Audrey Fikes is an Assistant Teaching Professor in the Department of Chemistry at North Carolina State University (NC State), affiliated with the Integrative Sciences Initiative (ISI). She focuses on inorganic and bioinorganic chemistry, particularly iron homeostasis mechanisms and metalloprotein interactions. Her research also explores osmium-based clusters, coordination polymers, and chemical probe development for cellular studies. Her affiliations include the College of Sciences and the ISI, with an office in Dabney Hall 840. While no awards or grants are explicitly listed, her work emphasizes interdisciplinary approaches to chemical tool design and materials synthesis. She is part of NC State's Department of Chemistry faculty, contributing to undergraduate and graduate education in chemistry. Research interests span synthetic inorganic chemistry, redox-active systems, and applications in biochemistry. Her publications highlight advancements in ligand behavior analysis, cluster reactivity, and microwave-assisted synthesis techniques.
Justin English serves as Assistant Professor of Biochemistry at the University of Utah School of Medicine, where he develops molecular tools to investigate human health and disease mechanisms through directed evolution and protein engineering approaches. Education: B.A. from Cornell University Ph.D. from University of North Carolina at Chapel Hill Research Focus: Dr. English's laboratory specializes in Directed Evolution and Protein Engineering to create molecular tools for studying G-protein Coupled Receptors (GPCRs) , cell signaling pathways , and neuroscience applications . His work integrates synthetic biology with classical pharmacology to develop innovative platforms like VEGAS for mammalian cell evolution and TRUPATH for GPCR transducerome analysis, with significant implications for drug discovery and therapeutic development. Publication Trends: Analysis of his 2019-2025 publications reveals consistent focus on GPCR biology, featuring breakthroughs in biosensor development (nanobody-based receptor monitoring), chemogenetic tools (BioTAC system), and high-throughput screening platforms. His research demonstrates strong translational potential in neuroscience, particularly through engineered mouse models for psychedelic drug studies and molecular tools for mapping small-molecule interactomes. Research Environment: Dr. English leads an active laboratory within the University of Utah's Department of Biochemistry, leveraging institutional core facilities for biochemical and molecular studies. His research program maintains strong collaborative ties with neuroscience and pharmacology groups, with ongoing projects focused on advancing molecular engineering techniques for biomedical applications as detailed on his lab website.
Cuiyun Gao is a Full Professor and PhD Supervisor at the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen. She has established herself as a prominent researcher in the intersection of artificial intelligence and software engineering. Her educational background includes a PhD from the Chinese University of Hong Kong (completed in 2018), followed by postdoctoral work at CUHK and a Research Fellowship at Nanyang Technological University. She also had a visiting period at University College London supervised by Prof. Mark Harman and Prof. Federica Sarro. Dr. Gao's research primarily focuses on Software Repository Mining, Natural Language Processing, Code Analysis, Large Language Models, Source Code Understanding, User Review Analysis, Vulnerability Detection, and Mobile Advertising Analysis . Her work bridges the gap between traditional software engineering practices and modern AI techniques, particularly in the context of code intelligence and software maintenance. Her recent publications (2024-2025) demonstrate a strong emphasis on Large Language Models for code-related tasks, including code generation, optimization, vulnerability detection, and software engineering applications. Her research shows a clear trend toward addressing practical challenges in integrating LLMs into the software development lifecycle while maintaining code quality and security. Scientific Awards: Distinguished Paper Award at ASE 2023 Best Paper Award of the Track at ICSE 2024 Distinguished Paper Award at ICSE 2024 Dr. Gao actively supervises multiple PhD and Master's students, contributing to the next generation of software engineering researchers. She has served on numerous conference committees including FSE, ISSTA, ICSE, ASE, and SANER. Her research has received significant attention in the software engineering community, with multiple papers published in top-tier venues like FSE, ICSE, ASE, and TSE. Her lab appears to be actively engaged in both theoretical research and practical applications, particularly in the context of WeChat and other industry collaborations, demonstrating strong industry-academia connections.
Chang Xu is a Professor and Ph.D. supervisor at Nanjing University, affiliated with the State Key Laboratory for Novel Software Technology, School of Computer Science, and Institute of Computer Software (ICS). He has been a full-time faculty member since 2010, when he joined as an associate professor and was later promoted to full professor in 2015. Education: Ph.D. from The Hong Kong University of Science and Technology (HKUST) in 2008 (advisor: Prof. S.C. Cheung) M.Eng. from Institute of Software, Chinese Academy of Sciences (ISCAS) in 2003 B.Eng. from University of Science and Technology of China (USTC) in 2000 Research Interests: Professor Xu's research focuses on big data software engineering, intelligent software testing and analysis, and adaptive and autonomous software systems. His recent work centers on constructing and providing runtime support for intelligent software in open environments, with emphasis on inconsistency detection and resolution for environments, and quality assurance for adaptive, concurrent, learning-based, smartphone-based, and spreadsheet-based applications. His work bridges theoretical foundations with practical applications in software engineering, particularly in program analysis, software testing, and self-adaptive systems. Scientific Awards: ACM SIGSOFT Distinguished Paper Award from ICSE 2025 Best Student Paper Award from EUROSYS 2025 ACM Distinguished Member in 2024 Best Paper Award from SOSP 2023 Best Paper Candidate from ISSRE 2022 Yangtze River Scholar by the Ministry of Education in 2021 Multiple ACM SIGSOFT Distinguished Paper Awards from conferences including ASE, ICSE National Science and Technology Progress Award (Second Class) in 2011 Academic Service and Advising: Professor Xu has served on numerous program committees for top software engineering conferences including ICSE, ASE, ESEC/FSE, and ISSTA. He is an editorial board member for several journals including Journal of Computer Science and Technology and Frontiers of Computer Science. He has supervised numerous Ph.D. and MSc students, with research topics spanning program analysis, software testing, self-adaptive systems, and more. His students have gone on to successful careers in both academia and industry. Research Groups: Professor Xu is associated with the SPAR research group at Nanjing University and the CASTLE research group at HKUST, focusing on software analysis, reliability, and testing.
Ningchuan Xiao is a Professor of Geography at The Ohio State University's Department of Geography. His work bridges Geographic Information Science (GIScience) with computational methods, emphasizing spatial optimization, cartography, and machine learning integration. Education: Ph.D. in Geography from The University of Iowa (2003). Courses taught include GIS fundamentals, cartography, and Python-based spatial analysis. Research Interests: Spatial Optimization: Developing algorithms for land acquisition, redistricting, and resource allocation. Machine Learning & Cartography: Exploring AI-driven map interpretation and ethical visualization of complex data. Census Data: Innovating privacy-preserving techniques while maintaining data utility, including temporal/spatial modeling. Open Source Tools: Authored GIS Algorithms (2016) and maintains GitHub repository 'gisalgs' for accessible code. Publications: Recent work (2023-2025) highlights advancements in synthetic microdata generation, privacy-utility tradeoffs in census aggregation, and AI-driven cartographic recognition. His 2022 studies include traffic camera analytics and choropleth map QA systems. Awards: Not explicitly listed in the provided texts. Advising & Grants: Collaborated with researchers like Y. Lin, J. Li, and S. Bao. Projects include the Sustainable Columbus Observatory (SCO) for urban sustainability metrics. Research is supported through academic partnerships and computational initiatives.
Prof. Jacco van Ossenbruggen is a Full Professor in Intelligent Information Systems at Vrije Universiteit Amsterdam (VU), affiliated with the Network Institute. He serves on the Management Board of ODISSEI, a national research infrastructure for social sciences and economics. His academic background includes a PhD in Computer Science (2001) from VU’s Faculty of Science, focusing on hypermedia processing. Research Interests: His work centers on cultural AI, FAIR data principles, ontology engineering, and semantic web technologies. Key areas include inclusive cultural heritage metadata, bias mitigation in AI systems, and knowledge discovery via linked data. Recent projects involve leveraging large language models (LLMs) for metadata enrichment and ontology construction. Key Contributions: He leads initiatives like the Cultural AI Lab, exploring AI applications for cultural heritage. His research bridges technical innovations (e.g., semantic integration of restricted-access data) with societal impacts (e.g., ethical AI frameworks for public-sector applications). Developed frameworks for evaluating entity alignment in knowledge graphs Pioneered FAIR-aligned data management plans for scientific communities Designed tools like Alter Heritage for collaborative metadata curation Grants & Projects: Principal Investigator of the ODISSEI Portal project (2020–2024), advancing open data infrastructures. Active in funding initiatives promoting reproducible research and ethical data practices. Labs/Teams: Cultural AI Lab at VU, focusing on AI-driven solutions for cultural heritage preservation and accessibility.
Diogo Barradas is an Assistant Professor in the Department of Computer Science at the University of Waterloo. His research focuses on network security, internet censorship circumvention, and anonymous communication systems. He holds a Ph.D., M.Sc., and B.Sc. from Instituto Superior Técnico, Universidade de Lisboa, Portugal. His work addresses challenges such as website fingerprinting defenses, programmable network security, and steganographic techniques for censorship resistance. Education: Ph.D., Instituto Superior Técnico, Universidade de Lisboa (2021) M.Sc., Instituto Superior Técnico, Universidade de Lisboa (2016) B.Sc., Instituto Superior Técnico, Universidade de Lisboa (2014) Research interests include: Network traffic analysis and obfuscation Security of programmable network infrastructures Digital forensics and information hiding Covert channels in multimedia protocols His recent publications explore cutting-edge topics like time series analysis for website fingerprinting detection, distributed traffic correlation on programmable networks, and satellite-based censorship circumvention. These contributions highlight advancements in both theoretical frameworks and practical tools for privacy-preserving communication.
John Byabazaire is a Research Fellow at the School of Computer Science, University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2024), following a BSc (Gulu University, 2013) and MSc (Waterford Institute of Technology, 2018). His research focuses on IoT systems for data collection, remote sensing, AI-driven end-to-end system management, and fog analytics. He has held academic roles including Assistant Lecturer at Gulu University (2018–2019) and teaching roles at UCD since 2019, including Occasional Lecturer and Senior Teaching Assistant. His research spans smart agriculture, data quality in IoT, and education technology. Notable contributions include frameworks for yield mapping in precision agriculture, trust-based data validation in IoT, and machine learning approaches for livestock health monitoring. He has secured grants like the National ICT Initiatives Support Program (Uganda Government, 2019–2020). Teaching includes courses on cloud computing, web development, and distributed systems. His articles emphasize IoT data quality, agricultural analytics, and educational technology innovation. He actively promotes technology adoption in African education and agriculture sectors through collaborative projects.
Michael Bode is a Professor in the School of Mathematical Sciences at Queensland University of Technology (QUT). His research focuses on applying mathematical and computational methods to ecological and conservation challenges, particularly in marine ecosystems and coral reef management. He holds a PhD in Applied Mathematics from the University of Queensland. Research Interests: Professor Bode specializes in larval dispersal modeling, conservation prioritization, and the application of ecosystem models to real-world management scenarios. His work integrates disciplines like applied mathematics, statistics, and ecology to address issues such as coral reef resilience, invasive species control, and climate change impacts. Grants & Projects: Securing Antarctica's Environmental Future (2020) Conserving Coral Reef Fish and Sustaining Fisheries in the Anthropocene (2019) Tackling Pests Using Game Theory (2019) New Methods for Conserving Dispersing Species on Coral Reefs (2017) Awards: 2020 Fenner Medal (Australian Academy of Science) Eureka Prize finalist (2020) Australian Research Council Future Fellowship (2017) Labs & Teams: He leads the QUT Applied Mathematical Ecology Group (QUTAMEG), which develops quantitative tools for ecological and conservation problems. His work is published in journals like Conservation Biology , Nature Sustainability , and PLoS Biology .
Matthijs L. Noordzij is a Full Professor in the department of Psychology, Health & Technology. His research focuses on wearable technology, mental health interventions, and compassionate design principles. He has contributed to over 90 publications, emphasizing the intersection of psychophysiology and healthcare innovation. Recent work explores stress management through wearable devices and ethical considerations in digital mental health tools. Research interests: Wearables, stress measurement, compassionate technology, sensor-based healthcare. Key activities include organizing conferences and delivering invited talks on topics like 'Compassionate Technology or Stress Trigger?'. Notable contributions: Developed the Compassionate Technology Scale and published on accelerometer-based stress research. Media engagements include expert commentary on smartwatch applications and data privacy in wearable tech.
Bruno Castro da Silva is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He holds a PhD in Computer Science from UMass Amherst (2014), and MSc and BSc degrees from the Federal University of Rio Grande do Sul (UFRGS), Brazil. Prior to UMass, he was an Associate Professor at UFRGS and a postdoctoral researcher at MIT's Aerospace Controls Laboratory. His research focuses on reinforcement learning (RL), robotics, and AI safety, aiming to develop algorithms that ensure safe and autonomous task decomposition while meeting user-specified safety criteria. Key areas include hierarchical policies, active learning, and biologically-plausible mechanisms. He has published in top venues like ICML, NeurIPS, and Science, and received awards such as the Best Paper at RLDM 2022 and Distinguished Reviewer distinctions. He teaches courses in reinforcement learning and machine learning at UMass, emphasizing accessibility and safety. His work also extends to fostering diversity in STEM education. He leads the Autonomous Learning Laboratory and collaborates with organizations like Adobe Research and the Laboratory of Computational Neuroscience in Rome.
Bart J. Hengeveld is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Industrial Design department within the Faculty of Industrial Design. He leads the Future Everyday research cluster, focusing on the intersection of sound design and IoT systems. His work explores how embedded technologies can communicate through sound and tangible interaction in ways that enhance human experience. Education: BSc/MSc in Industrial Design Engineering, Delft University of Technology (2003) PhD in Industrial Design, TU/e (2011), with concurrent training in double bass at Codarts Conservatory Research Interests: Acoustic communication in IoT systems Tangible interaction design Social implications of ubiquitous computing Sound as a medium for human-technology interaction Recent Research Trends: Focus on multimodal interfaces combining sound/light/haptics Studies on sound design for healthcare and smart living environments Investigations into ethical and inclusive IoT soundscapes Awards: Two Teacher of the Year awards at TU/e, reflecting his commitment to pedagogical excellence. Advising & Projects: Supervised 113 student projects including interactive installations like 'Naktis' and 'Sonostapp' Leading the €1.5M 'Role of Sound in Hybrid Work Environments' project (2023–2026) Organized TEI 2016 conference and serves on its steering committee Teams/Labs: Core member of TU/e's Future Everyday cluster, collaborating with multidisciplinary teams on embodied interaction research.
Professor Thierry Langer is a Full Professor of Pharmaceutical Chemistry at the University of Vienna’s Faculty of Life Sciences (Department of Pharmaceutical Sciences). He leads research in computational drug design, with a focus on pharmacophore modeling, 3D-QSAR analysis, and AI-driven molecular design. His work bridges theoretical and experimental chemistry, addressing targets like viral proteases (e.g., SARS-CoV-2), GABA receptors, and dopamine transporters. Research interests include: Pharmacophore-guided drug discovery for anti-viral and CNS therapies Development of next-generation computational tools (e.g., PharmacoMatch, QPhAR) Protein-ligand interaction modeling using neural networks and graph-based algorithms Recent studies focus on: Inhibitors for herpesvirus nuclear egress complexes, AI-optimized antivirals, and dopamine transporter inhibitors for cognitive enhancement. His lab collaborates on projects like the NeuroDeRisk initiative to de-risk neurotoxic compounds. Publications emphasize drug repurposing, metabolic pathway analysis, and scalable synthesis methods for promising drug candidates.
Wagdi George Habashi is a Professor and NSERC-Industrial Research Chair at McGill University's Faculty of Engineering, Department of Mechanical Engineering. He leads the Computational Fluid Dynamics (CFD) Lab, focusing on aerodynamics, fluid mechanics, and icing-related simulations. His research emphasizes in-flight icing prediction, computational wind engineering, and CFD-driven optimization of aircraft and jet engine systems. Education: Ph.D., Cornell University M.Eng., McGill University B.Eng., McGill University Research Interests: Habashi's work bridges analytical and computational methods to address multi-physics/multi-scale engineering challenges. Key areas include in-flight ice crystal ingestion in jet engines, ice surface roughness modeling, supercooled droplet dynamics, and CFD-based risk management for icing. His team develops tools like FENSAP-ICE for real-time aero-icing simulations and explores mesh adaptation, parallel computing, and reduced-order modeling. Labs/Teams: Computational Fluid Dynamics Lab (CFD Lab).
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
