جست‌وجوی استاد و پژوهشگر

Stephan A. Sieberمشاهده پروفایل

استاد

• Bioorganic Chemistry
• Antibiotic Development
• Chemical Proteomics
• +۵ مورد دیگر

Professor Stephan A. Sieber is a leading researcher in bioorganic chemistry at the Technical University of Munich (TUM), where he holds the Chair of Organic Chemistry II within the TUM School of Natural Sciences. His research program focuses on developing new drugs against multidrug-resistant bacteria through a multi-disciplinary approach that integrates synthetic chemistry, functional proteomics, microbiology, and protein biochemistry. His laboratory has made significant contributions to identifying unprecedented antibacterial targets beyond the scope of current antibiotics and exploiting these for chemical manipulation. Recent work has increasingly incorporated machine learning approaches to accelerate antibiotic discovery, with notable publications on AI-guided pipelines, drug-target interaction prediction, and high-throughput screening optimization. Sieber's research has resulted in the discovery of new active substances, some of which are currently being optimized for medical applications. His group's publications reveal a strong focus on chemical proteome mining, natural product mode of action studies, and novel antibacterial target identification. The lab has published extensively in top journals including Nature Chemistry, Nature Communications, and ACS Central Science. Inhoffen Medal (2024) Max Bergmann Medal (2023) ERC Advanced Grant (2023) Merck Future Insight Prize (2020) Klaus Grohe Prize (2020) ERC Consolidator Grant (2016) Professor Sieber leads an active research group that maintains a strong presence in the scientific community through regular publications, conference presentations, and collaborations. His laboratory website and BlueSky presence (@sieberlab.bsky.social) demonstrate ongoing research activities and engagement with the broader scientific community. He has successfully secured significant research funding including multiple ERC grants that have supported his innovative work in antibiotic discovery.

Sergey Tulyakovمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Generative AI
• +۳ مورد دیگر

Sergey Tulyakov is the Director of Research at Snap Inc. , leading the Creative Vision team. His work focuses on enhancing creator capabilities through computer vision , machine learning , and generative AI , with applications in 2D/3D/4D video generation, editing, and personalization. He pioneered video generation frameworks like MoCoGAN and First Order Motion Model , and has been recognized for BEST IN SHOW AWARD at SIGGRAPH Real-Time Live! 2020. PhD (2012-2017): University of Trento, Italy MSc (2010): Belorusian State University of Informatics and Radioelectronics B.Eng (2009): Belorusian State University of Informatics and Radioelectronics His research interests span computer vision , generative models , 3D reconstruction , and personalization , with a focus on making large models efficient and mobile-compatible . Recent publications highlight advancements in 4D video generation , text-guided 3D composition , and lightweight architectures . Key scientific awards include the SIGGRAPH Real-Time Live! 2020 Best in Show for Interactive Video Stylization. He has also served on technical program committees for top-tier conferences like CVPR, ICCV, SIGGRAPH, and NeurIPS since 2022. His team organizes tutorials and keynotes, including courses on Deep Generative Models and Efficient Neural Networks . While no direct student names are listed, his collaborative work spans 60+ top-tier publications.

James C. Davisمشاهده پروفایل

استادیار

• Empirical software engineering
• Security
• Safety
• +۷ مورد دیگر

James C. Davis is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. He leads the Duality Lab, which focuses on the engineering of software-intensive computing systems with particular interest in how these systems fail and how those failures can be mitigated. His research takes a socio-technical approach, considering both human and technical perspectives in system engineering. Dr. Davis received his PhD in Computer Science from Virginia Tech, where he was advised by Dongyoon Lee. His research interests span empirical software engineering, security, safety, testing, and web technologies, with a strong emphasis on practical impact and measurement. He applies a socio-technical philosophy to his work, believing high-quality systems must be engineered considering both human and technical perspectives. His recent research focuses on software supply chain security, regular expression vulnerabilities (particularly ReDoS), pre-trained model security, and failure analysis in software systems. His work often involves empirical studies of real-world software systems and security practices, with a strong emphasis on practical impact and measurable results. Dr. Davis has received significant funding from the National Science Foundation, Google, Cisco, and Rolls Royce for his research. His publications appear in top-tier venues including ICSE, FSE, ASE, and USENIX Security. He has served on program committees for many major software engineering and security conferences. Among his notable achievements are being elevated to IEEE Senior Member in 2022, receiving the Ruth and Joel Spira Outstanding Teacher Award from ECE@Purdue in 2022, and multiple Best Paper and Best Poster awards. He has successfully mentored numerous PhD and Master's students, with several completing their theses on topics related to software security and engineering. Dr. Davis actively recruits graduate and undergraduate research assistants for his Duality Lab, which has produced influential work on software failure analysis, regular expression security, and machine learning supply chain security. His lab is supported by multiple federal and industry grants focused on improving the security and reliability of software systems.

Sören Laueمشاهده پروفایل

استاد

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +۳ مورد دیگر

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

Anne Lauscherمشاهده پروفایل

دانشیار

• Conversational Artificial Intelligence
• Fairness in AI
• Natural Language Processing
• +۳ مورد دیگر

Prof. Dr. Anne Lauscher is an Associate Professor of Data Science at the University of Hamburg Business School, specializing in fair, inclusive, and sustainable conversational AI systems. Her research focuses on improving algorithmic fairness through demographic factors in NLP systems and exploring ethical implications of large language models. She holds a PhD from the University of Mannheim, where her work on computational argumentation was awarded summa cum laude, and has conducted research at Grammarly and the Allen Institute for AI. Key contributions include gender-fair machine translation datasets (e.g., Building Bridges), bias detection frameworks (e.g., SHADES), and multilingual benchmarking tools like MultiQ. Her work has been recognized with the Maria Gräfin von Linden-Award and inclusion in the '100 Brilliant Women in AI Ethics' list. Research spans ethical NLP, multilingual AI, and societal impacts of AI technologies. Education: PhD in Data and Web Science (University of Mannheim, 2021), Postdoc at Bocconi University's NLP group (2021-2022). Academic roles include adjunct positions and international collaborations across Europe and the US. Research Interests: Conversational AI fairness, multilingual NLP systems, ethical AI evaluation, bias mitigation in LLMs, and interdisciplinary applications of machine learning in scientific discovery. Publications (select highlights): Over 55 peer-reviewed works in top-tier venues like ACL, EMNLP, and AAAI. Recent focus on LLM hallucination analysis, cross-cultural NLP benchmarks, and gender-neutral language resources. Awards: 2021 Maria Gräfin von Linden-Award (Baden-Württemberg), 2023 '100 Brilliant Women in AI Ethics', 2022 Dissertation Award Nominee (GI). Labs/Teams: Leads the UHH Data Science Research Group, collaborating with industry partners like Grammarly and academic institutions worldwide. Active in initiatives promoting gender equity in STEM and sustainable AI development.

Matthias Rareyمشاهده پروفایل

استاد

• Bioinformatics
• Cheminformatics
• Algorithmic Molecular Design
• +۵ مورد دیگر

Prof. Dr. Matthias Rarey is a computer scientist and Professor at the University of Hamburg's Center for Bioinformatics. He holds a Ph.D. in Computer Science from the University of Bonn (1996) and has been leading the Algorithmic Molecular Design working group since 2002. His research focuses on molecular design algorithms, cheminformatics tools, and 3D bioinformatics. Co-founder of BioSolveIT GmbH Former cheminformatics group leader at Fraunhofer SCAI Former researcher at SmithKline Beecham and Roche Bioscience Head of Helmholtz Data Science Graduate School DASHH Director of Center for Data and Computing in Natural Science (CDCS) Research interests span algorithmic molecular design, cheminformatics, structure-based drug discovery, and machine learning applications in bioactivity prediction. His group developed widely used tools like FlexX, PoseView, and SpaceLight for molecular modeling and fragment space analysis. Recent publications focus on geometric pattern matching in protein-ligand interfaces, combinatorial fragment space encoding, adverse drug reaction network analysis, and efficient shape-based virtual screening. The work emphasizes scalable algorithms for billion-sized compound libraries and integration of machine learning with traditional cheminformatics approaches. Scientific awards include: GMD Award 1996 (Best Dissertation) GMD Award 2000 (Best Project) NRW Wissenschaftspreis 2002 Corwin Hansch Award 2005 Emerging Technologies Award 2011 Norddeutscher Wissenschaftspreis 2020 Academic leadership roles: Founding director of Center for Bioinformatics Co-founder of M.Sc. Bioinformatics and B.Sc. Computing in Science programs Chair of doctoral committee at Faculty of Computer Science Member of EMBL-EBI's Molecular and Cellular Structure advisory board Former Associate Editor of Journal of Chemical Information and Modeling

• Computational Physics
• Biophysics
• Light-Harvesting Complexes
• +۷ مورد دیگر

Prof. Dr. Ulrich Kleinekathöfer is a Full Professor of Theoretical Physics at Constructor University (formerly Jacobs University Bremen) in the School of Science. His research focuses on computational physics and biophysics, particularly on light-harvesting complexes, membrane transport, and quantum dynamics in biological systems. He leads the Computational Physics and Biophysics research group and coordinates the MSCA Doctoral Training Network "PhotoCaM". His educational background includes: PhD from Max-Planck-Institut für Strömungsforschung, Göttingen (1996) Diploma in Physics from Universität Göttingen (1993) Habilitation in Physics from Technische Universität Chemnitz (2002) Prof. Kleinekathöfer's research spans multiple areas of computational biophysics and theoretical physics. His primary interests include excitation energy transfer in light-harvesting complexes , molecular transport through membrane channels and nanopores , and quantum dynamics in open systems . His group develops and applies advanced computational methods including molecular dynamics simulations, quantum chemistry calculations, and machine learning approaches to study these phenomena. A significant portion of his work focuses on photosynthetic systems, particularly how energy is transferred and converted in natural light-harvesting complexes, with implications for renewable energy technologies. His recent publications demonstrate a strong trend toward integrating machine learning with traditional computational methods, particularly in the fields of quantum chemistry and molecular dynamics. There's a clear focus on multifidelity approaches that balance computational efficiency with accuracy. His work spans from fundamental quantum dynamics to applied research on antibiotic transport mechanisms, showing remarkable breadth while maintaining depth in computational methodology development. His notable recognition includes: Tan Chin Tuan Exchange Fellowship, NTU Singapore (2019) Prof. Kleinekathöfer has supervised numerous PhD students and postdoctoral researchers, with a current group comprising several PhD candidates and research associates. His research is supported by multiple funding sources including the Deutsche Forschungsgemeinschaft (DFG), European Union through MSCA Doctoral Network PhotoCaM, and previously through the Innovative Medicines Initiative "Translocation" and Marie Curie Training Program "Translocation". His collaborative network spans internationally, with partnerships at institutions in Germany, USA, Greece, and Switzerland. The Computational Physics and Biophysics Group operates within Constructor University's research infrastructure, utilizing high-performance computing resources for their simulations. The group maintains active collaborations with experimental groups to validate and inform their computational models, creating a strong interdisciplinary research environment focused on understanding fundamental biophysical processes at the molecular level.

• Quantum Optics
• Quantum Metrology
• Gravitational Waves
• +۳ مورد دیگر

Daniel Braun is a Professor at the University of Tübingen, affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Physics. He holds the Theoretical Physics (Braun Chair) and has been active in academia since October 1, 2013. Email: daniel.braun@uni-tuebingen.de Research Interests: His work bridges quantum optics, metrology, and gravitational physics. He explores quantum-enhanced measurement techniques, nonlinear optical phenomena in curved spacetime, and mechanical systems for fundamental tests of physics. Institutional Affiliation: Institute for Theoretical Physics (ITP) Recent Publications (2025-2024): Focus on quantum-limited interferometry, machine learning applications in quantum channels, gravitational effects in particle accelerators, and nonlinear soliton dynamics in relativistic settings. Scientific Awards: No specific awards mentioned in the provided data.

• Hyperdimensional Computing
• Neuromorphic Hardware
• In-Memory Computing
• +۳ مورد دیگر

Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.

Saman Amarasingheمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Domain-Specific Languages
• +۷ مورد دیگر

Professor Saman Amarasinghe is a full Professor in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), and Principal Investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Commit compiler research group, which focuses on programming languages and compilers that maximize application performance on modern computing platforms. His work spans multiple academic departments and research centers, with strong affiliations to both MIT's School of Engineering and CSAIL. Professor Amarasinghe's research interests center around high-performance domain-specific languages and compiler technology . His work combines language design with sophisticated compilation techniques to deliver unprecedented performance for targeted application domains. His research spans multiple areas including image processing (Halide), sparse tensor algebra (TACO), graph analytics (GraphIt), stream computations (StreamIt), and bioinformatics (Seq). A significant thread throughout his work is the application of machine learning for compiler optimizations, from Meta optimization in 2003 to the OpenTuner autotuner framework. Analysis of Professor Amarasinghe's recent publications reveals a strong focus on sparse computing , compiler vectorization , and domain-specific language implementation . His work consistently bridges theoretical compiler concepts with practical performance gains across diverse application domains. The progression from earlier work on StreamIt and Halide to more recent projects like GraphIt and TACO shows an evolution toward more specialized, high-performance DSLs targeting specific computational patterns. His 2020-2025 publications particularly emphasize sparse tensor operations, GPU acceleration, and machine learning integration with compiler technology. ACM Fellow (2019) Professor Amarasinghe has made significant contributions to academic entrepreneurship and student development. He founded Determina, Inc. (acquired by VMware) based on security research from his MIT lab and co-founded Lanka Internet Services, Ltd., Sri Lanka's first ISP. As faculty director of MIT Global Startup Labs, his programs across 17 countries have helped create over 20 successful startups. His teaching includes the popular Performance Engineering of Software Systems (6.172) course with Professor Charles Leiserson, as well as innovative project-based courses like the Open Source Software Project Lab and Bring Your Own Software Project Lab. His educational approach emphasizes hands-on experience with compiler and language design concepts. Professor Amarasinghe leads the Commit compiler research group at MIT CSAIL, which has produced numerous influential domain-specific languages and compilers including Halide, TACO, Simit, StreamIt, and GraphIt. The lab maintains strong industry connections through projects like OpenTuner and Determina, and collaborates with researchers worldwide on compiler technology. The group's work spans both theoretical compiler research and practical implementation, with a consistent focus on bridging the performance gap between high-level programming abstractions and hardware capabilities.

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

Daniel Müller-Gritschnederمشاهده پروفایل

استاد مدعو

• TinyML
• Embedded Software Development
• Soft Error Resilience
• +۴ مورد دیگر

Daniel Müller-Gritschneder is an Adjunct Teaching Professor (Privatdozent) at the Technical University of Munich (TUM), affiliated with the Chair of Electronic Design Automation. He leads the 'Electronic System Level' research group, focusing on embedded systems, TinyML, virtual prototyping, and hardware resilience. He temporarily served as head of the Chair of Real-Time Systems (2019–2020) and holds a senior membership in IEEE. His research spans: TinyML : Optimizing neural network inference for microcontrollers. Virtual Prototyping : Fast simulation for embedded software development (e.g., ETISS simulator). Runtime Verification : Hardware monitoring for safety-critical systems. Fault Tolerance : Cross-layer resilience against soft errors. Design Automation : NoC synthesis and RISC-V toolchain optimization. His publications emphasize RISC-V-based systems, TinyML deployment, fault injection, and embedded AI. Recent works show trends toward compiler-assisted security, thermal management, and automated design-space exploration for edge devices. Awards: Best Paper Award (SiPS 2019) Habilitation Award (Bund der Freunde der TUM, 2019) 2nd Best Paper (SMACD'15) Best Paper nominations at DAC'07, DATE'10, Analog'10, NOCS'13 He advises researchers in the Electronic System Level group and contributes to EU projects (e.g., Scale4Edge). His lab develops tools like ETISS, MLonMCU, and Seal5 for RISC-V and TinyML ecosystems.

• Machine Learning
• Optimization
• Federated Learning
• +۸ مورد دیگر

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

• Embedded Machine Learning
• Neural Network Hardware Acceleration
• Quantized Neural Networks
• +۳ مورد دیگر

Lukas Einhaus is a Researcher and PhD student in the Embedded Systems department at the University of Duisburg-Essen since April 2020, affiliated with the Intelligent Embedded Systems (IES) research group and contributing to initiatives including Elastic AI and the IoT Garage. His academic background includes: Bachelor of Science from University of Duisburg-Essen, thesis focused on programming abstractions for concurrent embedded systems Master of Science from University of Duisburg-Essen, specializing in distributed and reliable systems with thesis research on quantizing neural networks Einhaus's research centers on designing neural networks for efficient hardware implementation on FPGAs, with primary expertise in quantized or low-precision neural networks that reduce bit depth (typically 1-3 bits) for computations and information flow. This work enables energy-efficient AI solutions for embedded and IoT devices where resource constraints are critical. His publication record from 2021-2025 reveals consistent innovation in FPGA-based neural network optimization, with applications spanning fluid flow estimation, time-series analysis, and real-time stream processing. Core themes include Elastic AI for adaptive systems, precomputation techniques for convolutional layers, and hardware-aware neural architecture design. He previously contributed to the BMBF-funded project "KI-Sprung: LUTNet" (until March 2022), developing energy-efficient AI networks using elementary lookup tables for FPGA deployment. Einhaus actively mentors students through the IoT Garage initiative, supervising practical projects including drink-mixing machines, exoskeletons, and ball-challenge systems.

Helge Steinمشاهده پروفایل

استاد

• Digital Catalysis
• Battery Research
• Materials Science
• +۳ مورد دیگر

Prof. Helge Stein is a Professor in the Department of Chemistry at the Technical University of Munich (TUM), leading the Professorship for Digital Catalysis. He holds a doctorate in mechanical engineering from Ruhr University Bochum (summa cum laude) and conducted postdoctoral research at Caltech before joining TUM in 2023. His research focuses on accelerating materials discovery and optimization through digital tools like machine learning, robotics, and data management, with applications in catalysis and battery systems. Stein has pioneered the development of Materials Acceleration Platforms (MAPs) to streamline experimental and computational workflows globally. Education: Bachelor/Master in Physics, Georg August University of Göttingen (2008–2013) Doctorate in Mechanical Engineering, Ruhr University Bochum (2017, summa cum laude) Postdoc, California Institute of Technology (2017–2020) Tenure-track Professor, Karlsruhe Institute of Technology (2020–2023) Research Interests: Integration of robotics and AI in materials research High-throughput experimentation for battery and catalysis systems Data-driven approaches to nonlinear material-behavior analysis Development of decentralized Materials Acceleration Platforms (MAPs) Key Awards: ACS Engineering Au Rising Star (2023) Kit Innovation Award (2023) Masao Horiba Award (2021) Eickhoff Prize (2018) Teaching: Leads courses on high-throughput methods, data management in chemistry, and digital catalysis seminars. Collaborates with interdisciplinary teams across TUM's School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence. Labs/Teams: Directs research groups focused on automated electrochemistry, materials robotics, and AI-driven battery design, with partnerships spanning academia and industry for global MAP implementations.