Dr. Xinqun Zhu is an Associate Professor at the University of Technology Sydney (UTS) in the School of Civil and Environmental Engineering . He has held academic positions at Western Sydney University (2016-2017), University of Western Australia (2005-2009), and University of Manchester (2001-2005). His research spans structural health monitoring, steel-concrete composite structures, physics-informed machine learning, and advanced sensor systems.
Bozidar Stojadinovic is a Full Professor and Chair of Structural Dynamics and Earthquake Engineering at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering. He leads the Institute of Structural Engineering and previously held professorships at UC Berkeley and the University of Michigan. His research focuses on community disaster resilience, seismic design, and experimental methods like hybrid simulation. Education: PhD in Civil Engineering, UC Berkeley (1995) MS in Civil Engineering, Carnegie-Mellon University (1990) BS in Civil Engineering, University of Belgrade (1988) Research Interests: Performance-based probabilistic resilience evaluation of civil infrastructure. Earthquake engineering, including seismic isolation and response modification techniques. Development of experimental testing methods, such as hybrid simulations for dynamic structural analysis. Awards: ICE Journal John Henry Garrood King Medal (2023) ACI Chester Paul Siess Award (2017) NSF CAREER Award (1999) Teaching & Advising: Teaches courses on seismic design and structural dynamics at ETH. Advised 49 doctoral students to date. His work integrates advanced methodologies to enhance structural resilience against natural hazards. Labs/Teams: Leads ETH's Institute of Structural Engineering, advancing research in seismic protection and infrastructure resilience through experimental and computational innovations.
Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.
Dr. Chao Hu is the Collins Aerospace Professor in Engineering Innovation and Associate Professor at the University of Connecticut's Department of Mechanical Engineering within the College of Engineering. His research focuses on engineering design under uncertainty, battery health diagnostics, and structural health monitoring. He holds a B.E. from Tsinghua University (2007) and a Ph.D. from the University of Maryland (2011), with prior roles at Medtronic and Iowa State University. Research interests emphasize physics-informed machine learning for prognostics, battery degradation modeling, and reliability-based design optimization. Key publications include work on digital twin models for lithium-ion batteries, federated learning for fleet-wide fault diagnosis, and probabilistic machine learning pipelines for real-time state estimation. He has received awards like the ASME Design Automation Young Investigator Award and highly cited paper recognitions. Dr. Hu serves as Senior Editor for Engineering Optimization and Review Editor for Structural and Multidisciplinary Optimization . His work spans academic leadership in journals and industrial collaborations. Current projects include battery aging datasets (UConn-ILCC and UConn-ISU-ILCC), design for remanufacturing frameworks, and high-rate structural health monitoring techniques.
Xinyu Jia is currently a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich since June 2024, and concurrently serves as Associate Professor in the Department of Mechanical Engineering at Hebei University of Technology, China since October 2022. Her research focuses on advancing uncertainty quantification, structural reliability, and risk assessment methodologies for engineering systems. Her academic background includes: PhD in Mechanical Engineering, University of Thessaly, Greece (2018-2021) Bachelor of Engineering and Master of Science in Mechanical Engineering, Hunan University, China (2011-2018) Dr. Jia specializes in Bayesian learning frameworks for physics-based models, with particular expertise in uncertainty propagation in structural dynamics and industrial robotics applications. Her work develops hierarchical Bayesian approaches that integrate multi-level data to enhance predictive accuracy for complex engineering systems, addressing critical challenges in structural health monitoring and risk-informed decision making. Analysis of her 2022-2023 publications reveals a concentrated research trajectory in applying Bayesian inference to structural dynamics, with emphasis on hierarchical modeling techniques, variational inference schemes, and nonlinear model updating. These contributions predominantly appear in top-tier mechanical engineering journals, demonstrating methodological innovations that bridge theoretical statistics with practical engineering reliability problems. Her scientific recognition includes: Humboldt Research Fellowship (2023) Marie Curie Early Stage Researcher Fellowship (2018) No specific student advisement records are documented, though her Associate Professor role implies teaching responsibilities. Her fellowship awards represent significant research funding supporting her work in uncertainty quantification. As an active member of TUM's Engineering Risk Analysis Group, she contributes to high-impact projects including digital twins for ships, S3UQDyn, Navigating Risk, and infrastructure resilience initiatives like BIG-ROHU and INFRA.RELEARN, focusing on probabilistic risk modeling across civil and mechanical engineering domains.
Rabin Bhattarai is an Associate Professor in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign. His research focuses on understanding climate change impacts on agricultural systems, water quality management, and extreme weather event analysis. He has collaborated on projects involving crop yield optimization, nitrate loss reduction, and the development of novel biochar-based nutrient capture systems. His work integrates modeling tools such as SWAT and DRAINMOD to assess subsurface drainage systems and their environmental impacts. Key contributions include a globally recognized water quality database for lakes and studies on extreme precipitation regimes in Illinois. Bhattarai's publications emphasize interdisciplinary approaches, combining hydrology, climate science, and engineering to address agricultural sustainability challenges. Recent research highlights include analyzing socially vulnerable communities' exposure to compound extreme heat and precipitation events in the Upper Midwest and evaluating probabilistic approaches for extreme weather prediction. His datasets, such as the Framework of Simulating Structural Sediment Perimeter Barriers, demonstrate commitment to practical applications in erosion control and environmental engineering. Research Strengths: Climate-Driven Agricultural Systems, Hydrological Modeling, Sustainable Nutrient Management Notable Projects: Cover Crop Decision Support Tool, DIRECT4AG Project Series
Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.
Dr Ivan Petrunin is a Research Professor in Signal Processing for Autonomous Systems and a DARTeC Fellow at Cranfield University's School of Aerospace, Transport and Manufacturing. His work focuses on advancing sensor technologies, data fusion, and decision-making systems for Cyber-Physical Systems, with applications in aerospace, ground-based autonomous systems, and urban air mobility. Key areas include Position, Navigation and Timing (PNT), vehicle health management, and AI-driven fault detection. He leads research at facilities like the Muti-User Environment for Autonomous Vehicle Innovation (MUEAVI) and collaborates with industry partners like Airbus, Rolls-Royce, and Thales. Education: BSc and MSc in Design of Electronic Equipment from National Technical University of Ukraine (1996–1998), followed by a PhD in Signal Processing for Condition Monitoring from Cranfield University (2013). Prior to Cranfield, he was a Lecturer in Digital Signal Processing at NTU Ukraine (2001–2005). Research Interests: Autonomous Systems & Sensor Fusion Machine Learning in Navigation and Safety GNSS Integrity & Urban Air Mobility Condition Monitoring & Structural Health Multi-Agent Reinforcement Learning Publications: Over 100 journal/conference articles and book chapters, with recent works emphasizing hybrid sensor fusion, resilient navigation architectures, and AI-driven solutions for GNSS-denied environments. Notable contributions include multi-sensor fusion frameworks for UAVs and Bayesian filter innovations. Awards: FRIN Fellowship, SMAIAA Membership, IEEE and ION Fellowships, and FHEA recognition. His work is supported by ESA, Innovate UK, and EPSRC. Advising & Labs: Supervises PhD students in UAV navigation and machine learning. Leads Cranfield's facilities for autonomous systems experimentation and advanced timing node infrastructure.
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.
Dr. Colin Caprani is an Associate Professor and Head of Structural Engineering at Monash University's Department of Civil and Environmental Engineering. His research focuses on bridge traffic loading, structural reliability, vibration serviceability, and Intelligent Transportation Systems. He holds qualifications including a PhD, BSc(Eng), and DipEng, and is a Chartered Professional Engineer (CPEng) and Chartered Structural Engineer (CEng). He has contributed to editorial roles for journals like Advances in Structural Engineering and Computers & Concrete , and led community initiatives such as mini-symposiums on bridge loading and maintenance. His professional affiliations include the Institution of Structural Engineers and the International Association for Bridge and Structural Engineers. Recent projects include the ARC Research Hub for Nanoscience-based Construction Materials and studies on pavement behavior. Awards include the 2017 Best Paper Award and 2021 Institution of Structural Engineers Research Award. Teaching commitments span courses like CIV2226 and CIV4210, emphasizing structural analysis and bridge engineering.
Sriraam Natarajan is a Professor and Director of the Center for Machine Learning at the Erik Jonsson School of Engineering & Computer Science, University of Texas at Dallas. He previously served as an Associate Professor at Indiana University (on leave since 2017) and Wake Forest School of Medicine. His research focuses on artificial intelligence, machine learning, and their biomedical applications, particularly in relational learning, reinforcement learning, and graphical models. He leads the StaRLing Lab and holds fellowships from hessian.AI and RBCDSAI. Education: PhD in Computer Science from Oregon State University (2007), advised by Prasad Tadepalli. Postdoctoral research at University of Wisconsin-Madison under Jude Shavlik and David Page. Research interests span statistical relational AI, causal inference, and healthcare applications. Notable awards include AAAI Fellow (2025), UTD Outstanding Graduate Teaching Award, and roles as AAAI Program Co-Chair and CODS-COMAD 2024 co-chair. Students supervised include over 20 PhD/MS graduates and current advisees in AI and machine learning. Active in editorial roles for JAIR, Machine Learning Journal, and conference PCs (ICML, AAAI, NIPS).
Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.
Dr. Andreas Rauschecker is a neuroradiologist at the University of California San Francisco (UCSF), specializing in advanced imaging technologies (CT, MRI) for diagnosing nervous system disorders in adults and children. He employs AI and image-processing techniques to enhance diagnostic accuracy and collaborates with multidisciplinary teams to improve patient outcomes. Fellowship in Neuroradiology, University of California San Francisco (2020) MD PhD in Neuroscience, Stanford University (2013) MSc in Neuroscience, Oxford University (2005) BS in Biology & Psychology, Georgetown University (2004) His research focuses on applying artificial intelligence to neuroimaging, particularly for conditions like multiple sclerosis, brain tumors, and developmental disorders. He investigates how AI can standardize myelination assessments, detect lesions, and reduce reliance on contrast agents in MRI. Recent publications highlight his work on automated lesion segmentation, transfer learning for MRI analysis, and large language models in radiology. Collaborative efforts include multi-institutional datasets for meningioma and glioma segmentation, emphasizing reproducibility and open science. UCSF Chen Scholar (2024-2026) UCSF Weill Award for Clinician-Scientists (2023) ASNR/ASfNR MIT-E Scholarship (2019) NVIDIA GPU Seed Grant (2018) RSNA Roentgen Fellow Research Award (2019) Rauschecker mentors trainees and collaborates on grants related to AI-driven radiology tools. His work bridges clinical practice and computational innovation, aiming to integrate cutting-edge technologies into standard neuroradiology workflows.
Kallol Sett is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (SUNY), within the School of Engineering and Applied Sciences. His research focuses on risk and reliability analysis of civil infrastructure under extreme events, with expertise in uncertainty quantification, multi-hazard resilience, and geomechanics. He leads the Risk and Reliability Research Group, which develops computational tools integrating physics-based and data-driven modeling, stochastic calculus, and high-performance computing. Education includes a PhD from the University of California, Davis (2007), an MS from the University of Houston (2003), and a BE from Jadavpur University (1997). His work is funded by NASA, NSF, USDOT, NIST, and industry partners. Key research themes include probabilistic geotechnical site characterization, stochastic simulation of seismic ground motion, and life-cycle cost-benefit analysis of infrastructure systems. Advising includes mentoring over 10 PhD and MS students, with notable alumni now in academia and industry roles such as Assistant Professors at Embry-Riddle Aeronautical University and Tianjin University. His lab’s recent studies address real-time decision support systems for hurricane-impacted infrastructure, resilience deficit indices, and multi-hazard financial risk assessment of integrated infrastructure systems.
Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.
