Jason P. Jue is a Professor of Computer Science at the University of Texas at Dallas and director of the Advanced Networks Research Lab. He holds a Ph.D. in Computer Engineering from UC Davis (1999) following M.S. and B.S. degrees in Electrical Engineering from UCLA (1991) and UC Berkeley (1990). Research Interests: His work focuses on next-generation optical network architectures and protocols, including control and management in optical networks, multi-domain optical network protocols, and survivability methods. He has pioneered research in network slicing, software-defined networking, and AI-driven network optimization. Scientific Contributions: Recipient of the prestigious NSF CAREER Award (2002) Recognized for Ph.D. Education & Research (2005) at UTD's Erik Jonsson School Co-authored 3 IEEE Best Paper Awards (Globecom 2005, ONDM 2010, ICC 2011) Leadership Roles: Currently serves on editorial boards of IEEE Communications Surveys and Tutorials and Springer Photonic Network Communications . Former Associate Editor (2009-2012) for IEEE/OSA Journal of Optical Communications and Networking . Organized key conferences as Technical Program Chair of OptiComm 2000 and Co-Chair of multiple IEEE symposia. Lab & Team: Leads the Advanced Networks Research Lab at UT Dallas, mentoring students and collaborating with researchers like Genya Ishigaki, Riti Gour, and Ashkan Yousefpour. The lab focuses on fog computing, optical network survivability, and AI-driven network resource allocation.
Danny Raz is a Professor at the Technion - Israel Institute of Technology, specializing in computer science and networking. His work focuses on cloud computing, network function virtualization (NFV), resource allocation, and online algorithms. Institution: Technion - Israel Institute of Technology, Haifa, Israel Research interests include: Network Function Virtualization (NFV) and service chaining Stochastic and dynamic resource allocation Online algorithms in random-order models 5G/edge computing infrastructure Blockchain network analytics Over the past decade, his publications in venues like IEEE/ACM Transactions on Networking and INFOCOM address: Optimal deployment of cloud services TCAM-based classification and flow measurement Game-theoretic approaches to load balancing Cost-aware live migration and fault recovery Wireless network optimization (4G/5G) Collaborations with researchers like Yuval Shavitt, Joseph Naor, and Haim Kaplan highlight his interdisciplinary work bridging theory and practice in networking and cloud systems.
Dr. Teodor Buchner is a faculty member at the Faculty of Physics, Warsaw University of Technology. He received his doctoral degree on April 25, 2002, with a dissertation titled "Symbolic dynamics and local ordering measures of selected dynamical systems" under the supervision of Prof. Dr. hab. Jan Jacek Żebrowski. His research focuses on nonlinear dynamics, symbolic dynamics, heart rate variability analysis, electrocardiography, and biomedical physics. Dr. Buchner has made significant contributions to understanding the complex dynamics of physiological systems, particularly focusing on cardiac signals and their relationship with respiratory patterns. His work bridges physics, mathematics, and medical applications, developing novel analytical methods for physiological time series. Analysis of Dr. Buchner's recent publications reveals a strong interdisciplinary focus on applying advanced mathematical and computational techniques to cardiac electrophysiology. His research spans from fundamental theoretical investigations of signal propagation to practical clinical applications, including studies on the effects of SARS-CoV-2 on heart function and the development of deep learning models for ECG analysis. A notable trend is his persistent questioning of conventional assumptions, such as his groundbreaking work on the finite velocity of ECG signal propagation. Dr. Buchner has contributed to educational initiatives at Warsaw University of Technology, including quantum engineering education programs. His work extends beyond traditional physics research into areas such as cryptography and network security, demonstrating the breadth of his expertise and ability to apply physical principles across diverse domains. His research methodology combines theoretical modeling, experimental investigation, and advanced computational techniques to address complex problems in biomedical physics and beyond.
Arpit Gupta is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where he co-directs the Systems and Networking Lab (SNL). He also serves as a Faculty Scientist at Lawrence Berkeley National Laboratory and holds the Marjorie & Charles Benton Opportunity Fund Fellowship at the Benton Institute. His research focuses on two main areas: developing production-ready machine learning systems for self-driving networks that ensure secure and performant connectivity with limited infrastructure, and enabling data-driven policymaking to address digital inequity through better broadband measurement and analysis. His work bridges networking, security, and analytics to solve real-world problems at scale. Gupta's research has resulted in several influential systems including Trustee (for ML interpretability in networks), BQT (for broadband plan analysis), PINOT (programmable data collection), netUnicorn (network data collection platform), and netFound (network foundation models). His team develops practical solutions that have been deployed in production environments, including at Tencent. NSF CAREER Award (2025) Google Research Scholar Award (2025) Google ML and Systems Junior Faculty Award (2025) IETF/IRTF Applied Networking Prize (2025, 2023) SIGCOMM Doctoral Dissertation Award (2024) Best Paper Honorable Mention, ACM CCS (2022) As a mentor, he has advised multiple award-winning students including Udit Paul who received the SIGCOMM Doctoral Dissertation Award. His research is supported by substantial funding from NSF (including a $700k CAREER award), DoE, Google, Verizon, Cisco, and state agencies including the California Public Utility Commission.
Jacqueline Walker is an Associate Professor in the Department of Electronic and Computer Engineering at the University of Limerick, Ireland. She is affiliated with the Centre for Research Training in Foundations of Data Science and the Centre for Robotics and Intelligent Systems. Education: Bachelor of Engineering (University of Western Australia, 1993) B.A. (University of Western Australia, 1988) Research Interests span telecommunications synchronization, nonlinear signal processing, and higher-order statistics with applications in biomedical, speech, and music domains. Key areas include: Satellite and network timing transfer Software Defined Radio (SDR) systems Musical sound synthesis and transcription Biomedical signal reconstruction (EMG/MEAP) Jitter analysis and metastability Recent Publications highlight trends in SDR for offshore energy networks (2023), direct-sequence spread spectrum (2021), and cross-disciplinary work in music processing (2014–2003) and biomedical signal analysis (2005–2001). Her work integrates spectral modeling, genetic algorithms, and bispectrum techniques. Labs & Teams: Active in the Centre for Research Training in Foundations of Data Science and Centre for Robotics and Intelligent Systems, focusing on data-driven and robotic applications.
Vishrant Tripathi is an Assistant Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He leads the Multi-Agent Intelligent Networks (MAIN) Lab , focusing on communication networks, robotics, and control theory. Prior to Purdue, he earned a Ph.D. in Electrical Engineering and Computer Science at MIT under Prof. Eytan Modiano, with foundational work on age of information metrics in wireless networks. Education: Ph.D. in EECS, MIT (2023) B.Tech. (Honors) in Electrical Engineering, Indian Institute of Technology Bombay (2015) Research interests span real-time wireless networking , multi-agent robotics , edge computing , and federated learning , with an emphasis on optimizing information freshness via age of information (AoI) frameworks. His group develops theoretical models and practical systems for wireless networks in search-and-rescue, industrial monitoring, and autonomous fleets. Recent publications include works on AoI-based scheduling, software-defined networking for UAV swarms, and correlation-aware resource allocation in wireless networks. His research has earned Best Paper Runner-Up Awards at ACM MobiHoc and Best Presentation Awards at LIDS Student Conferences. Teaching highlights include ECE 302 (Probabilistic Methods) at Purdue and TA roles at MIT for courses like 16.36 (Communication Systems) and 6.7700 (Fundamentals of Probability). He is a participant in MIT's Kaufman Teaching Certificate Program. Professional service includes co-chairing LIDS Student Conferences, organizing MIT's LIDS & Stats Tea Talks, and reviewing for IEEE journals and conferences. He also builds wireless systems for drones and autonomous vehicles in collaboration with industry partners like Google and Facebook.
Francesco Raviglione è Ricercatore a tempo determinato (Law 240/10 art.24-a) presso il Department of Electronics and Telecommunications (DET) del Politecnico di Torino. Con expertise in Connected Vehicles , Intelligent Transportation Systems , e Wireless Communication , svolge attività di ricerca e insegnamento. Università: Politecnico di Torino Dipartimento: Electronics and Telecommunications Ruolo: Assistant Professor Interessi di Ricerca: Si concentra su architetture di comunicazione per veicoli autonomi e connessi, con particolare attenzione a 5G-based Collective Perception , Time-Sensitive Networking , e ETSI Standards Compliance . Pubblicazioni Recenti: I suoi ultimi lavori trattano Telemetry Analysis , Dynamic Mapping , e Network Optimization per sistemi veicolari, presentati in conferenze IEEE e MedComNet 2024. Insegnamenti: Collabora in diversi corsi post-laurea tra cui Software-defined Communication Systems , Networking Technologies for Connected Vehicles , e Mobile and Sensor Networks . Patents: Co-inventore di un International Patent per V-Edge , una soluzione per Vehicular Edge Intelligence in bande di spettro non licenziate.
Dr. Todd Humphreys is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He directs the Radionavigation Laboratory, focusing on software-defined GPS receivers and LEO-based positioning systems. His research emphasizes defending against GNSS spoofing/jamming and exploring satellite navigation innovations. Research interests include satellite navigation, orbital dynamics, and signal processing with applications in ionospheric remote sensing and cybersecurity. He co-founded Coherent Navigation to develop hardened GPS systems using Iridium signals. Recent work focuses on LEO mega-constellations (Starlink, OneWeb) for resilient PNT solutions, spoofing detection via single/dual-satellite geolocation, and multi-modal fusion for urban navigation (TEXR Dataset). His publications address OFDM signal design for ranging, radar-inertial positioning, and anti-spoofing countermeasures. Current projects involve beamforming optimization for LEO terminals and TITAN inertial-terrain navigation.
David Brumley is a Professor of Electrical and Computer Engineering at Carnegie Mellon University with a courtesy appointment in the Computer Science Department. He previously served as Director of CyLab, CMU's Security and Privacy Institute, from 2015 to 2017. His research focuses on developing systems that automatically check software for exploitable bugs using program analysis with security-specific properties. Brumley received his Ph.D. in Computer Science from Carnegie Mellon University, an MS in Computer Science from Stanford University, and a BA in Mathematics from the University of Northern Colorado. Before his academic career, he served as a Computer Security Officer for Stanford University from 1998-2002. Brumley's research focuses on software security techniques that provide users with guarantees. His work sits at the intersection of model checking, formal methods, compilers, and logic, all applied to security problems. He develops efficient symbolic execution, reasoning about bit-level arithmetic in finite fields, sound decompilation, and decision procedures. His research also extends to network security and applied cryptography, focusing on efficient protocols, signature schemes, and privacy-preserving cryptography. A key aspect of his work involves binary code analysis, which allows reasoning about the security of code that actually executes. Brumley's publication record shows a consistent progression from theoretical foundations in program analysis to practical security systems. His work spans symbolic execution, fuzzing, exploit generation, and binary analysis. The Mayhem Cyber Reasoning System, which won the DARPA Cyber Grand Challenge, represents the culmination of his research vision for automated vulnerability detection and patching. His publications demonstrate how theoretical advances in program analysis can be translated into real-world security tools. USENIX Security Best Paper Awards (2003, 2007) International Conference on Software Engineering Distinguished Paper Award (2014) NSF CAREER Award (2010) United States Presidential Early Career Award for Scientists and Engineers (PECASE) (2010) Sloan Foundation Award (2013) DARPA Cyber Grand Challenge Winner ($2,000,000) (2016) Brumley has mentored numerous PhD students who have gone on to successful careers in academia and industry, including co-founders of ForAllSecure. He served as faculty mentor for the CMU Hacking Team Plaid Parliament of Pwning (PPP), which has been ranked #1 internationally and won DefCon 2013. His research has been supported by significant grants including DARPA programs and the NSF CAREER award. He also runs PicoCTF, an annual computer security contest for high school students that has become one of the largest cybersecurity education initiatives of its kind. Brumley leads the development of security systems through both academic research and commercialization. He is the CEO of ForAllSecure, which commercializes the Mayhem system developed through his academic research. His work bridges the gap between theoretical security research and practical security tools used by industry, creating a pipeline from academic innovation to real-world impact.
Mihai Lazarescu is Associate Professor in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University. His research focuses on computer networks, cloud computing security, and wireless sensor networks with particular emphasis on energy efficiency and reliability. His research interests include green networking strategies for software-defined networks, energy management in rechargeable wireless sensor networks considering battery imperfections, and developing forensic readiness frameworks for cloud platforms and IoT ecosystems. Recent work addresses multi-stage upgrade optimization for SDNs with budget constraints. Additional research explores anomaly detection in network traffic, real-time industrial monitoring systems for rotary kilns, and robust stream clustering algorithms for dynamic datasets. His work combines theoretical modeling with practical applications in network infrastructure and cybersecurity.
Dr. Ryan Green is an Assistant Professor in the Department of Electrical and Computer Engineering at Mississippi State University. His academic background includes a Ph.D. in Engineering from Virginia Commonwealth University (2019), an M.S. in Electrical and Computer Engineering from Mississippi State University (2013), and a B.S. in Electrical Engineering from the same institution (2011). His research focuses on cutting-edge wireless technologies including: Smart-city communication infrastructures Internet of Things (IoT) network design Optically transparent antennas and RF filters Wireless medical telemetry systems Biocompatible implantable devices Advanced antenna measurement techniques Analysis of his recent publications reveals strong thematic emphasis on: Novel antenna designs using transparent conductive oxides and e-textiles Biomedical applications of wireless technology (implantable devices, biosensors) Environmental monitoring systems using unmanned platforms Innovations in electromagnetic propagation and measurement Smart-city network infrastructure solutions No scientific awards or student advising relationships are documented in the provided materials.
Mert D. Pesé is an Assistant Professor of Computer Science and Founding Director of the TigerSec Laboratory at Clemson University's School of Computing. His research focuses on autonomous vehicle security, adversarial machine learning, generative AI applications in security, and automotive data privacy. He holds a PhD in Computer Science and Engineering from the University of Michigan (2022), an MSc in Electrical Engineering from Technische Universität München (2016), and dual BSc degrees in Electrical Engineering and Computer Science (2015). His work involves collaboration with automotive companies like BMW, General Motors, Ford, Audi, DENSO, and Harman, supported by grants from the US Army GVSC and NSA. Recent projects include DENSO-funded research with Purdue University and leadership in the TigerSec Lab, which has onboarded PhD students Alkim Domeke and David Fernandez and Master’s student Jan de Voor. Key research trends in his publications emphasize securing automotive networks (e.g., CAN Bus vulnerabilities), adversarial attacks on AI-driven systems, and privacy-preserving techniques for vehicular data. His frameworks like FuzzSense and AutoWatch showcase innovations in automotive software testing and driver behavior analysis. Grants and collaborations highlight applied security solutions for modern vehicles, while his TigerSec Lab serves as a hub for exploring cutting-edge automotive cybersecurity challenges.
Dr. Uday Tupakula is Associate Professor in Cybersecurity at the University of New England's School of Science and Technology. With qualifications including BE (Electronics), MIT, and PhD in Cybersecurity from Macquarie University, he previously served as Deputy Director of the Advanced Cyber Security Engineering Centre at University of Newcastle. Research encompasses: Virtualization and cloud security Software Defined Networking (SDN) security Malware analysis techniques 5G network security frameworks Healthcare system security He developed UNE's Master of Cybersecurity and Graduate Certificate programs, creating multiple cybersecurity courses across institutions. With 100+ publications, he chaired ACM ASIACCS 2009 conference and serves on program committees for major cybersecurity conferences. Professional memberships include IEEE, ACM, and Australian Information Security Association.
Wajahat Ali Khan is an Associate Professor in Artificial Intelligence at the University of Derby's College of Science and Engineering. His research focuses on AI-driven solutions for healthcare informatics, cybersecurity, and data integration. He leads interdisciplinary projects involving knowledge graphs, semantic web technologies, and clinical decision support systems. Research interests include applying machine learning to healthcare challenges such as disease subtyping, health data interoperability, and personalized medicine. He also explores cybersecurity in network systems and SDN environments. Key contributions include platforms like the Ubiquitous Health Profile (UHPr) for health data interoperability and the Intelligent Medical Platform for dialogue-based healthcare services. His publications span topics from anti-DDoS models to sentiment analysis for depression detection. Collaborations with institutions like Sungkyunkwan University (South Korea) highlight global impact. Khan’s work emphasizes bridging semantic gaps in healthcare systems through adaptive mediation frameworks and data-driven knowledge acquisition methods.
Hamed Rezaei is an Adjunct Professor in the Department of Computer Science at the University of Wisconsin-Milwaukee, affiliated with the College of Engineering & Mathematical Sciences. He works as a full-time researcher at Rockwell Automation, focusing on computer networks, particularly congestion control and low-latency applications. Education: PhD in Computer Science, University of Illinois at Chicago MS in Computer Science, University of Illinois at Chicago BS in Computer Science, Razi University, Iran His research interests include: Congestion Control Software Defined Networking (SDN) Network Function Virtualization (NFV) Programmable Data Planes Publications highlight expertise in datacenter networking, congestion control, and low-latency systems. Key areas include network protocols, SDN-based traffic management, and security frameworks for large-scale networks. His work spans both theoretical and applied domains, including contributions to datacenter topologies (Superways), flow scheduling (ResQueue), and DDoS detection mechanisms. Contact: rezaeih@uwm.edu
