Siobhán Clarke is a Professor at the School of Computer Science and Statistics, Trinity College Dublin, specializing in software systems for smart urban environments . Her work addresses dynamic software adaptation in large-scale, mobile IoT ecosystems , with a focus on QoS optimization and collaborative agent models . Director, Enable : National SFI IoT Research Programme Director, Future Cities Centre for Smart & Sustainable Cities Co-Lead, ADVANCE : SFI Centre for Advanced Networks Co-PI, CONNECT (Future Networks) and Lero (Software Research) Her research spans smart city infrastructure , edge computing , and multi-agent coordination , informed by 15+ years of publications on service-oriented architectures , QoS prediction , and self-adaptive systems . Key project contributions include DIVERSIFY (2016) and TRANSFoRm (2015). Scientific awards include election to the Royal Irish Academy (2023) and a Best Student Paper at IEEE ICWS 2011. She has supervised 20+ PhD/MSc students, including Fan Li (2020: SLA Negotiation Systems), Gary White (2020: IoT QoS Forecasting), and Andrei Palade (2019: Stigmergic Optimization).
Christopher Kucha is an Assistant Professor in the Department of Food Science & Technology at the University of Georgia's College of Agricultural and Environmental Sciences. His work focuses on integrating engineering principles with digital technologies to address challenges in agri-food production. Dr. Kucha's research centers on the development and application of sensing technologies, data analytics, and systems modeling to improve food quality, safety, and processing efficiency. His lab, the Precision Food Process Engineering Lab, aims to reduce food waste, conserve resources, and enhance sustainability in the food system through innovations such as machine vision, artificial intelligence, and non-destructive assessment techniques. Key research areas include machine vision systems for food analysis, AI and chemometrics for food safety, process analytical technologies, intelligent food design, and big data applications in food processing. He teaches courses such as Introduction to Food Science and Technology (FDST 3000) and Introduction to Artificial Intelligence in Food Systems (FDST 2001). His recent publications (2020-2025) demonstrate a strong focus on hyperspectral imaging and machine learning for food quality and safety assessment, with applications across meat, fruits, nuts, and protein products. The work emphasizes non-destructive, real-time monitoring and the integration of digital technologies like augmented reality and digital twins.
Mina Mortazavi is a Senior Lecturer at the University of Technology Sydney's School of Civil and Environmental Engineering with over 15 years of experience specializing in structural engineering. Her academic journey includes a PhD in Structural Engineering from Western Sydney University, an MEng in Structural Engineering from Amirkabir University of Technology in Tehran, and a BSc in Civil Engineering from Shahid Beheshti University in Tehran. Her research interests focus on three interconnected fields: cold-formed steel profile assessment and section optimization, modularization in construction, and prefabrication of seismic mounting systems for building services. Mortazavi has developed expertise in applying machine learning techniques to structural engineering problems, particularly in thermal buckling analysis, seismic performance evaluation, and concrete material behavior prediction. Her publication record demonstrates consistent output in high-impact journals such as Thin-Walled Structures , Automation in Construction , and Journal of Building Engineering . Recent research shows increasing integration of artificial intelligence methods with traditional structural engineering problems, particularly in thermal analysis, seismic performance evaluation, and material behavior prediction. Research Innovation Connection grant recipient Multiple contract research projects with industry partners Active PhD and Masters student supervision Mortazavi's teaching portfolio includes courses in Steel and Composite Design, Steel and Timber Design, Mechanics of Solids, and Application of Timber in Engineering Structures. Her industry collaborations demonstrate strong practical application of research findings to real-world structural engineering challenges.
Susan A. Gauthier is Professor of Neuroscience at the Brain and Mind Research Institute, Professor of Neurology in the Department of Neurology, and Professor of Neurology in Radiology at Weill Cornell Medical College. She serves as Director of Clinical Research at the Judith Jaffe Multiple Sclerosis Center, where she leads a translational research program focused on uncovering the biological mechanisms of multiple sclerosis using advanced quantitative neuroimaging. Dr. Gauthier completed her undergraduate studies at the State University of New York at Buffalo, earned her D.O. from the Philadelphia College of Osteopathic Medicine, and obtained her MPH from Harvard School of Public Health. After completing neurology residency and chief residency at Boston University Medical Center, she was awarded a Clinical Trial Training Fellowship from the National Multiple Sclerosis Society at Brigham and Women's Hospital. Dr. Gauthier's research focuses on developing imaging biomarkers to track inflammation, demyelination, and clinical disability in multiple sclerosis. Her work spans quantitative susceptibility mapping (QSM), TSPO-PET imaging, brain connectivity analysis, and artificial intelligence applications in neuroimaging. She has made significant contributions to understanding chronic active lesions, paramagnetic rim lesions, and their relationship to disability progression in MS. Analysis of Dr. Gauthier's recent publications (2022-2025) reveals a strong focus on quantitative neuroimaging biomarkers for multiple sclerosis. Her work increasingly integrates advanced computational methods including artificial intelligence and machine learning to analyze structural and functional connectivity. A key theme across her recent work is the validation of imaging techniques against pathological findings, particularly regarding chronic active lesions and iron deposition. She has also contributed to several consensus statements that aim to translate imaging research into clinical practice for MS patients. Sylvia Lawry Fellowship in Clinical Trials, National Multiple Sclerosis Society (2002) Dr. Gauthier has served as Principal Investigator on multiple NIH and National Multiple Sclerosis Society grants, including studies on multi-scale imaging assessment of cognitive impairment in MS, quantification of innate immune activity within chronic lesions, and establishing the clinical relevance of chronic active MS lesions. She has built the MS Center's research infrastructure at Weill Cornell and forged strong collaborations with the Department of Radiology. As a dedicated mentor, she has trained numerous students, residents, fellows, and junior faculty who have gone on to academic and clinical leadership positions. Dr. Gauthier leads a research team within the Judith Jaffe Multiple Sclerosis Center that collaborates closely with the Department of Radiology at Weill Cornell. She serves on the Steering and Executive Committees of the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative, which brings together experts from multiple institutions to advance imaging research in MS. Her team utilizes advanced MRI techniques including 7T MRI and PET imaging to study MS pathophysiology.
Kamesh Madduri is an Associate Professor in the Department of Computer Science and Engineering at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His research focuses on graph analytics, parallel algorithms, and high-performance computing for large-scale data analysis. NSF CAREER Award (2013) His work contributes to the development of scalable graph partitioning algorithms, extreme-scale sparse data analytics, and heterogeneous computing frameworks. Recent projects include multilayer network analysis (NetSplicer) and GPU-accelerated graph processing (Jet). Key research areas include network science, computational biology, and distributed-memory graph algorithms. His publications highlight applications in genomic workflows, advertising keyphrase recommendation (Graphite/BroadGen), and large-scale hydrology data management. Collaborative Research: CCRI (2021-2023) SHF: Medium: NetSplicer (2020-2024) PPoSS: Extreme-scale Sparse Data Analytics (2018-2022) XPS: Genomic Workflows Acceleration (2014-2020) EAGER: SME Manufacturing Integration (2024-2026)
Genda Chen is a Professor and the Robert W. Abbett Distinguished Chair in Civil Engineering at Missouri University of Science and Technology. He serves as Director of the System and Process Assessment Research Laboratory, Director of the INSPIRE University Transportation Center, and Associate Director of the Mid-America Transportation Center. Dr. Chen's research spans multiple domains in civil and structural engineering with a strong emphasis on integrating advanced technologies for infrastructure assessment and resilience. His work prominently features structural health monitoring, smart infrastructure systems, bridge engineering, and the application of machine learning and computer vision techniques to civil infrastructure problems. Specific research interests include ultra-high performance concrete applications, structural connections, resilient infrastructure design for extreme events, unmanned vehicle systems for infrastructure inspection, and digital twinning of civil infrastructure. Analysis of Dr. Chen's recent publications reveals a strong trend toward integrating artificial intelligence and machine learning with traditional civil engineering practices. His work increasingly focuses on computer vision applications for infrastructure inspection, including crack detection, bridge element segmentation, and delamination detection using thermal imaging. There's also a significant emphasis on developing resilient infrastructure systems, particularly for bridges, with research on SMART shear keys for recovery after extreme events. His publications demonstrate a multidisciplinary approach that bridges civil engineering, materials science, robotics, and data science. Robert W. Abbett Distinguished Chair in Civil Engineering As Director of the System and Process Assessment Research Laboratory and the INSPIRE University Transportation Center, Dr. Chen leads research initiatives focused on transportation infrastructure assessment and innovation. His work with the Mid-America Transportation Center involves regional transportation research and technology transfer, with numerous projects addressing bridge inspection, structural monitoring, and resilient infrastructure design. Dr. Chen's laboratory work centers on structural assessment technologies, with particular emphasis on sensor development, unmanned systems for infrastructure inspection, and digital twin applications for civil infrastructure. His research team appears to be actively engaged in developing next-generation tools for bridge inspection, structural health monitoring, and resilient infrastructure design, with strong connections to transportation agencies and industry partners.
Pinar Okumus serves as Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. Her research focuses on advancing infrastructure resiliency through low-damage seismic systems, prefabricated concrete structures, and high-performance materials for rapid construction and repair of bridges and buildings. Her academic credentials include: PhD in Civil Engineering, University of Wisconsin, Madison (2012) MS in Civil Engineering, University of Wisconsin, Madison (2008) BS in Civil Engineering, Middle East Technical University (2006) Dr. Okumus' research integrates nonlinear structural analysis, material-scale testing, and in-situ monitoring to develop rapidly deployable infrastructure solutions. Her work emphasizes practical applications of pre-tensioned, post-tensioned, and reinforced concrete components for extreme event resilience, with particular focus on coastal infrastructure vulnerability and seismic retrofitting. The Dr. Okumus Research Group employs advanced methodologies including machine learning for structural assessment and optical fiber technologies for long-term monitoring. Recent publications (2023-2025) reveal strong thematic trends in corrosion effects on coastal infrastructure, 3D-printable cementitious composites for rapid repair, and tessellated structural-architectural systems. Her work increasingly incorporates machine learning for shear strength prediction and crack pattern analysis while maintaining core expertise in post-tensioned systems and seismic retrofit solutions. Research funding is secured through competitive grants from the National Science Foundation and Federal Highway Administration, supporting experimental validation of novel concepts like self-centering shear walls and ultrahigh-performance concrete retrofits. The group actively collaborates with transportation agencies to translate laboratory findings into field applications for bridge and building systems. The Dr. Okumus Research Group operates as an interdisciplinary team investigating structures that enable rapid reoccupation after extreme events. Current projects focus on modular systems with interlocking components, optical sensing integration for tendon force monitoring, and material innovations for climate-resilient infrastructure, maintaining strong connections with industry partners for practical implementation.
Dr. Evelyn Hsieh Donroe, MD, PhD, is an Associate Professor of Medicine (Rheumatology) and Chronic Disease Epidemiology at Yale University, with additional roles as Chief of Rheumatology at VA Connecticut Healthcare System and Network Lead for the Yale Network for Global Non-Communicable Diseases (NGN). She bridges biomedical and behavioral sciences to develop prevention strategies for osteoporosis, sarcopenia, and fractures in low-resource settings. Education: AB in Molecular Biology (Princeton), MD (Stony Brook), MPH (Harvard), PhD in Investigative Medicine (Yale) Research Focus: HIV-related musculoskeletal comorbidities in economic transition countries (China, Peru), secondary osteoporosis in chronic conditions (RA, breast cancer), and VA-based studies leveraging the Veterans Aging Cohort. Key Projects: Quarterly Vitamin D supplementation trials in China, fracture risk prediction tools for veterans, and NCD care integration for HIV patients in Peru. Scientific Contributions: Developed BoneScore NLP algorithm for DXA data extraction, conducted cross-national studies on HIV-aging, and leads global capacity-building programs through CMB Fellowships and Fulbright scholarship. Awards: 2018-2019 U.S.-China Fulbright Scholar 2016 Stony Brook 40 under Forty Alumni Award American College of Rheumatology Distinguished Fellow (2013) Mentorship: Directs Global Health Emerging Scholars Program, CMB Global Health Fellowship Programs, and NIH T32 Training Program in Rheumatology.
Laura Bofferding is a Professor of Mathematics Education in the Department of Curriculum and Instruction at Purdue University's College of Education. Her career spans over a decade of progressive academic appointments, including Assistant Professor (2011-2017), Associate Professor (2017-2024), and current Professor (2024-present). She maintains active research and teaching roles focused on early mathematics cognition. Her educational background includes: Ph.D. in Curriculum Studies and Teacher Education from Stanford University (2011) M.A. in Learning, Design, and Technology in Education from Stanford University (2007) B.S. in Elementary Education from University of Wisconsin, River Falls (2002) Bofferding's research centers on cognitive development in early mathematics, particularly children's understanding of negative numbers, spatial reasoning through tangram puzzles, and the application of contrasting cases in instruction. Her work bridges theoretical cognitive science with practical classroom applications, emphasizing game-based learning and emergent bilingual education. Recent projects explore AI-generated word problems and programming debugging in elementary contexts, demonstrating her commitment to innovative pedagogical approaches. Her publication trends reveal sustained focus on integer conceptualization (2014-2023), expanding into spatial reasoning (2022-2023) and AI integration (2024). Key thematic clusters include measurement misconceptions, tangram-based shape transformation, and debugging strategies in computational thinking. Notable awards include: Purdue Faculty Engagement Scholar (2021) Purdue Faculty Scholar (2019) AMTE STaR Fellow (2012) Bofferding secures significant grant funding including an NSF CAREER award ($680,504) for integer understanding research and a Purdue Launch the Future Grant ($25,000) investigating AI-generated word problems. Her advising focuses on mathematics education doctoral students through courses like EDCI 62000 (Developing as a Mathematics Researcher), with grant collaborations spanning computational thinking (NSF ITEST) and emergent bilingual education (Purdue Small Research Grant).
Dario Duca is a Full Professor in the Department of Physics and Chemistry at the University of Palermo . His research focuses on computational chemistry and catalysis, particularly using Density Functional Theory (DFT) and microkinetic modeling to study reaction mechanisms on nanoscale catalysts. He teaches General and Inorganic Chemistry (10 CFU) and Higher Inorganic Chemistry (8 CFU) for the Chemistry degree program. Fields of Interest: Computational chemistry, catalysis, DFT, materials science, nanotechnology, surface chemistry, reaction kinetics His recent work includes DFT studies of CO/H2 purification over MnO2 catalysts, biomass conversion using halloysite nanotubes, and platinum particle growth in zeolites. He has developed computational tools like the Empathes code for transition state analysis. Contact: dario.duca@unipa.it | Office hours: Mon-Fri 1:00-2:00 pm, Sat 10:00-13:00 at Ed.17, University of Palermo.
Karim Abu Salem serves as a Fixed-term Assistant Professor in the Department of Mechanical and Aerospace Engineering (DIMEAS) at the Polytechnic University of Turin, affiliated with the College of Mechanical, Aerospace, and Automotive Engineering. He additionally holds invited membership in the College of Management and Production Engineering. His teaching portfolio includes Aerospace Vehicle Design, Space Flight Mechanics/Structures, Space Environment Operations, and Aeronautical Legislation courses for both bachelor's and master's programs in Aerospace Engineering. Dr. Abu Salem's research centers on sustainable aviation innovation, specializing in box-wing aircraft configurations, hybrid-electric and hydrogen propulsion systems, and advanced structural design methodologies. His work addresses critical challenges in emissions reduction, flight dynamics optimization, and climate impact mitigation through computational modeling, metamodeling techniques, and multidisciplinary design analysis. Key focus areas include unconventional aircraft architectures, power management systems, and metamaterial applications for next-generation aerospace vehicles. Analysis of his recent publications (2023-2025) reveals a concentrated research trajectory toward decarbonizing regional and medium-range aviation. His work demonstrates increasing emphasis on liquid hydrogen propulsion, box-wing aerodynamic efficiency, and holistic environmental impact assessment beyond CO 2 emissions. The publications exhibit strong collaboration patterns with researchers like G. Palaia and E. Carrera, primarily targeting high-impact journals in aerospace engineering and sustainability. As an active educator, Dr. Abu Salem contributes to curriculum development across multiple aerospace engineering programs, bridging theoretical concepts with emerging sustainable aviation technologies through his course collaborations and lectures.
Federico Visi (he/they) serves as Guest Faculty in the Sound Studies and Sonic Arts Master's program at Berlin University of the Arts. Based in Berlin, Germany, Visi is a researcher, composer, and performer whose work bridges technology and artistic expression through innovative musical interfaces and performance systems. Visi completed doctoral research on instrumental music and body movement at the Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, UK. They have held postdoctoral positions at Luleå University of Technology (Sweden) and Goldsmiths, University of London (UK), establishing a foundation for their interdisciplinary approach to music technology. Visi's research focuses on gesture in music, motion-sensing technologies, interactive machine learning, and embodied interaction. Their work explores how body movement and physiological signals can be harnessed in electronic music creation through both theoretical frameworks and practical implementations. They have developed novel instruments like the Sophtar and biosignal-based performance systems that translate physiological data into sound. Recent scholarly output reveals a trajectory toward increasingly sophisticated integration of machine learning in musical contexts, with significant contributions spanning computer music, psychology, and human-computer interaction domains. Visi's publications demonstrate how algorithmic processes can be embedded in musical instruments to create new forms of musical expression and co-creativity between humans and machines. As an active member of the research community, Visi co-edited a thematic issue of Organised Sound on 'Embedding Algorithms in Music and Sound Art' and organized the Embedding Algorithms Workshop at Berlin University of the Arts. They contribute to the Wilding AI collective that explores creative applications of AI in spatial audio environments. Visi releases music under the moniker AQAXA, combining conventional electronic music production with machine learning exploration of personal sonic memories. Their work has been supported by the European Research Council, Swedish Research Council, Boström Fund, and Helge Ax:son Johnsons foundation.
Dr. Ying Wang is an Associate Professor and doctoral supervisor at the Software College of Northeastern University (China), where she has been working since February 2019. She serves as Assistant Dean at the School of Software and is an active member of several CCF committees including the System Software Committee, Software Engineering Committee, Open Source Development Committee, and Women's Committee. Dr. Wang received her Ph.D. in Software Engineering from Northeastern University in January 2019 under the supervision of Professor Zhiliang Zhu. She completed postdoctoral research at the Hong Kong University of Science and Technology (HKUST) from 2022 to 2023 under Professor Shing-Chi Cheung and was a visiting scholar at Microsoft Research Asia through the StarTrack Program in 2021. Her research focuses on intelligent software development technologies, large AI models, open source software big data analysis, and software supply chain security. She has made significant contributions to the governance of open source software ecosystems across multiple programming languages including Java, C#, Python, Go, JavaScript, Android, and Rust. Her work has led to the development of practical tools like 'League of Legends' for monitoring dependency defects in open source ecosystems, with several technologies commercialized by Huawei and Microsoft. Dr. Wang's recent publications demonstrate her expertise in cross-language dependencies, software component analysis, software refactoring, and the application of large language models in software engineering. Her work spans both theoretical foundations and practical applications, with a strong emphasis on real-world impact through industry collaboration. Among her notable achievements are the ACM SIGSOFT Distinguished Paper Awards at ICSE 2021 and ESEC/FSE 2023, making her the first researcher from Northeastern University to receive this honor. She has also received multiple awards for her doctoral dissertation and prototype implementations. Dr. Wang actively contributes to the academic community as an Associate Editor for IEEE Transactions on Software Engineering and serves on program committees for top conferences including ASE, ICSE, and ESEC/FSE. She mentors a large group of doctoral and master's students, with many alumni securing positions at major technology companies including Huawei, Microsoft, Alibaba, and Tencent.
Anna Huang is an Assistant Professor at the Massachusetts Institute of Technology (MIT), affiliated with the PI Core/Dual program. Her research focuses on AI-driven music technologies, including human-AI collaboration, generative music models, and interactive creative tools. She specializes in developing frameworks for real-time music jamming, adaptive accompaniment systems, and novice-friendly AI co-creation platforms. Huang has contributed to projects like the Bach Doodle and the AI Song Contest , demonstrating scalable applications of machine learning in music composition. Her work bridges computer science and musicology, with a particular emphasis on cross-cultural music generation (e.g., Hindustani classical music modeling) and expressive control mechanisms for generative systems. Key areas include MIDI signal processing, source separation algorithms, and the design of user interfaces that empower both professionals and novices to co-create with AI. Huang’s publications emphasize interdisciplinary innovation, with trends spanning reinforcement learning for music performance, hierarchical generative modeling, and ethical considerations in AI-assisted creativity. Though no awards are explicitly listed, her impactful projects suggest recognition in computational music research. Her research also involves dataset development (e.g., MAESTRO dataset) and open-source tools like Coconet, fostering reproducibility and community engagement in music technology.
Patrizia Savi is a Tenured Associate Professor at the Polytechnic University of Turin within the Department of Electronics and Telecommunications. She actively participates in the Power Electronics Innovation Center (PEIC) and serves as a Senior Member of IEEE and the International Union of Radio Science . Teaching: She has been the Titolare del corso for 'Campi Elettromagnetici' (Electromagnetic Fields) since 2019-2021 at the Polytechnic University of Turin. Key Collaborations: Works with researchers across Italy and international institutions on projects involving GNSS-R for environmental monitoring. Research Interests: Her work focuses on GNSS Reflectometry for soil moisture retrieval, carbon-based composites (graphene, biochar, nanotubes) for microwave applications, and graphene tunable devices including biosensors. She also explores electromagnetic shielding using sustainable materials. Recent Publication Trends: Recent work emphasizes machine learning integration with GNSS-R data, biochar composite shielding in construction materials, and graphene-based biosensors for glucose and HRP detection. Key applications span climate action , environmental monitoring , and medical diagnostics . Scientific Awards: IEEE Fellow (2016-) IEEE Senior Member (2016-) International Union of Radio Science Senior Member (2024-) Advising: Supervises PhD students Simone Gaetano Ballaera and Fabio Peinetti , focusing on graphene sensors and tunable devices.
