Renny Edwin Fernandez is an Associate Professor in the Department of Engineering at Norfolk State University's College of Science, Engineering and Technology. His multidisciplinary research focuses on microsensing platforms for healthcare, pollution control, and agriculture applications. Education: PhD in Electrical Engineering (2010) from Indian Institute of Technology Madras His research integrates microfabrication, microfluidics, and machine learning to develop wearable biosensors, disposable electrodes, and IoT-enabled soil monitoring systems. Key trends in his recent publications include: Real-time health monitoring via flexible nanosensors Machine learning integration in agricultural IoT Plasma-aided printing of conductive nanomaterials Smart PPE systems with NFC technology Scientific Awards: Research Initiation Award (2020) for cognitive monitoring systems in extreme environments Dr. Fernandez mentors graduate and undergraduate researchers at NSU, with prior teaching experience at University of Indianapolis and Florida International University. He holds a patent for biosensor technology and has developed innovative solutions for: Salivary cortisol detection Soil nutrient analysis Cell viability assessment Smart irrigation systems
Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.
Greg Distelhorst is an Associate Professor at the University of Toronto, holding appointments in the Rotman School of Management and the Centre for Industrial Relations and Human Resources. Previously, he taught at MIT Sloan School of Management and Saïd Business School, Oxford. He holds a BA in Cognitive Science from Yale University and a PhD in Political Science from MIT. His research focuses on global trade and worker rights, as well as politics and policy in contemporary China. Key areas include multinational management, industrial relations, and the intersection of political economy with Chinese governance. His work explores how corporate practices in global supply chains affect labour standards, and how authoritarian regimes manage public discourse and accountability. Distelhorst has published in leading journals including Management Science , Organization Science , and American Journal of Political Science . His research has been recognized with awards such as the 2018 Responsible Research in Management Award and the American Political Science Association Dorothy Day Award. His research on Chinese governance examines mechanisms of public accountability under authoritarian rule, including how social media activism and grassroots participation influence policy outcomes. He has conducted fieldwork in China, including fellowships with the U.S. Fulbright Program and the Yale-China Association. Distelhorst’s work bridges management studies, political science, and labour economics, with a focus on systemic challenges in global supply chains and the socio-political dynamics of emerging markets.
Andres Kwasinski is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), part of the Kate Gleason College of Engineering. He serves as Graduate Program Director for the Ph.D. in Electrical and Computer Engineering and M.Sc. in Computer Engineering. He co-directs the Networking and Information Processing (NetIP) Lab and holds editorial roles with IEEE publications, including Chief Editor of the IEEE Signal Processing Repository and Associate Editor of IEEE Signal Processing Magazine. Education: Ph.D. and M.Sc. in Electrical and Computer Engineering from the University of Maryland, College Park (2004 and 2000), and B.Sc. in Electrical Engineering from the Buenos Aires Institute of Technology (1992). Prior to RIT, he worked at Texas Instruments, Lucent Technologies, and the University of Maryland. Research Interests: Cognitive radios, machine learning for dynamic spectrum access, 5G/6G networks, VR communications, cross-layer resource allocation, smart infrastructures, and signal processing. His work emphasizes sustainable and resilient communication systems, integrating renewable energy and AI-driven solutions. Notable Contributions: Authored/co-authored books on cooperative communications and 3D visual communications. Over 70 peer-reviewed publications, including works on energy-efficient wireless networks, microgrid integration for base stations, and deep reinforcement learning in cognitive radio. His research is funded by the NSF, Harris Corporation, and the Air Force Research Laboratory. Grants & Awards: Supported by grants from NSF and industry partners. Recognized for contributions to IEEE standards and technical leadership in signal processing and communications. Labs & Teams: Co-director of the NetIP Lab, focusing on networking, signal processing, and smart infrastructure. Collaborates on interdisciplinary projects in robotics, warehouse automation, and 5G/B5G systems.
Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.
Mark R. Greenstreet is a Professor in the Department of Computer Science at the University of British Columbia (UBC). He holds a BSc from Caltech (1981), MA (1988), and PhD (1993) in Computer Science from Princeton University. His primary research focuses on formal verification of analog and mixed-signal (AMS) circuits, VLSI design, and hybrid systems. Notable contributions include the STARI signaling technique, tools like Coho for reachability analysis, and PReach for parallel model checking. He has advised numerous graduate students and collaborators, including Brad Bingham, Chao Yan, and Yan Peng. His work has been recognized with a Best Paper Award at the ASYNC Symposium. Supported by NSERC, Intel, and Oracle, his research bridges theoretical foundations and practical challenges in circuit design and verification. He teaches courses on formal methods, computer architecture, and automata theory at UBC.
Hossein Valavi is a Lecturer and Assistant Director of Undergraduate Studies at Princeton University, contributing to advancements in computer architecture and hardware acceleration. His research focuses on in-memory computing, neural networks, and energy-efficient systems, with notable work in reconfigurable architectures and mixed-signal processing. He has received multiple teaching awards, including recognition for innovative pandemic-era Car Lab courses and collaborative work honored by the Edison Patent Award. His academic contributions span academic positions since 2018, emphasizing both research and pedagogical excellence. Key technical areas include scalable in-memory computing systems, analog neural network accelerators, and low-power matrix factorization algorithms. His work addresses critical challenges in data movement reduction and hardware-software co-design for modern computing systems. Awards: Teaching Excellence Awards (2021, 2023), Edison Patent Award (2023) Grants & Projects: Leading developments in in-memory computing accelerators and embedded microprocessor designs Research teams under his guidance have produced impactful IP in semiconductor layouts, CNN accelerators, and programmable architectures, aiming to bridge theoretical computer science with practical hardware implementations.
Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.
Onur Mutlu is a Professor of Computer Science at ETH Zurich, affiliated with the Department of Information Technology and Electrical Engineering. He also holds adjunct professorships at Carnegie Mellon University and Bilkent University. His research focuses on computer architecture, systems security, bioinformatics, and energy-efficient computing. He has pioneered work on memory-centric computing paradigms, RowHammer security vulnerabilities, and bio-inspired computing systems. He teaches courses such as Digital Design & Computer Architecture and supervises the SAFARI research group, which explores cutting-edge topics in memory systems, AI accelerators, and genomics. Recent activities include keynote talks at ISCA, HiPEAC, and IEEE conferences, emphasizing emerging hardware-software co-design principles. Key contributions include foundational work on memory reliability, cross-layer system design, and accelerating genomic data analysis. His research has been showcased in over 200 publications and industry collaborations with tech leaders like Intel, Huawei, and Micron.
Simon Yang is a Professor in the School of Engineering at the University of Guelph, part of the College of Engineering and Physical Sciences. His research focuses on artificial intelligence, robotics, sensors, control systems, and bio-inspired intelligence. He has contributed to advanced robotics applications, including mobile robot navigation, underwater vehicle control, and agricultural automation. Dr. Yang holds editorial roles for journals such as the International Journal of Robotics and Automation and IEEE Transactions on Cybernetics . His work bridges theoretical advancements with practical implementations in areas like sensor networks, machine learning, and multi-agent systems. Recent projects include developing robust control frameworks for autonomous systems, digital twin applications, and bio-inspired neural network algorithms. His research emphasizes real-world challenges in robotics, environmental monitoring, and precision agriculture, with a focus on integrating AI-driven solutions for enhanced decision-making and system reliability. Professional contributions include advisory roles in multiple journals and conference committees, reflecting his leadership in the field.
Chris Bryan is an Assistant Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He leads the Sonoran Visualization Laboratory (SVL @ ASU), focusing on data visualization, human-computer interaction, and advanced interfaces for data science. His research includes explainable AI, augmented/virtual reality, and privacy-preserving visualization techniques. Educations: Ph.D. Computer Science, University of California, Davis (2018) B.S. Computer Science, University of Arkansas (2008) Research Interests: Bryan’s work spans data visualization, human-computer interaction, explainable AI, and immersive visualization. He develops tools for collaborative analysis, privacy-aware systems, and visual analytics for complex data. Current projects involve VR/AR interfaces, bias reduction in NLP tasks, and educational visualization tools. Recent Achievements: Recipient of the 2024 and 2023 Top Five Percent Faculty Award at ASU’s Ira A. Fulton Schools of Engineering. NSF grants for privacy-preserving visualization (SaTC #2224066) and visualization education (IUSE #2216452). Multiple publications in top venues like IEEE VIS, CHI, and EuroVis, including work on differential privacy, mind wandering in visualization, and LLM prompt exploration. Advising & Grants: Advises Ph.D., MS, and undergraduate students on visualization and HCI research. Collaborates with institutions like Los Alamos National Laboratory, Phoenix Children’s Hospital, and Nankai University. Lab focuses on mentoring and preparing students for academic and industry roles in visualization and AI. Labs & Teams: Leads the SVL @ ASU, which uses advanced hardware (HTC Vive, HoloLens 2) and tools like D3.js, React, and LaTeX. The lab emphasizes interdisciplinary projects with domain experts in medicine, engineering, and security.
Emma Tegling is a Senior Lecturer (Associate Professor) at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University, Sweden. She joined the department in January 2021 and holds a prestigious WASP (Wallenberg AI, Autonomous Systems and Software Program) professorship. Her research focuses on the analysis and control of large-scale networked systems, with applications in distributed electric power networks and socio-epidemiological networks. She is actively involved in multiple research projects, supervises several PhD students, and contributes to major academic events in control theory. Education: Ph.D. in Electrical Engineering, KTH Royal Institute of Technology (2019) M.Sc. in Engineering Physics, KTH Royal Institute of Technology (2013) B.Sc. in Engineering Physics, KTH Royal Institute of Technology (2011) Emma Tegling's research centers on the fundamental limitations of distributed control, particularly in large-scale and non-normal network systems. Her work addresses critical challenges in vehicular formations, power grids, and social networks. She develops scalable control designs, consensus protocols, and optimal control strategies for complex networked environments. Her recent publications highlight breakthroughs in string stability, transient performance, and distributed optimization. The trend in her articles shows a strong focus on mathematical control theory, network dynamics, and real-world applications in socio-technical systems. Scientific Awards: WASP professorship (Wallenberg AI, Autonomous Systems and Software Program) Emma Tegling leads and co-leads several significant research grants, including WASP NEST: Learning in Networks and Dynamics of Complex Socio-Technological Network Systems. She actively supervises PhD students such as Jonas Hansson and David Ohlin, whose work has led to novel consensus protocols and optimal control formulations. Her academic leadership extends to organizing the European Control Conference and co-organizing interdisciplinary workshops on power and democracy in modern societies. She is also involved in public engagement and academic service through supervision and project coordination. Emma Tegling is a key member of the Department of Automatic Control at Lund University, contributing to research teams focused on networked systems, control theory, and AI integration. She collaborates extensively within ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and participates in cross-disciplinary labs working on AI, digitalization, and natural/artificial cognition. Her work is aligned with UN Sustainable Development Goals related to sustainable energy and resilient infrastructure.
Dr. Huadong Mo is a Senior Lecturer at the School of Systems and Computing, University of New South Wales (UNSW) Canberra, Australia. He holds a B.E. degree in automation from the University of Science and Technology of China (2012) and a Ph.D. in systems engineering and engineering management from the City University of Hong Kong (2016). Prior to his current position, he was a research associate at ETH Zurich's Reliability and Risk Engineering Lab (2016-2019) and a Lecturer at UNSW Canberra (2019-2021). Dr. Mo's educational background includes a strong foundation in systems engineering with international experience across China, Switzerland, and Australia. His career trajectory demonstrates a progression from academic research to faculty positions with increasing responsibilities in teaching and research leadership. His research focuses on enhancing the resilience, performance, and security of complex systems using learning-based algorithms, primarily in power and energy systems, cyber-physical systems, and manufacturing systems. He applies data analytics to understand system evolution under uncertainties, with particular emphasis on prognostics and health management, sustainable transportation, robust operation of power systems under extreme events, and reinforcement learning-based asset management. His work bridges theoretical advances with practical applications in critical infrastructure. Analysis of Dr. Mo's recent publications reveals a strong focus on energy systems, particularly in the integration of machine learning with power grid management, battery storage systems, and resilience against cyber threats. His research shows a clear trajectory toward increasingly complex system integration, with growing emphasis on multi-vector energy communities, cross-domain prediction, and uncertainty-aware energy management. The interdisciplinary nature of his work spans electrical engineering, computer science, and operations research. 2024 IEEE SMC Early Career Award 2023 Visiting Research Fellowship (Jean d'Alembert Pour Fellowship) Gold Medal in 2024 China International College Student Innovation Competition (as supervisor) Arc PGC Supervisor Award (2021) IEEE SMC Outstanding Chapter Award (2021) Alumni Achievement Award from City University of Hong Kong (2019) Dr. Mo actively supervises numerous HDR students working on cutting-edge research topics including battery health monitoring, quantum control, reinforcement learning for power systems, and explainable AI for energy management. He leads multiple significant research grants totaling over 3 million AUD, including projects funded by ARC, Energy Innovation Fund, and international collaborations with institutions like ETH Zurich, Cambridge, and Tsinghua University. His research group maintains strong international connections, facilitating student exchanges and collaborative research. As Postgraduate Course Coordinator of Systems Engineering and Chair of IEEE SMC ACT Chapter, Dr. Mo plays a significant role in academic leadership and professional community building. His research team collaborates with industry partners on practical implementations of their theoretical work, particularly in the energy sector.
Stephen Brown is a Professor at the University of Toronto within the Department of Electrical and Computer Engineering under the Faculty of Applied Science and Engineering. He earned his B.A.Sc and M.A.Sc in Electrical Engineering from the University of Toronto and New Brunswick, respectively, and a Ph.D. in Electrical Engineering from the University of Toronto (1992). His career spans over two decades in academia and industry collaboration. Education : B.A.Sc, University of New Brunswick M.A.Sc, University of Toronto Ph.D, University of Toronto Professor Brown’s research focuses on field-programmable gate arrays (FPGAs) , CAD algorithms , and computer architecture , with applications in machine learning and high-level synthesis . He is a principal investigator in the LegUp project , an open-source high-level synthesis framework that bridges software and hardware design. His work also extends to optimizing FPGA interconnect delays, physical synthesis, and logic block architectures. Key trends in his publications include advancements in high-level synthesis tools, FPGA architecture evaluation, and timing-driven design methodologies. His contributions often intersect with design automation , resource sharing , and embedded systems . Scientific Awards : NSERC 1992 Doctoral Prize Hart Professorship for Innovation in Teaching (2017) Multiple teaching excellence awards Best Paper Award at ICCAD 1990 Best Paper Award nomination at Canadian Conference on VLSI (1989) As Director of the FPGA University Program at Intel Corporation, he leads industry-academia initiatives. His teaching portfolio includes courses like ECE253 (Digital Logic) and ECE1733F (Switching Theory).
Yoon Chae is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), joining the faculty in January 2025. His work bridges wireless networking and low-power IoT systems, with a focus on millimeter-wave technologies and backscatter communication. Education: PhD in Computer Science, George Mason University, 2024 MS in Computer Science, George Mason University, 2022 MS in Electrical and Computer Engineering, University of Minnesota - Twin Cities, 2012 BS in Electrical Engineering, Yonsei University, 2012 His research centers on enabling reliable and high-speed mmWave backscatter by leveraging the unique properties of millimeter waves, with applications in vehicular networks, IoT, and spectrum efficiency. He has pioneered techniques using commodity WiFi and FMCW radar for backscatter communication, significantly advancing the field of low-power wireless systems. The recent publications highlight a strong trend in utilizing commodity hardware (like WiFi and radar) for novel wireless sensing and communication. His work spans mmWave backscatter, vehicular networking using street view imagery, and interference management between ZigBee and WiFi. The research demonstrates innovation in spectrum reuse, low-power design, and integration of real-world data for network optimization. Scientific Awards: Best Paper Award, ACM MobiSys 2022 SIGMOBILE Research Highlight, 2023 Yoon Chae actively contributes to the academic community as a reviewer for top journals including IEEE Transactions on Mobile Computing, IEEE/ACM Transactions on Networking, and ACM Transactions on IoT, as well as conferences like INFOCOM, MobiCom, and SenSys. He serves on program committees and has held leadership roles such as Registration Co-chair for ICNP'25 and Artifact Evaluation Committee for MobiSys'25. He is currently building a research lab and seeking motivated Ph.D. students and research interns to join his team. He has been involved in collaborative projects with Prof. Parth Pathak (George Mason) and Prof. Song Min Kim (KAIST), and his work has been presented at major venues including NSDI, MobiCom, MobiSys, and SenSys. His lab focuses on experimental systems design, wireless sensing, and next-generation IoT networking.
