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Michael F. P. O'BoyleView profile

Professor

• Optimizing compilation
• Machine learning
• Heterogeneous systems
• +3 more

Michael F. P. O'Boyle is a Professor of Computer Science at the University of Edinburgh's School of Informatics. He is a leading researcher in compiler technology, specializing in optimizing compilation, machine learning for compilation, and heterogeneous systems. His work addresses the critical challenges of compiling software for increasingly diverse hardware architectures in the post-Moore's Law era. Professor O'Boyle's research interests focus on: Optimizing compilation techniques Machine learning applications in compilation Heterogeneous computing systems Program synthesis Neural machine translation for code Hardware/software co-design His recent publications demonstrate a strong focus on tensor optimization, compiler infrastructure for heterogeneous systems, and machine learning applications in program analysis and transformation. O'Boyle's work bridges traditional compiler techniques with modern AI-driven approaches to code optimization, addressing the growing complexity of hardware-software interfaces. Professor O'Boyle has received several notable honors and awards: ACM CGO Test of Time award (2017) Senior EPSRC Research Fellow Fellow of the British Computer Society (BCS) He holds significant leadership roles including Director of the ARM Research Centre of Excellence at Edinburgh and Director of the EPSRC Centre for Doctoral Training in Pervasive Parallelism. O'Boyle is also a founding member of HiPEAC, a European network for high-performance and embedded architecture and compilation, and has delivered keynote addresses at major conferences including PPoPP 2019 where he presented his vision for "Rethinking Compilation in a Heterogeneous World."

Nagarajan KandasamyView profile

Professor

• Computer Engineering
• Embedded Systems
• Self-Managing Systems
• +5 more

Nagarajan Kandasamy is a Professor and Interim Department Head in the Department of Electrical and Computer Engineering at Drexel University. His research focuses on computer engineering, with expertise in neuromorphic computing, embedded systems, fault-tolerant architectures, and distributed systems. Prior to Drexel, he worked as a research scientist at Vanderbilt University's Institute for Software Integrated Systems. PhD, University of Michigan (2003) MS, University of Connecticut BE, Guindy Engineering College, Anna University, Chennai, India His research interests include neuromorphic computing, spiking neural networks, and reliable system design. He has contributed to fields like deformable image registration, self-testing hardware, and wireless security frameworks. Kandasamy's publications emphasize neuromorphic architectures, medical imaging algorithms, and secure communication protocols. His work often intersects hardware-software co-design and machine learning applications. National Science Foundation Early Faculty (CAREER) Award (2007) Best Paper Award, IEEE International Conference on Autonomic Computing (2006) He collaborates across disciplines in neuromorphic engineering, with grants from NSF and industry partners. His team develops tools for radiation oncology, low-power AI systems, and FPGA-based security protocols.

• Signal Processing
• Machine Learning
• Computer Engineering
• +3 more

Ehsan Namjoo serves as a Research Fellow in the Department of Electronics & Computer Engineering at the University of Limerick, with a primary affiliation at Lero – the Irish Software Research Centre. His multidisciplinary work bridges theoretical signal processing, machine learning applications, and hardware implementation for real-world systems. His research spans signal processing (DOA estimation under non-ideal noise conditions, EEG source imaging), machine learning (explainable AI for medical diagnostics, feature selection in cancer detection), and computer engineering (polar code decoders, visible light communication systems). Notable biomedical applications include breast cancer diagnosis and epileptic source analysis, while cybersecurity work focuses on intrusion detection in cloud environments. Recent publications (2021-2025) reveal a consistent trajectory toward developing lightweight, interpretable models for tabular medical data alongside robust signal processing frameworks for communication systems. His work increasingly integrates hardware-aware algorithms, particularly in polar coding implementations, and emphasizes practical validation through experimental demonstrations in visible light networks. As an active Lero researcher, Namjoo participates in Ireland's national software research ecosystem, contributing to interdisciplinary projects that connect theoretical innovations with healthcare, communications, and security applications through collaborative frameworks.

• Software Testing and Verification
• Compiler Testing and Optimization
• High-Performance Computing
• +7 more

Michael Philippsen is a Professor at the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Chair of Programming Systems (Lehrstuhl für Informatik 2). His research spans software engineering, programming languages, high-performance computing, and machine learning applications. He has directed multiple significant research projects including Holoware (software visualization in VR/AR), ORKA (OpenMP for FPGAs), and CS4MINTS (computer science education initiatives). Prof. Philippsen's research focuses on improving software quality and developer productivity through innovative approaches. His work in software testing includes novel methods for detecting flaky tests using version history and test execution data. In compiler research, he has pioneered automated testing techniques and optimization methods, particularly for FPGA acceleration using OpenMP extensions. His Holoware project revolutionized software visualization by applying city metaphors in virtual reality to enhance program comprehension. Additionally, he has made significant contributions to machine learning applications in software engineering, including few-shot out-of-domain detection in natural language processing systems. His publication record demonstrates a strong trend toward interdisciplinary research bridging traditional software engineering with emerging technologies. A significant portion of his recent work focuses on optimizing compiler techniques for heterogeneous architectures, particularly FPGA acceleration through OpenMP extensions. His research in software visualization has produced award-winning work on layered software city metaphors that significantly improve program comprehension compared to traditional visualization techniques. The consistent theme across his work is applying practical, measurable solutions to real-world software engineering challenges. Best Paper Award for 'Multipurpose Cacheing to Accelerate OpenMP Target Regions on FPGAs' (2023) Best Paper Award for 'A Layered Software City for Dependency Visualization' (2020) Prof. Philippsen has secured substantial research funding from the German Federal Ministry for Economic Affairs and Energy (BMWE), the Bavarian State Ministry of Science and the Arts (StMWK), and the Fraunhofer Society. His projects often involve industry collaboration to ensure practical applicability. He has supervised numerous student theses contributing to his research in compiler testing and software visualization. His research group maintains specialized laboratories for VR/AR software visualization, FPGA acceleration, and educational tool development as part of the CS4MINTS project.

• Neuromorphic Electronics
• Neuroscience
• Biointerfaces
• +3 more

Dr. Hongwei Tan serves as Group Leader at the Department of Molecular Electronics, Max Planck Institute for Polymer Research (MPIP) in Mainz since November 2024. Previously, he was Research Fellow (2022-2024) and Postdoctoral Fellow (2017-2022) at Aalto University's Department of Applied Physics, following postdoctoral work at the University of Massachusetts Amherst on neurointerface devices. His academic credentials include: Bachelor of Physics, Nankai University (2011) Doctorate in Materials Physics and Chemistry, University of Chinese Academy of Sciences (2016), focusing on neuromorphic photomemristors Tan's research pioneers neuromorphic biointerfaces—integrating brain-inspired electronics with biological systems to enable energy-efficient computation and seamless biointeraction. His work targets transformative applications in brain-machine interfaces, prosthetics, and diagnostics through interdisciplinary convergence of neuroscience, materials science, and engineering. His publication portfolio (2015-2024) reveals a clear evolution from foundational optoelectronic memristor research toward complex neuromorphic systems for sensory processing. Key thematic trajectories include photomemristor-based vision systems, tactile coding with spiking nerves, and nanoionic human-machine interfaces, demonstrating consistent innovation in bioinspired hardware. As MPIP Group Leader, Tan directs research strategy and team mentorship within his neuromorphic electronics program, though specific advisees and grant details remain unlisted in available materials. His laboratory focuses on developing integrated neuromorphic systems—from novel materials and devices to algorithms—that facilitate bidirectional communication between electronic and biological neural networks for next-generation neurotechnologies.

Ying XuView profile

Researcher

• Neuromorphic Engineering
• Neuromorphic Auditory Systems and Applications
• Mixed-signal VLSI Design
• +2 more

Dr. Ying Xu serves as a Researcher at the International Centre for Neuromorphic Systems (ICNS) within Western Sydney University, specializing in neuromorphic computing and hardware implementations. Her academic qualifications include: PhD from Western Sydney University (2019) M.Sc. in Microelectronics from Waseda University, Japan (2008) Bachelor of Electrical Engineering from Xidian University (2006) Her research focuses on biologically inspired computing systems with expertise in neuromorphic auditory processing , analog-digital circuit design , and hardware-accelerated machine learning . She bridges theoretical neuromorphic principles with practical VLSI implementations for next-generation computing architectures. Based at Western Sydney University's Penrith (Werrington South) campus in Room BA.2.01, she contributes to the ICNS research ecosystem advancing neuromorphic technologies through mixed-signal hardware solutions.

Bashir M. Al-HashimiView profile

Professor

• Energy-Efficient Computing
• Battery-Free Systems
• Internet of Things
• +2 more

Professor Sir Bashir M. Al-Hashimi is Vice President (Research & Innovation) at King’s College London and co-Director of the King's Institute for Artificial Intelligence. A renowned computer engineer, his research focuses on energy-constrained computing, semiconductor chips, and battery-free systems enabling IoT technologies. Significant contributions include founding the Arm-ECS industry-academia center, leading large-scale interdisciplinary research programs, and publishing over 350 technical papers. Honors include knighthood (2025), CBE (2018), and fellowships in Royal Society, IEEE, and Royal Academy of Engineering.

Matteo RinaldiView profile

Professor

• Micro/Nanoelectromechanical Systems (M/NEMS)
• Piezoelectric and Ferroelectric Materials
• Nanoscale Sensors
• +5 more

Matteo Rinaldi is a Professor in the Department of Electrical and Computer Engineering at Northeastern University's College of Engineering and serves as Director of the Institute for NanoSystems Innovation. His work focuses on advanced micro/nanoelectromechanical systems (M/NEMS) for sensing and communication applications. Ph.D. in Electrical and Systems Engineering, University of Pennsylvania (2010) M.Sc. and B.S. in Electronic Engineering, University of Rome Tor Vergata (2007, 2004) Research Interests: Rinaldi's research spans fundamental and applied aspects of piezoelectric nanomaterials, with emphasis on Aluminum Nitride (AlN) and Scandium Aluminum Nitride (ScAlN) based NEMS Plasmonically enhanced infrared and chemical sensors Low-power reconfigurable radio communication systems Integration of MEMS/NEMS with electronics Nanomaterials for harsh environments Publications & Research Trends: Rinaldi's recent work explores high-frequency resonators, infrared detection systems, and zero-power sensing technologies. Key trends include plasmonics for energy-efficient devices, ScAlN integration for improved performance, and machine learning applications in nanoscale device design. Scientific Recognition: 2025 IEEE IFCS Walter G. Cady Award 2024 Northeastern Global Network Accelerator Award Optica Fellow IEEE Sensors Council Early Career Award DARPA Young Faculty Award NSF CAREER Award Advising & Funding: Rinaldi advises graduate researchers like Antea Risso. His lab secures major grants from DARPA, NSF, DOE, and the Bill & Melinda Gates Foundation, focusing on next-generation sensing technologies and 6G communication systems. Research Infrastructure: Leads the Northeastern Sensors & Nano Systems Laboratory (NS&NS Lab) and co-directs the bicoastal Institute for NanoSystems Innovation with Boston and Oakland campuses, advancing nanoscale semiconductor technologies.

Simha SethumadhavanView profile

Professor

• Computer Architecture
• Computer Security
• Hardware Security
• +3 more

Simha Sethumadhavan is a Professor of Computer Science at Columbia University and an Alfred P. Sloan Research Fellow. His research focuses on computer architecture and security, particularly on securing hardware first and systematically hardening systems stacks. Hardware Security Microarchitectural Side Channels Memory Safety Analog Accelerators His recent work explores mechanism design for security, memory safety, reliability co-design, and cloud security. Publications span journals like Communications of the ACM and conferences including IEEE Security and Privacy, CCS, ISCA, and MICRO. Distinguished Paper Awards IEEE Micro Top Picks Best Student Paper Awards Best of CAL 2016 He advises PhD students such as Adam Hastings, Evgeny Manzhosov, and Miguel Arroyo, and has taught courses like Computer Architecture (CSEE 4824) and Hardware Security (COMS 6424). He has served on editorial boards and program committees for ISCA, IEEE Security and Privacy, and MICRO.

Andreas AndreouView profile

Professor

• Neuromorphic Engineering
• CMOS Microsystems
• Sensory Information Processing
• +6 more

Andreas Andreou is a Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), with secondary appointments in Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the JHU Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab, focusing on brain-inspired microsystems, neuromorphic engineering, and biomedical sensors. His research spans CMOS-based neuromorphic processors, event-based vision systems, and wearable health monitoring devices like the StethoVest. Key contributions include silicon retinas, polarization-sensitive imagers, and algorithms for pattern analysis. Research Interests: Neuromorphic Computing: Designing energy-efficient brain-inspired chips using 3D CMOS, FETs, and memristive technologies. Biomedical Microsystems: Wearable acoustic sensors for cardiac monitoring and vestibular prosthetics. AI Hardware: Neuromorphic accelerators for edge computing, leveraging LLMs for automated circuit design. Notable Achievements: IEEE Fellow (since 2020) Recipient of the 3rd Best Paper Award at IEEE BioCAS 2018 $2M DARPA grant for bio-inspired event cameras Labs/Teams: The Andreou Lab collaborates with the Kavli Neuroscience Discovery Institute and NSF-funded neuromorphic projects. Ongoing work includes neuromorphic Ising machines, LLM-driven chip design, and quantum sensing for medical applications.

Andrea CiniView profile

Researcher

• Machine Learning
• Graph Deep Learning
• Time Series Forecasting
• +3 more

Andrea Cini is a postdoc researcher affiliated with the Graph Machine Learning Group and the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) at the University of Lugano (USI). He also holds a position as a SNSF postdoc fellow at the University of Oxford under Prof. Michael Bronstein, focusing on machine learning for time series forecasting and graph processing. His research integrates graph deep learning methodologies with spatiotemporal dynamics, emphasizing applications in healthcare, energy systems, and intelligent systems. Education: PhD in Computer Science and Engineering (USI, 2020), supervised by Prof. Cesare Alippi MSc and BSc in Computer Science and Engineering (Politecnico di Milano) Visiting researcher at Imperial College London (Prof. Danilo Mandic) Research interests span graph neural networks , time series forecasting , and spatiotemporal data processing . His work has introduced influential methods such as the Torch Spatiotemporal library, and has been recognized with a best paper award. Recent publications emphasize applications in relational conformal prediction, hierarchical forecasting, and energy grid optimization. Awards include the Best Paper Award for contributions to graph-based forecasting methodologies. Current projects are funded by the Swiss National Science Foundation, exploring graph-based reinforcement learning and spatiotemporal modeling at the University of Oxford. Collaborations include affiliations with the Northernmost Graph Machine Learning group at UiT the Arctic University of Norway. His research bridges theoretical advancements and industrial applications in fields like healthcare dynamics prediction and smart grid optimization.

• Materials Science
• Photovoltaics
• Semiconductor Physics
• +3 more

Bruno Ehrler is a Professor at the University of Groningen (honorary) and Group Leader of the Hybrid Solar Cells Group at AMOLF in Amsterdam since 2014. His research focuses on perovskite materials science, including fundamental studies and device applications like solar cells. He holds significant grants (ERC Starting, NWO Vidi) and is a WIN Rising Star award recipient. He previously worked at the University of Cambridge, where he earned his PhD in Physics under Prof. Neil Greenham. His expertise spans optoelectronics, quantum dots, and singlet fission photovoltaics. Ehrler contributes to national energy initiatives and serves on advisory boards for the Dutch Chemistry Council and nanoGe conferences. Education: PhD in Physics, University of Cambridge (2009–2012) MSci in Physics, University of London (Queen Mary College, 2005–2009) Studies at RWTH Aachen and University of London Research Interests: Perovskite materials, solar cell efficiency, ion migration dynamics, photonic materials, and sustainable energy technologies. His work bridges fundamental material science with applied device engineering, addressing challenges in scalability, stability, and energy conversion efficiency. Grants & Awards: ERC Starting Grant (2020): Artificial synapses from halide perovskites NWO Vidi Grant (2017): Metal halide perovskites WIN Rising Star Award (2018) Advising & Labs: Leads the Hybrid Solar Cells Group at AMOLF, focusing on perovskite innovation. Collaborates on national energy projects like the Netherlands Energy Research Alliance (NERA). Active in mentoring early-career researchers through advisory roles and grant programs.

• High-Performance Computing
• Parallel Computing
• Data Compression
• +4 more

Kento Sato is a researcher specializing in High-Performance Computing (HPC), with a focus on checkpointing systems, data compression, and parallel computing optimization. His work addresses challenges in fault tolerance, algorithmic efficiency, and resource management in large-scale computing environments. Collaborating with institutions like the Joint-Laboratory of Extreme Scale Computing, he explores trade-offs between compression efficiency and hardware costs, as well as automatic variable identification for checkpointing. His contributions span theoretical advancements and practical implementations, with notable publications in venues such as SC, IPDPS, and the International Journal of High Performance Computing Applications. Key Research Themes: Checkpointing protocols, lossy compression for scientific data, HPC workflow optimization. Long-term Collaboration: Regular co-authorship with experts like Satoshi Matsuoka and Martin Schulz. Publications consistently highlight innovations in fault tolerance mechanisms, adaptive hardware design, and scalable algorithms for machine learning and distributed systems.

Keith UlmerView profile

Associate Professor

• Experimental Elementary Particle Physics
• Supersymmetry
• Dark Matter Models
• +3 more

Keith Ulmer is an Associate Professor in the Department of Physics at the University of Colorado Boulder. His research focuses on experimental particle physics, particularly using the CMS experiment at CERN's LHC. He leads projects for the CMS Global Track Trigger and US-CMS Trigger/DAQ upgrades. His work includes searches for physics beyond the Standard Model, such as supersymmetry and dark matter, and detector R&D leveraging FPGA-based machine learning. Education: Ph.D. in Physics (2007, University of Colorado Boulder), B.A. from Amherst College. Awards include the 2024 LPC Distinguished Researcher, CU Teaching Excellence, and CERN Scientific Associate roles. Research interests span high-energy collider physics, detector innovation, and advanced computing. He advises graduate students and collaborates internationally on SUSY searches, trigger system upgrades, and Higgs physics. Notable contributions include the first observation of B_s→μμ decay and leadership in CMS Phase-2 upgrades.

Adnan Siraj RakinView profile

Assistant Professor

• AI Security
• Deep Learning
• Computer Vision
• +3 more

Adnan Siraj Rakin is an Assistant Professor at Binghamton University's School of Computing. He holds a PhD and MS in Computer Engineering from Arizona State University (2022 and 2021) and a BS in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2016). His research focuses on AI security, including adversarial attacks on deep learning systems, model stealing, and hardware vulnerabilities. Notable contributions include defenses against bit-flip attacks, weight duplication frameworks, and RowHammer exploits. Research Interests: Adversarial Attacks (Input/Weight/Model Stealing) Deep Learning Security Hardware Vulnerabilities (e.g., FPGA/DRAM) Robust Neural Network Design Publications highlight advancements in detecting and mitigating adversarial perturbations, with work featured in CVPR, ICCV, and IEEE Security & Privacy. His recent efforts address LLM vulnerabilities and edge computing efficiency. He received the 2022-2023 Educator of the Year award from Binghamton's CS department. Current projects include developing full-stack obfuscation frameworks, secure domain adaptation, and exploring adversarial impacts on robotics systems.