جست‌وجوی استاد و پژوهشگر

Ricardo Eusebiمشاهده پروفایل

استاد

• High-Energy Physics
• Particle Physics
• Standard Model
• +۴ مورد دیگر

Ricardo Eusebi is a Professor at Texas A&M University, affiliated with the CMS collaboration at the Large Hadron Collider (LHC) in Switzerland. He specializes in high-energy physics, focusing on fundamental particle interactions, electroweak symmetry breaking, supersymmetry, and lepton flavor universality violation. His research leverages particle accelerator data to explore new physics beyond the Standard Model, including Higgs boson properties and B-meson decay anomalies. His institutional partnerships include the Mitchell Institute for Fundamental Physics & Astronomy, CMS Detector at the LHC, and the LPC at Fermilab. Key contributions include developing high-performance track-fitting algorithms for ultra-fast electronics, as published in Nuclear Instruments and Methods in Physics Research . Eusebi advises a research team comprising Valeria D’Amante, Patrick Flanagan, and others. His work is funded by the U.S. Department of Energy, State of Texas, and Texas A&M’s Mitchell Institute. He has been a CMS member since 2003, contributing to the Higgs boson discovery and subsequent precision measurements.

José Luis Lázaro Galileaمشاهده پروفایل

استاد

• Electronics Engineering
• Intelligent Systems
• Positioning Systems
• +۳ مورد دیگر

José Luis Lázaro Galilea is a Professor in the Department of Electronics at the University of Alcalá. He is affiliated with the GEINTRA research group, focusing on Electronic Engineering applied to Intelligent Spaces and Transport. His doctoral thesis (1998) explored infrared modeling for mobile robot guidance. Key research areas include visible light positioning systems, FPGA-based hardware architectures, and remote laboratory development. He has contributed to vehicular VLC systems, indoor navigation algorithms, and biomedical applications like closed-loop insulin delivery systems. Notable projects include the ALCOR initiative for optimizing robot guidance in smart environments and the Helpresponder system for emergency intervention safety. Education: PhD in Electronics from University of Alcalá (1998). Supervised by Dr. Manuel Ramón Mazo Quintas. Academic focus spans digital electronics, embedded systems, and sensor networks. Active in educational innovation, including adapting digital electronics practices for blended learning models. Research emphasizes practical applications of electronic systems in smart spaces, with over 70 publications since 1998. Recent work addresses multipath effects in optical positioning, reconfigurable hardware platforms, and human-centered lighting systems. His interdisciplinary approach integrates robotics, telecommunications, and biomedical engineering. Notable contributions include: Development of high-precision indoor positioning systems using PSD sensors Optimization of sensor deployment via evolutionary algorithms Design of FPGA-based real-time processing architectures Innovative remote laboratory frameworks for STEM education Current projects explore vehicular visible light communication, wearable medical devices, and intelligent transportation systems. His work bridges theoretical advancements with practical implementations in both academic and industrial contexts.

Philip Harrisمشاهده پروفایل

دانشیار

• Particle Physics Experiment
• Nuclear Physics Experiment
• Dark Matter Detection
• +۳ مورد دیگر

Philip Harris is an Associate Professor of Physics at the Massachusetts Institute of Technology (MIT), specializing in particle and nuclear physics experiments. He leads research on dark matter detection and Higgs boson properties, leveraging machine learning and deep learning techniques. His affiliations include the Laboratory for Nuclear Science and Particle Physics Collaboration (PPC). He also serves as experimental coordinator of the Institute for AI and Fundamental Interactions (IAIFI) and deputy director of the A3D3 Institute. Education: B.S. in Physics from Caltech (2005), Ph.D. from MIT (2011). Research focuses on real-time FPGA-based systems, jet substructure in heavy ion collisions, and AI-driven analysis of LHC data. He co-founded the Fast Machine Learning Organization and pioneered AI strategies for Higgs measurements. Awards include the 2023 MIT Teaching with Digital Technology Award and 2021 DOE Early Career Award. His work bridges particle physics with cutting-edge AI, emphasizing open data and collaborative tools like Jupyter notebooks. Key initiatives include the Fast Machine Learning Lab and multi-messenger astronomy projects integrating gravitational wave detection with particle physics. He advocates for advancing AI in fundamental physics through interdisciplinary collaboration.

Ralf Ramsauerمشاهده پروفایل

پژوهشگر ارشد

• Modern Operating System Architectures
• Embedded Systems
• Embedded Virtualisation / Hardware Partitioning
• +۳ مورد دیگر

Dr.-Ing. Ralf Ramsauer is a postdoctoral researcher at the OTH Regensburg , affiliated with the Systems Architecture Research Group . His work focuses on Modern Operating System Architectures , Embedded Systems , and Mixed-Criticality System Design , with a particular interest in hardware/software co-design and real-time constraints. Academic Rank: Research Fellow Email: ralf.ramsauer@oth-regensburg.de Ramsauer's research explores Embedded Virtualisation , Hardware Partitioning , and Quantum-Classical Co-Design , often leveraging open-source development practices. His publications reveal trends in Database/OS Stack Optimization , Security Vulnerability Analysis , and Real-Time System Evaluation . Scientific Awards: 2013: Best Graduation Award in Computer Engineering, OTH Regensburg He has supervised numerous students across Bachelor's and Master's theses , including work on RISC-V Implementation , Linux Kernel Modules , and Hypervisor Optimization .

Alin-Sasa Tisanمشاهده پروفایل

مدرس ارشد

• Neuromorphic Systems
• Pervasive Healthcare
• Pattern Recognition
• +۹ مورد دیگر

Alin-Sasa Tisan is a Senior Lecturer in the Department of Electronic Engineering at Royal Holloway, University of London. His research focuses on neuromorphic systems, pervasive healthcare, and sensor-based applications. He holds a PhD in Electronic Engineering from Technical University of Cluj Napoca (2008) and has certifications in teaching and learning from Anglia Ruskin University (2012). Key research areas include neuromorphic designs for smell recognition, body hydration monitoring, low-cost air quality systems, and FPGA-accelerated signal processing. His work contributes to UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action. Recent projects include developing air quality monitoring systems for Tanzania, creative sensing methods for indoor environments, and cross-disciplinary approaches to industrial electronics challenges. He serves as an External Examiner at the University of Huddersfield and has supervised multiple collaborative projects involving international teams.

Ayesha Khalidمشاهده پروفایل

مدرس ارشد

• Hardware Security
• Post-Quantum Cryptography
• Lattice-Based Cryptography
• +۵ مورد دیگر

Dr. Ayesha Khalid is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen’s University Belfast (QUB), leading research in post-quantum cryptography within the Centre for Secure Information Technologies (CSIT). She holds a PhD from RWTH Aachen University (Germany), supported by a DAAD scholarship. Her expertise focuses on secure, efficient hardware/software implementations of lattice-based cryptography for embedded systems and IoT devices. Key research contributions include developing timing-attack resilient Gaussian samplers, lightweight post-quantum schemes for constrained environments, and hybrid quantum-safe communication protocols. She has led EU H2020 projects (SAFECrypto) and Innovate UK initiatives (AQuaSec), and collaborates with institutions like Rolls-Royce and Purdue University. Her work addresses vulnerabilities in cryptographic hardware through side-channel analysis and fault injection countermeasures. Dr. Khalid is an IEEE Senior Member, IACR member, and serves on technical committees for conferences like VLSI-SoC and ISCAS. Awards include the EEECS Teaching Fellowship (2017). She actively contributes to standards development and has delivered tutorials at FPL 2022 and Schloss Dagstuhl seminars. Her research bridges theoretical cryptography with practical implementation challenges in the quantum era. Education: PhD (Magna Cum Laude) in Applied Cryptography from RWTH Aachen University Key Projects: EPSRC UK Quantum Communications Hub, Rolls-Royce Cyber Security Collaboration Expertise: FPGA-based cryptographic accelerators, post-quantum algorithm hardening, and secure embedded systems design

• Embedded Systems
• Machine Learning
• Internet of Things (IoT)
• +۳ مورد دیگر

Prof. Dr. Gregor Schiele is a Full Professor at the University of Duisburg-Essen , where he leads the Embedded Systems Department of Computer Science since 2014. His research focuses on self-organizing embedded systems in the Internet of Things (IoT), with emphasis on local embedded intelligence using deep learning and neural networks, as well as programming abstractions for adaptive systems . PhD in Computer Science (2007) from the University of Stuttgart Prior affiliations: Insight Centre for Data Analytics (Ireland), National University of Ireland, Galway, University of Mannheim Current research explores Embedded Machine Learning , FPGA-based AI Accelerators , and Energy-Efficient IoT Systems , with recent publications on quantization-aware training , configuration-aware power management , and medical signal processing . His 15 most recent articles highlight expertise in FPGA optimization , soft sensors , and adaptive architectures for time-series forecasting and fluid flow estimation . Key collaboration networks include the Elastic AI Ecosystem , ZaKI.D , and TransfAIr projects. He teaches courses like Programmieren in C , Internet of Things: Protocols and System Software , and Embedded Systems , while supervising research assistants and student teams in the IoT Garage initiative.

Andre Kleinwächterمشاهده پروفایل

پژوهشگر

• Fluid Dynamics
• Marine Technology
• Cavitation Research
• +۳ مورد دیگر

Andre Kleinwächter works at the Teaching Transfer Center of the University of Rostock , focusing on technical experimentation and measurement systems. His projects include KickMeToScience (technical education transfer), SPURT-Schullabor (laboratory engineering), and contributions to the Ocean Technology Campus Rostock in talent development. Research Areas: Fluid dynamics, PIV measurement techniques, marine propulsion systems, and cavitation analysis. Courses: Conducts Technical Experiments and Tests (WS 2025/26), emphasizing hands-on engineering education. Recent publications highlight advancements in single-camera position estimation, FPGA-based velocity field analysis, and full-scale PIV measurement applications for marine technology. His work bridges experimental validation with computational models (e.g., ANSYS CFD) to improve propeller design and cavitation prediction.

Christoph Niemannمشاهده پروفایل

پژوهشگر

• Reliability Engineering
• Nanometer CMOS Technologies
• FPGA Design and Optimization
• +۵ مورد دیگر

Dr.-Ing. Christoph Niemann is a researcher at the Institute of Computer Engineering within the School of Computer Science and Electrical Engineering at the University of Rostock. His work bridges computer engineering and biomedical applications, with a focus on hardware design for medical devices and energy-efficient computing systems. Dr. Niemann's primary research interests include reliability and aging in nanometer CMOS technologies and contributions to increasing the energy efficiency of FPGA-based applications . His work spans both theoretical hardware design and practical biomedical implementations, particularly in the field of deep brain stimulation systems. His research demonstrates a unique intersection of computer engineering principles applied to critical medical technologies. Analysis of Dr. Niemann's recent publications (2020-2025) reveals a strong focus on energy efficiency in implantable medical devices, particularly for deep brain stimulation applications. His work combines FPGA optimization techniques with biomedical requirements, creating hardware solutions that address the stringent power constraints of implantable systems. The publications show consistent collaboration with Dirk Timmermann, Christian Haubelt, and Franz Plocksties, indicating a well-established research group focused on medical device engineering. Dr. Niemann has made significant contributions to the STELLA platform for deep brain stimulation research and has developed novel approaches for sensor-based adaptive voltage scaling on FPGAs. His work demonstrates both theoretical innovation in hardware design and practical implementation in medical contexts.

Santosh Nagarakatteمشاهده پروفایل

استاد

• Compilers
• Programming Languages
• Verification
• +۴ مورد دیگر

Santosh Nagarakatte is a Professor and Undergraduate Program Director at the Department of Computer Science, Rutgers University, New Brunswick. He joined Rutgers in January 2013 after obtaining his Ph.D. in Computer Science from the University of Pennsylvania. His research spans hardware-software interfaces including compilers, programming languages, verification, and computer architecture, with a focus on building efficient, robust, and secure software systems. Education: Ph.D. in Computer Science, University of Pennsylvania His research interests include compilers, programming languages, verification, computer architecture, numerical methods, and formal methods. He leads projects on correctly rounded math libraries (RLIBM), verified eBPF ecosystems, lightweight formal methods for LLVM, and tools for parallel programming and debugging. His publications demonstrate a consistent focus on numerical accuracy, verification, and systems building, with recent work emphasizing correctly rounded math functions, eBPF verification in the Linux kernel, and debugging tools for numerical errors. This research bridges theoretical foundations with practical implementations across compilers, architectures, and security domains. Scientific Awards: ACM Distinguished Member (2023) eBPF Foundation Research Gift (2024) Intel Corporation Research Gift (2024) ACM SIGPLAN Dissertation Awards (2018, 2022 as advisor) Distinguished Paper Awards at PLDI, POPL, CGO, ICSE NSF CAREER Award (2015) Google Research Award (2014) IEEE Micro Top Picks (2010, 2013) He leads the Research on Abstractions for Programming and Learning (RAPL) group, advising PhD and Master's students. His research is supported by grants from NSF, Intel, Google, Facebook, and the eBPF Foundation, focusing on verified systems, numerical accuracy, and compiler technologies.

Juan Escárate Muñozمشاهده پروفایل

پژوهشگر

• Machine Learning for Control Systems
• Reinforcement Learning
• Electrical Motor Drives
• +۶ مورد دیگر

Juan Escárate Muñoz is a doctoral researcher affiliated with the Electromechanics and Power Electronics (EPE) group in the Electrical Engineering department at Eindhoven University of Technology (TU/e) . His work focuses on integrating artificial intelligence with control systems to enhance electrical motor drives. Specializing in reinforcement learning for real-time optimal control Developing FPGA-based acceleration of AI algorithms Contributing to energy-efficient industrial systems Research trends include magnetic saturation effects , cross-coupling dynamics , and adaptive control strategies . Key applications span motor drives and power electronics . Collaborates with the EAISI institute and contributes to the UN Sustainable Development Goals (SDG7: Affordable Energy; SDG9: Industrial Innovation).

• Machine Learning
• IoT Security
• Deep Learning
• +۴ مورد دیگر

Ponnusamy Vijayakumar is a researcher affiliated with SRM University in Kanchipuram, India, within the College of Engineering and Department of Electrical & Computer Engineering . His work spans interdisciplinary domains including Machine Learning , IoT Security , and Deep Learning , with additional expertise in Blockchain , Augmented Reality , and Cyber-Physical Systems . Research Interests : Vijayakumar focuses on applying advanced machine learning techniques to real-world problems such as energy sector optimization , food safety , and medical diagnostics . His recent work explores federated learning for secure IoT environments, predictive analysis using stochastic methods, and computer vision for rehabilitation and security applications. Article Trends : Over the past five years, he has contributed to IoT security through anomaly detection frameworks, augmented reality for plant disease detection, and blockchain for credentialing systems. His publications also address deep learning applications in agricultural quality analysis and medical imaging for musculoskeletal disorders. Collaborations : Vijayakumar has collaborated extensively with experts in Serbia, India, and Germany, particularly with researchers like Nemanja Zdravkovic , Aman Kumar Mishra , and Sowmya Natarajan , across conferences such as BISEC and journals like IEEE Access .

• Embedded Systems
• FPGA
• High Performance Computing
• +۵ مورد دیگر

Alberto Dassatti is a Full Professor at HES-SO University of Applied Sciences Western Switzerland and Director of the Reconfigurable & Embedded Digital Systems (REDS) Institute. He is affiliated with the School of Engineering and Management of the Canton of Vaud. His work focuses on embedded systems, FPGA applications, high-performance computing, and topological data analysis. He has led and collaborated on numerous projects funded by institutions like Innosuisse and HES-SO Rectorat, totaling over CHF 1.2 million in grants. His research spans computational storage systems, medical sensor technologies, and signal processing frameworks for wireless communication. Key projects include developing the CarbonViz energy visualization tool, advancing topological methods for AI reliability, and creating the BB-Sensors system for neonatal vital monitoring. He has contributed to open-source frameworks like giotto-tda and gr-dnn, emphasizing interdisciplinary collaboration between hardware and software domains. His publications span topics such as FPGA-GPU communication, real-time embedded systems optimization, and heterogeneous computing architectures. He actively participates in conferences like NeurIPS and IEEE SOC, showcasing innovations in reconfigurable computing and energy-efficient systems.

• FPGA-based Systems
• Embedded Systems
• Hardware Accelerators
• +۶ مورد دیگر

Thoma Yann is a Professor at the Haute école d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), leading the Reconfigurable and Embedded Digital Systems (REDS) Institute. His work focuses on FPGA-based systems, hardware accelerators, and bio-medical applications. He holds a PhD in Computer Science from EPFL and has led multiple projects in therapeutic drug monitoring (TDM) and embedded systems. Research interests include FPGA design, bio-medical signal processing, and precision medicine. Notable projects include the SpikeOnChip neural activity analysis platform, TUCUXI TDM software, and the TheraPer POC system for drug dosage optimization. His work spans collaborations with institutions like CHUV, University of Geneva, and international partners. Funding includes projects like DNoC FPGA design (CHF 261,100), MOVABLE POC diagnostics (CHF 143,774), and therapeutic drug monitoring initiatives. Awards and recognitions are not explicitly listed in the provided text. Current roles include academic leadership at HEIG-VD and collaborative research in embedded systems and medical technology. No retirement status or former staff indication is present.

• Data Management Systems
• Database Systems
• Operating Systems
• +۴ مورد دیگر

Prof. Jana Giceva Makreshanska is a Professor of Database Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. Her research focuses on systems support for data management, leveraging modern hardware architectures including operating systems, compilers, and computer architecture. She holds a PhD from ETH Zurich (2017) and previously served as faculty at Imperial College London. Notable awards include the ERC Starting Grant (2024), VMware Early Career Faculty Award (2019), and ETH Medal (2018). Education: PhD in Computer Science, ETH Zurich (2017) Research roles at Microsoft Research and Oracle Labs Research Interests: Her work addresses holistic system design challenges in data management, including optimizing query processing for modern hardware, compiler integration, and emerging architectures like chiplets. She explores efficient data processing techniques for big data and transactional systems. Recent Contributions: Her recent work includes innovations in disaggregated systems (HotOS 2023), chiplet-aware scheduling (2025), and universal graph data structures (VLDB 2022). Her publications span topics like NVMe array optimization, FPGA-based partitioning, and hybrid OLTP/OLAP systems. Awards: ERC Starting Grant (2024) Best Paper Runner-Up at VLDB 2022 ACM SIGMOD Highlights Award (2023) Teaching & Service: Current courses include 'Cloud-Based Data Processing' and 'Data Processing on Modern Hardware'. She chairs diversity initiatives at SoCC 2023, serves on program committees for OSDI, VLDB, and SIGMOD, and leads gender equality efforts at TUM. Labs/Teams: Maintains the Lehrstuhl III (I3) database systems group at TUM, collaborating on projects like LakeVilla and ARCAS.