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.
Rui Zhang is an Associate Professor in the Department of Computer and Information Sciences at the University of Delaware. He holds a PhD in Electrical Engineering from Arizona State University (2013) and has held academic positions at the University of Hawaii and industry roles at UTStarcom. His research focuses on cybersecurity, wireless networks, and privacy-preserving technologies, with notable work on secure edge computing and mobile authentication systems. Education: PhD, Electrical Engineering, Arizona State University, 2013 M.E., Communication and Information Systems, Huazhong University of Science & Technology, 2005 B.E., Communication Engineering, Huazhong University of Science & Technology, 2001 Research Interests: Rui’s work spans secure wireless systems, mobile crowdsourcing, accessibility technologies for visually impaired users, edge computing, and social network privacy. He has led NSF-funded projects on trustworthy hierarchical edge computing and secure mobile cloud sensing. Grants & Awards: Recipient of the NSF CAREER Award (2017) and multiple NSF grants totaling over $1M. Current focus includes NSF-funded research on edge computing security and privacy-preserving data aggregation. Teaching: Has taught courses in algorithms, network security, and computer networks at both University of Delaware and University of Hawaii. Labs & Teams: Leads research projects in secure edge computing and collaborates with Arizona State University on federally funded initiatives. Active in organizing academic conferences like ISC 2016 and serving on editorial boards of IEEE journals.
Anthony Rowe is the Siewiorek and Walker Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU) and a Chief Scientist at Bosch Research. His primary affiliation is with the CyLab and the Wireless, Sensing and Embedded Systems (WiSE Lab) at CMU. He specializes in networked embedded systems, sensor networks, and extended reality (XR) technologies. His research emphasizes energy-efficient sensing, real-time localization, and XR integration with physical systems. Research Focus: His work spans XR systems (e.g., AR/VR edge networking in ARENA), mmWave radar for sensing (e.g., tire wear monitoring via Osprey), distributed edge computing (Silverline), and low-power wide-area networking (OpenChirp). Recent efforts include AI-integrated XR platforms (XaiR) and radar tomography (DART). Grants & Projects: Leads the CONIX Research Center ($27.5M NSF/DARPA grant), Bosch-funded edge computing projects, and DOE initiatives on microgrids. Notable projects include ARENA (XR edge architecture), GridBallast (smart grid control), and rural microgrid deployments in Haiti. Awards: Best Student Paper (ISMAR 2024), Best Paper (IPSN 2020), and the Steven J. Fenves Research Award (2015). Recognized for innovations in localization (MobiCom 2021), radar (ICRA 2023), and energy systems (BuildSys 2010). Teaching: Teaches courses on embedded systems (18-349/18-449), real-time systems, and mixed reality (18-453). Courses emphasize hands-on design and real-world applications. Labs & Teams: Directs the WiSE Lab, collaborating with Bosch Research and industry partners. The lab develops open-source frameworks like ARENA and OpenChirp, and contributes to standards for edge computing and sensing.
Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).
Maria Gorlatova is an Associate Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where she leads the Intelligent Interactive Internet of Things (I3T) Lab. She also holds a secondary affiliation as Faculty Network Member of the Duke Institute for Brain Sciences and has previously served as Assistant Professor of Computer Science. Dr. Gorlatova earned her Ph.D. in Electrical Engineering from Columbia University (2013), following M.Sc. and B.Sc. (Summa Cum Laude) degrees in Electrical Engineering from University of Ottawa, Canada. Prior to joining Duke, she was an Associate Research Scholar in the Electrical Engineering Department and Associate Director of the Princeton EDGE Lab at Princeton University (2016-2018). She also has industry experience with Telcordia Technologies, IBM, and D. E. Shaw Research. Her research focuses on advancing intelligent behavior in Internet of Things systems and applications, particularly in mobile pervasive systems and the Internet of Things. Her work crosses traditional discipline boundaries, requiring thinking across multiple layers of system and protocol stacks. Current research themes include breaking barriers for technologies that enable fundamentally new deployments and experiences, such as energy harvesting, artificial intelligence adapted to IoT constraints, and augmented reality. Her lab specifically develops edge- and IoT-enabled intelligent augmented reality platforms, with applications in healthcare and human-robot collaboration. Analyzing her recent publications reveals a strong focus on augmented reality systems, particularly for medical applications. Her work spans computer vision for AR, spatial tracking, SLAM systems, vision-language models for AR security, and VR/AR applications in neurosurgery and rehabilitation. A significant portion of her recent work addresses challenges in mixed reality for medical procedures, demonstrating the translational impact of her research. Google Anita Borg USA Fellowship Canadian Graduate Scholar CGS NSERC Fellowships Columbia University Presidential Fellowship Columbia University Jury Award for Outstanding Achievement in Communications ACM SenSys Best Student Demonstration Award IEEE Communications Society Young Author Best Paper Award IEEE Communications Society Award for Advances in Communications Best Research Artifact Award, IEEE IPSN (2020) N2 Women Rising Star, Networking Networking Women (N2Women) (2019) Dr. Gorlatova's research has been supported by various funding sources that enable her work on edge computing for augmented reality, IoT systems, and medical applications. She actively mentors graduate students who frequently appear as first authors on her publications, indicating strong student involvement in her research. Her I3T Lab at Duke focuses on creating human-facing pervasive mobile computing platforms that enable transformative applications, with recent emphasis on creating advanced augmented reality platforms that integrate edge computing and IoT technologies. The I3T Lab is developing next-generation AR systems with capabilities in edge AI, collaborative spatial awareness, AR user cognitive context sensing, and AR QoS/QoE evaluation. Current projects include applications in healthcare (particularly neurosurgery guidance and rehabilitation) and human-robot collaboration scenarios, demonstrating the lab's focus on real-world impact of pervasive computing technologies.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.
Sujaya Maiyya is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. Prior to this, she completed a postdoc at Cornell University and earned her PhD from the University of California, Santa Barbara. Her research focuses on distributed systems, databases, and privacy/security, particularly in designing secure and efficient data management systems. She leads projects on oblivious databases, trusted execution environments (TEEs), and scalable privacy-preserving systems. Education: PhD in Computer Science, University of California, Santa Barbara (2018) MSc in Computer Science, University of California, Santa Barbara (2017) BE in Information Science, PESIT Bangalore (2014) Research Interests: Distributed systems, database privacy, oblivious datastores, genomics data security, and secure computation using TEEs. Her work emphasizes practical solutions for privacy-preserving storage and query processing, including tunable-privacy mechanisms and fault-tolerant ORAM systems. Awards and Grants: CFI/ORF Infrastructure Grant (2024-2029) NCC Research Awards (2024-2028) NSERC Discovery Grant (2023-2027) MIT EECS Rising Stars (2021) Teaching: Courses include CS348 (Introduction to Databases) and CS848 (Privacy Enhancing Data Systems). She emphasizes foundational concepts and system internals in database design and secure systems. Professional Service: Chair of Ontario Database Day (2024), PC member for SIGMOD, EDBT, VLDB, and ICDE. Frequent reviewer for journals like TKDE and DKE.
Vijay Raghunathan is a Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. His work focuses on hardware and software architectures for embedded systems, wireless sensors for IoT, and wearable/implantable electronics with emphasis on low power design, energy harvesting, emerging memory technologies, and secure system design. Academic Rank: Professor Department: Electrical and Computer Engineering University: Purdue University Research Focus: Energy-efficient embedded systems, IoT, wearable devices His research explores low power design at both board-level and system-on-chip scales, micro-scale energy harvesting , and reliable/secure system design for medical devices. Recent work focuses on Processing-in-Sensor/Memory for battery-free AIoT devices, state space models for neural processing units, and security coprocessor integration in autonomous systems. Analysis of his publications reveals trends in energy-efficient neural network acceleration , approximate computing for edge inference , and compute-in-memory architectures . Key subfields include collaborative edge-cloud partitioning , sparse DNN accelerators , and security frameworks for medical devices. Vijay's work also addresses energy-accuracy tradeoffs in multimodal cognitive systems and intermittent computing using non-volatile memory technologies. His contributions span from microcontroller energy management to security protocols for implantable electronics.
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.
Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.
Professor Hanumant Singh leads the Electrical and Computer Engineering department at Northeastern University, with a joint appointment in Mechanical and Industrial Engineering , and serves as Program Director for the Master of Science in Robotics. He earned his Ph.D. from MIT/WHOI Joint Program in 1995 and has conducted over 60 expeditions globally, focusing on marine geology, polar studies, and coral reef ecology. His research emphasizes field robotics , including SLAM, underwater manipulation, and imaging in extreme environments. He developed the Seabed AUV and Jetyak ASV , widely used in scientific research. His labs include the Field Robotics Lab and the Institute for Experiential Robotics . Research Interests: Machine Learning for Fisheries SLAM with dynamic objects Underwater imaging and manipulation Autonomous surface and aerial systems Polar and marine robotics Awards: ICRA Best Student Paper Award, IEEE Oceanic Engineering Society Distinguished Faculty Award (2025), Lifetime Achievement Award (2022), and IEEE Fellow status. His work has been featured in Nature Geoscience , Polar Biology , and media outlets like WGBH. Students & Collaborations: Advises students like Srinidhi Pattala (MS Robotics) and Dennis Giaya (PhD Computer Engineering). Collaborates with institutions on projects such as Antarctic sea ice thickness estimation and deep-sea submersible missions.
Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park, with an appointment in the University of Maryland Institute for Advanced Computer Studies. He leads the Data Systems Lab at Maryland (DSLAM) and has made significant contributions to database system architecture and implementation, particularly in scalable and distributed systems. Prof. Abadi's research focuses on database system architecture, especially at the intersection with scalable and distributed systems. He is best-known for the development of the storage and query execution engines of the C-Store (column-oriented database) prototype, which was commercialized by Vertica and eventually acquired by Hewlett-Packard, and for his HadoopDB research on fault tolerant scalable analytical database systems which was commercialized by Hadapt and acquired by Teradata. His current work includes deterministic distributed systems like Calvin and SLOG, which provide strictly serializable, low-latency, geographically replicated database transactions. Analysis of his recent publications reveals a strong focus on modern database challenges including transaction processing, distributed systems architecture, data mesh concepts, schema evolution, and IoT data management. His work consistently addresses the tension between consistency, availability, and performance in distributed database systems, with recent emphasis on moving beyond traditional two-phase commit protocols and exploring novel approaches to data architecture like data mesh. ACM Fellow Sloan Research Fellowship Churchill Scholarship NSF CAREER Award VLDB Best Paper Award Two VLDB Test of Time Awards (for C-Store and HadoopDB) 2008 SIGMOD Jim Gray Doctoral Dissertation Award 2013-2014 Yale Provost's Teaching Prize 2013 VLDB Early Career Researcher Award PhD dissertation advisor for Alexander Thomson and Jose Falerio, whose dissertations won SIGMOD Jim Gray Doctoral Dissertation Awards in 2015 and 2020 respectively Prof. Abadi has advised multiple PhD students, including Alexander Thomson and Jose Falerio, both of whom received the prestigious SIGMOD Jim Gray Doctoral Dissertation Award. His research has been supported by numerous grants including NSF funding for projects like SLOG. He maintains an active presence in the database community through his widely-read blog DBMS Musings and through service on program committees for major conferences including SIGMOD, VLDB, and CIDR. He leads the DSLAM research group at the University of Maryland, which focuses on cutting-edge database system research with strong industry connections and practical impact.
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.
