Liu Lili is a Lecturer (Educator Track) in the Department of Computer Science at the School of Computing, National University of Singapore. She holds a Ph.D. from Nanyang Technological University and a Master's in Computer Science from Shanghai University. Prior to NUS, she served as a Senior Research Scientist at Singapore Polytechnic and a Scientist at A*STAR's Institute of High-Performance Computing. Her research focuses on Machine Learning, Computer Vision, and Multi-modal Learning, with applications in FinTech, Social Media Analysis, and Algorithms & Theory. Notable projects include AI-driven coating inspection systems for marine assets and behavioral competency assessment tools for navigational safety. She has contributed to robotics for construction quality assessment and interactive virtual environments for rehabilitation. Liu's publications span AI applications in finance, robotics, and material science, reflecting her expertise in bridging theoretical computer science with practical industrial solutions. Her work emphasizes automation, anomaly detection, and multi-modal data integration.
Suranga Chandima Nanayakkara is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the Centre for Holistic Inquiry into Lifelong Learning (CHILL) and the Smart Systems Institute (SSI). He holds roles such as AI+HCI Theme Lead at NUS+CNRS IPAL Lab and Residential Fellow at NUS College. His research focuses on assistive human-computer interfaces, emphasizing technologies that enhance perceptual and cognitive capabilities for individuals with sensory deficits. He earned his PhD and BEng from NUS, followed by postdoctoral work at MIT Media Lab. Notable projects include the Augmented Human Lab, iTILES for rehabilitation, and the FingerReader device for visually impaired shoppers. His work has garnered awards like the MIT TR35, TOYP, and INK Fellowship. Research interests include Intelligent Systems, Human Augmentation, and Design Science. He explores applications in assistive technologies, healthcare informatics, and educational tools. Over 15 publications highlight innovations in wearables, stress management, and multimodal interaction. Awards: 40+ accolades, including MIT TR35, TOYP, and multiple design awards for projects like Kiwrious and FingerReader. Grants: Led projects funded by institutions like NUS and Singapore’s innovation initiatives. Labs: Augmented Human Lab (founded 2011), focusing on humanizing technology through natural interfaces.
Dr. Qiang Lee is an Associate Professor in the Electrical and Computer Engineering Department at Hampton University, located in the Franklin W. Olin Engineering Building. She holds a Ph.D. in Electrical Engineering from Georgia Institute of Technology (2006), an M.S. in Computer Information Science from Clark Atlanta University (2002), and a B.Sc. in Electrical Engineering from Beijing University of Aeronautics and Astronautics (1995). Her research focuses on multi-modal sensor fusion, multiple target tracking, signal processing, and geospatial data analysis. Notable projects include NASA's ULI initiative on spectroscopy sensors for hypersonic flight control and ARL-funded work on sensor networks for target tracking. She has served as Principal Investigator (PI) on NSF and ARL grants, and co-investigator on NASA projects. Dr. Lee's publications span machine learning applications in spectroscopy, scramjet control systems, and multitarget tracking algorithms. Her work bridges aerospace engineering, data science, and sensor network optimization. She contributes to engineering education research, particularly in minority-serving institutions. Lab affiliations include Hampton University's School of Engineering research groups focused on sensor systems and aerospace applications. Grants highlight her role in advancing sensor technology for defense and aerospace industries.
Reza Ghabcheloo is a Professor at Tampere University, affiliated with the Faculty of Engineering and Natural Sciences and the Department of Automation Technology and Mechanical Engineering. He leads the Robotics major and the international Automation Engineering program. His research focuses on autonomous mobile machines, robotics, control systems, and safety engineering, with specific interests in construction robotics, sensor fusion, and hydraulic systems. He co-leads the Autonomous Mobile Machines Group and is associated with the Robotics and Intelligent Machines Lab and the Innovative Hydraulics and Automation Lab. His research emphasizes developing autonomous systems for off-road machinery, safe control strategies, and energy-efficient automation. He has published extensively on topics such as reinforcement learning for crane control, radar-based perception, and safety architectures for autonomous systems. His work bridges robotics, control theory, and industrial automation, addressing challenges in heavy-duty machinery and real-world robotic applications. Research Group: Autonomous Mobile Machines Group Labs: Robotics and Intelligent Machines Lab, Innovative Hydraulics and Automation Lab Key Projects: Safety of automated off-road machinery, machine learning for autonomous loading, and trajectory optimization
F. Levent Degertekin is a Regents' Entrepreneur and the George W. Woodruff Chair in Mechanical Systems and Professor at the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His office is located in Love Building, room 311B, and his contact email is levent.degertekin@me.gatech.edu. Dr. Degertekin's academic journey includes a Ph.D. in Electrical Engineering from Stanford University (1997), an M.S. in Electrical Engineering from Bilkent University, Turkey (1991), and a B.S. in Electrical Engineering from Middle East Technical University, Turkey (1989). Dr. Degertekin's research focuses on micromachined ultrasonic devices and systems for medical applications, particularly in intravascular ultrasound imaging, therapeutic ultrasound, and acousto-optical sensors for MRI. His work spans from fundamental research on novel transduction methods to complete catheter-based imaging systems close to commercialization. He has made significant contributions to capacitive micromachined ultrasonic transducers (CMUTs), developing diffraction grating based optomechanical sensing methods now commercialized by Silicon Audio, novel atomic force microscopy imaging probes, and micromachined ultrasonic ejector structures for cell transfection commercialized by OpenCell Technologies. His research integrates acoustics, optics, and their combinations for various medical applications, utilizing conventional microfabrication (MEMS) and integrated circuit technologies. The Degertekin lab exposes students to applied physics, electrical, mechanical and biomedical engineering, biology, and biomimetic systems, providing them with thorough theoretical and experimental education in acoustics and optics while learning interdisciplinary research. Dr. Degertekin's work has received significant media attention, including coverage in IEEE Spectrum, Wired Magazine, The New York Times, and Fox Business News, highlighting innovations such as handheld ultrasound probes, MRI safety sensors, and minimally invasive cardiac imaging technologies. IEEE Fellow for 'Contributions to micromachined ultrasonic and optomechanical transducers and systems,' 2022 IEEE UFFC Society Inaugural Carl Hellmuth Hertz Ultrasonic Achievement Award, 2014 George W. Woodruff School Outstanding Achievement in Commercialization and Entrepreneurship Award, 2024 National Science Foundation CAREER Award, 2004-2009 Whitaker Foundation Biomedical Engineering Research Grant Award, 2001 66 US and 6 International Patents Dr. Degertekin has mentored numerous students who have gone on to make significant contributions in the field. Several of his students have received IEEE Ultrasonics Symposium Best Student Paper Awards, including Jeff McLean (2003), Sheng-Yu Peng (2006), Rasim O. Guldiken (2005 and 2007), and Toby Xu (2014). His research has been supported by various grants including the NSF CAREER Award and Whitaker Foundation grant. His work has led to multiple commercial ventures including Silicon Audio and OpenCell Technologies. The Degertekin Group at Georgia Tech focuses on transducers and systems for medical imaging and sensing, with current projects including capacitive parametric transducers, acousto-optic sensors for MRI, novel transducer methods for focused ultrasound in the brain, microsystems for intravascular and intracardiac ultrasound imaging, and CMUT-on-CMOS systems for IVUS imaging.
Dr. Ameer Abdelhadi is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. His research focuses on application-specific custom-tailored computer architectures, hardware-efficient deep learning, neurotechnology, and reconfigurable computing. He holds a PhD from the University of British Columbia and has held academic positions at the University of Toronto, Imperial College London, and Simon Fraser University, alongside industry experience in semiconductor design. Education: PhD in Computer Engineering (University of British Columbia, 2016). Research Interests: Hardware acceleration for machine learning and neurotechnology Reconfigurable computing and FPGAs/ASICs Asynchronous circuits and synchronization protocols VLSI physical design and CAD algorithms Publications span high-impact venues such as IEEE Journal of Solid-State Circuits, IEEE Hot Chips, and IEEE Micro. Notable achievements include the 2017 Best Paper Award at ASYNC for work on synchronization FIFOs. Teaching includes COMPENG 4DV4 (VLSI System Design) and ELECENG 4OI6B (Engineering Design). His lab focuses on advancing hardware systems for next-generation applications in AI and biomedical engineering.
Paolo Samorì is a full-time Professor at the Université de Strasbourg , where he serves as Director of the Nanochemistry Laboratory and Emeritus Director of the Institut de Science et d'Ingénierie Supramoléculaires (ISIS) . He is affiliated with multiple prestigious academies, including the German National Academy of Science and Engineering (ACATECH) , Royal Society of Chemistry (FRSC) , and European Academy of Sciences (EURASC) . Education: Laurea (MSc) in Industrial Chemistry (University of Bologna, 1995), PhD in Chemistry (Humboldt University Berlin, 2000, summa cum laude). His research focuses on Nanochemistry , 2D materials , and supramolecular systems at interfaces , with applications in organic electronics , optoelectronics , and sensing . He pioneered methods for scanning probe microscopies and photoresponsive nanodevices , including graphene-based systems and diarylethene molecular switches. His scientific awards include the ERC Advanced Grant (2019) , Blaise Pascal Medal (2018) , and Catalán-Sabatier Prize (2017) , among 20+ honors. He has trained over 130 students and researchers , including 34 professors now active globally.
Răzvan Sandu ENOIU is a Professor at the Department of Motor Performance, Faculty of Physical Education and Mountain Sports, Transilvania University of Brașov. His work focuses on sport training methodologies, physical education innovation, and the integration of technology in athletic performance analysis. He is affiliated with the university’s research initiatives and has contributed to interdisciplinary studies bridging sports science with educational technology. Research Interests: Sport training programming and periodization Applications of wearable sensors and modern technology in sports Optimization of athletic performance through data-driven approaches Impact of extracurricular activities on motor skills development His publications explore topics such as Alpine skiing performance analysis, vital sign monitoring in athletics, and the sociological effects of technology during the pandemic. Recent work includes advancements in javelin throwing biomechanics and balance training methodologies using Bosu balls. Professor ENOIU collaborates with international journals and contributes to conferences on sports science and child development.
Saurabh Bagchi is a Professor at Purdue University, West Lafayette, USA. He holds a PhD in Computer Science from the University of Illinois Urbana-Champaign (2001). His research focuses on distributed systems security, networking, and embedded systems. Key areas include IoT security, cyber-physical systems resilience, and machine learning applications in edge computing. Bagchi's work spans theoretical and applied domains, addressing challenges in distributed algorithms, fault tolerance, and secure communication protocols. His contributions to firmware analysis, serverless computing optimization, and anomaly detection in industrial IoT systems have been widely recognized. He has published over 300 papers in top-tier conferences and journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and CVPR. He collaborates with researchers in academia and industry to advance resilient networked systems, including projects funded by NSF and industrial partnerships. His lab explores cutting-edge topics like federated learning security, edge computing architectures, and game-theoretic approaches to cyber defense.
David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)
Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.
Dr. Saeed Gazor is a full Professor in the Department of Electrical and Computer Engineering at Queen's University. He holds a cross-appointment in the Department of Mathematics and Statistics. His research focuses on signal processing applications in electrical energy systems, communications, and medical imaging. He has supervised postdoctoral fellows Babak Ghaffari and Yaser Esmaeili Salehani. Professional affiliations include Senior Member IEEE and membership in the Institution of Engineering and Technology. Education: PhD (1994) in Signal and Image Processing from Télécom ParisTech; M.Sc. (1989) and B.Sc. (1987) from Isfahan University of Technology with highest honors. Academic roles include former Assistant Professor at Isfahan University of Technology (1995–1998) and research associate at University of Toronto (1999). Research interests span detection theory, smart energy systems, hyperspectral imaging, and medical signal processing. Notable contributions include innovations in radar signal processing, sparse signal reconstruction, and adaptive filtering. Active in academic service, including editorial roles in IEEE journals. Awards: Professional Engineer designation from Professional Engineers Ontario. Over 200 peer-reviewed publications with recent focus on AI-driven hyperspectral analysis, robust beamforming, and energy-efficient communication systems. Labs/Teams: Leads signal processing research initiatives at Queen's, collaborating on projects involving smart energy grids, distributed radar networks, and biomedical signal analysis. Current work emphasizes integrating deep learning with traditional signal processing techniques.
Prof. Dr. Tunç ÇATAL is a Professor of Molecular Biology and Genetics at Üsküdar Üniversitesi. He holds a PhD from İstanbul Technical University (2008) and conducted postdoctoral research at Oregon State University and the National University of Ireland Galway. His expertise spans microbial biotechnology, molecular biology, and hydrogen production. Education: BSc in Biology, İstanbul University (2001) MSc in Biology, İstanbul University (2004) PhD in Molecular Biology-Genetics and Biotechnology, İstanbul Technical University (2008) Administrative Roles: Head of Molecular Biology and Genetics Department (English Program) Director of PROMER Research Center Bologna Coordinator and Erasmus Coordinator His research focuses on microbial electrochemical systems, bioremediation, and bioenergy. Notable contributions include optimizing hydrogen production using microbial electrolysis cells and studying the effects of pharmaceuticals on microbial fuel cell efficiency. He has supervised 3 graduate theses and holds TÜBİTAK awards for impactful publications. His work integrates environmental science and molecular biology, with applications in sustainable energy (e.g., hydrogen production) and wastewater treatment. Recent studies explore novel curcumin compounds for toxicity mitigation and marine mucilage-based bioelectrochemical systems.
Dr. Jennifer Koch is an Associate Professor at the Laboratory of Geo-information Science and Remote Sensing, part of Wageningen University & Research. Previously, she served as an Associate Professor and Associate Research Director at the University of Oklahoma's Data Institute for Societal Challenges. Her research integrates data-driven methods like simulation modeling to address socio-economic and climate change challenges, focusing on sustainable urbanization and environmental management. Education: She holds a Diplom (Univ.) in Geoecology from the University of Bayreuth and a Dr.-Ing. in Electrical Engineering/Computer Science from the University of Kassel. She teaches courses on geo-information management and data analytics. Research emphasizes multi-scale modeling, stakeholder engagement, and participatory approaches to socio-ecological systems. Recent work explores urban growth in Africa, methane emission monitoring, and renewable energy siting. Articles highlight interdisciplinary methods in GIS, climate policy, and community geography. Professional service includes roles with iEMSs, IALE, and the AAG. No ancillary activities reported. Her work bridges technical geospatial tools with societal challenges, emphasizing practical policy applications.
Sverre Steen is a Professor and Head of the Department of Marine Technology at the Norwegian University of Science and Technology (NTNU). He leads the Kongsberg Maritime University Technology Centre focused on 'Ship Performance and Cyber-physical Systems' and is a member of the standing committee for the Symposium of Marine Propulsors. His research emphasizes ship propulsion, hydrodynamics, and big data analysis of in-service vessel performance. Key interests include seakeeping, high-speed marine vehicles, and model testing techniques. Steen teaches TMR 4217 Hydrodynamics of High-Speed Marine Vehicles , covering cavitation, experimental hydrodynamics, and propulsion systems. He collaborates internationally on projects like the Norwegian Ocean Technology Centre. His recent work explores wave-energy extraction via hydrofoil vessels, resistance modeling for fast ferries, and propulsion efficiency in real sea states. He has contributed to global shipping emission models (MariTEAM) and reliability analysis of structural components under vibration. Steen's publications span propulsion in waves, engine-propeller dynamics, and data-driven methods for ship performance monitoring. His applied research bridges experimental testing and computational modeling to address challenges in sustainable maritime transport and operational safety.
