جست‌وجوی استاد و پژوهشگر

Clara Wanjuraمشاهده پروفایل

پژوهشگر

• Non-Hermitian Topology
• Quantum Optics
• Neuromorphic Computing
• +۵ مورد دیگر

Clara Wanjura is a leading theoretical physicist at the Max Planck Institute for the Science of Light (MPL) since December 2024, where she heads a Minerva Fast Track Research Group. Her interdisciplinary research bridges quantum optics, solid-state physics, and machine learning. PhD in Physics (2018-2022) from University of Cambridge Master's (2016-2018) and Bachelor's (2013-2016) in Physics from Ulm University Her research focuses on: Non-Hermitian Topology: Exploring directional amplification, sensing, and non-reciprocity in quantum systems with gain/loss dynamics. Neuromorphic Computing: Developing energy-efficient optical hardware for machine learning using linear wave scattering and physical training frameworks. Her recent publications highlight transformative work in topological amplification, quantum interfaces for gravity detection, and photonic neuromorphic architectures. Collaborations span institutions like AMOLF (Amsterdam) and Cavendish Laboratory. Scientific Recognition: DPG SAMOP Thesis Prize (2024) Sam Edwards Thesis Prize (2023) CSAR PhD Students Award (2022) Cavendish PhD Prize (2020) She actively mentors students and welcomes applications for research projects in quantum technologies and computational physics.

Alessandro Biondiمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems
• +۶ مورد دیگر

Alessandro Biondi is an Associate Professor at Scuola Superiore Sant'Anna in Pisa, Italy, affiliated with the Real-Time Systems Laboratory and the Department of Excellence in Robotics & AI. His research focuses on real-time and embedded systems, cyber-physical systems, and security for critical systems. Biondi's work spans real-time operating systems, virtualization, schedulability analysis, safe machine learning for CPS, and synchronization protocols. He collaborates extensively with industry partners in automotive, railway, avionics, and drone domains, working on projects involving autonomous driving software, safety-critical systems, and multi-domain platforms. He has received several prestigious awards including the IEEE TCCPS Early Career Award and ACM SIGBED Early Career Award. Biondi serves as Associate Editor for several journals including IEEE Transactions on Emerging Topics in Computing and Springer Real-Time Systems.

Naeimeh Mohseniمشاهده پروفایل

استاد

• Quantum Technology
• Quantum Optics
• Quantum Simulation
• +۵ مورد دیگر

Naeimeh Mohseni is a Professor and Chair of Theoretical Physics II at the Department of Physics of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Her research focuses on quantum technology, with an emphasis on quantum optics and its applications in quantum simulation, computation, and metrology. Recently, her work has expanded to bridge quantum technology and machine learning, particularly leveraging deep learning techniques to tackle quantum many-body dynamics problems. Her research interests also include broader areas such as quantum information science and interdisciplinary applications of machine learning in quantum systems. No scientific awards, grants, or advised students are explicitly listed in the provided text.

Paarijaat Adityaمشاهده پروفایل

استاد

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +۴ مورد دیگر

Paarijaat Aditya is a faculty member at the Max Planck Institute for Software Systems (MPI-SWS) , focusing on interdisciplinary research at the intersection of theoretical computer science and applied systems. Their work spans algorithms, programming languages, security, and distributed systems , with a particular emphasis on serverless computing, federated learning, and privacy-preserving urban sensing. Research Areas : Machine Learning Security (e.g., model hijacking, federated learning) Serverless Computing (e.g., GPU sharing, high-performance frameworks) Privacy Engineering (e.g., urban sensing, image capture, mobile compliance) Distributed Systems (e.g., NFV, peer-assisted content delivery) Their publications reflect a trajectory of advancing scalable AI training, secure collaborative systems, and privacy-compliant infrastructures. Recent works address challenges in public cloud adaptability, while earlier contributions explore mobile privacy risks and secure resource management.

Debasmita Loharمشاهده پروفایل

پژوهشگر ارشد

• Program Analysis
• Approximate Computing
• Probabilistic Analysis
• +۵ مورد دیگر

Debasmita Lohar is a Postdoctoral Researcher at Karlsruhe Institute of Technology (KIT), working with Bernhard Beckert in the Application-oriented Formal Verification group at KASTEL — Institute of Information Security and Dependability. She is set to join the IT University of Copenhagen as an Assistant Professor starting December 2024. Her educational background includes: PhD from Saarland University in collaboration with the Max Planck Institute for Software Systems (MPI-SWS), Saarbrücken, advised by Eva Darulova Graduate studies at the Indian Institute of Technology (IIT) Kharagpur, advised by Soumyajit Dey Dr. Lohar's research focuses on the intersection of program analysis, approximate computing, and probabilistic analysis, with applications spanning embedded systems, scientific computing, and machine learning. Her work addresses critical challenges in numerical error analysis, particularly in the context of finite-precision arithmetic and its implications for system reliability and safety. She has made significant contributions to sound static program analysis techniques for neural networks and cyber-physical systems, developing novel approaches for mixed-precision tuning and quantization that balance computational efficiency with numerical accuracy guarantees. Her recent publications demonstrate a strong trajectory in formal methods for numerical programs, with increasing focus on neural networks and cyber-physical systems. The research shows a clear progression from fundamental numerical error analysis to practical applications in machine learning systems, reflecting the growing importance of formal verification in AI safety-critical domains. Her scientific recognition includes: Selected to participate in the 10th Heidelberg Laureate Forum, 2023 Best Presentation Award (iFM PhD Symposium), 2019 Dr. Lohar has extensive teaching experience across multiple institutions, having taught courses on Floating-Point Analysis, LLMs in Formal Verification, Neural Networks in Formal Verification at KIT, Advanced Program Analysis and Program Analysis at Saarland University, and Fault Tolerant Systems, Theory of Computation, and Computer Organization and Architecture Lab at IIT Kharagpur. Her research is supported through her position at KASTEL and likely through competitive research grants given her publication record in top-tier venues. She leads research in the Application-oriented Formal Verification group at KASTEL, collaborating with researchers like Bernhard Beckert, Eva Darulova, and others. Her work on the Aster project demonstrates her leadership in developing practical tools for sound mixed fixed-point quantization of neural networks.

Tim Gollischمشاهده پروفایل

استاد

• Neurobiology
• Retinal Processing
• Neural Coding
• +۵ مورد دیگر

Prof. Tim Gollisch is a Professor of Sensory Processing in the Retina at the School of Medicine, University of Göttingen. His research focuses on understanding how retinal neural networks process visual information, emphasizing neural coding and computation. Key areas include retinal dysfunction analysis, optogenetic restoration of vision, and adaptive mechanisms in retinal circuits. Education: Diploma in Physics, University of Heidelberg (2000) PhD in Biophysics, Humboldt University Berlin (2004) Research Interests: Neural coding mechanisms in retinal neurons Synaptic connectivity and functional diversity Optogenetic therapies for degenerative retinal diseases Integration of experimental data with computational models Research Trends in Publications: Advances in nonlinear signal processing and receptive field modeling Applications of machine learning in retinal circuit analysis Investigations into primate retinal responses to natural stimuli Labs/Teams: Head of the Sensory Processing in the Retina research group at University Medical Center Göttingen Collaborations with Harvard University and the Max Planck Institute of Neurobiology

Sebastian Huberمشاهده پروفایل

مدرس

• Theoretical Physics
• Quantum Statistical Mechanics
• Machine Learning Applications
• +۳ مورد دیگر

Prof. Dr. Sebastian Huber is a Lecturer at the Department of Physics at ETH Zürich. He holds an ERC Consolidator Grant and leads a research group focused on theoretical and experimental investigations of classical topological wave phenomena and applications of machine learning to quantum statistical many-body systems. His academic journey includes a diploma (2004) and doctoral studies at ETH Zürich under Prof. Gianni Blatter, followed by postdoctoral research at the Weizmann Institute of Science supported by prestigious fellowships. He returned to ETH Zürich in 2012 as an SNSF Professor and transitioned to his current role in 2018. Education: Diploma in Physics (ETH Zürich, 2004), Ph.D. (ETH Zürich, supervised by Gianni Blatter) Research interests bridge theoretical physics with interdisciplinary methods, including information theory and machine learning. His work has been recognized through awards such as the Koshland Prize and SNSF fellowships. He currently oversees a research group funded by an ERC grant, exploring foundational questions in quantum systems and topological wave dynamics. Labs/Teams: Research group under ERC Consolidator Grant framework at ETH Zürich’s Institute for Theoretical Physics

• Electrochemistry
• Materials Science
• Energy Storage
• +۶ مورد دیگر

Dr. Zhenyu Wang is a Researcher at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany, specializing in computational materials design with a focus on electrochemistry and corrosion science. His research emphasizes ab-initio modeling of electrochemical solid/liquid interfaces, energy storage systems, and novel materials for photovoltaics and thermoelectrics. Education: B.Sc. in Physics, Xi'an Jiaotong University (2009-2013) Ph.D. in Electrical Engineering, Xi'an Jiaotong University (2013-2020) Visiting Ph.D. in Chemistry, University College London, UK (2016-2018) Research Interests: His work spans several key areas including: Ab-initio modeling of electrochemical interfaces Energy storage mechanisms in metal-air and metal-ion batteries Two-dimensional hybrid perovskites for photovoltaic applications High-performance thermoelectric materials and transparent conducting oxides Defect chemistry analysis in energy conversion materials Professional Background: Since 2021: Postdoctoral Researcher at Max-Planck-Institut für Eisenforschung and Max Planck Institute for Sustainable Materials Publications: His recent work focuses on advancing computational methods for modeling electrochemical systems, exploring novel materials for energy storage and conversion applications, and understanding interface phenomena through first-principles calculations and molecular dynamics simulations. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Collaborations: Affiliated with the Department of Computational Materials Design, contributing to interdisciplinary research at the Max Planck Institute.

• Wireless Communications
• Integrated Sensing and Communications (ISAC)
• Millimeter-wave Systems
• +۷ مورد دیگر

Kai Wu is an Associate Professor at Xidian University's School of Electronic Engineering, working within the MOE Key Laboratory of Intelligent Perception and Image Understanding in Xi'an, China. His research spans wireless communications, integrated sensing and communications (ISAC), and signal processing, with a particular focus on millimeter-wave systems, radar signal processing, and MIMO technologies. He maintains strong collaborative ties with international researchers, particularly with J. Andrew Zhang, Y. Jay Guo, and Xiaojing Huang from institutions including University of Technology Sydney. Dr. Wu's research interests center on the integration of communication and sensing technologies, with significant contributions to frequency-hopping MIMO radar-based communications, angle-of-arrival estimation, and RIS-assisted ISAC systems. His work addresses critical challenges in wireless communications including spectrum efficiency, channel modeling in complex environments, and the development of energy-efficient communication systems. Recent research has expanded into transformer-based deep learning approaches for signal processing applications and the application of magnetic particle spectroscopy in biomedical contexts. Analysis of Dr. Wu's publication record reveals a strong focus on practical implementations of theoretical concepts, with numerous experimental validations of proposed communication and sensing techniques. His work demonstrates a clear progression from fundamental signal processing techniques to integrated communication-sensing systems, with increasing emphasis on RIS-assisted technologies and machine learning applications in recent years. The interdisciplinary nature of his research spans telecommunications engineering, signal processing, and biomedical applications. Dr. Wu has established himself as a leading researcher in the field of integrated sensing and communications, with over 40 publications in top-tier IEEE journals and conferences. His collaborative network includes prominent researchers in wireless communications, and he serves as a frequent reviewer for major IEEE publications in his field. While specific grant information isn't detailed in the available records, his extensive publication output suggests successful funding from national research programs in China.

• Computer Architecture
• Distributed Systems
• Cloud Computing
• +۵ مورد دیگر

Randy H. Katz is a distinguished Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley's College of Engineering. With an extensive publication record spanning over two decades, he has established himself as a leading researcher in computer systems, distributed computing, and energy-efficient architectures. His work bridges theoretical foundations with practical implementations in cloud computing, networking, and smart infrastructure systems. Professor Katz's research interests span a broad spectrum of computer systems topics, with particular emphasis on energy-efficient computing, distributed systems design, and infrastructure for emerging applications. His work on FireSim represents a major contribution to FPGA-based system simulation, while his research on energy-aware datacenters and smart grids addresses critical sustainability challenges in computing infrastructure. He has pioneered approaches in serverless computing, blockchain systems, and mobile augmented reality that balance performance with energy constraints. Analysis of Professor Katz's recent publications reveals a strong focus on hardware-software co-design for emerging computing paradigms. His work consistently addresses the tension between performance and energy efficiency across multiple domains including cloud infrastructure, smart buildings, and mobile systems. A notable trend is his focus on making complex systems more accessible and manageable through innovative abstractions like FireSim for hardware simulation and Cirrus for serverless machine learning workflows. Scientific Awards: IEEE James H. Mulligan, Jr. Education Medal (2010) - Recognizing his exceptional contributions to engineering education Professor Katz has advised numerous PhD and Master's students who have gone on to influential positions in both academia and industry. His research has been supported by major grants from NSF, DARPA, and industry partnerships with leading technology companies. His work on Mesos, a cluster resource management system, has had significant industry impact, and his energy-efficient computing research has informed datacenter design practices across the industry. His research group at UC Berkeley has been instrumental in developing frameworks like FireSim for hardware acceleration and FirePerf for performance profiling. These tools have become important resources for the computer architecture community, enabling researchers to explore new hardware designs with unprecedented efficiency. His work on smart buildings through projects like SnapLink demonstrates his commitment to applying computing research to solve real-world sustainability challenges.

• Wireless Sensor Networks
• Mobile Computing
• Crowdsourcing Systems
• +۵ مورد دیگر

Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines, with a distinguished research career spanning over two decades in wireless sensor networks, mobile computing, and distributed systems. Their work bridges theoretical foundations with practical applications in augmented reality, robotics, and smart city infrastructure. Research interests focus on three interconnected domains: 1) Networked robotic systems including UAV-UGV collaboration and swarm intelligence, 2) Mobile augmented reality with emphasis on real-time performance and spatial accuracy, and 3) Crowdsourced data systems for smart city applications. Recent work demonstrates sophisticated integration of these areas, particularly in energy-aware path planning, communication-resilient robot teams, and quality-aware AR systems. Analysis of recent publications (2022-2025) reveals a strategic shift toward integrating large language models with physical systems, developing more robust communication frameworks for drone networks, and creating comprehensive evaluation metrics for AR applications. The research trajectory shows increasingly complex system integration, moving from single-device solutions to coordinated multi-agent systems with sophisticated networking requirements. Principal Investigator for multiple NSF-funded projects on networked robotic systems Recipient of best paper awards at MobiQuitous and DCOSS conferences Senior member of IEEE Computer Society Organizing committee member for ACM MobiSys and IEEE PerCom conferences As a research advisor, Qi Han has mentored numerous graduate students who have gone on to publish in top-tier venues and secure positions in both academia and industry. Their lab maintains strong industry partnerships with companies developing AR/VR technologies and drone systems. Current research directions include energy-aware coordination of heterogeneous robot teams, privacy-preserving crowdsensing frameworks, and adaptive AR systems for industrial applications.

Andreas Schmidtمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Business Management
• +۳ مورد دیگر

Andreas Schmidt is affiliated with the University of Kassel, Germany, and holds a PhD in Computer Science from the University of Koblenz and Landau (2022). His research spans interdisciplinary domains including artificial intelligence, business management, and neuroscience. He has published extensively in journals like NeuroImage, SIAM Journal on Scientific Computing, and Remote Sensing of Environment, demonstrating expertise in computational methods, neural networks, and applied data science. Key research themes include propagation network modeling, home-office productivity analysis, and autonomous driving safety frameworks. His work integrates technical innovation with practical applications, such as optimizing district heating systems and advancing neurovascular coupling studies in primates. While no formal awards are listed, his contributions reflect impactful interdisciplinary collaboration across computer science, environmental science, and medical imaging.

• Artificial Intelligence
• Distributed Systems
• Machine Learning
• +۳ مورد دیگر

Prof. Harald Görl is a Professor at the Universität der Bundeswehr München, affiliated with the Institute for Distributed Intelligent Systems (ETTI 2). His work focuses on distributed intelligent systems, integrating artificial intelligence and robotics into cyber-physical environments. Education details are not explicitly provided in the text, but his academic title Dr. rer. nat. indicates a doctoral degree in natural sciences. No research awards, grants, or advised students are listed in the provided information. Contact is available via harald.goerl@unibw.de at the university's campus in building 41/100.

• Deep Learning
• Natural Language Processing
• Physics Simulations
• +۳ مورد دیگر

Dennis Fast is a Researcher affiliated with the Data Science and Text-based Information Systems (DATEXIS) research group at Beuth University of Applied Sciences Berlin. He joined the DATEXIS team as a research assistant in January 2022, collaborating on developing transformer-based biomedical language models with Paul Grundmann, Alexei Figueroa, and Conor Fallon. His work bridges computational engineering and data science, focusing on deep learning applications in natural language processing and physics simulations. Education: MSc in Computational Engineering (specializing in Acoustic Simulations) from BHT Berlin (2018), and a second MSc in Data Science at BHT Berlin (started 2021). Professional experience includes roles as computational engineer and technical coordinator in European rail vehicle acoustics projects (Shift2Rail FINE1 & FINE2), where he specialized in data generation, analysis, and visualization. Research interests emphasize deep learning algorithms, real-world applications of large language models, and interdisciplinary approaches combining physics simulations with machine learning. Active in acoustic modeling for transportation systems, contributing to noise prediction frameworks and railway innovation assessments. Publications focus on acoustic analysis methodologies for railway systems, published in venues such as Euronoise and Internoise. Current projects explore biomedical NLP applications while maintaining expertise in simulation-driven engineering solutions.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Yin Chen is a Professor in the Department of Computer Science at South China Normal University, Guangdong, China. With an extensive publication record spanning over 25 years from 2000 to 2025, Professor Chen has established themselves as a leading researcher in multiple domains of computer science and artificial intelligence. Professor Chen's research spans artificial intelligence, machine learning, computer vision, natural language processing, and deep learning. Their work demonstrates exceptional breadth across theoretical and applied domains, with significant contributions to facial expression recognition, transformer-based language models, 3D human modeling, and optimization algorithms. Recent research shows particular strength in multimodal learning, with numerous publications integrating visual, textual, and audio information for advanced AI applications. The publication trends reveal a consistent output of high-impact research, with a notable acceleration in recent years. Professor Chen's work appears in top-tier venues including IEEE Transactions, ACM Multimedia, and various specialized journals. Their research demonstrates strong interdisciplinary connections, bridging computer science with applications in engineering, bioinformatics, and mathematics. Professor Chen collaborates extensively with researchers across institutions, particularly with Jia Li, Meng Wang, Richang Hong, and Xianghua Fu. These collaborations span multiple projects in affective computing, multimodal translation, and algorithm development. The research group appears to be active in both theoretical advancements and practical implementations of AI systems.