Prof. Jay Guo is a Distinguished Professor and Founding Director of the Global Big Data Technologies Centre at the University of Technology Sydney (UTS). He also leads the New South Wales Connectivity Innovation Network (CIN) and the TPG-UTS Network Sensing Lab. With over 700 publications, 350+ IEEE journal papers, and 27 patents, his work focuses on 5G/6G antennas, integrated sensing and communications (ISAC), and environmental sensing using 5G/6G networks. Research Interests: 5G/6G Antenna Systems (reconfigurable arrays, multibeam antennas) In-Band Full-Duplex Wireless Systems Integrated Sensing and Communications (ISAC) Environmental Sensing via Network Infrastructure Awards & Recognition: IEEE Schelkunoff Prize Paper Award (2023) Fellowships: IEEE, Australian Academy of Engineering, Royal Society of NSW Australia Engineering Excellence Award (2007) Australia's Top Researcher in Electromagnetics (2020-2023) Highly Ranked Scholar (top 0.05% globally) Grants & Projects: NSW Government-funded environmental sensing projects Industry collaborations with TPG Telecom, Telstra, and CSIRO Leadership in global antenna conferences (e.g., IEEE APS, ISAP) Labs & Initiatives: Global Big Data Technologies Centre TPG-UTS Network Sensing Lab NSW CIN (Connectivity Innovation Network)
Prof. Andrew Zhang is a Professor at the School of Electrical and Data Engineering, University of Technology Sydney (UTS). He leads the UTS Radio Sensing and Pattern Analysis (RaSPA) Lab and serves as Technical Director of the UTS-TPG Network Sensing Lab. His research focuses on integrated sensing and communications (ISAC), wireless signal processing, and autonomous vehicular networks. He holds a PhD from the Australian National University and has over 15 years of industry experience, including roles at CSIRO and ZTE Corp. Education: B.S. (Xi’an Jiaotong University), M.Sc. (Nanjing University of Posts and Telecommunications), Ph.D. (Australian National University). Research Interests: ISAC, radio sensing, machine learning for communications, and 6G waveform design. Key projects include developing perceptive mobile networks and flood/storm sensing via ISAC. Publications: Over 290 papers, 5 patents, and notable works on ISAC frameworks, joint communication-sensing systems, and mmWave technologies. Recent trends emphasize ISAC, 6G waveforms, and IoT integration with federated learning. Awards: CSIRO Chairman’s Medal, Australian Engineering Innovation Award, and multiple best paper awards. Active in IEEE leadership roles, including Editor-in-Chief of ISAC-Focus. Grants: ~$8M in research funding. Advises on ISAC-ETI initiatives and collaborates with industry partners like TPG Telecom. Labs: RaSPA Lab (radio sensing analytics) and UTS-TPG Lab (ISAC industrial solutions).
Dr Christine Guo is a Senior Lecturer at Birkbeck Business School, University of London, with expertise in Financial Economics. She holds a PhD from Imperial College London and has taught at the University of Newcastle. Her research focuses on market microstructure, asset pricing, stochastic differential equations, and inflation targeting. Notably, her paper Constructing Asset Pricing Models with Specific Factor Loadings won the ABACUS Best Paper Award. Dr Guo supervises PhD students and teaches modules like Financial Management (ACCA-accredited) and Introduction to Accounting. She has published extensively in top journals including Abacus , European Journal of Finance , and Pacific Basin Finance Journal . Education: PhD in Economics, Imperial College London MSc in Economics and Financial Economics, University of Nottingham BSc in Economics and Econometrics, University of Nottingham Research Interests: Dr Guo’s work bridges theoretical and applied finance, with emphasis on: - Quantitative financial modeling - Market efficiency and regulatory frameworks - Inflation dynamics and monetary policy - ESG transparency and firm valuation Recent Publications Trends: Her articles analyze market structure (e.g., ETFs, multilateral trading facilities), Chinese financial markets, and ESG impacts. Methodologically, she applies stochastic models and spatial econometrics. Awards: ABACUS Best Paper Award (2012) for groundbreaking asset pricing methodology critiques Supervision & Teaching: Supervised 4 doctoral researchers (e.g., Stella Zhixin Xu on urban sustainability). Teaches BUMN145S5 (Financial Management) and BUMN131H4 (Accounting). Actively engages in PhD mentorship. Labs/Teams: Collaborates with interdisciplinary teams on financial regulation and ESG metrics through her research network.
Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
Tien Chih is an Assistant Professor of Mathematics at Oxford College, a unit of Emory University. He previously taught at Newberry College and Montana State University Billings (MSUB) after earning his PhD in Mathematics from the University of Montana (2014). His work bridges pure mathematics and education innovation. Education: BA (University of Hawaii at Hilo, 2007), MA (University of Montana, 2009), PhD (University of Montana, 2014) Research focuses on discrete homotopy theory , particularly ×-homotopy of graphs, and its connections to groupoids and categorical combinatorics . He creates open educational resources (OERs) and EdTech tools for interactive learning, with emphasis on undergraduate research mentorship. Recent publications explore graph homotopy categories, fundamental groupoids for graphs, and innovative teaching methods like asynchronous inquiry-based learning. His mathematical outreach includes community collaboration and a MAA-funded Math Circle program. Scientific awards: CURM funding for undergraduate research, MAA grant for Math Circle He supervises undergraduate research projects and develops open educational resources to bridge the gap between mathematics' reputation and its accessible content.
Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.
Michel Gendreau is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal . His research focuses on the application of Operations Research to Transportation , Telecommunications , and Energy Systems , with an emphasis on Stochastic Optimization and Real-time Planning . He co-directs the Intelligent Transportation Systems Laboratory and is affiliated with the CIRRELT , IVADO , and Trottier Energy Institute . Education : Ph.D. in Computer Science (1984), Université de Montréal His work includes developing metaheuristics for complex optimization problems and dynamic transportation systems . Recent projects address smart supply chains and real-time logistics . He has supervised over 40 doctoral and master's students, including notable graduates like Sanchez-Martinez, Guillen Reyes, and Parada Pradenas. Dr. Gendreau has been recognized with prestigious fellowships from IFORS (2022) and INFORMS (2010). His academic contributions span 420 publications, with recent studies appearing in Reliability Engineering and System Safety and Networks , focusing on stochastic programming , multiperiod routing , and UAV network design . He collaborates extensively with industry partners and has secured grants from organizations like FRQNT and CIRRELT . His research integrates machine learning with operations research to solve real-world challenges in transportation , energy , and logistics .
Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.
Sebastian Stich is a tenured faculty member at the CISPA Helmholtz Center for Information Security , where he leads research in Trustworthy Information Processing . He has been a tenure-track faculty since 2021 and was promoted to tenured professor in 2025. He is also a member of the European Lab for Learning and Intelligent Systems (ELLIS) . Education: PhD in Computer Science, ETH Zurich (2010–2014) MSc and BSc in Mathematics, ETH Zurich (2005–2010) Research Scientist, EPFL (2016–2021) Research at CORE/ICTEAM, UCLouvain (2014–2016) His research centers on optimization for machine learning , with a focus on federated, decentralized, and distributed learning . He investigates methods for communication efficiency , adaptive stochastic optimization , privacy-preserving training , and generalization theory . His work bridges theoretical guarantees with practical scalability. His recent publications (2023–2025) consistently address gradient compression , error feedback , local updates , and decentralized consensus , demonstrating a strong trend toward making distributed learning more efficient, robust, and scalable—especially under heterogeneous data and limited bandwidth. Scientific Awards: ERC Consolidator Grant 2024 (CollectiveMinds) Google Research Scholar Award (2023) Meta Privacy-Enhancing Technologies Research Award (2022) Sebastian Stich actively advises PhD students and postdocs, including Anton Rodomanov , Xiaowen Jiang , and Yuan Gao . He has secured competitive grants such as the ERC CollectiveMinds project, supporting collaborative research on scalable federated learning. He teaches advanced courses at Saarland University and serves as an area chair for NeurIPS, ICML, and ICLR. He leads a research group at CISPA focused on trustworthy and efficient machine learning systems , contributing to both foundational theory and real-world applications in privacy and security.
Professor Forrest Brewer is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. His research spans VLSI design, computer-aided design tools, and low-power computing, with a focus on unconventional engineering solutions. Education: PhD in Computer Science, University of Illinois BS in Physics (with honors), California Institute of Technology His work includes CMOS pulse-gate asynchronous logic for high-performance systems, sigma-delta modulation for signal processing, and formal verification strategies for asynchronous circuits. Applications range from radiation-hardened communication links for the Large Hadron Collider (LHC) to spiking neural networks for low-power computing in LIDAR/RADAR systems. Affiliations: California Nanosystems Institute Allosphere Steering Committee (Media Technology) With over 100 publications and 40 years of systems design experience, Brewer has contributed to defense programs, founded UCSB's Computer Engineering program, and served as Intel Faculty Fellow (1997). His lab, the Systems Synthesis Lab, explores collective dynamics and high-resolution, low-latency computation.
Leonid Reyzin is a Professor and Associate Chair of Academics at Boston University, specializing in cryptography. His research focuses on enabling secure computation, communication, and collaboration in untrusted environments, with contributions to biometric security, cryptographic protocols, and privacy-preserving technologies. He earned his Ph.D. from MIT and has advised on industry standards and received prestigious awards including the NSF CAREER Award and Boston University’s Neu Family Award for Excellence in Teaching. His work spans cryptographic primitives such as fuzzy extractors, verifiable random functions, and proofs of space, addressing challenges like secure key derivation from noisy data and blockchain security. He has collaborated on standards like RFC 9381 and contributed to cryptographic tools for distributed systems and privacy-enhancing technologies. Reyzin’s research emphasizes practical applications, including secure authentication systems, cryptographic protocols for blockchain and distributed ledgers, and mitigating threats like side-channel attacks. His recent work explores advancements in proofs of space, memory-hard functions, and asynchronous authenticated data structures, reflecting a balance between theoretical rigor and real-world applicability. Awards: NSF CAREER Award, Neu Family Award for Excellence in Teaching Key Contributions: Fuzzy extractors, VRFs, cryptographic standards development, blockchain security frameworks Education: Ph.D. in Computer Science, MIT
Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .
Chih-Chun Wang is a Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University, with additional leadership roles as Associate Head for Facilities, Planning, and Staff. He earned his Ph.D. in Information Sciences and Systems from Princeton University in 2005, following an M.S. (2002) and B.S. (1999) in Electrical Engineering from Princeton and National Taiwan University, respectively. Research Interests: His work spans Network coding (graph-theoretic capacity, wireless network coding, feedback mechanisms) Coding theory (LDPC codes, Reed-Solomon decoding, iterative algorithms) Information theory (multi-user detection, network information theory) Signal processing (turbo equalization, space-time codes) Control theory (optimal stopping theory) Scientific Contributions: He has published extensively on Age-of-Information (AoI) minimization, low-latency coding, and multi-hop relay optimization. His research trends include Integrating machine learning with network coding Wireless security for Beyond-5G systems Distributed storage networks with intelligent helper selection Delay-constrained communication protocols Awards: Recognized as an IEEE Fellow in 2024 for contributions to network coding and information theory. Teaching: He teaches undergraduate courses like ECE301: Signals and Systems and graduate courses such as ECE639: Error Control Coding , with a focus on iterative decoding, LDPC codes, and network information theory. Advising: Supervised 15+ Ph.D. students, including current advisees Pin-Wen Su (delay-oriented coding), Wonjun Lee (cyber-physical systems), and Giles Bischoff (low-latency systems). Former students hold prominent roles at institutions like Google, Meta, and Intel.
Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.
