Keith Winstein is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Electrical Engineering. His research focuses on creating innovative networked systems, particularly in communication, compression, and computing. Notable projects include Mosh (an interactive remote shell for mobile clients), Puffer (a video-streaming platform), Lepton (a compression tool), Mahimahi (network emulators), and the gg framework for distributed computing. He has received prestigious awards such as the SIGCOMM Rising Star Award, Sloan Research Fellowship, and NSF CAREER Award. Winstein's academic journey includes undergraduate and graduate studies at MIT. Before academia, he worked at The Wall Street Journal as a reporter and at Ksplice (now part of Oracle), where he held roles in product management and business development. His research spans network protocols, video streaming optimization, cloud computing, and machine learning applications in networking. His work emphasizes practical systems that bridge theoretical concepts with real-world implementation. Recent projects explore computation-centric networking, in-network performance enhancements, and low-latency video streaming. He advocates for reproducible experiments through tools like Mahimahi and has contributed to open-source software widely used in academia and industry. Key Projects: Mosh, Puffer, Lepton, Mahimahi, gg Awards: SIGCOMM Rising Star Award, Sloan Fellowship, NSF CAREER Expertise: Networked Systems, Compression Algorithms, Distributed Computing
Dr. Hasan Abbas is a Senior Lecturer at the University of Glasgow's James Watt School of Engineering, affiliated with the Electronic & Nanoscale Engineering department. He holds a BSc from the University of Engineering and Technology, Lahore (2009) and a PhD from Texas A&M University (2017) under a Fulbright scholarship. Previously, he worked as a lecturer at UET Lahore (2009–2012) and a postdoctoral researcher at Texas A&M Qatar (2018–2019). His research focuses on AI-driven electromagnetic design, real-time microscopy techniques, and sustainable antenna technologies. Research interests include plasmonic microscopy for biological systems, AI-powered antenna design, and energy-efficient THz communication. He leads projects funded by grants and collaborates with industry partners. Abbas serves as Secretary of the IEEE AP/MTT Scotland Chapter and an executive member of the IET Electromagnetics Professional Network. Recent work emphasizes RF sensing for healthcare (e.g., gesture recognition, vital sign monitoring) and terahertz applications in biomedical imaging. His contributions span 99+ publications, including peer-reviewed journals and conferences like EuCAP, IEEE AP-S, and IEEE RadarCon.
Dipankar Mitra is an Assistant Professor in the Department of Computer Science & Computer Engineering at the University of Wisconsin-La Crosse (UW-L). He holds a Ph.D. and M.S. in Electrical and Computer Engineering from North Dakota State University (NDSU), and a B.Sc. in Electrical and Electronic Engineering from Chittagong University of Engineering and Technology, Bangladesh. His research focuses on transformation optics/electromagnetics, metamaterials, 3D-printed antennas, and RF circuits for IoT and biomedical applications. He has published over 50 peer-reviewed articles and is a reviewer for several IEEE journals and conferences. Education: Ph.D., Electrical and Computer Engineering, NDSU (2021) M.S., Electrical and Computer Engineering, NDSU (2016) B.Sc., Electrical and Electronic Engineering, Chittagong University of Engineering and Technology (2013) Research Interests: Mitra’s work spans transformation-based antenna design, metamaterials, RF MEMS for IoT, 3D-printed flexible electronics, and electromagnetic applications in medicine. Recent projects include non-invasive physiological monitoring and microwave-based tissue characterization. Awards: NDSU Doctoral Dissertation Fellowship (2020-2021) UW-L Early Start Award Nominated as NDSU GTA of the Year (2019-2020) Teaching & Grants: Teaches courses in computer architecture, digital logic, and software design. His research is funded by UW-L, NASA, AFRL, and industry partners like NextFlex/Uniqarta. He collaborates with the U.S. Air Force Research Lab and Mayo Clinic. Labs & Teams: Active in the Applied Electromagnetics Lab at NDSU and the RFIC Lab. Leads UW-L projects on conformal antennas and wearable sensors.
Wayne Burleson is a Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on embedded security, hardware security, VLSI circuit design, and VLSI architectures for digital signal processing (DSP), cryptography, and graphics. He has pioneered work on physically unclonable functions (PUFs), remote power attacks on FPGAs, and thermal management strategies in chip multiprocessors. He holds a B.S. and M.S. from MIT (1983) and a Ph.D. from the University of Colorado (1989). His honors include the 2011 IEEE Fellow distinction and the 1999 Ben Dasher Award for Best Paper. He is actively involved in IEEE, ACM, ASEE, and Sigma Xi societies. Burleson’s recent work addresses grand challenges in embedded security, including IoT device vulnerabilities, medical device cybersecurity, and mitigation of hardware Trojans. His lab explores novel approaches in reconfigurable hardware security, energy-efficient asynchronous interconnects, and adaptive systems-on-chip.
Prof. Simona Lohan is a Full Professor at the Electrical Engineering unit of Tampere University, Finland, and a visiting professor at Universitat Autònoma de Barcelona. Her research focuses on wireless positioning, GNSS algorithms, and wearable computing, with an emphasis on privacy-aware localization and interference mitigation. She leads the Signal Processing for Wireless Positioning research group and coordinates the H2020 MSCA European Joint Doctorate A-WEAR (2019-2023). She holds a PhD in Telecommunications from Tampere University of Technology (2003), and has authored/co-authored over 250 peer-reviewed publications. Education background includes an MSc in Electrical Engineering from Polytechnics University of Bucharest (1997), a DEA in Econometrics from École Polytechnique, Paris (1998). Her research spans satellite navigation (Galileo/GPS/GLONASS), UMTS/WCDMA positioning, 5G positioning, IoT localization, and medical applications of positioning systems. She is an associate editor for the RIN Journal of Navigation and IET Journal on Radar, Sonar, and Navigation . Key projects include the H2020 A-WEAR project (wearable health tech), Academy of Finland ULTRA (2020-2022), and SJU GATEMAN (2018-2019). Her work addresses global navigation challenges through LEO-PNT constellations, low-cost positioning solutions for Africa, and fusion of 5G/mmWave radar for airport surveillance. She has pioneered datasets like TUJI1 for indoor localization and developed open-source tools like SyDR for GNSS algorithm benchmarking. Her research themes include: LEO satellite-based positioning systems Anti-spoofing techniques for GNSS RF fingerprinting for device identification Privacy-preserving localization algorithms IoT and industrial internet applications Embedded signal processing systems Her team's innovations bridge communication and navigation domains, with applications in aviation, healthcare, and smart infrastructure. Recent work emphasizes energy-efficient GNSS signal processing and multi-constellation PNT solutions for global accessibility.
Yan Yao is a Professor at Hefei University of Technology, School of Computer Science and Information Engineering, with a distinguished research career spanning over two decades. Her work bridges theoretical foundations with practical implementations across wireless communications, cloud computing, and medical informatics, demonstrating both technical depth and interdisciplinary versatility. Her primary research domains include: Wireless Communications - Pioneering work on distributed wireless communication systems, MIMO technologies, and physical layer security Cloud and Edge Computing - Innovative resource allocation mechanisms using game theory and auction models Medical Informatics - Applying machine learning to critical healthcare challenges including sepsis prediction and diabetic retinopathy analysis Blockchain and Security - Developing secure data sharing frameworks for industrial IoT applications Analysis of Yan Yao's publication trajectory (2002-2025) reveals a strategic evolution from foundational wireless communications research to interdisciplinary applications. Her early work (2002-2008) established her expertise in distributed wireless systems architecture and MIMO technologies, frequently collaborating with Tsinghua University researchers. More recently (2018-2025), she has expanded into cloud-edge computing, blockchain applications, and healthcare analytics, demonstrating remarkable adaptability while maintaining technical rigor. Her most impactful recent contributions integrate multiple domains to solve complex real-world problems, such as applying game theory to cloud-edge resource allocation and developing machine learning solutions for clinical prediction. Yan Yao's research impact is reflected in her consistent publication record in high-impact venues including IEEE Transactions, BMC Medical Informatics and Decision Making, and top-tier conferences. Her work shows a clear progression from technical contributor to research leader, with increasing emphasis on interdisciplinary applications that address significant societal challenges.
George Panagopoulos is an Assistant Professor at the School of Electrical and Computer Engineering (ECE) of the National Technical University of Athens (NTUA). He holds a BSc in Computer and Communications Engineering from the University of Thessaly (2006) and a PhD in Electrical and Computer Engineering from Purdue University (2012), specializing in semiconductor device variability and reliability modeling. His research focuses on analog/RF circuit design for communication systems, including front-end wireless systems, device characterization, and energy-efficient co-design strategies. He also explores spin-based devices for machine learning applications. At Intel Corp. (2012–2018), he led device modeling teams for analog/high-frequency applications, contributing to WiFi, Thunderbolt, Bluetooth, RADAR, and CPU timing solutions. He has participated in over 20 tape-outs across semiconductor technologies from 65nm to 1.8nm. Awards: Bakalas Scholarship, State Scholarships Foundation Award, Technical Chamber of Greece Award, Public Electricity Enterprise S.A. Award Teaching: Undergraduate courses in electronics, digital systems, VLSI design, and analog systems; postgraduate course on integrated circuit design for telecommunications He actively promotes academic-industry collaboration and mentors students in integrated circuit design.
Dr. Anteneh Girma is a Professor and Cybersecurity Program Director at the University of the District of Columbia (UDC), leading the School of Engineering and Applied Sciences' Computer Science and Information Technology department. He has held academic roles at UDC since 2019, previously serving as Associate Professor and now full Professor, while concurrently holding adjunct positions at institutions like the University of Maryland and Montgomery College. Dr. Girma earned his Ph.D. in Computer Science/Cybersecurity from Howard University (Magna Cum Laud). Educational Background: Ph.D. in Computer Science/Cybersecurity, Howard University Research Interests: AI-driven cybersecurity solutions for IoT, cloud, and edge computing environments Machine learning for threat detection (e.g., malware, DDoS, insider threats) Cryptography and authentication mechanisms Cybersecurity policy and workforce development Grants & Impact: National Science Foundation grant for cybersecurity workforce development Department of Defense-funded machine learning research NSA support for establishing UDC as a Center of Academic Excellence in Cybersecurity Awards & Leadership: 2024 Leadership Award from UDC’s School of Engineering Founder of UDC’s Cybersecurity Program (launching in Fall 2025) Best Research Paper Award (2015) and international cybersecurity recognitions Teaching & Mentorship: Teaches advanced courses in cybersecurity governance, IoT security, and cryptography Mentors Ph.D. and M.S. students in cybersecurity research Key Contributions: Developed hybrid models for DDoS detection in cloud environments Advocated for blockchain-based election security systems Published over 30 peer-reviewed articles on cybersecurity topics
Nicholas Mastronarde is an Associate Professor and Associate Chair in the Department of Electrical Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. He leads the WIRED Lab (Wireless Networking, Reinforcement Learning, and Drones Lab), which conducts cutting-edge research in wireless communications, AI/ML for networks, and UAV systems. Ph.D. in Electrical Engineering from UCLA (2011) M.S. in Electrical Engineering from UC Davis (2006) B.S. in Electrical Engineering from UC Davis (2005, Highest Honors) Dr. Mastronarde's research focuses on wireless networking, reinforcement learning, and drone systems. His work spans AI/ML for wireless networks (reinforcement learning for energy harvesting wireless sensors, IoT, 5G scheduling), NextG wireless networks (active-passive coexistence, software-defined networking, mmWave networks), and modeling, simulation, and field experimentation for future networks. His research has significant applications in spectrum coexistence, digital twin technology, and UAV networking, with emphasis on practical implementation through platforms like UB-ANC and NeXT. His recent publications demonstrate a strong trend toward integrating AI/ML techniques with wireless communications to develop more efficient, adaptive, and intelligent network systems, particularly focusing on spectrum coexistence between satellite and terrestrial networks, digital twin-enabled network simulation, and reinforcement learning applications for next-generation wireless systems. Dimitris N. Chorafas Foundation Award (2011) UCLA Graduate Division Dissertation Year Fellowship (2010-2011) IBM Research Watson Lab Graduate Intern Fellowship (2010) 2020 SEAS Senior Teacher of the Year Award UB's Teaching Innovation Award 2022 Dr. Mastronarde has successfully advised numerous graduate students, including current PhD candidates and multiple alumni who have gone on to work at companies like Qualcomm. His research is supported by prestigious organizations including the US Air Force Research Laboratory, US SOCOM, the National Science Foundation (including the NSF SWIFT award), GE Aviation, US Ignite, ARMOR-IIMAK, and SUNY. His extensive publication record includes over 100 peer-reviewed articles in top-tier journals and conferences. As the leader of the WIRED Lab, Dr. Mastronarde oversees several key research initiatives including UB-ANC (unmanned aerial vehicle networking simulation/emulation), RF-SITL (software-defined transceiver and channel emulation), NeXT (digital twin-enabled multi-fidelity network simulator), and UnionLabs (cloud-based platform for testbed sharing). His team consists of current PhD students, research scientists, and undergraduate researchers working collaboratively on cutting-edge wireless communication projects.
Adam Wolisz is a full Professor of Electrical Engineering and Computer Science at Technische Universität Berlin (TU Berlin), where he founded and led the Telecommunication Networks Group (TKN) from 1993 until 2018. He also served as Executive Director of the Institute for Telecommunication Systems (2001-2018), inaugural Dean of the Faculty of Electrical Engineering and Computer Science (2001-2003), and is currently an Einstein Center Digital Future (ECDF) Fellow. Since 2005 he has held an adjunct appointment at the University of California, Berkeley, and is presently a visiting researcher at the Berkeley Wireless Research Center. Education Dipl.-Ing. in Control Engineering, Silesian Technical University, Gliwice (1972) Dr.-Ing. in Computer Engineering, Silesian Technical University, Gliwice (1976) Habilitation in Computer Engineering, Silesian Technical University, Gliwice (1983) Research Interests Professor Wolisz has spent five decades advancing the architectures, protocols, and performance evaluation of communication networks. His current work centres on mobile multimedia communication , wireless sensor networks , and cognitive/cooperative wireless systems . Methodologically, he combines rigorous analytical modelling with large-scale simulation and real-world experimentation, frequently within the open testbeds run by TKN. A cross-cutting theme is Quality of Service (QoS) —from early work on real-time operating systems and industrial field-buses to recent studies on QoE-driven adaptive video streaming and ultra-reliable low-latency vehicular communications. His group is internationally recognised for contributions to reinforcement-learning-based MAC scheduling , spectrum sharing between LTE-U and WiFi , and energy-efficient protocol design . Publication Impact & Trends Across more than 200 refereed publications, two clear trajectories emerge: (1) a continuous evolution from wired network modelling (WDM optical networks, ATM, early Internet QoS) toward fully wireless and mobile settings, and (2) an increasing reliance on machine-learning techniques to tackle uncertainty and dynamics in dense, heterogeneous wireless environments. Recent papers exploit deep reinforcement learning for scheduling, federated learning for context-aware services, and transfer learning for realistic mobile-app testing. Scientific Awards & Recognition Best Paper Awards: IEEE WoWMoM 2020, IEEE INFOCOM CNERT 2019, ACM MSWiM 2017, IEEE EW 2017, IFIP WD 2017, IEEE EW 2009 Best Demo Award: ACM/IEEE IPSN 2014 (EVARILOS benchmarking platform) Senior Member, IEEE & IEEE ComSoc; Member, ITG (VDE); Steering Board, GI/ITG KuVS Doctoral Advising, Projects & Funding Since establishing TKN in 1993, Professor Wolisz has supervised over 60 completed PhD dissertations . Current and recent funding includes the DFG Collaborative Research Centre 1053 “MAKI”, DFG priority programme “SmartSynch”, EU projects (e.g., Fed4FIRE+, H2020 5G-Infrastructure), and industrial collaborations with Deutsche Telekom, Nokia, and Rohde & Schwarz. The group operates large-scale indoor and outdoor testbeds (FIT/IoT-LAB Berlin, TKN campus testbed, EVARILOS benchmarking framework) that are open to external researchers. Laboratories & Teams At TU Berlin, Professor Wolisz heads the Telecommunication Networks Group (TKN) , comprising more than 25 researchers (post-docs, PhD candidates, MSc students, technical staff). TKN maintains four major labs: the Wireless Communication Lab (software-defined radios, mmWave, IEEE 802.11ax/ay), the Sensor Networking Lab (IoT, 6TiSCH, energy harvesting), the Networking Testbed (optical backhaul, network softwarisation), and the QoE & Multimedia Lab (adaptive streaming, immersive media). Multiple spin-off companies have emerged from TKN research, most recently “Wolisz Technologies” (founded 2020) commercialising AI-driven Wi-Fi optimisation.
Paul R. Prucnal is a Professor of Electrical and Computer Engineering at Princeton University and an Associated Faculty member in the Princeton Materials Institute (PMI). He directs the Lightwave Communications Research Laboratory, where he leads cutting-edge research in photonics, optical communications, and neuromorphic computing. Education: Ph.D., Columbia University, 1979 M.Phil., Columbia University, 1978 M.S., Electrical Engineering, Columbia University, 1976 A.B., Math and Physics, Bowdoin College, summa cum laude, 1974 Professor Prucnal's research focuses on ultrafast optical techniques with applications to communication networks and signal processing. His group investigates several key areas including physical (optical) layer network security, optical code division multiple access (CDMA), nonlinear optical signal processing for ultrafast networks, optical cancellation of RF interference, and the development of photonic neurons that operate a billion times faster than biological neurons. His work bridges the gap between photonics device physics and neural networks, pioneering the field of neuromorphic photonics. His recent publications reveal a strong trend toward neuromorphic photonics and brain-inspired optical computing. The research spans from fundamental optical physics to practical applications in secure communications and ultrafast signal processing, with emphasis on photonic neural networks, optical encryption techniques, silicon photonics implementations, and optical systems that emulate biological neural functions. Scientific Awards: National Academy of Inventors Fellow (2017) The President's Award for Distinguished Teaching, Princeton University (2015) Lifetime Achievement Award for Excellence in Teaching, Engineering Council, Princeton University (2015) School of Engineering and Applied Science Distinguished Teaching Award, Princeton University (2009) Fellow of the OSA (1997) Fellow of the IEEE (1992) Rudolf Kingslake Medal and Prize, SPIE (1990) Professor Prucnal has mentored numerous graduate students including Eric Blow, Eli Doris, Thomas Ferreira de Lima, Yusuf Jimoh, Hyuma Umeda, Yuxin Wang, Ben Wu, Lei Xu, and Jiawei Zhang. His research has been supported through collaborations with government and industrial research laboratories, focusing on next-generation optical signal processing, computing, and communications systems. His lab has produced significant innovations including graphene-based laser neurons and optical implementations of biological neural processing. The Lightwave Communications Research Laboratory offers students opportunities to work on innovative projects at the intersection of photonics, communications, and neural computing. Current research includes developing photonic neurons for machine learning applications, optical security techniques for fiber networks, and neuromorphic photonic systems that emulate visual, auditory, and motor functions found in biological organisms.
Dr. Linke Guo is an Associate Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University. He received his Ph.D. (2014), M.S. (2011), and B.Eng. (2008) in Electrical and Computer Engineering from the University of Florida and Beijing University of Posts and Telecommunications. Dr. Guo serves as Associate Editor for three prestigious IEEE journals and holds a 3-year sole PI grant from the Army Research Office. Education: Ph.D., Electrical and Computer Engineering, University of Florida (2014) M.S., Electrical and Computer Engineering, University of Florida (2011) B.Eng., Electronic Information Science and Technology, Beijing University of Posts and Telecommunications (2008) Dr. Guo's research focuses on security and privacy in wireless networks, mobile crowdsensing, IoT, eHealth systems, and machine learning algorithms. His work addresses challenges in federated learning, semantic communications, and robust resource orchestration across heterogeneous networks. His recent publications span top-tier venues like IEEE Symposium on Security and Privacy (Oakland), ACM CCS, ICML, NeurIPS, and INFOCOM. Funded by NSF and ARO grants, his research targets adversarial robustness in NextG wireless systems, spectrum efficiency, and sustainability in computing. Scientific Awards: Best Paper Award - Globecom 2015 Symposium on Communication and Information System Security Distinguished Best Paper Runner-up - MADWEB Workshop in NDSS 2024 Distinguished TPC Member - IEEE INFOCOM 2018 Dr. Guo advises Ph.D. students including Sihan Yu (now at Rowan University) and Xiaonan Zhang (now at Florida State University). His professional service includes editorial roles and program chair positions at major conferences.
Shigeru Shimamoto is a Professor at Waseda University's School of Fundamental Science and Engineering, Faculty of Science and Engineering. His research spans wireless communication systems, biomedical sensing technologies, and intelligent transportation solutions. Since 2014, he has led the Communication and Computer Engineering department at Waseda University, previously serving as Director of the Global Information and Telecommunication Institute (2020-2024). 2008: Visiting Professor at Stanford University's Electrical Engineering 2000-2002: Research Assistant at University of Electro-Communications Key research areas include: Wireless Communication: OTFS modulation, NOMA, RIS-aided systems, and microwave-based vital sensing Smart Healthcare: Non-contact blood pressure monitoring, SpO2 estimation using microwave reflection Transportation Optimization: On-street parking analysis, traffic flow modeling, and energy-efficient vehicular networks Awarded the 2024 Commendation for Science and Technology from MEXT, his work demonstrates strong interdisciplinary impact combining communication engineering with medical applications. Recent publications focus on machine learning integration in gesture recognition, vehicular detection, and resource allocation for autonomous systems.
Yuk Fai Leung is an Associate Professor in the Department of Biological Sciences at Purdue University, affiliated with multiple institutions including the Purdue Institute for Integrative Neuroscience and the Purdue Center for Drug Discovery. He holds visiting roles at Shantou University Medical College and serves on scientific advisory boards for institutions in China and Hong Kong. Current appointments: Affiliate Faculty at Purdue Institute for Integrative Neuroscience (2016–present), Visiting Professor at Shantou University (2012–present), Adjunct Assistant Professor at Indiana University School of Medicine (2011–present) Research focuses on drug discovery for retinal degeneration using zebrafish models, with two main directions: rapid drug discovery and gene network analysis. His lab has pioneered high-throughput screening methods and collaborated on spinal cord injury research. Key tools include the LeungLab, PULSe, and Zebrafish Information Network platforms. Major awards include the 2018 Outstanding Professor of Basic Science Award and multiple teaching accolades. His work has been supported by grants from NIH, Hong Kong Health Bureau, and others. Notable students include Beichen Wang (2024 travel grants and poster awards) and Emre Coskun (2018 drug discovery projects). Labs/teams: Active collaborations include Dr. Daniel Suter’s lab (spinal cord injury) and Dr. Rosa Chan’s lab (radiation studies). The lab participates in initiatives like the FLAME zebrafish consortium and organizes conferences on zebrafish models.
Razin Ahmed is an Adjunct Lecturer at the School of Engineering, specifically within the Department of Electrical, Electronic and Computer Engineering at The University of Western Australia. His research focuses on renewable energy systems, particularly photovoltaic power forecasting and machine learning applications. He has published extensively on topics such as LSTM-based ensemble methods for solar power prediction and advanced antenna design for wireless communication standards like IEEE 802.11p. Key research interests include photovoltaics, solar energy optimization, artificial neural networks, and multiband antenna technologies. His work contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG 7) and climate action (SDG 13). Ahmed's methodologies often integrate machine learning with traditional engineering approaches to enhance energy system efficiency and predictive accuracy. His most recent publications (2022) highlight advancements in adaptive weighting techniques and data segmentation for photovoltaic forecasting, while earlier works (2014–2016) explore innovative antenna designs using metamaterials and slot structures. Collaborations have focused on optimizing renewable energy systems and improving wireless communication infrastructure. Notably, his 2020 review article evaluates state-of-the-art PV forecasting techniques, influencing both academic and industry practices. While no specific awards or grants are listed, his high citation count (954 citations) and h-index (9) reflect significant scholarly impact in energy and electronics fields.
