Mustafa Hajij is an Assistant Professor in the Data Science program at the University of San Francisco. He holds a PhD in Mathematics from Louisiana State University, an MS in Computer Science, and completed postdoctoral training at University of South Florida and Ohio State University. Previously, he served as Assistant Professor at Santa Clara University and as an AI Research Scientist at KLA Corporation. His research develops foundational frameworks for topological deep learning, including cell complex neural networks and geometric learning architectures that operate beyond graph domains. He leads the NSF-funded project 'A Unifying Deep Learning Framework Using Cell Complex Neural Networks' (DMS-2134231, $547,626). Recent publications establish new paradigms for topological representation learning, including combinatorial complexes and simplicial networks, with applications in computational biology, 3D vision, and drug discovery. He organized the ICML Topological Deep Learning Challenges and develops open-source tools like TopoX for topological learning.
Shashi Raj Pandey serves as Assistant Professor in the Department of Electronic Systems at Aalborg University's Technical Faculty of IT and Design, Denmark. His research is anchored in the Connectivity section and Connectivity Classique-Center for Classical Communication in the Quantum Era, with office location at Fredrik Bajers Vej 7C, C1-111, 9220 Aalborg Øst. His core research spans Network Economics, Game Theory, and Wireless Networks, with specialization in Decentralized Machine Learning and Semantic/Goal-oriented Communications. Current work integrates Digital Twin technologies with 6G systems for industrial automation and earth observation, emphasizing resource-efficient protocols for Internet of Things and edge intelligence applications. Recent publications (2024-2025) reveal a clear trajectory toward AI-6G convergence, featuring semantic communications for satellite imaging, game-theoretic network resource allocation, and digital twin implementations for autonomous systems. Key themes include communication efficiency in distributed learning and physical-digital world integration. Notable recognitions include: Best PhD Thesis Nominee (2021) Excellent Paper at Korea Software Congress, KIISE 2021 Student Best Paper Award at APNOMS 2019 Best Paper at Korea Software Congress, KIISE, 2018 Brain Korea 21st Century Plus Fellowship Academic service includes external PhD examination for EU SNS projects and peer review for premier conferences (AAAI, ICLR, ICML). His lab work within the Connectivity Classique-Center explores classical communication frameworks applicable to quantum-era networks, with focus on semantic information theory and decentralized network architectures.
Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.
Hokeun Kim is an Assistant Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He previously held positions at Hanyang University (2021-2023) and worked in industry roles at Google, LinkedIn, and HP Labs. His research focuses on cyber-physical systems, IoT security, and computer architecture, with a particular emphasis on safety and security aspects of time-sensitive systems. Education: Ph.D. in EECS, University of California, Berkeley (2017) M.S. in EECS, Seoul National University (2012) B.S. in Computer Science and Engineering, Seoul National University (2010) Research Interests: Kim’s work spans secure IoT frameworks, real-time embedded systems, and edge computing. He develops tools like the Secure Swarm Toolkit (SST) and Lingua Franca, addressing challenges in distributed system security, interoperability, and performance. Key Contributions: Authored over 30 peer-reviewed publications in top venues like IEEE Transactions, ACM Conferences, and DATE. Received the ACM/IEEE Best Paper Award (IoTDI 2017) and IEEE Micro Top Picks Honorable Mention (2017). Active in organizing conferences (e.g., DATE, FDL) and serves on technical committees for top journals/conferences. Teaching: Courses include Computer Architecture I/II, Real-Time Embedded Systems, and IoT design at both undergraduate and graduate levels.
Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.
Dr. Graziano Fiorillo is an Assistant Professor in the Department of Civil Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a Ph.D. from the City University of New York and M.Sc./B.Sc. from the University of Naples, Italy. His research focuses on structural reliability, bridge systems analysis, and risk assessment, incorporating machine learning and high-performance computing. He has contributed to probabilistic frameworks for infrastructure resilience, filovirus outbreak modeling, and bridge redundancy evaluation. Education: Ph.D. Civil Engineering, City University of New York, 2016 M.Sc. Building Engineering, University of Naples Federico II, 2003 B.Sc. Building Engineering, University of Naples Federico II Research Interests: Dr. Fiorillo specializes in structural analysis of bridges, risk-based design, and machine learning applications in infrastructure. He develops probabilistic models for bridge network reliability and flood risk assessment, with a focus on Manitoba's infrastructure resilience. His work integrates computational fluid dynamics (CFD) and energy efficiency solutions for buildings. Publications: His recent work emphasizes interdisciplinary approaches to infrastructure challenges, including CFD for sediment transport, EnergyPlus-based building efficiency studies, and MPI parallel computing for reliability analysis. His 2024 studies on flood-overload interactions and additive manufacturing in construction highlight emerging trends in civil engineering. Awards: He received the 2012 New York State Intelligent Transportation Society Award for best student paper. His research has been applied to truck weight regulation strategies and bridge importance factor calibration. Advising & Grants: Offers M.Sc. opportunities in CFD, building energy efficiency, and bridge structures. Positions require expertise in OpenFOAM, EnergyPlus, or structural analysis software. No specific grants mentioned in the text.
Eunsuk Kang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. Their research focuses on the intersection of software engineering and formal methods, emphasizing rigorous modeling and analysis techniques to create safe, secure, and reliable systems. PhD in Computer Science from MIT Postdoctoral scholar at NSF ExCAPE program Former connected vehicles researcher at Toyota Their research interests span software design, requirements engineering, modeling, specification and verification, system safety, security, and cyber-physical systems (CPS). Recent projects explore robustness in evolving environments, specification engineering, automated reasoning for complex systems, and safety/resilience mechanisms in ML-based CPS. Publications highlight advancements in Signal Temporal Logic decomposition, LTL specification learning, and requirement-driven adaptation frameworks. Selected scientific contributions include: tl;dr: Chill, y’all – AI will not devour SE (Onward! Essays 2024): Critical perspective on AI integration in software engineering FairSense (ICSE 2025): Long-term fairness analysis for ML-enabled systems AlloyMax (ESEC/FSE 2021): Relational specification satisfaction techniques As an educator, Kang teaches graduate courses in software design and formal methods, including: 17-423/723: Designing Large-Scale Software Systems 17-614 & 624: Formal Methods 17-445/645: Software Engineering for AI-enabled Systems 17-651: Models of Software Systems Service activities include: Program co-chair for SEAMS 2026 Co-organizer of Dagstuhl Seminar on Specification Engineering Co-organizer of International Workshop on Designing Software Program committee member for ICSE, OOPSLA, ASE, and specialized conferences Notable research collaborations include work with: Ben-hau Chia (PhD student) Parv Kapoor (PhD student) Yiliang (Leo) Liang (PhD student) Sumon Biswas (Postdoc) Rômulo Meira-Góes (Postdoc)
Hsiao-Dong Chiang is a Professor in the School of Electrical and Computer Engineering at Cornell University. He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley, and has made significant contributions to nonlinear system theory and power system stability. His research spans theoretical development and practical applications in electric power systems, nonlinear optimization, and machine learning. B.S., Electrical Engineering, National Taiwan University, 1979 M.S., Electrical Engineering, National Taiwan University, 1981 Ph.D., Electrical Engineering, University of California, Berkeley, 1986 Chiang's research interests focus on nonlinear system theory , power system stability and control , nonlinear optimization , and their applications to modern power grids with high penetration of inverter-based resources. He is renowned for developing the BCU method and TRUST-TECH methodology , which have enabled fast direct stability assessment and global optimization in complex systems. His work bridges fundamental theory with industrial deployment through his companies, Bigwood Systems, Inc. and Global Optimal Technology, Inc. His recent publications (2024–2025) reflect a strong trend toward integrating machine learning and deep neural networks with power system analysis , particularly in state estimation, optimal power flow, and voltage control. There is a clear emphasis on handling uncertainty, non-convexity, and multi-scale dynamics in active distribution networks and integrated energy systems . His work increasingly focuses on resilience , real-time control , and user-centered methodologies for modern grid operations. Chiang has received numerous scientific honors, including: IEEE Fellow (1997) United States Presidential Young Investigator Award (1989) Multiple DOE Grid Optimization Challenge Awards (2020–2023) Best Paper Awards from IEEE Transactions and Conferences Outstanding Education Award, Cornell University (1990) He has successfully managed over 100 research projects and holds 28 U.S. and international patents. As the founder of Bigwood Systems, Inc., he has commercialized advanced software for utility companies across the U.S. and Japan. His team has published over 480 refereed papers and received more than 17,500 citations. He advises a large research group and leads innovations in computational methods for energy systems. His lab is actively involved in developing next-generation tools for grid security, optimization, and machine learning integration.
Mehmet Uğur KAHRAMAN is an Assistant Professor at the Department of Interior Architecture and Environmental Design at Antalya Bilim University, where he has served since 2017. Previously, he held a faculty position at Kayseri Nuh Naci Yazgan University (2015–2017). His academic journey includes a Doctorate from Hacettepe University (Interior Architecture and Environmental Design), a Master of Design (Interior Design) from Swinburne University of Technology (Australia), and a Bachelor’s degree from Hacettepe University’s Department of Interior Architecture and Environmental Design. Before academia, he worked as a construction site manager at KG Architecture in Istanbul, co-founded the food and beverage brand 'Shot&Bite' in Ankara, and later served as a designer at QUBİ Design Office. His research focuses on integrating artificial intelligence into design education, neurocognitive aspects of spatial design, sustainable materials, and pedagogical innovations in interior architecture education. He has authored over 20 peer-reviewed articles on topics ranging from AI-driven furniture design to multisensory hospitality spaces. His work bridges theoretical research with practical applications, such as developing curriculum models for design studios and exploring waste-to-art construction techniques. KAHRAMAN’s studies also address health impacts of building materials and the psychological dimensions of housing during crises like the COVID-19 pandemic. He maintains active research collaborations, particularly in Turkey and Australia, and has contributed to public infrastructure projects involving material conservation and adaptive reuse. His educational philosophy emphasizes student-centered learning, interdisciplinary approaches, and leveraging digital tools for contemporary design challenges.
Daniel Campbell is a Lecturer in Web Development & Web AI at the Computer Science department of Edge Hill University. His work contributes to UN Sustainable Development Goals related to health and innovation. He is affiliated with the Centre for Intelligent Visual Computing and the Data and Complex Systems Research Centre. Education: He completed his Doctoral Thesis in 2018 titled 'An Ontology-Driven Approach To Personalised mHealth Application Development' under supervisors E. Pereira, G. McDowell, and C. Balakrishna. Research focuses on mHealth applications, ontology-driven frameworks, machine learning for health monitoring, and software engineering practices like bug prediction and open-source repository analysis. Recent projects include a Knowledge Exchange initiative with the water industry (2024-2026) as a Co-Investigator. His articles explore topics ranging from accelerometer-based elderly activity prediction to automated classification of software repository messages. Collaborations span institutions globally, with active engagement in topics like healthcare technology and user-centric design.
Guanghan Meng is an Assistant Professor at the University of California, Berkeley , with dual appointments in the Herbert Wertheim School of Optometry and Vision Science and the Department of Electrical Engineering and Computer Science (EECS) . He leads the Visionary Optical Imaging Lab (VOILA) , focusing on interdisciplinary research combining optical physics and computational science to develop advanced microscopy technologies for eye and brain imaging. Education : PhD (2021, UC Berkeley), BE (2015, Shanghai Jiao Tong University) PhD Programs Affiliated With : Vision Science, Applied Science & Technology (AS&T), EECS His research integrates optical physics , computational biology , and artificial intelligence to create cutting-edge imaging tools. Recent work includes differentiable wave-optics libraries (Chromatix), super-resolution microscopy techniques, and high-speed neural imaging systems. Publications highlight applications in neuroscience (cerebral circulation, synaptic activity) and biomedical imaging (OCT, two-photon microscopy). VOILA is a highly interdisciplinary team spanning physics , engineering , and biology . In 2025, the lab will welcome 2 PhD students and 1 postdoc, though funding is currently at capacity for new members. Meng is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and Berkeley Center for Computational Imaging (BCCI) .
Tobias Ofner-Graff is a researcher at the Institute of Forest Growth within the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). Based at Peter-Jordan-Straße 82, 1190 Wien, his work focuses on advanced forest monitoring technologies. His research interests include: LiDAR and remote sensing applications in forestry Automated forest inventory systems Forest regeneration quantification Airborne Laser Scanning (ALS) data analysis Sustainable forest harvesting planning Recent project contributions include: Leading lidar-based forest monitoring systems development Developing spatial forest growth models Implementing digital inventory workflows His publications demonstrate expertise in: Quantifying forest resources through 3D point clouds Advanced timber stack measurement techniques ALS data integration for forest modeling Mobile laser scanning applications Forest climate adaptation strategies
Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).
Professor Lingxiao Jiang is a full-time faculty member at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU) , where he serves as Director of the Centre for Research on Intelligent Software Engineering (RISE) . His research and teaching focus on software engineering , program analysis , code search & reuse , and deep learning of code to enhance software quality, development productivity, and security. PhD, University of California, Davis (2009) Professor Jiang's research explores context-aware software deep learning , code clone detection , and security & privacy through tools like DECKARD (code clone detection), EqMiner (functionally equivalent code), and SmartEmbed (smart contract security). His work leverages distributed computing , symbolic execution , and neural program models for large-scale code analysis. Recent publications highlight trends in deep learning-based code analysis , smart contract vulnerabilities , and automated program transformation . Key themes include code clone detection , semantic patches , and cross-language API mappings . Scientific Awards ACM SIGSOFT Impact Paper Award (2018) for scalable code clone detection As advisor, Professor Jiang has mentored 14 graduate students, including Lucia (Ph.D. 2014) , Shaowei Wang (Ph.D. 2015) , and current advisees in intelligent software engineering. He leads multiple grants on code mining , security of digital platforms , and AI systems governance . RISE Lab at SMU develops tools like MANDO (smart contract analysis), iTiger (issue title generation), and TreeCaps (code processing with capsule networks). The lab actively recruits researchers in software engineering and AI.
Dr. Felix Härer is a Lecturer and researcher at the University of Applied Sciences FHNW, School of Business, Basel, Switzerland, and also teaches externally at the University of Fribourg. He is affiliated with the Digital Trust Competence Center, where he conducts research and teaching in IT Security, Cybersecurity, Digital Trust, Blockchain, AI, Cloud Computing, and Systems Modeling. His research interests span a broad and interdisciplinary range, including: Digital Trust and Cybersecurity Blockchain and Decentralized Systems AI and Knowledge-based Systems (including LLMs and RAG) Software and Systems Modeling (BPMN, ArchiMate) Data Science and ETL-based Analytics Zero Trust and Secure Architectures His recent publications (2020–2023) demonstrate a strong focus on blockchain interoperability, model-driven engineering, decentralized applications, and the integration of AI with conceptual modeling. He explores scalable architectures, cross-chain query languages, and secure attestation mechanisms, often combining modeling approaches with emerging technologies. His work bridges academic rigor with practical implementation in distributed and cloud environments. Scientific awards include: Best Paper Award at IEEE PKIA 2023 He actively supervises bachelor’s and master’s theses and student projects in digital trust and related domains. He has served on PhD committees externally and is a reviewer and program committee member for journals and conferences such as IEEE Transactions, WWW, CAiSE, and EMISAJ. His professional experience includes industry work at Siemens Healthineers in software engineering. He is a member of the IEEE Blockchain Group (Switzerland) and contributes to UN/CEFACT standards for e-commerce and supply chain data. He is involved in organizing workshops and conferences, including B4ISE 2025, B4TDS 2023–2024, and DESRIST 2023, and has delivered keynotes on Computational Trust and Blockchain Interoperability.
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