMU Munich and has held academic and research positions at institutions including LMU Munich, Leibniz-University Hannover, the German Aerospace Center (DLR), and UniBW Munich. His work bridges theoretical advancements with practical applications in spatial data analysis, visualization, and high-performance computing. Awards ACM SIGSPATIAL GIS Certificate of Appreciation (2019) 1st place ACM SIGSPATIAL GIS Cup (2015) IPIN Best Paper Award (2014) His recent publications emphasize geospatial AI, quantum computing for data processing, and environmental monitoring systems. Collaborations with industry partners like DLR highlight his focus on real-world geospatial challenges.

• Digital Humanities
• Artificial Intelligence
• Computer Vision
• +۴ مورد دیگر

Fabian Offert is an Assistant Professor in the History and Theory of the Digital Humanities at the University of California, Santa Barbara (UCSB), with affiliations spanning the Department of Germanic and Slavic Studies, Media Arts and Technology Program, and Comparative Literature Program. He directs the Center for the Humanities and Machine Learning and leads the Volkswagen Foundation-funded AI Forensics Project. His research explores artificial intelligence’s epistemological, aesthetic, and political dimensions, focusing on visual digital humanities and machine learning interpretability. Education: PhD in Media Arts and Technology (UCSB, 2014–2018), Diploma in Applied Theater Studies (Justus-Liebig University Gießen and UC Berkeley, 2006–2012) Research Interests: Machine visual culture, generative AI ethics, interdisciplinary methodologies in digital humanities, and technical close reading of AI systems His work bridges humanities scholarship with technical innovation, exemplified by projects like imgs.ai (a multimodal art history search engine) and the AI Forensics initiative. He has received over $2 million in grants, including a Volkswagen Foundation grant for AI Forensics and a UCHRI award for Critical Machine Learning Studies. Key Awards: Rudolf Wittkower Fellowship, Cambridge Center Residency, Fellow of the Regents (UCSB) Teaching: Courses include 'Critical Artificial Intelligence' and 'Digital Visual Studies,' emphasizing technical literacy and ethical critique Offert is actively involved in academic governance, serving on UCSB’s AI Policy Working Group and DEI committees. His interdisciplinary approach has positioned him at the forefront of AI’s societal and epistemological implications.

Klaus-Robert Müllerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Brain-Computer Interfaces
• +۸ مورد دیگر

Prof. Dr. Klaus-Robert Müller is a Full Professor and Chair of Machine Learning at Technical University of Berlin, leading the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds joint appointments at Korea University (Department of Artificial Intelligence) and collaborates with institutions like Google Research and the Max Planck Society. With a PhD in theoretical computer science from the University of Karlsruhe, Müller has pioneered work in statistical learning theory, brain-computer interfaces, and explainable AI. His research spans computational neuroscience, molecular dynamics, and medical imaging applications such as digital pathology and neuroimaging analysis. He has directed major initiatives like the Berlin Center for Machine Learning (BZML) and the Berlin Big Data Center (BBDC), now integrated into BIFOLD. Müller is a member of prestigious academies, including the Leopoldina and Berlin-Brandenburg Academy of Sciences, and has received awards like the Vodafone Innovations Award (2017) and Berlin Science Prize (2014). Education: PhD in Theoretical Computer Science (1992), University of Karlsruhe Diploma in Mathematical Physics (1989), University of Karlsruhe Research Interests: Müller’s work integrates machine learning with interdisciplinary challenges, emphasizing explainability and ethical AI. Key areas include neural network interpretability, non-stationary data analysis, and applications in healthcare (e.g., tumor classification, genomic data analysis) and materials science (e.g., quantum chemistry simulations). His contributions to brain-computer interfaces and neuroimaging have advanced understanding of neural oscillations and brain connectivity. Awards & Recognition: Member, German National Academy of Sciences Leopoldina (2012) Member, Berlin-Brandenburg Academy of Sciences (2017) Science Prize of Berlin (2014) Vodafone Innovations Award (2017) Labs & Leadership: Directs BIFOLD and co-leads initiatives like the TEA Challenge in molecular dynamics. Supervises interdisciplinary teams focused on AI in pathology, computational chemistry, and neurotechnology. His work bridges theory and practice, with impactful contributions to open datasets (e.g., QCML) and scalable machine learning frameworks.