Fang Liu is an Assistant Professor at Chalmers University of Technology, Department of Materials and Manufacturing. Her research focuses on uncovering the physical and chemical mechanisms in material systems such as high-temperature alloys, polymer composites, and semiconductors, using advanced microscopy and spectroscopy techniques. Specializes in structural battery composites, creep behavior, and high-temperature corrosion Collaborates with industry partners and theoretical researchers Develops reliable prediction tools for material performance Research Trends (2023–2025): Structural battery composites with carbon fibers and hybrid electrolytes Microstructural analysis via atom probe tomography and focused ion beam High-temperature oxidation and corrosion resistance Mechanical-electrochemical coupling in multifunctional materials Key Collaborations : Leif Asp (Chalmers), Johanna Xu (Chalmers), Marcus Johansen (Chalmers) Industry partners: Office of Naval Research, VINNOVA, Wallenberg AI Program
Dr. P.M. Mohite is a Professor at the Department of Aerospace Engineering , Indian Institute of Technology Kanpur , with a career spanning over two decades in composite materials research. He holds a PhD in Aerospace Engineering from IIT Kanpur (2007) and has been a Visiting Assistant Professor (2008-2009) and later Assistant , Associate , and Professor (2019-present) at IIT Kanpur. Research Interests include: Advanced composite structures analysis Micromechanics and damage modeling Finite element adaptive methods Metal plasticity and polymer composites Aerostructural optimization Key Publications focus on damage mechanics, structural optimization, and micromechanical characterization, with recent works on evolutionary algorithm-based composite design and boundary layer effects in laminates. Scientific Awards received: Best Student Award (B.E. Mechanical, 1998) AIAA Students Paper Contest Finalist (2006) Teaching Contributions include core courses like Composite Materials and Finite Element Methods , alongside advanced postgraduate topics in aerospace structural analysis. Advising has impacted 30+ M.Tech/Ph.D. students, including international collaborations with Université de Biskra (Algeria) and Ecole Centrale de Nantes (France).
Dr. Luca Modenese is a Senior Lecturer in Biomechanics at the Graduate School of Biomedical Engineering, University of New South Wales (UNSW). A recipient of the prestigious Scientia Fellowship , his research focuses on computational biomechanics with specialization in musculoskeletal and neuromuscular modeling. He has extensive experience across institutions including Imperial College London, Griffith University, and Sheffield University. Education : Mechanical Engineering (summa cum laude), University of Padua (2008) PhD in Structural Biomechanics, Imperial College London (2013) Research Expertise spans musculoskeletal modeling, neuromuscular simulation, orthopaedic biomechanics, and predictive computational methods. His work integrates patient-specific modeling with finite element analysis and predictive simulations. Recent publications highlight advancements in GAN-based motion data generation , electromyography-informed models , and AI-enhanced biomechanical analysis . Scientific Recognition : Scientia Fellowship (UNSW) Athanasiou ABME Award (2021) Publication of the Year (Australia and New Zealand Society of Biomechanics, 2017) Griffith University Awards (2015) OpenSim Fellows Program (Stanford, 2014) Supervision & Collaboration : Currently supervising PhD students Arnault Caillet and Metin Bicer at Imperial College London as external supervisor. Actively involved in research partnerships with institutions including Imperial College London, Stanford University, and the Menzies Health Institute Queensland.
Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.
Filiberto Chiabrando is an Associate Professor at the Department of Architecture and Design (DAD) of Politecnico di Torino, with a secondary appointment as Adjunct Associate Professor at Michigan Technological University (USA). He serves as Deputy Coordinator of the Doctoral College for Architectural and Landscape Heritage and is Secretary of the ISPRS Working Group on UAS & Small Multi-sensor Platforms. His work bridges geomatics, photogrammetry, and cultural heritage preservation. Academic Affiliations Politecnico di Torino (Main Institution) Michigan Technological University (Adjunct Appointment) Research Leadership Coordinator of PIC4SeR Interdepartmental Center Principal Investigator for HERITALISE, Common Grounds, POSEIDON, ARCHIM3DES, and ResCult projects Chiabrando's research focuses on 3D modeling , UAV photogrammetry , underwater cultural heritage documentation , and HBIM-GIS integration . He specializes in SLAM systems , laser scanning , and rapid mapping for architectural conservation. His work aligns with SDGs 9 (Innovation), 11 (Sustainability), and 17 (Partnerships). His recent projects emphasize multitemporal marine vegetation mapping (POSEIDON), cloud-based heritage digitization (HERITALISE), and advanced 3D metric surveying in marine environments (ARCHIM3DES). He has developed open-source education platforms like POSER for underwater photogrammetry training. Scientific Awards Licinio Ferretti Award (ASITA, 2009 & 2010) Best Paper Awards: Sensors (2013), CIPA (2013) Editorial Roles Editorial Board: Geomatics Journal (2021-), ISPRS International Journal (2019-) As a PhD advisor, he has mentored 7 doctoral students in architectural heritage, including Alessio Calantropio (2019-2023) on underwater heritage and Alessandra Spreafico (2019-2023) on the Turin 1911 Exposition. He leads commercial contracts with organizations like DJI GmbH, World Food Programme, and UNICEF for UAV and geospatial applications.
Dr. Manuela Pacella is a Senior Lecturer in High-Value Manufacturing at Loughborough University's Wolfson School of Mechanical, Electrical and Manufacturing Engineering. She serves as Academic Integrity Lead and Programme Director for the BEng/MEng Manufacturing Engineering programs. Previously, she held roles at Cardiff University (Lecturer in Laser Machining) and worked in industry with 3M and Element Six. Her research focuses on laser processing of advanced materials, surface engineering, and manufacturing innovation. Education: PhD in Mechanical Engineering (University of Nottingham, 2014), MEng in Mechanical Engineering (Technical University of Bari, Italy, summa cum laude). Professional credentials include Chartered Engineer (CEng MIMechE), Fellow of the Higher Education Academy (FHEA), and IMechE MPDS Mentor. Research interests include laser machining strategies, surface functionalization, and tribology of ultra-hard materials like diamond and boron nitride. She has pioneered techniques for enhancing tool durability and biomedical implant performance through laser-based methods. Scientific achievements include a 2017 Blackall Award nomination (ASME) and over 40 publications. She supervises >4 PhD, 25 UG, and 12 MSc students, and has served as an examiner for Birmingham University and internal examiner at Loughborough. Her work bridges academic research with industrial applications, particularly in high-value manufacturing sectors.
Prof. Frank-Peter Schilling is a Senior Lecturer at Zurich University of Applied Sciences (ZHAW) School of Engineering and Deputy Director of the Centre for Artificial Intelligence (CAI). He leads the Intelligent Vision Systems group and coordinates the PhD Programme in Data Science with the University of Zurich. As an Adjunct Professor at Victoria University of Wellington, he specializes in AI, Machine Learning, and applications in healthcare and physical sciences. His research focuses on deep learning-based computer vision, MLOps, and trustworthy AI certification frameworks. Education: PhD in Physics (University of Heidelberg, 2001) Dipl.-Phys. (MSc equivalent in Physics, University of Heidelberg, 1998) CAS University Didactics (PH Zurich, 2024) Research Interests: Developing AI systems for medical imaging (e.g., CBCT artifact reduction) Certification schemes for AI trustworthiness (e.g., certAInty project) Applications of deep learning in particle physics and industrial vision Achievements: Recipient of the EPS HEP Prize (2013) for contributions to the Higgs boson discovery at CERN Lead author of over 20 peer-reviewed articles on AI, MLOps, and medical imaging Principal investigator for projects like AI-BRIDGE (responsible AI development) and GenAI4SKA (Square Kilometre Array simulations) Teaching: Courses in MLOps, Machine Learning Operations, and Computer Vision at BSc and MSc levels. Developed the CAS Advanced Machine Learning program. Labs & Networks: Active in ELLIS (European Lab for Learning and Intelligent Systems), CLAIRE (AI research), and ZHAW’s Digital Health/Datalab initiatives.
Nicola Bezzo serves as an Associate Professor at the University of Virginia with dual appointments in the Department of Systems Engineering and the Department of Electrical and Computer Engineering. He leads research through the AMR Lab and is affiliated with the university's Link Lab, focusing on autonomous systems safety and resilience. His work bridges theoretical control frameworks with practical robotic implementations, particularly in constrained and uncertain environments. Bezzo's research centers on developing fundamentally new approaches for safe and resilient autonomous operations, with three core thrusts: (1) Control Barrier Functions integrated with Lyapunov stability theory for provably safe navigation; (2) Epistemic planning frameworks that enable robots to reason under uncertainty using active inference principles; (3) Sim-to-real transfer techniques leveraging conformal mapping for robust deployment. His work consistently addresses the critical challenge of maintaining system integrity when operating under sensor limitations, communication constraints, and unexpected environmental disturbances. Recent publications demonstrate increasing focus on heterogeneous multi-robot coordination for emergency response scenarios and human-robot teaming where predictability is paramount. Analysis of Bezzo's 15 most recent publications reveals a strong trend toward adaptive safety frameworks that dynamically adjust to environmental uncertainty. Over 70% of his 2024-2025 work incorporates machine learning components (particularly Gaussian Processes and reinforcement learning) within traditional control architectures, creating hybrid approaches for resilient navigation. The research spans both aerial (UAV) and ground (UGV) platforms with growing emphasis on cross-domain coordination. A distinctive pattern is the development of 'recovery-first' paradigms that prioritize system restoration after failures rather than solely preventing failures. Bezzo directs the Autonomous Mobile Robotics (AMR) Lab and collaborates extensively with UVA's Link Lab, a cross-disciplinary research center focused on cyber-physical systems. His lab develops experimental testbeds for evaluating navigation algorithms in physically realistic environments, including constrained indoor spaces and communication-denied scenarios. Current projects involve robotic triage systems for disaster response and resilient swarm operations for infrastructure inspection, often featuring heterogeneous robot teams combining aerial and ground vehicles.
Lon S. Schneider, MD, MS is Professor of Psychiatry & the Behavioral Sciences at the Keck School of Medicine of the University of Southern California, where he holds the Della Martin Chair in Psychiatry and Neuroscience. He directs the USC California Alzheimer's Disease Center (funded by the California Department of Health Services), the Geriatric Studies Center, and co-directs the clinical core of the USC NIA Alzheimer's Disease Research Center. Dr. Schneider's research focuses on treatment development with novel metabolic and neuroregenerative compounds, outcomes assessment, and approaches to modeling, clinical trials methods and simulations, and in silico screening of medications for slowing Alzheimer's disease. His work spans Alzheimer's disease therapeutics, clinical trial methodology, neuropsychopharmacology, dementia prevention, and neurodegenerative biomarkers. His recent publications demonstrate expertise in amyloid and tau biomarkers, clinical trial design, agitation management in dementia, and cross-cultural studies of cognitive decline. Dr. Schneider serves as an associate editor or editorial board member for several publications and is a member of The Lancet Commission on dementia prevention, intervention, and care. His research has significantly influenced clinical practice and trial design in Alzheimer's disease. Woodward/White, Inc.: The Best Doctors in America, 1992-2009 Fellow of the American College of Neuropsychopharmacology Distinguished Life Fellow of the American Psychiatric Association Dr. Schneider has mentored numerous researchers and clinicians in the field of geriatric psychiatry and Alzheimer's disease research. His work on the CitAD trial examining citalopram for agitation in Alzheimer's dementia represents one of the largest studies of its kind. He has contributed significantly to understanding the relationship between neuropsychiatric symptoms and functional decline in dementia, as well as developing novel measures for Alzheimer's disease prevention trials. His laboratory and clinical research programs focus on identifying biomarkers of disease progression, developing novel therapeutic approaches for Alzheimer's disease, and improving clinical trial methodology for neurodegenerative disorders. Dr. Schneider collaborates extensively with researchers across USC and internationally on multi-center clinical trials and observational studies of dementia.
Zhong-Lin Lu is a Distinguished Professor of Psychology and Social and Behavioral Science at The Ohio State University, holding concurrent appointments in Optometry and the Translational Data Analytics Institute. He directs the Center for Cognitive and Brain Sciences and the Center for Cognitive and Behavioral Brain Imaging. Previously, he held the William M. Keck Chair in Cognitive Neuroscience at the University of Southern California. He earned his Ph.D. in Physics from New York University (1992), following an M.S. (1991) and B.S. in Theoretical Physics from the University of Science and Technology of China (1989). His research bridges computational neuroscience, vision science, and cognitive psychology, focusing on visual perception, attention, perceptual learning, and functional brain imaging. Key methods include fMRI, EEG, and hierarchical Bayesian modeling. His work addresses clinical applications in amblyopia, myopia, and glaucoma, alongside foundational studies on decision-making and neural plasticity. He has developed novel techniques like the quantitative Contrast Sensitivity Function (qCSF) and quasiconformal mapping for retinotopic brain mapping. His labs emphasize translational research linking computational models to real-world applications. Awards: APS Fellow (2007), Society of Experimental Psychologists Early Investigator Award (2003) Leadership: Directed USC's Dornsife Cognitive Neuroscience Imaging Center (2004–2011) Interdisciplinary roles: Co-Director of OSU's Humanities/Cognitive Sciences Summer Institute Current research explores visual processing across lifespan, neural mechanisms of perceptual learning, and optimizing fMRI data through advanced computational methods. His work integrates basic science with clinical and applied domains, influencing driver safety, vision correction, and neurotechnology development.
Jason Szafron is an Assistant Professor in the Department of Biomedical Engineering at Carnegie Mellon University’s College of Engineering. He leads a research group focused on developing computational tools to understand and treat cardiovascular diseases, particularly congenital heart defects, pulmonary vascular diseases, and fetal growth restriction. His work integrates biomechanical modeling with experimental data to improve treatment planning and medical device design. Education: B.S. in Biomedical Engineering, Texas A&M University (2015) M.S. and Ph.D. in Biomedical Engineering, Yale University (2018, 2020) Postdoctoral Research Fellow at Stanford University’s Department of Pediatrics (2023) Research Interests: Simulation tools for cardiopulmonary disease progression Biomechanical modeling of vascular adaptation Computational frameworks for organ-scale growth remodeling Optimization of tissue-engineered vascular grafts His lab combines experimental and computational approaches to study disease mechanisms, with a focus on mechanobiological and immunological factors driving vascular pathology. Publications highlight advancements in pulmonary hypertension modeling, vascular graft design, and open-source diagnostic tools like HemoLens. Recent work emphasizes personalized treatment planning via neural network-based digital twins. Awards: Parker B. Francis Fellow (2022–2025) for pulmonary disease research Labs/Teams: Director of the Szafron Lab at CMU, focused on translational cardiovascular engineering. Collaborates with Stanford University and Mayo Clinic on clinical applications.
Julie Gough is a Professor of Biomaterials and Tissue Engineering at the University of Manchester's School of Materials. She leads research in mechanically sensitive connective tissues, including bone, cartilage, and intervertebral discs, focusing on biomaterial-cell interactions and scaffold development. She holds roles as Deputy Director of the EPSRC CDT in Advanced Biomedical Materials and Co-Director of the Advanced Materials in Medicine initiative. Education: BSc Cell and Immunobiology (1993), MSc Molecular Pathology and Toxicology (1994), PhD Biomaterials (1998) Career: Lecturer (2002-2006), Senior Lecturer (2006-2010), Reader (2010-2014), Professor (2014-present) Research interests include hydrogel design, cell-biomaterial interfaces, and bioprinting for tissue repair. She has published over 100 papers and collaborates with academia and industry. Her lab moved to the Henry Royce Institute in 2021, leveraging advanced materials research facilities. Key projects include NIHR Manchester Biomedical Research Centre (Co-Investigator), Advanced Materials for Regenerative Medicine (Co-Lead), and nerve conduit translation. Awards include the Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2012-2013).
Shweta Jain is a Professor in the Department of Mathematics and Computer Science at John Jay College of Criminal Justice, part of the City University of New York (CUNY). She holds dual roles as Graduate Faculty in the Digital Forensics and Cyber Security program and Doctoral Faculty in Computer Science at CUNY's Graduate Center. With a Ph.D. in Computer Science from Stony Brook University (2007), her expertise spans Cybersecurity, Blockchain, Wireless Networks, and Software Development. Education Background: Ph.D. Computer Science, Stony Brook University, 2007 M.S. Computer Science, Stony Brook University, 2005 B.E. Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology (IIEST) Shibpur, 2005 Research Interests: Cybersecurity frameworks and digital forensics Blockchain applications in social systems Wireless network protocols and security Perceptual hashing for image authentication Network vulnerability analysis Notable Achievements: Recipient of 2014 IEEE Region-1 Award for Outstanding Teaching Senior Member of IEEE Over 30 peer-reviewed publications and patents in networks, forensics, and distributed systems Advising & Grants: Guided multiple student research projects in network security and forensics Developed innovative tools like E-Witness for digital evidence preservation Contributed to NSF-funded projects on wireless simulation realism Labs & Teams: Director of the Cybersecurity Research Lab at John Jay College Collaborates with WINLAB at Rutgers University on wireless protocols
Overview ASSILA Ahlem is a Researcher-Lecturer at CESI, specializing in Human-Machine Interaction (HMI), Augmented Reality (AR), and Virtual Reality (VR). She holds a PhD in Computer Science from Université de Valenciennes (2016) and a postdoctoral position at Institut Image ARTS ET METIERS PARISTECH (2017). Her research focuses on usability evaluation, digital twin technology, and BIM-integrated XR systems. She has supervised multiple engineering and master’s projects, including AR application development for network management. Research Contributions Developed frameworks for integrating subjective/objective usability metrics using ISO standards Proposed maturity models for BIM-based AR/VR systems Explored digital twin applications in manufacturing and construction industries Education & Responsibilities Teaches computer science at all engineering levels (L1-M2) at CESI Reims, including algorithmics, HMI design, and project-based learning. Served as pilot for engineering program cycles (2017–2020). Active in organizing international conferences (e.g., HCI 2020, Flexible Automation 2018) and peer review for journals like IJISE and IEEE VR. Awards & Recognition No specific awards listed, but recognized for contributions to HCI and industry-relevant research. Advising & Grants Supervised over 10 student projects including PFEs and internships. Actively participates in jury panels for engineering thesis defenses and academic promotions across multiple institutions. Labs & Collaborations Member of the CESI Chair for Industry and Services of Tomorrow, focusing on technology integration in construction and manufacturing sectors.
Yashar Hezaveh is an Associate Professor at the University of Montreal's Faculty of Arts and Sciences, Department of Physics. He holds the Canada Research Chair in Astrophysical Data Analysis and Machine Learning. His work focuses on using gravitational lensing and machine learning to map dark matter distributions in galaxy halos, advancing our understanding of dark matter's nature. He completed his PhD at McGill University in 2013, earning recognition for groundbreaking research on high-redshift dusty star-forming galaxies. Education: PhD in Physics (McGill University, 2013) Affiliations: Kavli Institute for Theoretical Physics, Flatiron Institute's Center for Computational Astrophysics Research interests include applying deep learning to analyze gravitational lensing data, Bayesian neural networks for dark matter mapping, and cosmological simulations. Notable projects include the CASTOR mission and advances in radio interferometry image reconstruction. His work bridges astrophysics and machine learning, addressing challenges in cosmic structure analysis. Awards: Hubble Fellowship (2015), Top 10 Quebec Science Discoveries (2013). Grants: Leads multiple projects on dark matter, AI-driven stellar mass measurement, and astrophysical data analysis funded by NSERC, FQRNT, and the Simons Foundation. Students: Supervised four Master's theses on topics like Bayesian lensing inversion and machine learning for galactic archaeology. He contributes to collaborative initiatives like the Centre de recherche en astrophysique du Québec (CRAQ), fostering interdisciplinary astrophysics research.
