Dr. Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision and machine learning, with a particular emphasis on domain adaptation, meta-learning, and privacy-preserving techniques. He actively advises prospective graduate students through a dedicated webpage outlining application procedures. Research interests include few-shot learning, test-time adaptation, and cross-modal applications such as handwritten text recognition and gaze estimation. His work explores how models can adapt dynamically to new domains using limited labeled data, with applications in crowd counting, medical data analysis, and cybersecurity. He also investigates privacy-preserving methods for deep learning models to protect user attributes and sensitive information. Recent publications highlight advancements in meta-auxiliary learning frameworks and efficient user adaptation techniques. His contributions span journals and conferences, showcasing innovations in both foundational machine learning methodologies and real-world applications.
Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.
Lucy Bastin is a Professor in the School of Computer Science and Digital Technologies at Aston University, part of the College of Engineering and Physical Sciences. She holds academic roles since 2003, including leadership in the Digital Observatory for Protected Areas (DOPA) project at the European Commission. Her research focuses on biodiversity informatics, remote sensing, and citizen science, with applications in conservation planning and sustainable development. She advises PhD students on topics like GIS, remote sensing, and citizen observatories. Education: BSc Zoology (University of Nottingham), MSc GIS (University of Leicester), PhD in Spatial Population Ecology (University of Birmingham). She also holds a Postgraduate Certificate in Teaching and Learning in Higher Education. Research Interests: Essential Biodiversity Variables, metadata standards for citizen science, uncertainty in conservation models, disease mapping (e.g., MRSA), and environmental policy support. She developed the DOPA toolkit for protected area monitoring and co-authored the Bari Manifesto for biodiversity variables. Key Projects: DOPA Explorer 2.0, FLIERS EU project, EuroGEOSS initiatives Recent Awards: Midlands Women in Tech Finalist (2021), Best Paper Award (2018) Teaching: Software Engineering, Professional Ethics in Computing, GIS modules Labs/Teams: Part of the Computer Science Research Group and Aston Centre for Artificial Intelligence Research and Application. Collaborates with global partners on initiatives like BIOPAMA and the Green Deal Data Space.
Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.
Jingxian Wang is an NUS Presidential Young Professor and Assistant Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research builds next-generation wireless systems and satellite networks, with primary focus on integrating AI with wirelessly networked devices from WiFi to satellites. He earned his PhD from Carnegie Mellon University and previously served as a research scientist at Microsoft Research in Redmond, where he led the Smart Surface for 6G and Space initiative. His educational journey includes: PhD, Carnegie Mellon University Wang's research spans Wireless Systems , Satellite Networks , Artificial Intelligence , and Internet of Things , emphasizing AI-augmented wireless systems. His interdisciplinary work bridges robotics , materials science , and AI to develop sustainable sensing methods, robust communication networks, and multimodal AI techniques. Key projects include Multimodal AI for IoT (funded by Microsoft's Accelerate Foundation Models Program) and Satellite IoT Networks. His publication trends reveal accelerating integration of AI into wireless systems, with recent focus on satellite networking, soft robotics actuation, and generative models for IoT. The research consistently targets real-world deployment challenges in battery-free systems and space networks. His scientific contributions have earned prestigious recognition: ACM SIGMOBILE Doctoral Dissertation Award 2023 Communications of the ACM Research Highlights (2021, 2022) ACM SIGMOBILE Research Highlights 2021 Best Paper Awards at IPSN 2021 and UbiComp 2020 Microsoft Research Fellowship 2020 Emerging Rockstar in IEEE Pervasive Computing 2024 Wang actively mentors doctoral students and postdoctoral researchers through his AIoT Group. His grant portfolio includes Microsoft's Accelerate Foundation Models Research Program funding for multimodal AI projects, with ongoing work targeting satellite IoT infrastructure and wireless-powered soft robotics. Future directions emphasize foundation models for space networks and battery-free IoT systems. He leads the AIoT Group, fostering cross-disciplinary collaboration between computer scientists, roboticists, and materials engineers to pioneer wireless sensing and actuation technologies.
Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.
Professor Ahmed Hemani is a faculty member at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology, affiliated with the Digital Futures Faculty. He holds the role of PI for the project 'New Chip Architectures for Industrial Vision' and leads research in reconfigurable computing, memristor-based systems, and hardware acceleration for AI and edge computing. His work bridges theoretical computer science with practical VLSI design and embedded systems development. He actively contributes to cross-disciplinary initiatives at Digital Futures, a joint center with Stockholm University and RISE Research Institutes of Sweden focused on digital innovation. His research emphasizes scalable FPGA/HPC architectures, low-power neuromorphic systems, and optimization techniques for custom silicon solutions. Current projects include a Lego-inspired edge AI framework and memristor-driven MIMO acceleration. Teaching responsibilities span advanced courses in SOC design, digital system verification, and embedded systems. He supervises advanced-level degree projects across computer engineering and ICT innovation specializations, emphasizing hands-on hardware-software co-design methodologies. Recent publications highlight innovations in memristor applications, FPGA-based acceleration, and reconfigurable architectures for neural networks and bioinformatics. His work addresses challenges in dark silicon utilization, energy-efficient computation, and high-performance embedded systems.
Professor Raja Jurdak is a leading academic in distributed systems and applied data sciences at Queensland University of Technology (QUT), where he directs the Trusted Networks Lab. He holds dual roles as Professor of Distributed Systems and Chair in Applied Data Sciences, alongside leadership in the Centre for Data Science. His research focuses on dynamic network modeling, blockchain-based trust frameworks, and IoT applications, with particular emphasis on cybersecurity, energy efficiency, and mobility-driven diffusion processes. Jurdak formerly led CSIRO's Distributed Sensing Systems Group and maintains a visiting scientist role there. Education: PhD in Information and Computer Science, University of California, Irvine MS in Computer Networks and Distributed Computing, University of California, Irvine BE in Computer and Communications Engineering, American University of Beirut Research Interests: Network science, blockchain technology, IoT security, sustainable energy systems, and data-driven decision-making. His work bridges theoretical advancements with practical applications in smart grids, health surveillance, and urban mobility. Awards: Finalist for the 2019 Eureka Prize, multiple CSIRO accolades, and IEEE Senior Member status. His research has received industry recognition for interdisciplinary innovation, including the DiNeMo project's real-time disease surveillance system. Advisory & Grants: Leads high-impact projects funded by government and industry partnerships. Supervises PhD candidates in areas like decentralized data processing and privacy-preserving AI. Holds editorial roles at journals such as Ad Hoc Networks and PLoS ONE . Labs & Teams: Directs the Trusted Networks Lab at QUT, fostering collaborations with institutions like Oxford University and MIT. His work emphasizes cross-disciplinary teams to address global challenges in cybersecurity and sustainable systems.
Prof. (ret.) Dr.-Ing. habil. Wolfgang Schröder-Preikschat is a retired Full Professor at the Friedrich-Alexander University (FAU) Erlangen-Nuremberg, affiliated with the Chair of Computer Science 4 (System Software). His expertise focuses on distributed systems, operating systems, and resilient computing architectures. Affiliations: Faculty of Engineering, Department of Computer Science Key Roles: Former Chair of Computer Science 4, Co-lead scientist in Priority Program 2377 (Disruptive Main Memory Technologies) and Priority Program 2378 (Resilient Worlds) Research Contributions: Specializes in power-aware non-volatile memory systems (PAVE project), robust embedded data communication (ResPECT project), and fault-tolerant distributed systems. His work addresses challenges in main memory technologies, system resilience against failures/attacks, and energy-efficient computing. Projects: Leads initiatives like Power Failure-Aware Virtual Non-Volatile Memory (PAVE) and Robust, Power-Efficient Embedded Data Communication Stations (ResPECT), advancing system software reliability and efficiency. Contact: Room 0.054, Martensstr. 1, Erlangen. Email: wosch@cs.fau.de , Website: sys.cs.fau.de/~wosch
H. Jonathan Chao is a Professor in the Department of Electrical and Computer Engineering at New York University (NYU Tandon School of Engineering). He is the Director of the High-Speed Networking Lab, leading a team of 6 PhD students and 10 Master’s students. His research focuses on software-defined networking, network function virtualization, datacenter networks, and high-speed packet processing. Chao has held significant roles, including Head of the ECE Department (2004–2014) and former CTO of Coree Networks. He has authored over 200 publications and holds 58 patents. His awards include IEEE Fellow and National Academy of Inventors (NAI) Fellow. Education: B.S. and M.S. from National Chiao Tung University (Taiwan), Ph.D. from Ohio State University. Research Highlights Developing solutions for data center networks, network security, and quality of service control. Pioneering work in programmable packet schedulers, reinforcement learning for traffic engineering, and SDN security frameworks like SDNShield. Contributions to hybrid SDN networks, bufferless switch architectures, and energy-efficient data center designs. Awards Fellow of National Academy of Inventors (NAI) Fellow of IEEE Telcordia Excellence Award (1987) IEEE Best Paper Award (2001) IEEE New Jersey Coast Section Speaker of the Year (2003) Advisees & Labs Supervises 6 PhD and 10 Master’s students in the High-Speed Networking Lab. Collaborates with the Center for Advanced Technology in Telecommunications (CATT) to advance telecom innovations. Labs & Teams Directs the High-Speed Networking Lab, focusing on cutting-edge networking solutions, and contributes to CATT’s mission of technology transfer and entrepreneurship.
Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.
Dr. Panagiotis Andriotis is a Lecturer in Computer Science at the School of Computer Science, University of Birmingham, within the College of Engineering and Physical Sciences. He is also a GIAC Certified Forensic Examiner (GCFE, GASF) and a Senior Fellow of the Higher Education Academy (SFHEA). His interdisciplinary research spans Cyber Security, Human Factors, and Mobile and Ubiquitous Computing. He teaches courses in Computer Science, Cyber Security, and Digital Forensics. His educational background includes a PhD in Computer Science from the University of Bristol (2016), an MSc with Distinction in Computer Science from the same institution (2011), and a BSc in Mathematics from the National and Kapodistrian University of Athens (2004). Dr. Andriotis’s research interests focus on user-centered security, particularly in mobile environments. He investigates how users interact with Android’s permission systems, develops novel authentication mechanisms like Bu-Dash, and explores adversarial machine learning in cybersecurity. His work bridges technical and human aspects, aiming to improve both system robustness and user experience. His recent publications reflect a strong trend in adversarial machine learning, mobile malware detection, usable privacy, and the societal implications of AI in education. He has contributed to high-impact journals such as IEEE Transactions on Cybernetics, ACM Transactions on Privacy and Security, and Elsevier’s Journal of Information Security and Applications. Best Paper Award at HCI International 2020 Impact Award, UWE Bristol Student Union GIAC Certified Forensic Examiner (GCFE) GIAC Advanced Smartphone Forensics (GASF) SANS Lethal Forensicator Coin Dr. Andriotis has advised PhD students, including Andrew McCarthy, and has been involved in funded research projects such as those related to fuzzing, software security, and critical infrastructure protection in collaboration with Airbus. He has served as an External Examiner at Cardiff Metropolitan University and is currently on the editorial boards of Digital Threats: Research and Practice (ACM) and the Journal of Responsible Technology (Elsevier). He has held visiting roles at the National Institute of Informatics in Tokyo, including as a JSPS Fellow and Toshiba Fellow. He leads research in digital forensics and security, with a lab focus on mobile ecosystems, behavioral modeling, and AI-driven threat detection. His team explores both technical and human dimensions of cybersecurity, contributing to tools and frameworks that enhance mobile security and user awareness.
Bruce Stephen is a Senior Lecturer and Strathclyde Chancellor's Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, where he has been since 1999. His work lies at the intersection of data science and power systems engineering, with a strong focus on real-world industrial applications. His educational background includes a BSc in Aeronautical Engineering from the University of Glasgow (1997), an MSc from the University of Strathclyde (1998), and a PhD in Electronic and Electrical Engineering (2005) from the University of Strathclyde. Dr. Stephen's research centers on data-driven methodologies for solving complex engineering challenges in power systems, particularly under conditions of limited data or domain knowledge. His applications span the entire energy value chain—from generation (nuclear, wind, solar) to transmission, distribution, and end-use. He develops software solutions for condition assessment, anomaly detection, and predictive modeling to support asset management and future grid planning. Notably, he co-founded Silent Herdsman Ltd, a spin-out company applying intelligent systems to precision livestock farming. His recent publications highlight a strong trend toward advanced machine learning techniques such as transfer learning, surrogate modeling, and synthetic data generation (e.g., using CTGANs) to improve reliability and decision-making in power systems. These works emphasize explainability, uncertainty quantification, and scalability, particularly in renewable-rich and data-scarce environments. Dr. Stephen is currently the Principal Investigator on the EPSRC-funded Analytical Middleware for Informed Distribution Networks (AMIDiNe) project, aiming to identify barriers to Net Zero through improved data modeling of unmonitored networks. He has also contributed to major projects including EU FP7 ORIGIN, EPSRC APAtSCHE, AGILE, and Transactive Energy Supply Arrangements. He actively advises students and collaborates on interdisciplinary research. His professional activities include organizing the QFF Quarterly Forecasting Forum (2018) and delivering invited talks at industry workshops. He has supervised datasets and research involving structural health monitoring and industrial diagnostics. His work supports UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action.
Hua Ming is an Associate Professor of Computer Science and Associate Director of Academic Programs at the College of Innovation and Technology, The University of Michigan-Flint. His research focuses on scalable regression testing for high-performance microservices in cloud environments. Email: huaming@umich.edu Research Interests : Cloud Computing Microservices Architecture High Performance Computing Regression Testing Grants : National Science Foundation Collaborative Research Grant ($400,000.00) for "Scalable Regression Testing for High Performance Microservices in the Cloud" (2025-2029), Principal Investigator Current Courses : CSC 801
Marcelo Coelho is a Design Tech Innovation Fellow and Visiting Lecturer at Cornell University's Department of Design Tech within the College of Architecture, Art, and Planning. He is also a Lecturer at the MIT Department of Architecture and Director of the MIT Design Intelligence Lab. His interdisciplinary work bridges artificial intelligence, industrial design, and human-computer interaction, with a focus on physical expression and collaboration between humans and machines. Ph.D., MIT Media Lab Faculty, MIT Department of Architecture Director, MIT Design Intelligence Lab Design Tech Innovation Fellow, Cornell University Marcelo Coelho's research centers on Artificial Intelligence, Machine Learning, Interaction Design, and Industrial Design . His work explores how computation can be embodied in physical forms to enable new modes of creative expression and interaction. He investigates the materiality of computation through installations, products, and large-scale performances that merge art, technology, and design. Projects such as Six-Forty by Four-Eighty and Resolution explore redefining digital pixels in physical space, while Window to the Heart and Beyond Vision demonstrate how computation can transform public experiences. His recent publications reflect a strong trend in physical AI and generative design , particularly in creating intelligent objects and environments that respond to human interaction. Themes include tangible interfaces, crowd-driven assembly, and shape-changing technologies, indicating a trajectory toward more embodied, situated, and collaborative forms of artificial intelligence in design contexts. Marcelo Coelho has received numerous accolades for his innovative work, including: Prix Ars Electronica awards (multiple) Design Miami/ Designer of the Future Award (2010) Red Dot Design Award Fast Company’s Innovation by Design Award Core77 Design Awards (2021, 2014) AIGA 50 Books | 50 Covers (2020) Webby Honoree (2020) Times Square Valentine Heart Design Winner (2018) He has led design initiatives at Formlabs as Head of Design, directing an international team across disciplines including industrial design, software, and mechanical engineering. His work has been exhibited globally at venues such as the Rio 2016 Paralympics, Times Square, Ars Electronica, and the Tel Aviv Museum of Art. He collaborates with artists like Vik Muniz and Aranda\Lasch, and his projects often involve public participation and community engagement. His labs and teams, particularly the MIT Design Intelligence Lab, focus on experimental design research at the intersection of computation and physicality.
