Wenjie Yin is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in Data Mining and Machine Learning, focusing on Natural Language Processing (NLP), hate speech detection, and social media analysis. Her research addresses challenges like cross-domain generalization in hate speech detection and the impact of AI tools like ChatGPT on information platforms. She teaches postgraduate modules on Data Mining and Machine Learning, emphasizing practical algorithm exploration and real-world applications. Recent work includes SemEval tasks on explainable NLP and studies on implicit abusive language detection. No specific awards or grants are listed, and her academic contributions are centered on advancing NLP techniques for societal challenges.
Natalia Sidorova is an Assistant Professor at the Eindhoven University of Technology , affiliated with the Mathematics and Computer Science department and the Process Analytics research group. She also contributes to the EAISI Health initiative. Her work focuses on process mining, Petri nets, workflow analysis, and healthcare informatics. Research Interests: Dr. Sidorova specializes in formal process modeling, conformance checking, resource-constrained systems, and applications in healthcare and smart environments. Her research bridges theoretical foundations (e.g., soundness verification of workflow nets) with practical tools like log alignment techniques and adaptive workflow systems. She explores data-driven methods for refining process models and analyzing complex event logs. Key Contributions: Her publications cover topics such as log alignment algorithms, resource-aware Petri nets, and stress management systems using sensor data. She co-authored the 2018 Best Paper Award-winning work on intelligent environments and contributed to foundational studies on workflow net soundness undecidability (2008). Teaching: She teaches Foundations of Data Analytics (2023–present). Labs/Teams: Active in Process Analytics and EAISI Health, collaborating on artifact-centric processes and health informatics.
Prof. Koen Hindriks is a Full Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Department of Artificial Intelligence. He holds additional roles at the Network Institute and Social AI research groups. His research focuses on social robotics, human-robot interaction, and multi-agent systems, with applications in healthcare, education, and retail. He leads projects like SOROCOVA (social robotics for educational support post-COVID) and Droomrobot (a robot companion for pediatric medical stress reduction). Key research interests include dialogue management, empathy elicitation via robots, and long-term human-robot relationships. He teaches courses such as Multi-Agent Systems and Socially Intelligent Robotics. Recent work emphasizes ethical AI integration and practical deployment of social robots in real-world scenarios. His contributions span over 220 publications, addressing topics like robot learning from human demonstrations and automated speech processing in HRI contexts. Projects: Principal Investigator for SOROCOVA (2021-2023) Robot Companion for Childhood Cancer Patients (2019-2021) Current Droomrobot project (2024-2026) Awards: None explicitly listed, but recognized through extensive peer-reviewed contributions. Advising: Supervised 4 PhD theses, focusing on child-robot interaction and healthcare robotics. Prof. Hindriks also engages in ancillary roles, including Director at Ex Machina (Utrecht) and speaker at the Amsterdam Speakers Academy. His work aligns with UN SDGs, particularly in improving health and education through technology.
Dr. Stanisław Dunin-Horkawicz is affiliated with the University of Warsaw's Laboratory of Structural Bioinformatics. His research focuses on computational methods to study protein sequences, structures, and functions, with emphasis on machine learning applications, evolutionary origins of protein families, and fibrous proteins like coiled-coil domains. He leads a systems biology project on molecular pathways related to multicellularity (NCN OPUS grant 2020/37/B/NZ2/03268). Key contributions include developing tools like DeepCoil for coiled-coil prediction, pLM-BLAST for homology detection, and localpdb for protein structure management. His work spans structural bioinformatics, protein engineering, and evolutionary biology, addressing topics such as HAMP domain signaling, RNA structure prediction, and viral protein evolution. Collaborative projects involve international teams and cutting-edge methods like AlphaFold2 for protein modeling. His research has implications for understanding neurodegenerative diseases, bacterial pathogenesis, and enzyme specificity engineering.
Michael A. Long is the Thomas and Suzanne Murphy Professor of Neuroscience and Vice Chair for Education in the Department of Neuroscience and Physiology at NYU Grossman School of Medicine. He also holds a Professor position in the Department of Otolaryngology-Head and Neck Surgery. PhD from Brown University Postdoctoral Training at MIT's McGovern Institute for Brain Research His research focuses on neural circuits governing skilled movements, particularly vocal interactions, using a comparative approach across songbirds, parrots, singing rodents, and humans. Key areas include molecular/cellular neuroscience, systems/cognitive neuroscience, and computational modeling. The Long Lab investigates vocal communication mechanisms, neural dynamics during speech, cross-species brain network convergence, and develops advanced neurotechnologies like DREDge motion correction algorithms. Their work bridges basic neurobiology and translational applications for speech disorders. Grants from NYSCF, Rita Allen Foundation, Klingenstein Foundation, and Herschel-Weill Foundation support his research program.
Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, specializing in the digital humanities and the history of technology in music production. His research examines the intersection of computational methods with musicology, including early uses of digital computers in musical analysis and the cultural impact of formats like the CD. Previously, he was a postdoctoral Research Fellow at Trinity College Dublin (TCD), funded by the Irish Research Council (2019–2021). Bell holds a PhD in Music Theory from Columbia University (2019), where he explored computational approaches to musical scores under Joseph Dubiel, and a B.A. (Mod.) in Music and Mathematics from TCD (2013). His work spans topics such as the socio-technical history of CD audio formats, algorithmic creativity in early AI research, and the preservation of obsolete digital media. He teaches across computer science curricula, emphasizing interdisciplinary methods in digital humanities. Bell’s research has been supported by UK Research and Innovation (UKRI) and smaller institutional grants, and he contributes to projects like DRI initiatives for UK-based arts researchers. Key awards include the Government of Ireland Postdoctoral Fellowship. His teaching experience includes courses on digital music critique and music theory fundamentals at Columbia, alongside workshops in data visualization and music technology. Bell’s lab and team collaborations focus on emerging technologies in musicology, such as AI’s role in cultural analysis and the challenges of preserving born-digital artifacts.
University of California, Los AngelesUnited States
Keriann Marie Backus is an Associate Professor in the Department of Biological Chemistry at the University of California Los Angeles (UCLA) School of Medicine. Her research program focuses on developing and applying chemoproteomic approaches to understand protein function, particularly through cysteine profiling and covalent ligand discovery. Dr. Backus leads an active research laboratory that bridges chemical biology, proteomics, and systems biology to address fundamental questions in protein biochemistry and disease mechanisms. Dr. Backus's research interests center on chemoproteomics, with particular emphasis on cysteine reactivity profiling, protein interactome mapping, and covalent ligand discovery. Her laboratory develops innovative chemical and proteomic methodologies to characterize the cysteinome—the complete set of reactive cysteines in the proteome—and to understand how cysteine modifications impact protein function in health and disease. Her work spans fundamental protein biochemistry, chemical probe development, and translational applications in areas including inflammation, cancer, and infectious disease. The lab employs cutting-edge mass spectrometry, chemical biology, and bioinformatics approaches to map protein modifications, interactions, and functions at systems-level resolution. Her publication record demonstrates significant contributions to chemoproteomics methodology development and biological applications. Recent work has focused on creating scalable sample preparation techniques (CySP3-96), organelle-specific cysteine capture methods, cholesterol interactome mapping, and cysteine databases (CysDB). Her research bridges basic chemical biology with translational applications, including tuberculosis imaging and understanding vascular inflammation mechanisms. Dr. Backus has received significant research funding, including an NIH DP2 New Innovator Award (GM146246) for 'A systems-level approach to decipher the protein interactome' and an NIH F32 postdoctoral fellowship (GM108208) for 'Fragment-Based Ligand Discovery in Proteomes.' These awards reflect recognition of her innovative approaches to chemical proteomics and protein interaction mapping. Dr. Backus actively mentors graduate students and postdoctoral researchers in her laboratory, with numerous trainees appearing as first or co-first authors on high-impact publications. Her research program maintains strong collaborations across UCLA and with other institutions, particularly with researchers in the Cravatt laboratory (where she completed her postdoctoral training) and with clinical researchers applying chemoproteomic approaches to disease mechanisms. The Backus laboratory functions as an interdisciplinary research hub at the intersection of chemical biology and proteomics. It combines expertise in organic synthesis, mass spectrometry, computational biology, and cell biology to develop and apply chemoproteomic technologies. The lab maintains strong connections with the broader UCLA research community, particularly through the Graduate Program in Bioscience and collaborations with clinical departments studying inflammation, cancer, and infectious diseases.
Bingjie Li is an SRUC Challenge Research Fellow at Scotland's Rural College (SRUC), affiliated with the Integrative Animal Sciences Food Security Challenge Centre and the Department of Animal and Veterinary Sciences. His research focuses on quantitative genetics, animal breeding, and computational biology, particularly in managing omics data (phenomics, genomics, transcriptomics) to improve livestock genetics. He leads international projects and collaborates with industry partners to advance dairy cattle genetics and animal health. Education: PhD in Quantitative Genetics and Animal Breeding (2013–2018), Aarhus University and Swedish University of Agricultural Sciences MSc in Quantitative Genetics and Animal Breeding (2011–2013), AgroParisTech and Norwegian University of Life Sciences BSc in Veterinary Medicine and Computer Science (2007–2011), Southwest University Research Interests: Bingjie’s work integrates computational methods with genetic analysis to address challenges in livestock production. Key areas include genomic prediction of feed efficiency, genetic evaluation of dairy cattle traits, and understanding the genetic basis of complex traits like methane emissions and lameness resistance. His projects often involve multi-omics data integration and transcriptome analysis to enhance breeding strategies. Recent Contributions: His recent publications explore the genetic regulation of gene expression in poultry, rumen microbiome interactions affecting methane emissions, and the genetic architecture of claw horn lesions in cattle. He also contributes to tools like the FarmGTEx TWAS-server for transcriptome-wide association studies. Grants & Projects: PI of projects on genomics/transcriptomics for feed efficiency and dairy cow phenotypes Co-Investigator in studies on host-pathogen interactions in digital dermatitis Lead on the FarmGTEx project to create multi-tissue gene expression atlases Labs/Teams: Collaborates with interdisciplinary teams at SRUC’s Roslin Institute and global institutions, advancing precision breeding and sustainable livestock practices through genomic innovation.
Fabian C. Moss is an Assistant Professor for Digital Music Philology and Music Theory at Julius-Maximilians-Universität Würzburg (JMU), Germany. His research bridges humanities and computational methods, focusing on interdisciplinary approaches to music structure, including musicology, mathematics, data science, and digital humanities. He leads projects such as DigiMusTh (building a digital collection of historical music theory texts) and Digital Choro (exploring Brazil’s musical heritage). He is also part of the Zentrum für Philologie und Digitalität (ZPD) and the Graduate School Humanities (GSH) at JMU. Before joining JMU, Moss held positions as a Research Fellow in Cultural Analytics at the University of Amsterdam and as a postdoctoral and doctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL). He has conducted research visits at MIT and Escola Superior de Música de Catalunya (ESMUC). His work emphasizes computational modeling, corpus studies, and historical music analysis, with a focus on tonal evolution, harmonic progressions, and digital tools for musicology. Moss teaches courses in computational musicology, music theory, and digital tools. He serves on advisory boards for journals like Computational Humanities Research and Analitica , and actively contributes to conferences and workshops. His research outputs include datasets, software tools (e.g., MonodiKit, midiVERTO), and interdisciplinary analyses of musical corpora.
Ariangela Kozik is an Assistant Professor at the University of Michigan in the Departments of Molecular, Cellular, and Developmental Biology and Pulmonary and Critical Care Medicine. Her research focuses on the respiratory microbiome, host-microbe interactions, and multi-omic strategies to understand chronic respiratory diseases like asthma and COPD. She champions equity in STEMM through leadership in the Black Microbiologists Association (BMA) and initiatives like Black in Microbiology Week. Her recent work includes analyzing microbiome-immune relationships in obesity-associated asthma, developing computational tools for biological data, and addressing systemic racism in pediatric asthma research. She received the 2025 Equality, Diversity and Inclusion Prize from the Microbiology Society for her efforts in transforming research systems and fostering inclusivity. Key Research Themes: The 15 most recent articles highlight her dual focus on microbial ecology (respiratory/gut microbiome) and social equity (STEM inclusion, academic job market trends). Her work spans molecular microbiology and translational public health advocacy. Scientific Awards: Equality, Diversity and Inclusion Prize 2025 (Microbiology Society) for co-founding BMA Leadership & Outreach: Vice President of BMA, member of American Thoracic Society Health Equity Committee, advisor for 2030 STEM, and science communication across all education levels.
Andrew Cameron is a Professor in the Department of Biology at the University of Regina. He leads the Institute for Microbial Systems and Society, focusing on molecular genetics and microbial ecology. His research explores bacterial adaptation, gene regulation, and interactions in diverse environments. Bacteriology Molecular Genetics Microbial Ecology His lab uses genomic approaches (RNA sequencing, chromatin immunoprecipitation) to study bacterial lifestyles and protein-DNA interactions, particularly in Escherichia coli and Salmonella enterica . Key research themes include acid stress adaptation, horizontal gene transfer, and DNA topology regulation. Recent publications highlight multidrug resistance plasmids, quorum sensing, and cross-feeding dynamics in microbial communities. Research trends from his publications (2005–2025) span bacterial genomics, antibiotic resistance evolution, and host-pathogen interactions. Techniques include transcriptomics, proteomics, and phylogenomic analysis of environmental pathogens.
Ryan Gillespie is an Assistant Professor in the Department of Curriculum & Instruction at the University of Idaho, College of Education, Health and Human Sciences. His work focuses on mathematics education, teacher professional development, and the role of coaching in enhancing instructional practices. Gillespie holds a Ph.D. in Education (2021), M.Ed. in Curriculum & Instruction (2011), and dual B.S. degrees in Mathematics Education and Mathematics/Computer Science (2005/2002). His research emphasizes professional learning for teachers, particularly through coaching methodologies and video-based professional development. He has pioneered online teaching models, such as the Three-Part Synchronous Online Model for teacher training. Gillespie’s awards include the NSF Stem for All Video Showcase Presenters’ Choice Award (2019) and the Carbondale Colorado Rotary Distinguished Teaching Performance Award (2008). He teaches courses like EDCI 327: Elementary Mathematics Methods and EDCI 413: Data Analysis and Probability. His recent publications explore coach-teacher interactions, video annotation techniques, and synchronous online professional development design. Gillespie’s work bridges theory and practice, aiming to optimize teacher support through innovative coaching frameworks.
Daksitha Withanage Don is a Researcher at the Chair for Human-Centered Artificial Intelligence, affiliated with the University of Augsburg's Faculty of Applied Computer Science and Institute of Computer Science. His work focuses on developing socially interactive agents, affective computing, and generative AI applications. He holds an M.Sc. and actively contributes to projects like DEEP (Emotion Processing for Social Agents) and MITHOS (mixed reality teacher training). His research interests include self-supervised learning, multimodal analysis, and human behavior modeling. He advises students on topics such as GUI design for social agent frameworks, behavioral synchrony analysis using foundation models, and real-time listener behavior generation in Unreal Engine. Key Projects: MITHOS, FORSocialRobots, TherapAI, ReNeLiB Labs/Teams: Human-Centered Artificial Intelligence Team Supervised Theses (2024): Automated ICEP-R Annotation, Long-Term Memory Integration in LLMs, Mediapipe 3D Blendshapes for Behavior Modeling Publications include work on generative AI for HCI, automated behavioral annotation, and real-time interactive systems. Contact via email or visit his GitHub for open-source contributions.
Achim Rettinger is a full professor at Trier University, leading the research group krAil (Knowledge Representation Learning). He specializes in machine learning, natural language understanding, and human-centered AI. His work focuses on expressive knowledge representations and their applications in semantic technologies. Education: Studied Computer Science at Universität Koblenz (Germany), University of Georgia (USA), and University of Alberta (Canada). PhD in machine learning at TU Munich/Siemens AG, followed by habilitation at KIT (2016). Served as interim professor at Karlsruhe Institute of Technology (2018/19). Research interests include knowledge graphs, cross-lingual semantic annotation, and data-driven analysis in political and medical domains. Notable contributions include the X-LiSA framework and work on semantic web technologies. Awards include best paper and challenge awards at ISWC and ESWC conferences. Leadership roles include senior PC member at ISWC, track chair at ESWC, and membership in AI for Good Foundation. Active in EU projects, DFG grants, and large-scale collaborative initiatives. Key projects: BreXearch (cross-lingual Brexit analysis), xLiMe System (semantic search), and medical decision support systems for liver surgery. Collaborates with interdisciplinary teams on cognition-guided surgery and data integration. Publications span top venues like ISWC, NeurIPS, ICLR, and CVPR, with a focus on semantic web, machine learning, and applied AI solutions.
Bryan Gibb is an Associate Professor in the Department of Biological and Chemical Sciences at the New York Institute of Technology (NYIT), within the College of Arts and Sciences. He joined NYIT in 2015 after completing his postdoctoral research at Columbia University under Eric Greene. Gibb earned his Ph.D. from the University of Pennsylvania, studying DNA recombination enzymes with Gregory D. Van Duyne. Education : Ph.D. in Biochemistry/Structural Biology from the University of Pennsylvania, 2010s. Research Interests focus on molecular mechanisms of DNA-binding proteins, particularly in CRISPR pathways and bacteriophage therapy. Key areas include improving genome editing tools like CRISPR-Cas9 for gene therapy applications and developing bacteriophages as alternatives to antibiotics for treating drug-resistant infections. His lab employs single-molecule imaging techniques to study protein dynamics on DNA, such as RPA and RAD51 interactions. Recent Research Trends highlight advancements in phage therapy, CRISPR system optimization, and DNA repair mechanisms. His work bridges fundamental molecular biology with translational applications in biotechnology and medicine. Grants & Advising : While no grants are explicitly listed, his research projects imply funding for bacteriophage discovery and genome engineering. No advisees are listed in the provided texts. Labs/Teams : Active in NYIT’s Biological & Chemical Sciences Department, leading projects on CRISPR systems and phage-based therapies, often involving undergraduate students in phage isolation and engineering.
