Michael Feeley is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Institute for Computing, Information and Cognitive Systems (ICICS). His research focuses on operating systems, distributed systems, and their applications in scalable file systems, cloud storage, and mobile computing. He leads projects such as the Unico file system, Parallax storage system, and Remus virtual machine replication framework. His research interests include peer-to-peer file systems, mobile ad hoc networks, interference mitigation in wireless networks, and system software for workstation clusters. He has supervised numerous graduate students, contributing to advancements in distributed systems and networking. His work often addresses challenges in resource management, fault tolerance, and performance optimization. Publications span topics like interference detection in WiFi networks, dynamic program analysis (Tralfamadore), and distributed storage solutions. Feeley teaches courses such as CPSC 213, emphasizing operating systems fundamentals. His office is located in CISR 393, with contact details available via email and phone.
Justine Sherry is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University, affiliated with the College of Engineering. She holds a PhD (2016) and MS (2012) from UC Berkeley and a BS/BA (2010) from the University of Washington. Her research focuses on networked systems, including middleboxes, cloud computing, congestion control, and hardware acceleration (e.g., SmartNICs/FPGAs). Notable projects include Pigasus (open-source 100Gbps IDS), APLOMB (cloud-based middlebox scaling), and BlindBox (encrypted traffic scanning). Her academic roles include serving on the SIGCOMM CARES Committee, DARPA ISAT Study Group, and ACM CoNEXT Steering Committee. Awards include the Alfred P. Sloan Fellowship, VMware Systems Award, and IETF Applied Networking Prize. She advises over 15 students and collaborates with industry partners like Intel and VMware. Research highlights include radical shifts in datacenter architectures (SmartNIC compute control), fairness in congestion algorithms (BBR analysis), and database-proxy innovations (Tigger with eBPF). Her teaching emphasizes systems as science labs, integrating experimental design and hypothesis testing into projects. Education: PhD UC Berkeley (2016), MS UC Berkeley (2012), BS/BA University of Washington (2010) Labs/Teams: CyLab, SNAP Research Group, CMU Portugal Collaboration Grants: NSF, Intel, Google Faculty Awards
Prof. Bruno Clerckx is a Professor of Wireless Communications and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. He leads the Communications and Signal Processing Group and the Wireless Communications and Signal Processing Lab. Education: M.Sc. and Ph.D. in Electrical Engineering from Université Catholique de Louvain, Belgium Doctor of Science (DSc) from Imperial College London Research Interests: Focuses on wireless communications and signal processing for next-generation networks, including MIMO systems, reconfigurable intelligent surfaces (RIS), rate-splitting multiple access (RSMA), and integrated sensing and communications (ISAC). His work emphasizes 6G technologies, full-duplex systems, and energy-efficient architectures. Key contributions include pioneering research on beyond-diagonal RIS and RSMA prototyping. Awards: 2021 Blondel Medal (France) 2021 Adolphe Wetrems Prize (Royal Academy of Belgium) Fellowships from IEEE and IET IEEE Communications Society Distinguished Lecturer (2021-2023) Labs & Teams: Heads the Wireless Communications and Signal Processing Lab, and is affiliated with the IEEE Special Interest Groups on RSMA and BD-RIS. Collaborates with global institutions including Stanford University, Tsinghua University, and Samsung Electronics. Industry Experience: Former CTO of Silicon Austria Labs and contributor to 4G/5G standards at Samsung. Holds 80+ patents and authored two books on MIMO systems.
Dr. Kevin Kochersberger is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , with a career spanning academic research, technical innovation, and educational leadership. His work focuses on autonomous aerial systems , robotic control , and applied aerodynamics , particularly through the Uncrewed Systems Laboratory . Kochersberger's research has pioneered UAV-based radiation detection , 3D terrain mapping , and low-resource drone applications , including establishing the African Drone and Data Academy in Malawi . Education: Ph.D., Mechanical Engineering, Virginia Tech (1994) M.S., Mechanical Engineering, Virginia Tech (1984) B.S., Mechanical Engineering, Virginia Tech (1983) A.S., Engineering Science, Jamestown Community College (1981) Kochersberger's publications demonstrate expertise in UAV path planning , smart material actuation , and radiation source localization , with over $9M in research funding. His scientific awards include AIAA Associate Fellow (2009) and Aviation Week Aerospace Laureate (2003). Notable projects involve helicopter-deployable robotic systems and urban canyon navigation without GPS. Recent articles highlight BVLOS drone simulators , 2.5D terrain mapping , and autonomous negative obstacle traversal , reflecting his focus on real-time adaptive control and heterogeneous robotic systems . He teaches Drone Technology and Flight Operations and Advanced Design Projects , emphasizing student-driven innovation and industry collaboration .
Dr. Hongye Zhang serves as a Lecturer in Superconducting and Cryogenic Electric Machines at the School of Engineering, University of Edinburgh, while maintaining a Visiting Research Fellow position at the University of Manchester. He actively contributes to the European Society for Applied Superconductivity (ESAS) as a Board Member and chairs the international HTS 2026 workshop. His educational foundation includes: BSc and MSc in Electrical Engineering from Xi’an Jiaotong University (2015, 2018) Diplôme d’ingénieur (MEng) from École Centrale de Lyon (2018) PhD in Applied Superconductivity from the University of Edinburgh (2021) Dr. Zhang’s research centers on decarbonizing transport through superconducting/cryogenic electric machines for hydrogen-powered aircraft, integrating artificial intelligence with superconductor technology and cryogenic techniques. His work targets net zero emissions by developing high-power-density propulsion systems that leverage hydrogen energy and advanced numerical modeling of superconductors. Analysis of his 2022-2025 publications reveals dominant themes in superconducting machine design for wind energy and electric aviation, with significant contributions to loss mitigation, flux pump technology, and trapped field magnet applications. His research bridges fundamental superconductor characterization with practical system integration for renewable energy. Recognized with the 2021 IEEE Council on Superconductivity Graduate Study Fellowship, his professional engagements include: Early Career Editorial Board Member for Elsevier’s Superconductivity journal Technical Editor for IEEE Transactions on Applied Superconductivity Program Committee Member for SMT 2023 He leads critical research within the £54-million H2GEAR project developing hydrogen-electric aircraft propulsion, while teaching Power Engineering 2 and Electrical Machines courses. His advisory roles span doctoral supervision and industry collaboration through Energy Systems research institute. Based at the University of Edinburgh’s Faraday Building, Dr. Zhang directs a research group focused on hydrogen energy applications and superconducting machine testing, with strong ties to the H2GEAR consortium and ESAS working groups.
Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.
Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.
Azita Emami serves as the Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering at the California Institute of Technology (Caltech), where she concurrently holds leadership roles as Executive Officer for Electrical Engineering and Director of the Center for Sensing to Intelligence. Appointed to Caltech's faculty in 2007, she progressed from Assistant Professor to her current endowed professorship through demonstrated scholarly excellence. Her academic foundation includes: B.S. in Electrical Engineering from Sharif University of Technology (1996) M.S. in Electrical Engineering from Stanford University (1999) Ph.D. in Electrical Engineering from Stanford University (2004) Professor Emami pioneers mixed-mode integrated circuit systems that bridge theoretical innovation with practical applications. Her research emphasizes ultra-low power consumption and high reliability in scalable semiconductor technologies, targeting transformative solutions across multiple domains. Key thrusts include: Biomedical implantables for neural recording/stimulation and gastrointestinal monitoring Photonics-electronics co-design for energy-efficient optical interconnects Machine learning-enhanced signal processing for brain-computer interfaces Miniaturized magnetic sensors with unprecedented noise performance Her work consistently demonstrates how circuit-level innovations enable breakthrough capabilities in medical diagnostics and high-speed computing. Analysis of her 2021-2024 publications reveals a strategic convergence of biomedical sensing and intelligent signal processing . While maintaining strong contributions to optical interconnects (accounting for ~40% of recent output), her lab increasingly focuses on closed-loop medical systems where low-power analog neural networks interpret physiological signals. This evolution reflects growing NIH and industry interest in implantable/wearable health technologies, with her group leading in CMOS-based sensor miniaturization and energy efficiency. Her professional recognition includes: IEEE Solid-State Circuits Society Distinguished Lecturer appointment Mentorship excellence is evidenced by students receiving prestigious awards including the Jakob van Zyl Predoctoral Research Award (Saransh Sharma, Ryoto Sekine) and Charles Wilts Prize (Kuan-Chang Chen). Her research program leverages strategic partnerships with Heritage Medical Research Institute and industry collaborators, supported through center-based funding like the Center for Sensing to Intelligence. Administrative leadership spans departmental governance (as Executive Officer) and conference organization (ISSCC technical committees). She directs Caltech's Mixed-mode Integrated Circuits and Systems Lab (MICS) , which operates as a nexus for cross-disciplinary innovation between electrical engineering and medical applications. The lab's industry-collaborative framework accelerates translation of circuit concepts into real-world biomedical solutions through the Center for Sensing to Intelligence.
Scott Kerlin is a Senior Lecturer in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds an M.S. and B.S. in Computer Science from the University of North Dakota. Prior to academia, he worked at the Mayo Clinic on medical software systems and IBM as a build master for enterprise products. His career spans roles including network administrator, lab manager, and Undergraduate Director at UND. Dr. Kerlin's research focuses on bridging industry experience with academic curriculum, particularly in computer science education, project-based learning, and cybersecurity. He emphasizes practical applications of theoretical concepts, such as integrating 3D printing and scanning technologies with security systems for small satellites. His work also explores student efficacy in AI courses and scalable software project management methodologies. He has taught at multiple institutions, including the University of Minnesota and Augsburg University, and held roles at Michigan Tech as Senior Security Engineer. His 2024 Engineering+ Outstanding Teaching Award highlights his commitment to pedagogical innovation. Current projects include in-space 3D printing, solar energy systems, and cryptographic solutions for satellite communications. Key areas of contribution include: 3D printing/Scanning: Material characterization, key replication, and aerospace applications Cybersecurity: Intrusion detection, satellite communications security, and chaotic cryptosystems Educational Innovation: Active learning frameworks, PBL implementation, and student performance modeling His interdisciplinary approach connects computer science fundamentals with real-world engineering challenges, emphasizing sustainability and industry relevance in curricula.
Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.
Prof. Dr.-Ing. Thomas Zwick is a full professor and director of the Institute of High Frequency Engineering and Electronics (IHE) at the Karlsruhe Institute of Technology (KIT). He holds a Dipl.-Ing. (M.S.E.E.) and Dr.-Ing. (Ph.D.E.E.) from the University of Karlsruhe. His career includes roles at IBM Research (2001–2004), Siemens AG (2004–2007 managing automotive radar teams), and KIT since 2007. He leads research in high-frequency technologies, antennas, radar systems, and wireless communications. Research interests include radio wave propagation, antenna design, automotive radar architectures, and millimeter-wave systems. He has authored/co-authored over 400 papers, 20 patents, and received IEEE Fellow status (2018), honorary doctorate from Budapest University (2022), and membership in the Heidelberg Academy and acatech. His work emphasizes integrating sensing and communication systems, 3D-printed RF components, and high-frequency measurement techniques. Teaching focuses on high-frequency engineering, electronic circuits, and radar systems. He oversees the IHE’s laboratories, including the Microwave Engineering Lab and Student Innovation Lab. Recent work explores sub-THz communication, RIS-aided ISAC systems, and beamforming for reduced EMF exposure in urban scenarios.
Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).
Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.
Xiaojiang Du is the Anson Wood Burchard Endowed Professor at Stevens Institute of Technology, directing research in IoT security, AI security, and wireless networks. An IEEE Fellow and ACM Distinguished Member, he leads NSF-funded projects on secure IoT systems and cross-platform security vulnerabilities. Education PhD in Electrical Engineering, University of Maryland MS in Electrical Engineering, Tsinghua University BE in Electrical Engineering, Tsinghua University Research Focus: Develops security frameworks for IoT ecosystems and adversarial machine learning, with recent breakthroughs in smart home security anomaly detection. Honors: IEEE Fellow, ACM Distinguished Member, multiple best paper awards at IEEE conferences. Graduated PhD students hold faculty positions at UNC Charlotte, UL Lafayette, and ShanXi University. Professional Service: IEEE ComSoc Distinguished Lecturer, Associate Editor for IEEE Transactions, and General Co-Chair for IEEE/ACM IWQoS 2023. Secured $9M+ in research funding from NSF, NSA, and DOD.
Srdjan Lukic is the Deputy Director of the FREEDM Systems Center and a Distinguished Professor in the Department of Electrical and Computer Engineering at North Carolina State University. His research focuses on power electronics, power systems, and electric vehicle infrastructure. He holds a Ph.D. in Electrical Engineering from the Illinois Institute of Technology (2007). His work emphasizes high-power converters, DC microgrids, and extreme fast charging (XFC) technologies for electric vehicles. Recent projects include the development of a 13.2 kV solid-state transformer for EV XFC stations and resilient distributed control frameworks for smart grids. He has contributed to wireless charging systems and modular medium-voltage fast chargers. Key Awards: University Faculty Scholars (2022) Grants: DOE-funded XFC projects, modular EV charging infrastructure Labs/Teams: FREEDM Systems Center, RIAPS control architecture development Publications highlight innovations in microgrid control, power electronics modeling, and privacy-preserving distributed systems. His work addresses grid resilience, cyber-physical integration, and high-efficiency energy conversion.
