Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.
Dr. Liqiang Zhang is a Professor at the Department of Computer and Information Sciences, Indiana University South Bend. He holds a Ph.D. in Computer Science from Wayne State University (2005). His research focuses on wireless networks, mobile computing, resource allocation, IoT, cognitive radio networks, and network security. His work is supported by IU and the National Science Foundation. He has organized multiple conferences including ICCCN 2013 (Track Chair), GLOBECOM 2010 (Publicity Co-Chair), and founded/co-chaired WiMAN workshops. He serves as a Guest Editor for journals like ACM Transactions on Autonomous and Adaptive Systems and Elsevier's Computer Communications. He reviews for top journals including IEEE Transactions on Mobile Computing and IEEE Transactions on Parallel and Distributed Systems. Dr. Zhang teaches courses such as Computer Structures, Mobile App Development, and Network Security. Recent publications (2019-2009) include advancements in network scheduling, cognitive radio protocols, radar positioning, and digital design education. His research spans both theoretical and applied aspects of networking and distributed systems.
New Mexico Institute of Mining and TechnologyUnited States
Dr. Menake Piyasena is an Associate Professor in the Department of Chemistry at New Mexico Institute of Mining and Technology, where he leads the Bio-analytical and Microfluidics research group. His work focuses on developing novel analytical methods for environmental, biological, and chemical applications, including disease diagnostics and pollutant detection. Education: Postdoctoral Researcher, University of New Mexico, 2009 Postdoctoral Researcher, University of Maryland, 2007 Postdoctoral Fellow, California State University-Los Angeles, 2005 Ph.D., Analytical Chemistry, University of New Mexico, 2005 B.Sc., Chemistry, University of Kelaniya, 1997 Research Interests: Dr. Piyasena's research explores microsphere and polymer monolith-based bio-assemblies for disease diagnostics, acoustic focusing systems for biological particle separation, and microfluidic techniques for environmental contaminant removal. His group specializes in biosensors, acoustofluidic devices, and microfabrication techniques for analytical applications. Publication Trends: Recent work demonstrates a strong focus on environmental applications of microfluidics, particularly microplastic separation and pharmaceutical degradation analysis. His 15 most recent publications emphasize acoustofluidic particle manipulation, lipobead-based biosensors, and innovative microdevice fabrication for biological and environmental monitoring. Research Group: Leads the Bio-analytical and Microfluidics laboratory developing portable detection systems for environmental toxins and disease biomarkers.
Professor Mona Hella was a Full Professor in the Department of Electrical, Computer, and Systems Engineering (ECSE) at Rensselaer Polytechnic Institute (RPI), part of the School of Engineering. Her roles included leading RPI’s Chips Initiative and collaborations with industries such as Qorvo Inc. and GlobalFoundries. She held a PhD in Electrical Engineering from Ohio State University (2001) and joined RPI in 2004 after industry work in RF design. Notable recognitions include a Fulbright Scholarship (2015) and IEEE Senior Member status (2016). Her research focused on integrated circuits (IC) design, high-frequency systems (RF/mm-Wave/THz), power management, and biomedical applications. Collaborations spanned departments like Mechanical Engineering and Biomedical Engineering, leveraging clean rooms at RPI, Cornell, and MIT Lincoln Labs. She pioneered IC design education, establishing ECSE’s analog and RF curricula and leading the ECSE Maker Space and Mercer Lab. Research outputs included over 150 peer-reviewed publications, 5 patents, and industry partnerships with Analog Devices and Efabless. Her work emphasized THz gas sensing, silicon photonics, and energy-efficient circuits. A commemorative event highlighted her legacy as a mentor to 14 PhD, 8 master’s students, and many undergraduates, many of whom now lead roles in semiconductor and tech industries. Awards : Fulbright Scholar (2015), IEEE Senior Member (2016) Labs/Teams : ECSE Maker Space, Mercer Lab, Collaborations with MIT, Cornell, and international foundries Grants/Contracts : Supported by GlobalFoundries, TSMC, Fraunhofer Institute, and others
Julian Gardner is a Professor of Electronic Engineering at the University of Warwick's School of Engineering, leading the Electrical and Electronic Engineering Discipline Stream. He holds a First in Physics from Birmingham University, a PhD in Physical Electronics from Cambridge, and a DSc in Electronic Engineering from Warwick. His 30-year career includes 5 years in industry as an R&D Engineer and extensive research in chemical microsensors, founding companies like Cambridge CMOS Sensors (sold to ams in 2016) and spin-offs Flusso Ltd and Sorex Sensor Ltd. His expertise spans MEMS-based sensors, signal processing, and AI-driven data analysis. Research interests include chemical/environmental microsensors, MEMS devices, smart systems, electronic noses/tongues, and biomedical engineering. He teaches ES434 (ASICs, MEMS, and Smart Devices) and has authored 10 books, 500+ papers, and 20+ patents. Awards include the Royal Society Mullard Award and Fellowships from IET, IEEE, and the Royal Academy of Engineering. Education: B.Sc. (Hons) Physics, University of Birmingham Ph.D. Physical Electronics, University of Cambridge D.Sc. Electronic Engineering, University of Warwick Research Highlights: Electronic noses for healthcare and environmental monitoring CMOS-integrated gas sensors MEMS thermal flow sensors AI-driven sensor data analysis Awards: Royal Society Mullard Award (2018) IET & IEEE Fellowships Grants/Projects: Development of low-cost air quality monitoring systems Smart city IoT sensor networks Prostate cancer detection via lab-on-a-chip His labs include collaborations on particle sensing, thermal modulation techniques, and biomimetic systems. Ongoing work focuses on edge AI for real-time environmental sensing and wearable health monitoring devices.
Kazem Cheshmi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on compiler optimization techniques for accelerating scientific computing and machine learning applications on parallel architectures. He leads the SwiftWare Lab and teaches courses such as High-Performance Programming (COMPENG 4SP4/ECE 6SP4) and Special Topics in Computation (ECE 718). Education: B.Eng. (Ferdowsi University of Mashhad), M.A.Sc. (University of Tehran), Ph.D. (University of Toronto). He has held research positions at Microsoft Research, Adobe Research, Concordia University, and Rutgers University. Research Interests: High-performance computing, compiler design, sparse matrix computations, and their applications in machine learning and scientific computing. His work emphasizes optimizing sparse codes for parallel architectures and developing efficient QP solvers like NASOQ. Key Contributions: Developed Sympiler (a domain-specific compiler for sparse matrix codes) and NASOQ (a scalable QP solver). His awards include the ACM-IEEE CS George Michael Memorial HPC Fellowship (2020) and recognition for contributions to compiler-driven sparse computation optimization. Teaching and Service: Organizes SONAD’25, serves on program committees for PPoPP, Supercomputing, and IPDPS. Supervises students in compiler design, parallel programming, and high-performance computing. Labs/Teams: Leads the SwiftWare Lab focusing on compiler optimization and high-performance systems. Collaborates on open-source projects like Sympiler and NASOQ.
Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.
Kazuyuki Iwase serves as Associate Professor at Tohoku University's Institute of Multidisciplinary Research for Advanced Materials since April 2025, following progressive appointments as Senior Assistant Professor (2023-2025) and Assistant Professor (2019-2023). His academic journey includes postdoctoral research at Paul Scherrer Institute (Switzerland) and multiple JSPS Research Fellowships. He maintains active collaborations with prominent researchers including Prof. Itaru Honma and Prof. Takaaki Tomai. Dr. Iwase's research focuses on electrocatalysis for sustainable energy conversion, specializing in carbon dioxide reduction reaction (CO2RR) and oxygen evolution reaction (OER) systems. His work spans nanomaterials engineering, electrocatalyst design, and device integration for renewable energy applications. Key methodologies include supercritical hydrothermal processing, mechanical alloying, and machine learning optimization of electrochemical systems. His publication record demonstrates consistent high-impact output with 36 accepted articles through 2025, featuring 11 as corresponding author and 15 as first/equal-first author. Recent work explores manganese nanospinels for OER, Ag-Sn intermetallics for CO2RR, and machine learning approaches for reaction optimization, showing strong interdisciplinary connections between materials science, electrochemistry, and sustainable engineering. The 5th Symposium for The Core Research Clusters for Materials Science and Spintronics Poster Award (2021) Student Presentation Award, Chemical Society of Japan (2016) International Exchange Support Award, Electrochemical Society of Japan (2016) SIEMME Best Oral Presentation Award (2014) Dr. Iwase has secured significant research funding as Principal Investigator, including a JST PRESTO grant (¥40,000,000) for CO2 conversion research and multiple JSPS Grants-in-Aid totaling over ¥59,000,000. His academic service includes peer review for prestigious journals including Angewandte Chemie and Nature Sustainability. He maintains active international engagement through invited lectures in Japan, India, and Switzerland, focusing on nanomaterials for electrocatalysis.
Rohit Bhagat is a Professor and Centre Director at the Centre for E-Mobility and Clean Growth. His research focuses on advancing energy storage technologies, particularly lithium-ion batteries, through material science innovations and real-time monitoring systems. Key areas of interest include battery degradation mechanisms, sensor integration for diagnostics, and environmental impact assessments of battery production. Research highlights include developing predictive models for lithium plating detection using machine learning, investigating electrolyte degradation under elevated temperatures, and optimizing cathode materials for zinc-ion batteries. He has led projects such as the British Council MRes scholars initiative in STEM, emphasizing interdisciplinary collaboration. Bhagat’s work spans experimental design methodologies for battery modeling, thermal management strategies, and life cycle assessments. His contributions address critical challenges in battery safety, longevity, and sustainability, with applications in electric vehicles and renewable energy systems.
Dr Soroush Faramehr is an Associate Professor (Research) at Coventry University's Institute for Future Transport and Cities. He holds a PhD in Electrical Engineering from Swansea University (2015), specializing in wide bandgap semiconductor technologies. His research focuses on developing high-efficiency compound semiconductor devices for decarbonization in automotive, aerospace, renewable energy, and industrial sectors. He has over 10 years of R&D experience, with a strong publication record in peer-reviewed journals, successful grant acquisitions, and supervision of postgraduate researchers. Education: PhD in Electrical Engineering (Swansea University, 2015). Postdoctoral research at Swansea University before joining Coventry in 2019. Research Interests: Power semiconductor devices (GaN, SiC), magnetic sensors, thermal management, and E-mobility applications. His work aligns with UN Sustainable Development Goals related to climate action and sustainable infrastructure. Key Projects: Includes leadership in initiatives such as 'Gallium Nitride Smart Power Integrated Circuit Technology' (2021–2025) and 'GaN Hall Sensors' (2020). He has secured funding for projects like 'High-Voltage fast-charging efficient Electric vehicle Powertrains' (2025–2029). Advising: Supervises students researching topics like GaN HEMT switching behavior, thermal management in e-scooters, and second-life EV battery utilization. His advisees include Xuyang Lu, Arun Mambazhasseri Divakaran, and current students Louiza Mavrovounioti and Vartika Pandey. Publications: Over 30 articles in journals such as IEEE Access and IEEE Transactions on Power Electronics. Research spans CFD modeling, magnetic sensor development, and GaN device optimization.
Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .
Georges G.E. Gielen is a Full Professor in the Department of Electrical Engineering (ESAT) at KU Leuven (Belgium), where he has been faculty since 1993. He also serves as scientific research advisor at imec (10% appointment). Previously, he held leadership roles including Vice-Rector for Science, Engineering & Technology (2013-2017), Chair of Electrical Engineering Department (2012-2013, 2020-2024), Head of MICAS research group, and PI coordinator of Leuven CHIPS Center of Excellence. He received MSc (1986) and PhD (1990) degrees in Electrical Engineering from KU Leuven. His research focuses on analog/mixed-signal IC design and CAD tools, including design automation, modeling, simulation, optimization, synthesis, and testing. Key areas include data converters, sensor interfaces, low-power design, and EDA tools. He has supervised over 55 PhD graduates and coordinates multiple research projects, including an ERC Advanced Grant (AnalogCreate). Professor Gielen has authored 14 books and over 800 publications, with works spanning circuit design, CAD methodologies, testing techniques, and microelectronics. His publications consistently address emerging challenges in analog/digital integration and design automation. IEEE Fellow (2002) Royal Flemish Academy of Belgium (Technical Sciences) Academia Europaea (Engineering) IEEE CAS Mac Van Valkenburg Award (2015) IEEE CAS Charles Desoer Award (2020) EDAA Achievement Award (2021) 14 additional awards including 4 best paper prizes He leads the MICAS research group focusing on microelectronics and sensors, and has chaired the Leuven ICT research center. He regularly serves on editorial boards of IEEE Transactions and organizes major conferences (General Chair for DATE 2006, ICCAD 2007, ESSCIRC 2017, ETS 2021).
Yang Li is an Assistant Professor of Computer Science at Iowa State University, specializing in computer architecture, machine learning, and their intersection. He holds a Ph.D. and M.S. from Carnegie Mellon University (2020), an M.S.E. from the University of Texas at Austin (2013), and a B.E. from Tsinghua University (2011). Prior to academia, he worked as a Senior Research Scientist at Meta, a Research Scientist at Meta, and a Researcher at Microsoft. His research focuses on large language models (LLM) acceleration, on-device AI, cloud infrastructure optimization, and spatiotemporal forecasting. He has contributed to over 20 peer-reviewed publications at top venues like ASPLOS, EMNLP, and ICASSP. Research Interests: Algorithmic and systems-level acceleration of LLMs On-device AI co-design and privacy Cloud memory/power management Graph-based spatiotemporal forecasting Teaching: Taught COMS 6730 (Advanced Topics in ML), COM S 321 (Computer Architecture), and guest-lectured on graph signal processing. Recent teaching scores include 4.75/5.0 (Fall 2024) and 4.67/5.0 (Spring 2025). Awards: IBM Patent Application Award (2021) and multiple patents on power management systems for data centers. Service: Program committee member for DAC 2024, NeurIPS 2024, and ICLR 2025. Reviewer for ACM TACO, IEEE TPAMI, TPDS, and others.
Dr. Nicolai Stawinoga is affiliated with the Embedded Systems Architecture group at Technische Universität Berlin. His role involves research and academic contributions focused on embedded systems and related technologies. Contact details include office EN12, room E-N 646, and email nicolai@aes.tu-berlin.de . Research interests center on embedded systems architecture, real-time systems, and hardware-software co-design. His work addresses challenges in system-on-chip (SoC) design and computer engineering applications. No specific awards, grants, or advised students are listed in the provided information. Office hours are available by appointment.
Junming Zeng is a Researcher at the Department of Electrical and Electronic Engineering, Faculty of Engineering at Imperial College London. His work focuses on advanced CMOS technologies for biomedical applications, including lab-on-chip platforms and ion imaging systems. He holds a PhD from Imperial College London (2022), following a Master's in Analogue and Digital Integrated Circuit Design (2017) and a Bachelor's in Electronic Engineering (2016), both from UK institutions. His research interests span analogue/mixed-signal IC design, FPGA-based digital systems, and ultra-high-speed ion sensing solutions. He has pioneered CMOS lab-on-chip platforms for real-time chemical monitoring and developed compressed sensing techniques for optimizing sensor array performance. His work integrates deep learning for applications like diabetes glucose prediction and drift compensation in ISFET sensors. Zeng has received the Best Student Paper Award (1st Prize) at ISCAS 2018 and Imperial's Department PhD Scholarship. His research bridges electrical engineering and biomedical engineering, with a focus on scalable, energy-efficient systems for healthcare and diagnostics. He leads projects involving edge computing, temporal fusion transformers, and microfluidic integration, demonstrating expertise in both hardware innovation and algorithmic development. His lab develops cutting-edge systems such as 1000fps ISFET SoCs with programmable gain and high-throughput digital readout architectures. Recent work includes live demonstrations of real-time pH monitoring in 3D-printed microfluidic systems and spatio-temporal ion membrane characterization platforms.
