Xin Lou is an Associate Professor at the Infocomm Technology Cluster of Singapore Institute of Technology (SIT) and an Adjunct Senior Research Scientist at the University of Illinois at Urbana Champaign's Illinois Advanced Research Center Singapore (IARCS). He holds a PhD from City University of Hong Kong and a B.E. from Sichuan University. His research focuses on cyber-physical systems, AI security, autonomous systems, and sustainable smart infrastructure. Notable awards include the Best Paper Award (2025) and Best Demo Award (2024). He leads interdisciplinary projects on AI perception for autonomous vehicles and collaborates with industry partners like NVIDIA. His career includes roles as Senior Research Scientist at IARCS (2022–present) and Postdoctoral Researcher at Illinois at Singapore (2016–2018). He teaches courses in Computer Networks and Digital Signal Processing at SIT. Key grants include AI Singapore's Holistic Moving Target Defence (S$4M) and MOE-funded projects on AI perception and CO₂ reduction. His team develops datasets like the Water Supply System Dataset and explores smart grid cybersecurity through platforms like CRaaS. Professional activities include chairing ACM e-Energy 2024 and serving on TPCs for IEEE SmartGridComm and IEEE ICC. He advises EngD students under SIT-SINGA and collaborates internationally on robust autonomous systems, AI security, and sustainable energy.
Dr. Usman Adeel is an Associate Professor in Computer Science at Teesside University, specializing in Distributed Sensing Systems, IoT, and Smart Cities. He holds a PhD in Computing from Imperial College London (thesis: "Socio-economic aware data forwarding in mobile sensing networks and systems"). Previously, he worked as a Research Scientist at Intel Labs Europe (ICRI for Sustainable Connected Cities) and as a Research Associate at Imperial College London. His research focuses on Mobile Sensing, Low Power Networks, and Cyber-physical Systems. Recent work includes energy-efficient routing protocols for wireless sensor networks and intrusion detection systems for SDN-based VANETs. He leads the DUR007 project on Sustainable Drainage Systems innovation, emphasizing system innovation and water management. Key contributions span cybersecurity (e.g., IP camera vulnerability analysis) and biomedical applications (EEG-based brain-computer interfaces). No scientific awards are explicitly mentioned. He has supervised 2 academic works and collaborates widely in IoT, smart cities, and network security domains.
Mark Hempstead is a Professor in the Department of Electrical and Computer Engineering and Computer Science at Tufts University's School of Engineering. He leads the Tufts Computer Architecture Lab (TCAL) and has made significant contributions to computer architecture, systems research, and interdisciplinary applications of engineering tools to human subject research. Dr. Hempstead received his BS in Computer Engineering from Tufts University (Summa Cum Laude), and his MS and Ph.D. in Engineering from Harvard University, where he worked with Professors David Brooks and Gu-Yeon Wei. Prior to joining Tufts University in 2015, he was an Assistant Professor at Drexel University. His research focuses on increasing energy efficiency across circuits, architecture, and systems boundaries. Current research areas include: Computer architecture and systems Power-aware computing and embedded systems Mobile computing and machine learning systems Workload characterization and quantum computing Learning sciences and computer systems for human subjects research His group has published in several research communities including high-performance computer architecture, workload characterization, design automation, mobile systems, embedded systems, quantum computing, and Internet-of-Things. Recent publications show a strong trend toward machine learning systems, quantum computing architecture, and thermal management in modern processors, with applications spanning from embedded systems to high-performance computing platforms. Dr. Hempstead has received numerous scientific awards and honors: NSF CAREER award (2014) Allen Rothwarf Award for Teaching Excellence from Drexel University (2014) Excellence in Research Award from Drexel College of Engineering (2014) Winner of industry-sponsored SRC student design contest (2006) Best Paper Nominee in HPCA 2012 He has secured significant research funding including NSF Engineering Resource Center for Engineering Tools for Innovation and Research in Education (EnTIRE), multiple NSF grants including a CAREER award, DARPA funding, and industry collaborations with Google, Honeywell, and Facebook. His current research grants focus on hardware/software error detection, STEM education understanding, next-generation memory systems, and PCB assurance using thermal side-channel analysis. Dr. Hempstead leads the Tufts Computer Architecture Lab (TCAL), which investigates methods to increase energy efficiency across circuits, architecture, and systems. The lab has explored applications ranging from embedded systems and IoT to chip multiprocessors and high-performance computing. Current projects include systems support for machine learning, non-volatile memory design, thermal hotspot management, security implications of thermal side channels, automatic hardware accelerator generation, privacy-aware databases, and quantum computer architecture for ion-trap systems.
Alice Wang is an Assistant Professor of Instruction in the Department of Computer Science at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. Her research focuses on low-power electronics, energy harvesting systems, and advanced circuit design for mobile and embedded applications. She has contributed to innovations in ultra-low-power (ULP) SoCs, wireless sensor networks, and energy-efficient processor architectures. Key research interests include optimizing energy consumption in 3D-ICs, developing self-powered systems using multimodal energy harvesting, and improving thermal and power management in integrated circuits. Her work spans topics such as low-voltage circuit design, wake-up receiver architectures, and adaptive voltage scaling techniques for mobile processors. Her publications highlight advancements in low-power radio design, thermal-aware system architectures, and hardware-software co-optimization strategies. Recent work emphasizes industrial IoT applications, machine health monitoring systems, and energy-efficient embedded computing solutions. No academic awards or grants are explicitly mentioned in the provided texts. She has advised no students listed in the current data. Her research often intersects with industry challenges in battery-operated devices and scalable microsensor networks.
Ramtin Zand is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on hardware design for machine learning systems, neuromorphic computing, emerging nanoscale electronics (e.g., spintronic devices), and energy-efficient VLSI circuits. He leads the ICAS Lab, which explores reconfigurable architectures and low-power computing solutions. Education: Ph.D., Computer Engineering, University of Central Florida (2019) M.S., Electrical Engineering, Sharif University of Technology (2012) Research Interests: Dr. Zand’s work spans hardware-software co-design for AI, neuromorphic systems, and novel devices like MRAM and memristors. He emphasizes practical applications such as manufacturing anomaly detection, edge computing, and energy-efficient neural network deployment. Recent Trends in Articles: His publications emphasize hybrid PIM architectures, LLM-driven hardware design, and neuromorphic edge systems. Themes include optimizing communication efficiency, leveraging memristive crossbars, and bridging vision transformers with embedded platforms. Grants/Funding: Funded by AFRL for smart manufacturing projects ONR grants for perception systems research NSF support for in-memory computing Labs/Teams: The ICAS Lab collaborates on projects such as neuromorphic accelerators and low-power sensor systems. Recent milestones include DAC 2025 awards and CVPR 2025 demonstrations.
Dr. Nariman Farvardin serves as President and Professor of Electrical and Computer Engineering at Stevens Institute of Technology. Previously Senior Vice Provost at University of Maryland, he holds a PhD from Rensselaer Polytechnic Institute. His research focuses on data compression, wireless communications, and image processing. He has led major projects including a $5.5M Army Research Laboratory initiative on telecommunications and received honors including the Presidential Young Investigator Award and Academic Leadership Award from Carnegie Corporation. His patented inventions include image compression algorithms and wireless protocols. Funded by agencies including NSF, NASA and Mitsubishi, his work spans theoretical foundations to practical implementations like VLSI-based compression and CDMA network optimization. He has supervised over 30 graduate students and published 100+ works on communication systems.
Mark Yampolskiy is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University's Samuel Ginn College of Engineering. His research focuses on cybersecurity in additive manufacturing (AM), internet of things (IoT), and computer networks. Yampolskiy leads efforts to secure AM processes against cyber-physical attacks, including sabotage and data theft. He has received prestigious awards such as the ASTM International Additive Manufacturing Young Professor Award and led high-profile grants totaling over $685,000. Education: Ph.D. in Computer Science, Ludwig Maximilian University M.S. in Computer Science, Technical University Munich B.S. in Applied Mathematics, Moscow State Institute of Radiotechnics, Electronics and Automation Research Interests: Cybersecurity of digital & additive manufacturing IoT security and network defense Cyber-physical system vulnerabilities 3D printing integrity and sabotage detection Key Contributions: Developed frameworks for AM security awareness and threat modeling Pioneered studies on side-channel attacks in manufacturing Co-organized ASTM AM Security Working Groups and AMSec workshops Advocated for legal standards in AM intellectual property protection Awards: ASTM International Additive Manufacturing Young Professor Award America Makes Award ($260k+ for cybersecurity training) NSF Grant ($425k for sabotage prevention research) Labs/Initiatives: Active in Auburn's National Center for Additive Manufacturing Excellence (NCAME), McCrary Institute for Cyber and Critical Infrastructure Security.
Prof. Daniel Weiskopf is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute of Parallel and Distributed Systems. His research focuses on visualization techniques, eye tracking, and human-computer interaction, with applications in virtual/augmented reality (VR/AR), data analysis, and uncertainty modeling. He has contributed to advancements in scientific visualization, including ML-driven flow visualization, energy-efficient rendering, and gaze-aware interfaces. His work emphasizes empirical methodologies, such as eye-tracking studies for evaluating visualization literacy and collaborative learning in AR environments. Research interests include: Scientific Visualization and Uncertainty Representation Eye Tracking Methodologies and Human Factors VR/AR Systems and Immersive Analytics Machine Learning Applications in Visualization Recent publications highlight trends in optimizing visualization performance, improving data interpretation through gaze-aware systems, and integrating advanced visualization into gaming and engineering contexts. His work bridges computational methods with user-centric design principles. He leads efforts in the Institute of Parallel and Distributed Systems , collaborating on tools like Gazealytics for exploratory gaze analysis and mint for VR visualization integration. No awards or grants are explicitly listed in the provided information.
Dr. Martin Collier is an Associate Professor in the School of Electronic Engineering at Dublin City University and director of the Entwine Centre, focusing on IoT infrastructure. His research spans data and computer communications, network security, energy-efficient networking, and SDN/NFV. He leads projects like Horizon 2020's INPUT and FP7's ECONET, collaborating with IBM on data centre network design. His work emphasizes switch fabrics, green routers, and optical technologies. Research Interests: Switching/Routing, SDN, NFV, IoT, Energy Efficiency, Data Centre Networks. Recent Projects: INPUT (SDN/NFV personal cloud services), ECONET (energy efficiency), and IBM-funded data centre design. Laboratory activities include NetFPGA-based testing and SDN implementations. Teaching: Modules include Broadband Networks (EE552), Network Programming (EE562), and Communications Theory (EE450). Supervises final-year and MEng projects. Labs/Teams: Switching & Systems Lab (NetFPGA testbed), Entwine Centre (IoT infrastructure).
Longfei Shangguan is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He leads the Pittsburgh Experimental Research Group (PERG), focusing on hardware/software systems to advance computing, sensing, and communication in sensor/wireless technologies. Previously, he was a postdoc at Princeton and a senior researcher at Microsoft. He holds a PhD from HKUST (2015) under Professors Yunhao Liu and Ke Yi. Research Interests: Mobile/wireless systems, IoT, edge computing, and hardware/software co-design for sensor networks. Key areas include novel IoT applications, low-power hardware, and wireless protocol optimization. Key Projects Sensor-in-the-loop AI agents leveraging multimodal sensors Human-centric sensing using acoustic/vision modalities Wireless Digital Twin via neural ray tracing Professional Contributions Editor: IEEE Pervasive Computing, ACM GetMobile PC Member for top conferences including MobiCom, NSDI, SenSys Organized Dagstuhl Seminar on IoT Wireless Scenarios Awards NSF CAREER Award (2024) Google Research Scholar Award (2023) Best Paper Awards at MobiCom 2024 and MobiCom 2021 Advising & Grants Supervises 5 PhD students, 3 MS students, and undergraduate researchers Recipient of MassAITC Pilot Award (2025), NSF CAREER (2024) Labs/Teams: PERG lab focuses on experimental systems research with hands-on prototyping.
Nam Bui is an Assistant Professor in the Department of Electrical Engineering at the University of Colorado Denver, where he founded and directs the Intelligent Networked Systems and Cybernetics Laboratory (InsCy Lab). His research focuses on developing intelligent cybernetic systems at the intersection of AI, machine learning, and embedded systems, with applications in mobile/wearable health monitoring and physiological sensing. His research interests span: Innovative sensing techniques for health monitoring Embedded intelligent systems leveraging AI/ML Wearable technology for physiological measurement Multidisciplinary approaches to medical cybernetics Analysis of recent publications reveals consistent themes in wearable health technology development, particularly focusing on non-invasive monitoring techniques for vital signs (blood pressure, respiration, glucose) using ear-worn and behind-ear devices. Key technical innovations include compressed sensing frameworks, closed-loop acoustic stimulation systems, and novel sensing modalities like polarization-based optical detection. Awards and Honors: Best Paper Awards at ACM MobiCom (2019, 2017) ACM SigMobile Research Highlights (2020) Communication of ACM Research Highlights (2021) Colorado OEDIT Award (2024) NASA MUREP Program Award (2024) NSF Sense Award (2024) DoD STTR Grant (2024) Teaching Innovation Award (2023) Dr. Bui actively advises graduate students including Anh-Huy Dinh and undergraduate researchers including Luis Carlos Gutierrez and Alejandro Cordova. His laboratory has secured multiple grants from NSF, NASA, DoD, and Colorado economic development programs. He directs the Intelligent Networked Systems and Cybernetics Laboratory (InsCy Lab), which focuses on practical intelligent systems for health monitoring. The lab emphasizes multidisciplinary collaboration and has developed technologies like in-ear blood pressure monitors and closed-loop sleep aid devices.
Sanket Tavarageri is an Assistant Professor in the Computer Engineering Department at San José State University. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University and a B.Tech from National Institute of Technology Karnataka. His research encompasses big data systems, machine learning infrastructure, and high-performance compiler technologies. Research areas include: Polyhedral compilation techniques for deep learning workloads Automatic parallelization and optimization frameworks Hardware-software co-design for computational efficiency Scalable machine learning systems His publication portfolio demonstrates consistent innovation in compiler architecture and parallel systems from 2013-2021. Recent work focuses on AI-enhanced compilation, automatic parallelism for deep learning models, and hardware-aware optimizations. Publications appear in ACM TACO, IEEE Big Data, IPDPS, and PLDI conferences. Tavarageri maintains an active industry connection as a researcher at Google. Tools developed through his BRIGHT laboratory are available on GitHub and Bitbucket. His teaching covers compiler technology, parallel computing, and systems design with emphasis on practical implementation.
Gregory D. Abowd is the Dean of the College of Engineering and Professor of Electrical and Computer Engineering at Northeastern University. He previously held roles at Georgia Tech, including Regents’ Professor and Associate Dean of Research. His research focuses on Human-Computer Interaction (HCI), Ubiquitous Computing, and Software Engineering, with notable contributions like the Aware Home and autism technology initiatives. Abowd has advised 30 PhD students and received prestigious awards including ACM SIGCHI Lifetime Research Award and the ACM Eugene Lawler Humanitarian Award. Education: D.Phil. (1991) and M.Sc. (1987) in Computation from the University of Oxford, B.S. in Mathematics (1986) from the University of Notre Dame. Research interests span HCI, ubiquitous computing systems, and their societal impact. His work emphasizes technologies for education, healthcare, and sustainability. Recent projects include self-powered interfaces (e.g., SPIN, SATURN) and computational materials. He is affiliated with Northeastern’s Khoury College of Computer Sciences and Health Sciences programs. Awards highlight his mentorship and innovation, with recognition from ACM, IEEE, and the American Academy of Arts and Sciences. His academic leadership includes roles at Georgia Tech and Northeastern, fostering interdisciplinary collaboration and engineering education.
Gonzalo Mateos is an Associate Professor in the Department of Electrical and Computer Engineering and holds a secondary appointment in the Department of Computer Science at the University of Rochester. He is also the Asaro Biggar Family Fellow in Data Science and a member of the Goergen Institute for Data Science and Artificial Intelligence. Additionally, he serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Transactions on Signal and Information Processing over Networks , and is part of the IEEE SigPort Editorial Board. Dr. Mateos earned his B.Sc. in Electrical Engineering from Universidad de la Republica, Uruguay in 2005. He then pursued graduate studies at the University of Minnesota, Twin Cities, where he received his M.Sc. in 2009 and Ph.D. in 2011. Prior to joining the University of Rochester in 2014, he worked as a Systems Engineer at Asea Brown Boveri (ABB) in Uruguay from 2004 to 2006 and served as a visiting scholar at Carnegie Mellon University's Computer Science Department during the 2013 academic year. His research interests focus on statistical learning from Big Data , network science , decentralized optimization , and graph signal processing , with applications in dynamic network health monitoring , social networks , power grid analysis , and large-scale data analytics . His work emphasizes blind deconvolution, graph topology inference, fairness-aware methodologies, and explainable AI for medical and engineering systems. Dr. Mateos has been honored with the NSF CAREER Award (2018), the IEEE Young Author Best Paper Award (2017), and multiple conference best paper awards including ICASSP 2018, SSP Workshop 2016, and SPAWC 2012. His doctoral research was recognized with the University of Minnesota's Best Dissertation Award (2013, Honorable Mention) in Physical Sciences and Engineering. In advising, he mentors graduate students in his research areas, though specific names are not listed here. His grants include the NSF CAREER Award, and he collaborates on projects like fairness-aware graph filter design and dynamic network monitoring. His research teams and labs operate within the Goergen Institute, focusing on interdisciplinary data science and AI applications. He actively contributes to the Hajim School of Engineering and the broader University of Rochester community, fostering innovation at the intersection of electrical engineering and computer science. His industry experience and academic roles reflect a commitment to both theoretical and practical advancements in networked systems and machine learning.
Daniel Holcomb is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, part of the College of Engineering. He specializes in secure and reliable embedded systems, with research interests spanning embedded systems security, hardware security, formal verification, VLSI design, and cryptography. Education : B.S. Electrical & Computer Engineering, University of Massachusetts (2005) M.S. Electrical & Computer Engineering, University of Massachusetts (2007) Ph.D. Electrical Engineering and Computer Sciences, University of California, Berkeley (2013) His research focuses on mitigating hardware vulnerabilities in FPGAs and chiplet-based systems, including countermeasures against side-channel attacks, voltage attacks, and IP theft via reverse engineering. He has contributed to the development of secure design methodologies, PUF-based authentication, and fault-resistant cryptographic implementations. His publications highlight advancements in FPGA security, such as defeating memory protection units (MPUs), preventing trojan insertion during fabrication, and mitigating power distribution attacks in multi-tenant environments. Holcomb is an active member of IEEE and Tau Beta Pi Honor Society. Labs & Teams : His work aligns with the College of Engineering’s research strengths in hardware security, including collaborations with labs focused on embedded systems and VLSI design.
