Emre Gönülcü is a Researcher at the Department of Civil Engineering , Istanbul Technical University , focusing on structural dynamics, earthquake engineering, and numerical modeling. He holds a PhD in Structural Engineering (2020) from Istanbul Technical University, preceded by a Master's (2018) and a Bachelor's degree in Civil Engineering (2014-2017). His research emphasizes seismic safety, vibration control, and advanced materials for structural systems. His work includes developing polyurethane-based seismic isolation devices for high-voltage insulators and analyzing reinforced concrete behavior under dynamic loads. He has conducted experimental studies on rail fastening systems and shake-table testing of suspended ceiling systems. His contributions bridge theoretical numerical models with practical seismic protection solutions. Emre Gönülcü has been a Research Assistant at Istanbul Technical University since 2019. His research trends focus on enhancing structural resilience through advanced materials and numerical evaluation of critical infrastructure components. While specific grants or awards are not listed, his publications reflect a strong commitment to earthquake engineering and structural innovation.
Erzhuo "Ezra" Che is an Assistant Professor in the Department of Civil and Construction Engineering at Oregon State University's College of Engineering. His research focuses on 3D point cloud processing, geospatial data analysis, and infrastructure asset management. He teaches courses in 3D laser scanning, digital terrain modeling, and advanced point cloud programming. Education: Ph.D., Civil Engineering (Geomatics), Oregon State University, 2018 M.S., Photogrammetry and Remote Sensing, Wuhan University, 2015 B.Eng., Photogrammetry and Remote Sensing, Wuhan University, 2012 His research interests include lidar technology applications, automated infrastructure assessment, deformation monitoring, and error modeling. Recent work emphasizes ADA compliance evaluation using 3D point clouds and mobile lidar for road marking analysis. Dr. Che has received notable awards such as the 2022 Geo Week Achievement Award and the 2021 Martin Isenburg Best Paper Award. His projects include mapping historical structures at Silver Falls State Park and developing frameworks for efficient point cloud segmentation. His teaching and research bridge geomatics with civil engineering challenges, focusing on practical solutions for infrastructure maintenance and disaster resilience.
Giovanni Forchini is a Professor at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, Sweden. His research focuses on econometrics, panel data analysis, and their applications in health economics and epidemiological modeling. He holds the title of Docent, a Swedish academic qualification reflecting advanced expertise. His work bridges theoretical econometrics with practical policy analysis, particularly in pandemic preparedness and healthcare optimization. Research Themes: Econometric methodologies for panel data and structural equation models Quantifying pandemic impacts on healthcare systems and economies Optimization of resource allocation during public health crises Key Contributions: Developed the DAEDALUS model for integrated economic-epidemiological policy simulations Analyzed SARS-CoV-2 transmission dynamics and vaccine impact in multiple countries Pioneered statistical methods for handling multifactor structures in panel data Awards & Grants: USBSE Pedagogical Prize 2020 Funding from Forte (Swedish Research Council for Health, Working Life and Welfare) and Handelsbanken Teaching & Mentorship: Coordinates Master’s theses in Economics at USBSE Teaches advanced courses like Econometrics 1 & 2 and Analysis of Financial Data
Camelia D. Brumar is a PhD Candidate in Computer Science at Tufts University and a Visiting PhD Student at Harvard University's Visual Computing Group. She co-founded Boston Vis , a collaborative network for visualization researchers in the Greater Boston Area. Education: B.S. in Theoretical Mathematics from University of Maryland, College Park Research Focus: Systematic visualization design for decision-making processes, bridging gaps between problem spaces and design spaces through qualitative methods Her work intersects Visual Analytics , Human-Computer Interaction , and Machine Learning , with recent publications on decision-making taxonomies, dimensionality reduction explanations, and knowledge graph visualization. Key trends include: Interactive predicate logic for pattern explanation Domain expert challenges in automated data science Anomaly reasoning frameworks Medical AI applications for embryo grading Scientific Achievements: Organizer of Boston Vis (2024) Tutorial presenter on LLMs for research paper interaction (2024) IEEE Visualization 2024 Doctoral Colloquium participant Contributor to Dagstuhl Seminar on provenance in automated data science (2023) Industry experience includes roles at Tableau Research , Alife Health , and Bose Corporation , with collaborations spanning MIT Lincoln Laboratory, National Renewable Energy Laboratory, and Worcester Polytechnic Institute.
Aanuoluwapo Ojelade is a Distinguished Research Fellow in the Department of Industrial and Systems Engineering at the University at Buffalo, School of Engineering and Applied Sciences. His work bridges occupational ergonomics, biomechanics, and construction safety through advanced technologies. Education: PhD, Industrial and Systems Engineering, Virginia Tech University MEng, Industrial and Systems Engineering, Virginia Tech University BEng, Civil Engineering, Osun State University Research Interests: Focus on exoskeleton technology for construction workers, biomechanical analysis of manual tasks, and motion capture systems for ergonomic evaluation. His studies address physical demand reduction, workplace interventions, and technology adoption barriers. Recent Article Trends: Leverage machine learning (random forest, recurrent neural networks) for ergonomic analysis, compare exoskeleton efficacy across industries (construction, mining), and evaluate demographic impacts on technology readiness. Markerless motion capture and EMG-based force prediction are recurring methodologies. Contact: 317 Bell Hall, University at Buffalo, aojelade@buffalo.edu, (716) 645-4721.
Filippo Malandra is an Assistant Professor in the Department of Electrical Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on Internet of Things (IoT), wireless communications, 5G/4G cellular networks, network performance analysis, smart grid communications, optimization, and machine learning applications. PhD in Electrical Engineering from École Polytechnique de Montréal Master of Engineering in Telecommunications Engineering from Politecnico di Milano Bachelor of Engineering in Telecommunications Engineering from Politecnico di Milano His research interests include experimental testbed development for 5G-enabled smart grids, analytical modeling of cellular network delays, and machine learning frameworks for optimizing network performance under environmental factors. He has contributed to tools like WTTool for 5G network simulation and PeRF-Mesh for RF-mesh network analysis. Recent work emphasizes 5G testbed experimentation (e.g., ExTODS), weather-based signal prediction, and CBRS spectrum analysis. He has explored synergies between federated learning and O-RAN architectures for elastic network services. Active service roles include TPC Member for IEEE SECON 2019 and reviewer for IEEE journals and conferences like DRCN. Recipient of NSF CRII grant (2021) for CNS research on IoT-aware dynamic spectrum sharing. Collaborates on projects like UBSpot (aerial-ground wireless networks) and smartDESC (distributed energy storage control).
Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.
Luis G. Arboleda is an Associate Professor in the Department of Civil, Environmental and Construction Engineering at the University of Central Florida, College of Engineering and Computer Science. His research expertise spans geotechnical engineering, soil-structure interaction, numerical modeling, and earthquake engineering, with a focus on infrastructure resilience and underground construction. Education: Ph.D. in Geotechnical Engineering, Northwestern University, 2014 M.S. in Structural Engineering, Purdue University, 2006 B.S. in Civil Engineering, National University of Colombia, 2004 Dr. Arboleda's research interests include soil-structure interaction, geotechnical earthquake engineering, soil liquefaction, constitutive modeling of soils and rocks, field and laboratory testing, and sinkhole geomechanics. His work integrates advanced numerical simulations with experimental validation to improve the analysis and design of geostructures such as deep excavations and foundations. He has made significant contributions to understanding the nonlinear-inelastic behavior of soil and structures under seismic and construction loads. The trend in his recent publications reflects a strong emphasis on numerical modeling of soil-structure systems, particularly in deep excavations and tall buildings subjected to seismic and dynamic loading. His work bridges geotechnical and structural engineering, advancing predictive capabilities for ground deformations, soil response, and structural performance. Key themes include nonlinear modeling, field validation, and performance-based design under extreme events. Scientific Awards: Outstanding Reviewer, Journal of Geotechnical and Geoenvironmental Engineering (ASCE), 2021 Fellowship, Second Early Career Workshop for Junior Geotechnical Faculty, Case Western Reserve University, 2018 Dr. Arboleda actively advises graduate students and contributes to major research initiatives, including infrastructure protection from hurricanes. He has led and co-authored numerous studies on excavation-induced movements, seismic soil-structure interaction, and constitutive modeling. His prior industry experience at Janssen and Spaans Engineering Inc. informs his applied research on bridge and infrastructure systems. He is involved in research teams focusing on geotechnical performance, numerical simulation, and infrastructure resilience. His lab work includes laboratory testing of soils and advanced numerical modeling using finite element and hypoplasticity frameworks.
Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.
Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.
Prof. Sebastian Kaiser is a full professor at the University of Duisburg-Essen's Institute for Combustion and Gas Dynamics, where he leads research on reactive fluid dynamics since 2011. His academic background includes a Bachelor's from Dartmouth College, Diplomingenieur from RWTH Aachen, and PhD from Yale University, followed by postdoctoral work at Sandia National Laboratories. Research Focus: Kaiser specializes in optical diagnostics for reactive systems with emphases on: High-speed imaging of combustion processes Nanoparticle synthesis via spray-flame techniques Tribology and fluid-structure interactions Engine diagnostics using laser-based methods His work bridges experimental techniques and simulation development for energy and propulsion systems. Publication Trends: Recent articles (2023-2025) demonstrate consistent focus on advanced optical diagnostics applied to combustion systems, nanoparticle synthesis, and engine research. Key methodologies include laser-induced fluorescence, high-speed imaging, and machine learning for fluid dynamics analysis. Awards & Honors: Harding-Bliss Prize for Engineering Excellence (Yale, 2005) SAE Excellence in Oral Presentation Award (2008) NRW Returning Scientists Grant (2010) Professional Affiliations: Member of Society of Automotive Engineers (SAE) and The Combustion Institute, with extensive experimental facilities for reactive flow characterization.
Prof. Georg Jäggle is a Professor of Vocational Education at the University College of Teacher Education Vienna (PH Wien), specializing in educational robotics, STEM pedagogy, and sustainability education. He leads the Department of Vocational Education within the Institute for Secondary Level Vocational Education (I:SBB). His work integrates technology-enhanced learning environments, focusing on project-based methodologies to bridge gaps between education and industry needs. Key projects include the Recycling Heroes initiative (2022–2024), which employs citizen science to promote circular economy awareness, and the EU-funded ER4STEM project (2017–2018), fostering STEM engagement through robotics. Prof. Jäggle holds a Dipl.-Ing. (FH) and a Dr. in engineering education, with prior industry experience as an engineer and vocational school teacher. His research emphasizes digital competence development, intergenerational learning, and inclusive education strategies. He has coordinated interdisciplinary teams across 10+ EU and national projects, including RoboCoop (2018–2022) and Makers@school (2017–2019), which implemented robotics and maker culture in schools. His teaching spans applied mathematics, engineering, and automation technology at both academic and vocational levels. Research interests include technology integration in vocational training, lifelong learning frameworks, and the impact of educational robotics on student motivation. Recent publications (2022–2024) explore STEM career self-efficacy, sustainability curriculum innovation, and cross-generational robotics collaboration. He currently serves in the Center for Research Management (Zentrum Forschungsmanagement) at PH Wien, advancing applied research in MINT (STEM) education and educational technology.
Denis Duhamel is a Professor and researcher at the Navier Laboratory, affiliated with École des Ponts ParisTech. He teaches mechanics courses at École nationale des ponts et chaussées and previously lectured at École Polytechnique. He earned his doctorate from École des Ponts ParisTech (1994) and research accreditation from University of Marne la Vallée (1998). His research focuses on structural acoustics, railway dynamics, tire-road noise, and numerical modeling of vibrations using Wave Finite Element (WFE) methods. Key areas include railway track dynamics, vibration control, and acoustic barrier performance. Notable projects involve dynamic analysis of periodic structures like railway tracks and metamaterials. Recent publications (2020–2025) emphasize wave-based methods for periodic structures, nonlinear foundation modeling, and in-situ measurements of acoustic barriers. His work bridges theoretical models with practical applications in transportation and structural engineering. Lab affiliations include the Navier Laboratory, a leading center for mechanics and materials research. He collaborates on projects like DEUFRABASE for pavement noise evaluation and ODSurf for optimized road surface design.
Iraklis Lazakis is a Reader in Maritime Operations and Maintenance at the Department of Naval Architecture, Ocean and Marine Engineering (NAOME), within the Faculty of Engineering at the University of Strathclyde. He joined the university as a PhD researcher in 2007 and began his academic career in 2011, establishing himself as a key figure in maritime systems research and education. His research interests span a broad range of topics including ship operations, systems maintenance and reliability, condition monitoring, risk and asset management, shipyard productivity, and offshore renewable energy systems (wind, wave, and tidal). His work bridges academic theory with industrial application, drawing from his 8 years of prior industry experience in maritime surveys, accident investigations, and ship repairs. The trends in his recent publications reflect a strong focus on data-driven and digital solutions for sustainable maritime operations. Key themes include the development of simulation and optimization tools, application of virtual reality for safety, cost reduction in offshore wind O&M, and decarbonization strategies such as onboard CO2 capture. His work increasingly integrates AI, digital twins, and human factors to enhance system performance and crew wellbeing. He has received numerous accolades, including: SNAME Faculty Advisor of the Year (2024) SNAME WES Best Paper Award (2023) Multiple Knowledge Transfer Partnerships Certificates of Excellence (2020, 2022) Laureate of the Franz Edelman Award (2012) ISSC Committee IV.2 Membership (2012–2015) Lazakis actively supervises undergraduate, postgraduate, and PhD students, and leads or contributes to a wide portfolio of research and knowledge exchange projects. His recent projects include decarbonizing UK shipping, structural surveys of vessels like Calmac and the Royal Yacht Britannia, and development of low-cost underwater gliders. He plays a strategic role in supporting colleagues with funding applications, publications, and industry collaboration. His work contributes to UN Sustainable Development Goals related to sustainable energy and industry innovation.
Stephanie Forrest is a Professor of Computer Science at Arizona State University and serves as Director of the Biodesign Center for Biocomputation, Security and Society . She holds affiliations with the School of Computing and Augmented Intelligence , Global Futures Laboratory , and Santa Fe Institute External Faculty . Education: B.A. from St. John's College M.S. and Ph.D. in Computer Science from the University of Michigan Research Interests: Forrest specializes in the intersection of biology and computation , with key contributions to cybersecurity (anomaly detection, instruction-set randomization), automated software repair (evolutionary methods), and biological modeling (immune systems, SARS-CoV-2 spread). Her work bridges complex adaptive systems , AI/ML , and defense applications . Scientific Contributions: 2020 IEEE S&P Test of Time Award 2019 ICSE Most Influential Paper Award 2011 ACM/AAAI Allen Newell Award NSF Presidential Young Investigator (1991) IEEE Fellow Evolutionary Computation Pioneer award Publications & Grants: Her research appears in top venues (ICSE, IEEE S&P, PNAS) and is funded by the National Science Foundation , DARPA , Air Force Research Lab , and Santa Fe Institute . Key projects include Crispy (CRISPR-inspired DoS defense), GenProg (automated bug correction), and SIMCoV-GPU (agent-based pandemic modeling).
