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Akshay Satish BhosaleView profile

Research Fellow

• Compilers
• Compiler Optimization techniques
• Hardware-Software co-design
• +4 more

Akshay Satish Bhosale is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working within the Computer Architecture group under the supervision of Dr. Tim Jones. His research focuses on developing advanced compiler techniques for exploiting intra-core parallelism in processors, with particular emphasis on optimization techniques and intermediate representations for efficient code generation. His educational background includes a PhD from the University of Delaware under Dr. Rudi Eigenmann and a Bachelor's degree in Electronics Engineering from the University of Mumbai, India (2016). Dr. Bhosale's research interests span multiple areas of compiler technology and parallel computing: Compiler Optimization techniques for modern processor architectures Hardware-Software co-design methodologies Automatic Parallelization of complex computational patterns High Performance Computing applications Intermediate representation development His publication record demonstrates a consistent focus on solving the challenging problem of automatic parallelization of subscripted subscript patterns in scientific applications. Through his work, he has developed novel compile-time algorithms based on symbolic range aggregation that can detect properties of subscript arrays to prove parallelism in loops that conventional techniques cannot handle. His research has been published in top-tier conferences including PPoPP, ICS, and IPDPSW. Dr. Bhosale is actively involved in several significant research projects: ParaSol project (funded by EPSRC UK) - developing compiler analyses and transformations to extract parallelism for future processors Software Prefetching implementation in LLVM infrastructure Automatic Parallelization of Irregular Computations using the Cetus source-to-source translator He maintains an active presence in the compiler research community through his GitHub repositories and contributions to open-source compiler infrastructure projects, particularly the Cetus compiler framework for automatic parallelization.

Sajad Saeedi GharahbolaghView profile

Assistant Professor

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Focal-plane Sensor-processor Arrays (FPSP)
• +6 more

Sajad Saeedi Gharahbolagh is an Assistant Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University and an Honorary Research Fellow at Imperial College London's Department of Computing. His research spans robotics, SLAM, focal-plane sensor-processor arrays (FPSP), and deep learning for autonomous systems. Education : PhD in Electrical and Computer Engineering (2014) from the University of New Brunswick. Prior Roles : Dyson Research Fellow (2018-2019) at Imperial College London; Postdoctoral Fellow at University of New Brunswick (2014); R&D Engineer at 2G Robotics (2015). His research focuses on Simultaneous Localization and Mapping (SLAM) for single/multi-robot systems Focal-plane Sensor-Processor Arrays (FPSP) for high-speed, low-power vision processing Autonomous aerial/underwater robotics Deep learning integration with traditional robotics algorithms Control systems for heterogeneous robotic platforms His work addresses challenges in computational efficiency, robustness in GPS-denied environments, and real-time multi-sensor data fusion. Recent publications highlight advancements in Distributed NeRF for collaborative mapping MR.CAP multi-robot control/planning BIT-VIO visual-inertial odometry WiFi-based geometric mapping FPSP-optimized CNNs PathBench benchmarking framework Scientific Awards : Dyson Research Fellowship (2018-2019) Best Robotics Paper (CRV 2021) Best Student Presentation (IROS 2023) Research Team : PhD Students: Christopher Kolios, Navid Zarrabi, Messiah Esfahani, Ishaan Mehta, Mahboubeh Asadi, Jack Saunders MASc Students: Georgia Jovanovic, Hussein Ali Jaafar, Austin Vuong, Roni Sherman, Matthew Lisondra, Glenn Shimoda, Ali Babaei, Robel Efrem, Messiah Ataey, Christopher Kolios, Nikolas Kourtzanidis Laboratory Facilities : Robotics and Computer Vision Lab (RCVL) with Vicon motion capture system 14 TurtleBot 3 platforms (Waffle Pi/Burger variants) Germicidal UVC-equipped G-Robots Jetbots with onboard GPU processing OpenMANIPULATOR robotic arms

• Signal Processing
• Financial Signal Analysis
• Machine Learning
• +5 more

Ali Akansu is a Professor in the Department of Electrical and Computer Engineering at NJIT. His academic career spans multiple decades, with significant contributions to signal processing, financial engineering, and multimedia communications. He has led or co-led four major NSF-funded projects, including the Center for Digital Video & Media Research and the Laboratory for Multimedia Signal Processing and Communication. His research focuses on topics such as eigenanalysis for financial portfolios, machine learning in network analysis, and multimodal affective state assessment using EEG and fNIRS. Key research areas include financial signal processing, portfolio optimization, eigensubspace applications, and data hiding techniques. Notable projects involve FPGA-based real-time risk analysis systems and GPU implementations for eigenportfolio analysis. He has published over 190 peer-reviewed articles, with impactful work in journals like Journal of Investment Strategies and Brain Sciences . His recent work combines machine learning with signal processing to address challenges in network traffic forecasting and financial market dynamics. He has also contributed to biomedical signal processing through studies on affective state assessment using neuroimaging techniques. Awards and recognitions include a h-index of 27 and over 2,694 citations. Grants: National Science Foundation (1991-1993, 1997-2002): Real-Time Color Video Workstation, Multimedia Signal Processing Lab, and Digital Video Research Center Labs/Teams: Leadership in NJIT's Laboratory for Multimedia Signal Processing and Financial Signal Processing groups

Vladimir MilovanovićView profile

Associate Professor

• Electrical Engineering
• Computer Science
• Signal Processing
• +7 more

Dr. Vladimir Milovanović is an Associate Professor at the Faculty of Engineering, University of Kragujevac, Serbia, holding a position as Chair of the Department of Electrical Engineering and Computer Sciences. His academic career is anchored in interdisciplinary research at the intersection of electrical engineering, computer science, and biomedical applications. His research focuses on hardware design, radar sensor systems, biomedical signal processing, and machine learning applications. Notable areas include CMOS circuit design, FPGA-based implementations, and parameterizable hardware generators for signal processing tasks. He also explores medical imaging analysis using deep learning techniques for conditions like disc hernia diagnosis. Recent work emphasizes radar technology (FMCW transceivers, signal processing architectures), embedded systems (FPGA development, IP core utilization), and high-performance streaming data algorithms. His publications span over two decades, demonstrating expertise in analog/digital circuit design, comparative classifier analysis in biomedical contexts, and hardware-software co-design methodologies. Dr. Milovanović collaborates with industry on energy-efficient MIMO arrays and semiconductor gas sensors. His contributions include open-source HDL models for digital signal processors and comparator circuits optimized for low-power and high-speed applications.

Jordan GrecoView profile

Research Professor

• Biophotonics
• Photoactive Proteins
• Molecular Electronics
• +6 more

Jordan Greco is an Assistant Research Professor in the Department of Chemistry at the University of Connecticut. He is a member of the Birge Research Group and co-founder of LambdaVision Incorporated, a company focused on developing bacteriorhodopsin-based technologies for medical and industrial applications, including retinal implants and optoelectronic systems. His work bridges fundamental biophysical research with applied engineering, emphasizing protein-based solutions to societal challenges. Greco holds a B.S. in Chemistry and Anthropology from the University of Connecticut (2010), followed by a Ph.D. in Physical Chemistry (2015) and a postdoctoral fellowship (2015–2017) at the same institution. His education and training have equipped him with expertise in molecular spectroscopy, quantum mechanics, and genetic engineering. His research interests encompass theoretical simulations of excited-state properties in biological chromophores and retinylidene proteins, alongside the design of novel biophotonic devices such as protein-based retinal implants, holographic processors, and photovoltaic cells. He employs advanced techniques like time-resolved absorption spectroscopy and differential scanning calorimetry to study photoactive proteins, with recent focus on enhancing their functionality through quantum dot integration and microgravity manufacturing. Greco’s articles emphasize advancements in vision restoration, optoelectronic materials, and space-based biomaterial production. Key themes include optimizing retinal implant pixel arrays, understanding light-harvesting mechanisms in photosynthetic proteins, and developing methods for long-term data storage using photochromic proteins. As part of LambdaVision’s team, his contributions have been supported by grants from NASA and collaborations with Boeing and CASIS. These partnerships aim to leverage microgravity environments for scalable thin-film production of protein-based devices. Greco is affiliated with the Birge Research Group at UConn’s Department of Chemistry and LambdaVision Incorporated. His interdisciplinary approach, combining physical chemistry and engineering, drives innovations in medical technology, energy systems, and biomolecular electronics.

Laura Bégon-LoursView profile

Assistant Professor

• Neuromorphic Engineering
• AI Hardware
• Memristive Systems
• +3 more

Prof. Laura Bégon-Lours is an Assistant Professor at ETH Zürich's Department of Information Technology and Electrical Engineering, specializing in neuromorphic electronics and AI hardware. Her research focuses on developing analog in-memory computing systems using novel materials like conductive-metal-oxide/HfOx ReRAM and ferroelectric nanolaminates. She explores applications in bio-inspired computing, low-power neuromorphic processors, and beyond-CMOS device integration. Her work bridges material science and electronics engineering, emphasizing scalable, energy-efficient computing architectures. Key contributions include crossbar operation of ferroelectric tunnel junctions, BEOL integration of synaptic weights, and unsupervised learning models leveraging memristive systems. Current projects target multi-timescale synaptic weights and photonic-electronic hybrid systems for next-generation AI acceleration. Publications highlight advancements in resistive switching mechanisms, ferroelectric field effects, and neuromorphic processor design. Her research has been published in top journals, with recent focus on 2023-2025 innovations in analog computing and neuromorphic circuits.

James SheeView profile

Assistant Professor

• Quantum Chemistry
• Electronic Structure Theory
• Transition Metal Catalysis
• +7 more

James Shee is the Norman Hackerman-Welch Assistant Professor of Chemistry at Rice University, joining in 2023. His research focuses on developing ab initio computational methods to model weak and strong correlations in transition metal and f-block compounds, with applications in catalysis, magnetism, drug design, and energy storage. He specializes in quantum Monte Carlo techniques, quantum algorithms, and nuclear quantum effects. Prior to Rice, he was a postdoctoral researcher at UC Berkeley under Martin Head-Gordon. He holds a Ph.D. in Chemical Physics from Columbia University (2019) and an A.B. in Chemistry from Princeton University (2014). His research interests include quantum Monte Carlo (QMC) method development, electronic structure theory, and the intersection of quantum computation with chemistry. The Shee Lab emphasizes practical applications such as metal organic frameworks, drug design, and quantum information processing. He has been recognized with the Ruth L. Kirschstein National Research Service Award (2021) and the Louis Hammett Award (2019). Education: Ph.D. in Chemical Physics, Columbia University, 2019 A.B. in Chemistry, Princeton University, 2014 Awards: Ruth L. Kirschstein National Research Service Award (NIH, 2021) Louis Hammett Award for Excellence in Graduate Research (Columbia, 2019) His recent work bridges theoretical innovation with hardware-efficient algorithms for quantum computation, targeting challenges in transition metal chemistry. The lab collaborates on nuclear quantum effects and semi-classical potentials for simulating metal cofactors. Ongoing projects include quantum simulations of strongly correlated molecules using Rydberg atom arrays and optimizing AFQMC for scalable quantum hardware.

Visvesh S SatheView profile

Associate Professor

• Energy-Efficient Computing
• Implantable Electronics
• Power Management Systems
• +5 more

Visvesh S. Sathe is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. Previously, he served as Associate Professor at the University of Washington (2013-2022) and held roles at AMD (2007-2013), where he contributed to energy-efficient microprocessor design. His research focuses on energy-efficient computing, implantable electronics, and closed-loop neural interfaces. He leads the Processing Systems Lab (PSyLab), exploring computational techniques for hardware optimization in digital and mixed-signal systems. Education: B.Tech from IIT Bombay; M.S. and Ph.D. from the University of Michigan, Ann Arbor. Research Interests: Energy-efficient IC design, power management architectures, bi-directional neural interfaces, adaptive baseband processing, and SoC optimization. His work emphasizes run-time control of clocking, voltage regulation, and thermal management. Key Awards: NSF CAREER Award (2019), Intel Outstanding Researcher Award (2021), and IEEE Distinguished Lecturer (2021-2022). Notable contributions include the first resonant-clocked production processor and adaptive clocking for supply droop mitigation. Advisees/Grants: While specific grant details aren't provided, his lab’s work is funded by NSF and industry partnerships. His research spans from circuit-level innovations to system-level integration, with applications in biomedical and high-performance computing. Labs/Teams: PSyLab at Georgia Tech drives interdisciplinary projects in energy-efficient systems and neural interfaces, collaborating with industry and academic partners globally.

Per AnderssonView profile

Senior Lecturer

• Embedded Systems
• Code Generation
• Optimization
• +7 more

Per Andersson is a Senior Lecturer at the Department of Computer Science, Lund University. He serves as Director of first and second cycle studies and holds additional roles at ELLIIT (Linköping-Lund initiative on IT and mobile communication) and the Parallel Systems group. His research focuses on embedded systems, code generation, optimization, and reconfigurable hardware design. He contributes to UN Sustainable Development Goals through technology advancements. Professional roles include leadership in academic program administration and participation in major initiatives like the EASE project (Embedded Applications Software Engineering, 2008-2018). His work bridges computer science and education policy, particularly in recognition of prior learning (RPL) and higher education accreditation processes. Research interests span technical domains such as parallel architectures, reconfigurable systems, and medical informatics, alongside educational topics like lifelong learning systems. His 20+ publications reflect interdisciplinary engagement, with notable contributions in IEEE conferences and journals like Haematologica and International Journal of Lifelong Education. He collaborates extensively with industry and academia, evidenced by participation in projects involving software engineering, radio standard integration, and clinical trial analyses. His work emphasizes practical applications of theoretical advancements in both technical and educational fields.

• Fault-Tolerant Computing
• Digital Circuit Design
• Embedded Systems
• +3 more

Jakob Lechner is an External Lecturer in the Computer Engineering department at Vienna University of Technology. His work focuses on fault-tolerant digital circuit design, asynchronous systems, and robust hardware architectures. Education : PhD in Computer Engineering (2014), Diploma in Computer Engineering (2008) Research interests include: Fault-tolerant computing Asynchronous circuit design Transient error mitigation in FPGAs Triple Modular Redundancy (TMR) Metastability containment Single Event Transients (SET) resilience His publications (2006–2018) emphasize fault injection techniques, robust GALS circuits, and asynchronous communication protocols. Key projects include Intel CARS (2017–2019) on delay-insensitive codes. Supervision : Guided K. D. Pados' thesis on AXI4 bus systems (2013).

Ove EdforsView profile

Professor

• Radio Systems
• Statistical Signal Processing
• Massive MIMO
• +5 more

Ove Edfors is a Professor at the Department of Electrical and Information Technology, Lund University, affiliated with the Faculty of Engineering (LTH). His primary research focuses on radio systems, statistical signal processing, and massive MIMO technologies. He is a core member of the LTH Profile Area: AI and Digitalization and the LU Profile Area: Natural and Artificial Cognition. Edfors leads the NEXTG2COM Vinnova Competence Centre and contributes to ELLIIT initiatives. His research spans multi-carrier systems, low-complexity algorithms, and wireless communication applications. Key projects include 6G radio testbed development and millimeter-wave channel characterization. He has co-authored over 225 publications and supervised 22 graduate students. Notable achievements include the IEEE Signal Processing Society Donald G. Fink Award (2023) and the IEEE Communications Society Best Tutorial Paper Award (2018). Recent work emphasizes indoor localization via multi-sensor fusion (LuViRA Dataset) and energy-efficient MIMO processors. Active collaborations involve global institutions and industry partners in 5G/6G infrastructure. Edfors' contributions align with UN SDGs for affordable and clean energy (Goal 7) through energy-efficient wireless systems and innovation (Goal 9).

• Hardware Design
• FPGA Programming
• Parallel Computing
• +5 more

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

• Interconnects
• Integrated Circuits
• 3D Integrated Circuits
• +3 more

Vasileios Pavlidis is a Senior Lecturer specializing in Advanced Processor Technology, focusing on cutting-edge research in integrated circuits, 3D integration, and hardware security. His work aligns with UN Sustainable Development Goals related to Digital Futures. Research interests include interconnects, clock distribution networks, and process variation mitigation in semiconductor systems. He explores emerging technologies like monolithic 3D integration and energy-efficient serial interfaces. His publications (2021–2022) address hardware security (e.g., EM side-channel attacks), 3D IC design, and compact filter systems. He has supervised 10 research projects but no grants or awards are explicitly mentioned. His work is part of the Advanced Processor Technology group.

Zaijun ChenView profile

Researcher

• Optical Computing
• Neuromorphic Photonics
• Quantum Sensing
• +5 more

Zaijun Chen is a Researcher and Principal Investigator in the Laboratory of Intelligent and Quantum Photonics at the Viterbi School of Engineering, University of Southern California. He holds a PhD in Physics from the Max-Planck Institute of Quantum Optics and Ludwig Maximilian University of Munich (2019). His research focuses on optical computing architectures, neuromorphic photonic circuits, and precision sensing. He leads DARPA-funded projects NaPSAC and INSPIRED, developing next-generation AI processors and on-chip quantum sensing technologies. Key awards include the 2023 Optica Foundation Challenge Award and SPIE AI/ML Award. He advises PhD students and postdocs in photonic computing and sensing. Education: PhD, Physics, Max-Planck Institute of Quantum Optics & LMU Munich (2019); Postdoctoral research at MIT’s Quantum Photonics Group (2021-2022). Research Interests: Optical neural networks, photonic AI accelerators, integrated photonics, quantum sensing, dual-comb spectroscopy, and neuromorphic engineering. Notable Publications: Over 30 peer-reviewed articles in Nature Photonics , Science , and top optics journals, focusing on photonic computing and precision metrology.

• High-Performance Computing
• Energy Efficiency in Computing
• Parallel Algorithms
• +4 more

Dr. Lennart Johnsson is a Professor of Computer Science at the University of Houston, with affiliations at the Royal Institute of Technology (KTH) in Sweden. He has held faculty positions at Caltech, Yale University, Harvard University, and KTH, and industry roles at ABB Research and Thinking Machines Corp. His research focuses on High-Performance Computing (HPC), energy-efficient systems, parallel algorithms, and grid computing. He has collaborated with institutions like PRACE, and companies including AMD, Intel, and Texas Instruments, leading to innovations such as energy-efficient HPC servers and DSP-based architectures. Research interests include optimizing HPC for energy efficiency, embedded processors (e.g., DSPs), and novel interconnection networks. He has pioneered software libraries like CMSSL and contributed to standards such as MPI and High-Performance Fortran. Awards include the Machtey Best Student Paper Award and recognition in the Gordon Bell Prize competition. Dr. Johnsson has supervised numerous students, including those working on adaptive scheduling, grid computing, and bioinformatics. He founded the Texas Learning and Computation Center and led initiatives like the Texas GigaPoP and RENoH network. Current projects explore energy-efficient HPC using DSP architectures. Key contributions include the first No. 1 system on the Top500 list (1993), grid computing frameworks, and infrastructure for distributed applications. He has served on boards for PRACE, NSF, and Swedish research councils, and advises on national HPC strategies.