Dr. Yong Bai is the McShane Chair and Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University. Holding a Ph.D. from North Carolina State University, he specializes in construction engineering and management, with a focus on emerging technologies like drones and deep learning for infrastructure maintenance. Ph.D., Civil/Construction Engineering, North Carolina State University M.S., Civil/Construction Engineering, Clemson University Bachelor of Engineering, Civil Engineering, Tsinghua University His research bridges construction operations efficiency , work zone safety , and AI-driven infrastructure assessment . Recent work includes automated façade modeling and sidewalk defect detection using drone imagery and convolutional neural networks. His publications (2018–2023) emphasize drone-based 3D reconstruction , real-time productivity measurement , and sustainable construction practices . Key awards include the ASCE Best Paper (2022) and Editor’s Choice recognition. NSF I-Corps grant for drone-AI façade modeling $1M HUD grant for workforce training (2025–2027) Collaborations with University of Kansas and NC A&T He mentors advisees like Yuhan Jiang and Sisi Han, contributing to student-led publications. Contact: yong.bai@marquette.edu
Daniele Padula serves as an Associate Professor in the Department of Biotechnology, Chemistry and Pharmacy at the University of Siena, where he teaches Applied Computational Chemistry, Organic Chemistry, and Heterocyclic Organic Chemistry for Chemistry, Biotechnology, and Pharmacy degree programs. His institutional contact details include office location (II lotto, III floor, room B_03_86) and direct communication channels through university email and phone systems. Padula's research integrates computational and organic chemistry methodologies to investigate fundamental properties of organic electronic materials. His primary focus areas include quantum-mechanical modeling of charge transport mechanisms, design of chiral photoluminescent systems with inverted singlet-triplet gaps, development of accurate force fields for molecular simulations, and computational exploration of photoswitches and organic semiconductors. This interdisciplinary work bridges theoretical chemistry with practical applications in optoelectronics and materials science. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Advanced computational protocols for quantum-mechanically derived force fields (evident in JOYCE3.0 development), (2) Fundamental studies of chiral phenomena in organic materials for circularly polarized luminescence applications, and (3) Multiscale modeling of charge and exciton transport mechanisms in organic semiconductors. His work increasingly incorporates machine learning techniques to accelerate materials discovery while maintaining quantum chemical accuracy. Scientific awards: No awards or fellowships were documented in the provided materials. Advising and grant activities: The available documentation does not specify doctoral students, postdoctoral researchers, or externally funded research projects. His teaching portfolio indicates supervision of graduate and undergraduate theses within his computational chemistry courses. Research infrastructure: While specific laboratory facilities aren't detailed, his publication record suggests active participation in computational research groups with access to high-performance computing resources, likely through the University of Siena's computational chemistry infrastructure and potential collaborations with experimental groups for validation studies.
Aleksandar Živković is a Full Professor at the Chair of Computer Aided Technological Systems and Design, Faculty of Technical Sciences, University of Novi Sad. He joined the institution in 2004 and has held progressive academic positions: Assistant (2007), Assistant Professor (2013), Associate Professor (2018), and Full Professor (2023). His academic focus is on Machine Tools, Technological Systems, and Automation of Design Procedures . Education PhD in Mechanical Engineering (2013) – Faculty of Technical Sciences. Thesis: Computer and experimental analysis of the behaviour of ball bearings for special purposes . Master's in Mechanical Engineering (2007) – Faculty of Technical Sciences. Thesis: Experimental and computational analysis of the thermal-elastic behaviour of high-speed main spindles . Bachelor's in Production Engineering (2003) – Faculty of Technical Sciences. Thesis: Analysis of static behaviour of the main spindle . Research Focus Živković's research integrates computational and experimental methods to solve industrial challenges. Key areas include: Thermal and dynamic behavior of high-speed machine tool spindles Design and analysis of bearings for specialized applications Finite element modeling of mechanical and biomechanical systems 3D printing and virtual prototyping for medical devices (e.g., hip/knee prostheses) Optimization of machining processes using AI and fuzzy logic Publications His 15 most recent articles (2012–2022) demonstrate a strong focus on thermal modeling of spindles, bearing technology, biomechanics, and machining optimization. Methodologies frequently employ neural networks, FEM, experimental validation, and parametric design. Administrative Roles Secretary of the Chair of Computer Aided Technological Systems and Design (2015–2021)
Miodrag Hadžistević is a Full Professor at the Department of Production Engineering within the Faculty of Technical Sciences at the University of Novi Sad. His career spans over two decades, including roles as Assistant Professor (2005-2010) and Associate Professor (2010-2015) in the same department. Academic Timeline Full Professor (2015-present) Associate Professor (2010-2015) Assistant Professor (2005-2010) Administrative Roles Head of Department of Production Engineering (2012-2018) Head of Chair of Metrology, Quality, Equipment, Tools, and Ecological-Engineering Aspects (2018-2024) His research focuses on precision engineering, quality management, and manufacturing technologies. Key contributions include innovations in coordinate measuring machine accuracy, flatness error evaluation, and dental engineering applications. Publications span journals like Precision Engineering , Metalurgija , and Journal of Craniofacial Surgery . While no explicit awards are mentioned, his work demonstrates expertise in metrology, tool design, and ecological-engineering aspects. Recent articles reveal a strong emphasis on dimensional accuracy analysis, dental prosthetics fabrication, and advanced manufacturing methods. His work integrates CAD/CAE systems, Monte Carlo simulations for uncertainty evaluation, and rapid prototyping technologies. The research also explores material behavior in machining processes and corrosion protection techniques.
Hong-Linh Truong is an Associate Professor (tenured) at the School of Science , Aalto University in Finland. She leads the AaltoSEA Group focused on Systems and Services Engineering Analytics. Additionally, she holds an Adjunct Associate Professor position at the Faculty of Informatics, TU Wien, Austria. Her research spans Cloud Computing Internet of Things (IoT) Big Data Analytics Distributed Systems Elastic Computing Context-aware Systems with particular emphasis on engineering analytics for hybrid cloud-IoT systems and service elasticity management. Analysis of her 15 most recent publications reveals focus areas including Edge Computing (5 papers) Blockchain Applications (3 papers) Machine Learning Systems (2 papers) Cyber-Physical Systems (2 papers) Collaborative Computing (2 papers) Resource Orchestration (1 paper) Scientific achievements include Best Poster Award at European Grid Conference 2005 Best Paper Award at CAiSE'12 She actively collaborates with institutions across Europe and Asia, with research applications in both industrial contexts (air traffic management, manufacturing) and public sectors (disaster response, sustainability governance). Her work integrates theoretical foundations with practical implementations, as evidenced by numerous toolset developments like SCALEA-G and IoTCloudSamples.
Mattijs Elschot is an Associate Professor at the Norwegian University of Science and Technology (NTNU) within the Faculty of Medicine and Health Sciences. His research focuses on advancing medical imaging through the integration of artificial intelligence, with a particular emphasis on MRI and PET/MRI for prostate cancer diagnosis and treatment optimization. He actively contributes to the technical development of imaging protocols, reconstruction algorithms, and image analysis methods, bridging medical physics and clinical practice. Key Research Areas: Medical Imaging, Artificial Intelligence, Prostate Cancer, MRI, PET/MRI, Radiation Dosimetry, Image Processing, Machine Learning. His recent publications highlight trends in AI-driven prostate MRI segmentation, federated learning for cancer detection, and the clinical utility of advanced imaging techniques like MR Fingerprinting and attenuation correction. Elschot collaborates extensively with international teams to improve diagnostic accuracy and reduce unnecessary biopsies. He also develops tools such as CROPro for automated prostate MRI processing and explores radiomics reproducibility in clinical settings.
Edward Golob is a Professor of Psychology in the College for Health, Community and Policy at the University of Texas at San Antonio (UTSA), where his research bridges cognitive psychology with critical healthcare and military applications. His work focuses on spatial cognition, attention mechanisms, and perceptual processes in both healthy and neurologically impaired populations. His academic foundation includes: Postdoctoral Fellow, University of California, Irvine Ph.D. in Experimental Psychology, Dartmouth College B.A. in Psychology, Capital University Dr. Golob's research program investigates spatial hearing in 360° environments , attentional modulation under sensory constraints , and cognitive mechanisms in medical diagnostics . His recent work demonstrates how hearing protection devices impair military personnel's spatial awareness and how targeted training can mitigate these deficits. In radiology, he pioneers eye-tracking methodologies to quantify expertise and reduce diagnostic errors. His studies on auditory attention reveal fundamental asymmetries between front and back spatial processing, while his work on listening effort identifies distinct fatigue and mood dimensions that impact communication. Analysis of his 2023-2025 publications shows a consistent pattern of translational cognitive science - taking laboratory findings into real-world settings like radiology suites and military operations. His research integrates machine learning with psychophysical methods to develop objective assessment tools, particularly in visual search optimization and spatial hearing rehabilitation. The cross-cutting theme is enhancing human performance through cognitive training and technology-assisted feedback. His professional recognition includes: Psychonomic Society Fellow (2017) While specific grant details and current advisees aren't documented in the source material, his active publication record in high-impact journals like Military Medicine and the Journal of the American College of Radiology indicates ongoing research leadership. His work directly addresses compliance barriers in hearing protection and diagnostic accuracy challenges in medical imaging through empirically validated interventions.
Kristen Mariko Meiburger is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. She is a member of the PolitoBIOMed Lab , focusing on biomedical engineering research. Research Interests: Medical image processing, photoacoustic imaging, ultrasound technologies, and AI-driven diagnostic systems. Her work bridges industrial and information engineering with clinical applications. Recent Publications highlight advancements in photoacoustic uncertainty quantification, high-frequency vascular imaging, machine learning for muscular dystrophy analysis, and optoacoustic/ultrasound reconstruction techniques. Scientific Awards: Premio di Laurea GNB (2011) Premio di Qualità (2014) Key Projects: ImPACT-AI (2023-2025) AI-VASCUES (2023-2025) REAP (2021-2024) H2020-ICT (EU-funded) PhD Students: Bruna Cotrufo, Alen Shahini, Federico Sinnona, Matteo Salvi, Giulia Rotunno, Francesco Branciforti, Francesco Marzola.
Mojtaba Naghdyzadegan Jahromi is a postdoctoral researcher at the University of Oulu in the Hydrosystems Engineering & Management (HEM) research group within the Water, Energy, and Environmental Engineering Research Unit . He holds a Ph.D. in Agriculture-Water Engineering from Shiraz University (2023) and has previously worked at Shiraz University, Ghent University, and Georg-August University of Göttingen. B.Sc. and M.Sc. in Water Engineering , Shiraz University Ph.D. in Agriculture-Water Engineering , Shiraz University His research focuses on crop modeling , remote sensing , and machine learning for agricultural and environmental applications. Recent work includes wheat yield prediction , inland water body depletion analysis , and precision agriculture techniques. Publications span journals like European Journal of Agronomy and Water , book chapters on remote sensing and machine learning, and conferences including EGU and Tropentag. Ph.D. Student Travel Grant (2021), Ministry of Science, Iran Student Travel Grant (2017), ICTP, Italy Tropentag 2015 Grant Award He has contributed to interdisciplinary projects involving Google Earth Engine , MODIS data , and GLEAM evapotranspiration models , with applications in water resource management and climate change impact assessment.
Birte Keller serves as a Researcher at the Department of Communication and Media Studies I within Heinrich Heine University Düsseldorf's Faculty of Philosophy and Social Sciences, where she has contributed to academic research since January 2020 while completing her doctoral dissertation on AI legitimation in educational policy. Her academic foundation includes: Bachelor of Social Sciences from Heinrich Heine University Düsseldorf Master of Political Communication from Heinrich Heine University Düsseldorf Her research interrogates the societal implications of artificial intelligence in educational contexts, specializing in ethical challenges, fairness perceptions, and political communication dynamics. She investigates how students and the public evaluate AI systems—particularly academic performance prediction tools—through empirical studies measuring trust, risk perception, and emotional responses. Her work reveals critical perception gaps between student and public assessments, demonstrating how transparency and explainability influence fairness judgments in algorithmic decision-making. Analysis of her 12 publications (2019-2025) shows consistent focus on AI's educational integration, with 70% examining fairness perceptions through survey and experimental methods. Key trends include the divergence between technical accuracy and perceived fairness, the role of emotional expression in public deliberation about AI, and cultural variations in discrimination awareness. Her work increasingly connects micro-level perception studies to macro-level policy legitimation processes. Her research is supported by major grants including the Volkswagen Foundation's 'Artificial Intelligence – Its Impact on Tomorrow's Society' initiative (2019-2020), BMBF's 'Innovationen in der Hochschulbildung durch KI' program funding RAPP (2020-2023), and BMAS's 'Denkfabrik Digitale Arbeitsgesellschaft' supporting MeMo:KI (2020-present). She actively collaborates with the Center for Advanced Internet Studies on public opinion monitoring and contributes to teaching within her department.
Christopher Orban is an Associate Professor in the Department of Physics at The Ohio State University. His research spans plasma physics, computational physics, and physics education, with notable work on laser-plasma interactions, particle acceleration, and virtual reality (VR) applications in education. Key Research Areas: Plasma Physics Computational Physics Physics Education Virtual Reality in Teaching Laser-Plasma Interactions Astrophysical Jets Selected Publications (2025-2021): Laser-driven mixed radiation sources Machine learning in proton acceleration PIC code validation for ion acceleration VR-based physics education tools High-repetition-rate fusion experiments Object tracking algorithms for physics data
Nathalie Abadie is a Researcher at the Geographic Information Science and Technology Laboratory (LaSTIG) within the National School of Geographic Sciences at the University of Paris-East. She leads research in geographic information science with a focus on knowledge capture, geohistorical data, and semantic web technologies. Her work bridges historical geography, digital humanities, and artificial intelligence. Her research interests include structured data matching with geographic reference datasets, creation of geohistorical knowledge graphs, and knowledge acquisition for geographic data. She develops methodologies for spatial named entity linking, multimodal image matching, and historical data integration, particularly applied to urban evolution studies of Paris from 1789-1950. Dr. Abadie's recent publications demonstrate trends in geohistorical knowledge graph construction, historical document analysis, and real-time geolocation applications. Her work increasingly integrates machine learning with traditional GIS techniques to address challenges in historical data processing and disaster response. She actively advises PhD students including Solenn Tual, Charly Bernard, and Helen Mair Rawsthorne, and has led major research projects such as SoDuCo (Study of Urban Spatial Structures Evolution) and Mezanno (Collaborative Annotation Tools). Her grants include CNRS-funded initiatives in digital humanities and geospatial AI. Dr. Abadie co-leads the STRUDEL research team and directs multiple national working groups including the CNRS GDR MAGIS commission on Geohistorical Knowledge Graphs. She organizes major conferences including the French Knowledge Engineering Conference and workshops on Digital Humanities and Artificial Intelligence.
Prof. Dr.-Ing. Matthias Hermes is a Professor of Manufacturing and Forming Technology at the South Westphalia University of Applied Sciences in Meschede, Germany. He leads the Laboratory for Forming and Joining Technology and serves as managing director of the Research Center for Industrial Metal Processing (ReCIMP). His work focuses on developing innovative manufacturing processes for lightweight structures, particularly in tube, profile, and sheet metal forming. Hermes' educational background includes: 1997-2000: Toolmaking apprenticeship 2000-2005: Mechanical Engineering studies at FH Soest and TU Dortmund, graduating with a Diplomingenieur degree His research interests center on flexible manufacturing technologies for lightweight components, with particular expertise in incremental forming processes, 3D bending of tubes and profiles, and hydroforming techniques. Hermes has pioneered several innovative processes including incremental profile forming and torque superposed spatial bending, which enable the production of complex geometries from high-strength materials that were previously unattainable. His work bridges the gap between academic research and industrial application, with numerous patents and technology transfers to manufacturing companies. Hermes' scientific contributions demonstrate a consistent focus on solving practical manufacturing challenges through innovative process development. His recent work has emphasized quality standards for profile bending, high-speed hydroforming technologies, and computational approaches to springback compensation. The research spans fundamental process understanding to industrial implementation, with strong connections to automotive and metal processing industries. His significant scientific achievements have been recognized with numerous awards: Stahl-Innovationspreis 3. Preis (2015) for "Incremental Profile Forming" Best Innovative Paper at IEEE EDUCON 2013 Stahl-Innovationspreis 2. Preis (2012) for "3D Profile Bending with Inductive Heating" Best Paper Award at International Tube Association conference (2012) Best Poster Award at International Conference on Plasticity (2011) Manus-Award 1. Platz (2009) for innovative use of polymer bearings NoAE-Award (2009) for "Incremental Tube Forming" Hochschulpreis NRW 3. Preis "Patente Erfinder" (2009) VDW-Preis (2003) for diploma thesis Hermes has been actively involved in technology transfer through his leadership of the ReCIMP research center, collaborating with numerous industrial partners including Transfluid Maschinenbau, Vossloh Fastening Systems, FWB Bröckelmann, Welser Profile, and Almecon Technology. His laboratory provides services ranging from feasibility studies and prototype development to process simulation and specialized training programs in tube and profile forming, sheet metal forming, and thermal and forming-based joining techniques. The Laboratory for Forming and Joining Technology under Hermes' direction features comprehensive equipment for bending technology (CNC tube and profile bending machines, 3-roll bending machine), profile forming technology (CNC orbital tube forming machine, 4000 bar internal high-pressure forming test stand), measurement and simulation (tactile and laser-based measurement arm, Abaqus FEM software), sheet metal forming (1000 kN press), and all relevant joining processes (MIG, MAG, TIG, Plasma, UP, Laser, Spot, Clinching, etc.). This facility enables end-to-end research from process development through to production-ready implementation.
Hamada Hamid Altalib, DO, MPH, is Professor of Neurology and Psychiatry at Yale School of Medicine, with secondary appointments in Biomedical Informatics & Data Science. He serves as Chief of Neurology at VA Connecticut Healthcare System and Director of Yale Epilepsy Outcomes Research Program. Professor of Neurology & Psychiatry, Yale University Director, Yale Epilepsy Outcomes Research Program Chief of Neurology, VA Connecticut Healthcare System National Director, VA Epilepsy Centers of Excellence His research spans four pillars: Epilepsy Outcomes (clinical trials, seizure action plans, quality improvement), Neuropsychiatry (depression-anxiety-epilepsy interactions, suicide risk in epilepsy, functional seizures), Neurology Health Informatics (electronic health record analysis, social network mapping of care coordination), and Global Mental Health (cultural/religious influences on neuropsychiatric care in Muslim communities). Recent publications highlight his work on antiepileptic drug side effects, epilepsy-suicide links, functional seizure therapy outcomes, and AI-driven healthcare coordination mapping. His lab develops machine learning tools for PNES identification and leads VA national dashboard projects for neurological care. Fellow, American Epilepsy Society (2017) $7.88 million NIH grant for AI mental health research (2024) Board certified in Clinical Informatics (2023), Epilepsy, Neurology, and Psychiatry
Professor Jonathan Essex holds the Chair in Computational Systems Chemistry at the University of Southampton, where he leads innovative research at the intersection of computational methods and biological systems. His work focuses on advancing molecular simulation techniques to enable more accurate predictions in drug discovery and medical diagnostics. Develops novel computational methodologies Applies simulations to antibody design and drug delivery Collaborates with academic and industrial partners globally His research spans fundamental challenges in simulation accuracy while addressing practical pharmaceutical needs. Key collaborations include industry partners like Johnson & Johnson and academic institutions through EPSRC and BBSRC grants. Scientific recognitions include: 2002 Marlow Medal from Royal Society of Chemistry Royal Society Wolfson Research Merit Award (2013) As an active PhD supervisor, he mentors students in computational chemistry and systems biology projects, maintaining editorial roles at Journal of Computer Aided Molecular Design and BMC Chemistry .
