Cicek Cavdar is an Associate Professor at the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology , Sweden. She leads the Intelligent Network Systems research group and specializes in Telecommunication Networks , with a focus on Beyond 5G/6G Mobile Networks , Energy Efficiency , and AI-Assisted Network Management . PhD in Computer Science (2009) from University of California, Davis and Istanbul Technical University Her research spans Cell-Free Massive MIMO , Reconfigurable Intelligent Surfaces (RIS) , UAV Communication Systems , and Green Network Technologies . She actively contributes to 6G Network Architecture and Non-Terrestrial Networks , including satellite and aerial systems. Recent publications highlight AI-driven network optimization for handover management, energy-aware resource allocation , and multi-agent reinforcement learning in complex communication environments. She teaches advanced courses in Communication Systems , Machine Learning , and Software Engineering at KTH.
Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.
Bahar Asgari is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in CS, UMIACS, and ECE, and directs the Computer Architecture and Systems Lab (CASL). Her research focuses on domain-specific architecture design, near memory processing, and reconfigurable computing. Before joining UMD, she worked at Google, contributing to system optimization and memory utilization strategies. Education: Ph.D., Georgia Institute of Technology, 2021. Research Interests: Developing energy-efficient architectures for sparse problems, scientific computing acceleration, FPGA optimizations, and reconfigurable hardware systems. Her work addresses challenges in memory efficiency, parallelization, and hardware-software co-design across domains like machine learning and IoT. Key Awards: 2024 UMD Excellence in Teaching Award 2023 DOE Early Career Award Advising & Grants: Supervises 9 graduate students and leads projects sponsored by NSF, DOE, and industry partnerships. Her lab explores systolic arrays, near-data processing, and fault-tolerant computing. Labs/Teams: Directs CASL, which builds next-generation computing systems through hardware innovation and algorithm-architecture co-design.
Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.
Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.
I-Hong Hou is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a B.S. in Electrical Engineering from National Taiwan University (2004), and M.S./Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign (2008/2011). His research focuses on wireless networks, cloud/edge computing, and machine learning with notable contributions to real-time systems and network optimization. Education : B.S., Electrical Engineering, National Taiwan University, 2004 M.S., Computer Science, University of Illinois at Urbana-Champaign, 2008 Ph.D., Computer Science, University of Illinois at Urbana-Champaign, 2011 Research Highlights : Hou’s work emphasizes Age of Information (AoI) , distributed learning, and scheduling algorithms for edge computing. He has pioneered frameworks integrating machine learning with network protocols, such as deep reinforcement learning for restless bandits and second-order optimization for wireless systems. His methods address real-time communication challenges in multi-hop networks and dynamic environments. Awards : Best Paper Awards at ACM MobiHoc (2017, 2020) Best Student Paper, WiOpt 2017 C.W. Gear Outstanding Graduate Student Award, UIUC Advising & Grants : Advised PhD student Siqi Fan (graduated 2024). His research has been supported by grants exploring edge-cloud reconfiguration, real-time video delivery, and neural Whittle index networks. Recent work includes optimizing freshness of information in multi-user systems and developing threshold-optimal policies for complex decision-making. Labs/Teams : Leads the Computer Engineering and Systems Group (CESG) at Texas A&M, collaborating on projects blending networking, machine learning, and distributed systems.
Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.
Aditya K. Jagannatham is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). With expertise in wireless communications and signal processing, he has established himself as a leading researcher in 5G/6G technologies, MIMO systems, and cognitive radio networks. His educational background includes: PhD in Electrical and Computer Engineering from UC San Diego (2007) M.S. in Electrical and Computer Engineering from UC San Diego (2004) B.Tech. in Electrical Engineering from IIT Bombay (2001) Professor Jagannatham's research primarily focuses on next-generation wireless communication systems, with special emphasis on 5G and 6G technologies. His work spans OTFS modulation, Terahertz communications, Visible Light Communication (VLC), Intelligent Reflecting Surface (IRS) technology, Massive MIMO, mmWave MIMO, Non-Orthogonal Multiple Access (NOMA), and Filter-Bank Multi-Carrier (FBMC) systems. His research integrates theoretical analysis with practical implementation challenges, addressing critical issues in modern wireless networks. His recent publications demonstrate a strong trend toward advanced signal processing techniques for next-generation wireless systems, particularly focusing on Sparse Bayesian Learning approaches for channel estimation, cooperative communication systems with energy harvesting capabilities, and millimeter wave MIMO technologies. His work bridges theoretical communication theory with practical implementation challenges in emerging wireless standards. Professor Jagannatham has received numerous prestigious awards and fellowships: Arun Kumar Endowed Chair Professorship (2019) Qualcomm Innovation Fellowship (2018) P.K. Kelkar Young Faculty Research Fellowship for excellence in research (2015-2018) IEEE Signal Processing Society travel grant to attend ICASSP 2015 Gopal Das Bhandari Memorial Distinguished Teacher Award (2012-13) Cal(IT)2 fellowship for graduate study at UC San Diego As an educator, Professor Jagannatham has received commendation letters from the Director of IIT Kanpur for excellence in teaching courses including EE624 Information and Coding Theory, EE670 Wireless Communications, and EE320 Principles of Communication Systems. His research has attracted significant funding, though specific grant details are not provided in the available information. He likely supervises graduate students working on cutting-edge wireless communication research. Professor Jagannatham is based in the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, where he leads research in wireless communications. His work appears to be closely connected with the Center for Developing Intelligent Systems (CDIS) and other research centers at IIT Kanpur focused on next-generation communication technologies.
Dr. Mohammad Zulkernine is a Full Professor and Canada Research Chair in Cyber-Physical System Security at Queen’s University’s School of Computing (Faculty of Arts and Science). He leads the Queen’s Reliable Software Technology (QRST) research group and directs the Queen’s Centre for Security & Privacy. His research focuses on secure software systems for cyber-physical systems, including autonomous vehicles, IoT, and cloud computing. Dr. Zulkernine holds a cross-appointment in Electrical and Computer Engineering and is a licensed Professional Engineer in Ontario. Education: BSc (Bangladesh BUET), MEng (Japan), PhD (University of Waterloo). He joined Queen’s in 2003 and has held sabbaticals at University of Trento, Italy and Irdeto Canada. He has published over 250 papers, led 35+ research projects, and supervised 120+ students. Awards include Canada Research Chairs (Tier I and II), Queen’s Excellence in Graduate Supervision Award, and Distinguished Supervision Award from the School of Computing. Research Interests: Cyber-Physical System Security, Software Reliability, IoT Security, Vehicle Networks, Secure Software Engineering, and Cybersecurity Risk Assessment. Active industry collaborations include EU-funded projects and Canada-Africa initiatives.
Indian Institute of Technology Hyderabad (IITH)India
Antony Franklin is an Associate Professor and Head of the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IITH), India. He leads the Networked Wireless Systems (NeWS) Lab and is actively involved in 5G research, wireless networks, and cybersecurity. He earned his Ph.D. from IIT Madras in 2010. Education: Ph.D. in Computer Science and Engineering, IIT Madras, 2010 Research Interests: His research spans wireless networks , 5G systems , mobile edge computing , low-latency transport protocols , and cybersecurity . He focuses on next-generation mobile systems, especially 5G, aiming to meet the demands of ultra-low latency, high data rates, and quality of experience for services like IoT, autonomous driving, and VR. He has been involved in developing testbeds and prototypes to validate real-world performance of 5G technologies, including network slicing, LTE-WiFi integration, and edge computing. Scientific Awards: Best Academic Demo Award, IEEE COMSNETS 2018 Second Best Paper Award, IEEE ANTS 2017 Grants & Projects: Network Slice Life-cycle Management for 5G Mobile Networks – SPARC, MHRD (2019–2021) DNS/IPv6 for IoT Security – NASSCOM (2018–2019) CCRAN: Energy Efficiency in Cloud RAN – Intel India (2018–2021) End-to-End 5G Test-Bed – DoT, GoI (2018–2021) M2SMART Smart Cities – SATREPS (2018–2023) Ultra-Reliable Low Latency Protocols – STINT, Sweden (2017–2018) Low Latency 5G Protocols – SERB ECR (2016–2019) Lab & Team: He leads the NeWS Lab (Networked Wireless Systems) at IITH, which focuses on 5G, wireless networks, and IoT systems. The lab is actively involved in developing real-world testbeds and publishing in top-tier conferences and journals.
Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.
Xing Gao is an Assistant Professor at the University of Delaware, affiliated with the Department of Computer and Information Sciences and jointly with the Department of Electrical and Computer Engineering. His office is located at 316B FinTech Innovation Hub on the STAR Campus. He holds a PhD from the College of William and Mary (2018) and a BS from Beijing Institute of Technology (2011). Research Interests: Cybersecurity in Software Supply Chain, Web 3, High-Performance Computing, Large Language Models, with a focus on Security, Cloud Computing, and Mobile Computing. Education: PhD | 2018 | College of William and Mary BS | 2011 | Beijing Institute of Technology His recent work explores cybersecurity vulnerabilities in cloud gaming services (CCS'22), container registries (USENIX-SEC'22), and software supply chains, with specialized attention to GPU cache attacks (USENIX-SEC'24) and Ethereum smart contracts (WWW'24). His research spans theoretical and applied aspects of system security, including CI/CD pipelines (CCS'24), SDN backdoors (INFOCOM'23), and hardware-level threats (ACSAC'21). Scientific Awards NSF CAREER Award (2024) NSF CRII Award (2020) NDSS Distinguished Poster Award (2016) He serves as Registration Chair for ACM/IEEE Symposium on Edge Computing (2023) and Publicity Co-Chair for IEEE Conference on Communications and Network Security (2022). He is actively involved as TPC Member in multiple top-tier conferences including USENIX Security (2026,2025), CCS (2026,2025,2024), and IEEE DSN (2024). He has also reviewed for journals like IEEE Transactions on Dependable and Secure Computing. Labs & Teams X-Lab at the University of Delaware, a research group focused on cybersecurity in emerging technologies.
Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .
Dr. Hiren Patel is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He holds a Doctorate in Computer Engineering from Virginia Tech and previously worked as a postdoctoral fellow at UC Berkeley under Edward A. Lee. His research focuses on real-time embedded systems, computer architecture, machine learning hardware, and cybersecurity. He teaches courses like ECE 150 (Programming), ECE 320/429 (Computer Architecture), and ECE 327 (Digital Systems). Research Interests: Cyber-physical systems and hybrid architectures Hardware/software co-design methodologies Predictable cache coherence protocols IoT and edge computing systems Security in embedded and real-time systems Recent work emphasizes cache coherence solutions for safety-critical systems and GPU acceleration strategies. His publications address challenges in multicore predictability, FPGA bandwidth optimization, and autonomous robotics orchestration. No specific awards are listed, though his extensive publication record indicates significant contributions to embedded systems research. He currently oversees graduate student applications focusing on his core research areas.
Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).
