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Lee HughesView profile

Associate Professor

• Microbiology
• Bacteriophages
• Genomics
• +4 more

Dr. Lee Hughes is an Associate Professor of Biology and Associate Dean for Undergraduate Studies at the University of North Texas (UNT) College of Science. His research spans two domains: (1) microbiology, focusing on bacteriophages infecting Streptomyces and their genomic diversity, and (2) science education research, emphasizing early research experiences for biology majors and innovative teaching strategies like blended and online learning. Education: B.S. in English, M.S. in Biology, Ph.D. in Microbiology – all from UNT. In microbiology, Dr. Hughes leads UNT's Phage Hunters Advancing Genomics and Evolutionary Science (PHAGES) program, a Howard Hughes Medical Institute (HHMI) Science Education Alliance initiative. His lab isolates novel Streptomyces -infecting phages to study their genetic elements, evolutionary relationships, and potential applications in biotechnology and medical microbiology. Recent publications highlight his work on phage genome sequencing, cluster classification, and diversity analysis across bacterial genera. In science education, he investigates pedagogical frameworks that enhance student engagement and retention, particularly for historically underrepresented groups in STEM. His research includes program evaluations (e.g., NSF-funded FOCUS Scholarships) and development of assessment tools like the Microbiology Concept Inventory to identify and address student misconceptions. Current grant-funded projects include the NIH G-RISE (Graduate Research Training Initiative for Student Enhancement) grant (2021–2026), where he serves as Co-PI, and ongoing HHMI-affiliated education initiatives. Prospective students may engage with Dr. Hughes' research through the UNT PHAGES program or the Research Experiences and Activities in Life Sciences (REALSCI) webpage. His lab's work bridges cutting-edge genomics and transformative educational practices, fostering training opportunities at both undergraduate and graduate levels.

• Computer Vision
• Robotics
• Machine Learning
• +5 more

Saurabh Gupta is an Associate Professor in the Electrical and Computer Engineering Department at the University of Illinois Urbana-Champaign (UIUC), where he is based at the Coordinated Science Lab (CSL 319). He previously served as a Research Scientist at Facebook AI Research in Pittsburgh working with Prof. Abhinav Gupta. His educational background includes a PhD in Computer Science from UC Berkeley advised by Prof. Jitendra Malik, and an undergraduate degree in Computer Science and Engineering from IIT Delhi, India. Gupta's research focuses on building intelligent agents that can interact with the physical world, with particular emphasis on computer vision, robotics, and machine learning. His work explores representations that enable physical interaction and learning from active engagement with environments. Key research directions include spatio-semantic and topological representations for visual navigation, skill discovery, learning from videos, and active visual learning. His recent publications reveal a strong trend toward practical robotics applications with an emphasis on real-world deployment. The research spans multiple domains including humanoid robotics, egocentric vision, physical reasoning, and wireless sensing. Significant attention is given to bridging the sim-to-real gap and developing systems that work in practical environments rather than controlled laboratory settings. As an educator, Gupta teaches advanced courses including Deep Learning for Computer Vision (CS 444/ECE 494), Computer Vision (ECE 549/CS 543), and Learning-Based Robotics (ECE 598 SG). Gupta advises multiple PhD students including Arjun Gupta, Shaowei Liu, Aditya Prakash, Xiaoyu Zhang, Runpei Dong, and Xialin He, with former student Matthew Chang completing his PhD in 2024 on Robot Learning from Videos. His research group operates within the Coordinated Science Laboratory at UIUC, collaborating with researchers across computer vision, robotics, and machine learning domains. The group maintains strong connections with industry research labs including Meta AI Research, where former students have pursued research scientist positions.

Julien PerretView profile

Researcher

• Geographical Information Science
• Urban Dynamics
• 3D Urban Simulation
• +6 more

Julien Perret is a senior researcher at the National Institute of Geographic and Forest Information (IGN) in France, affiliated with the LASTIG laboratory and STRUDEL research team. His work spans geographical information science, urban dynamics, and historical cartography. Current Affiliation: National Institute of Geographic and Forest Information (IGN) Research Team: LASTIG, STRUDEL Academic Rank: Senior Researcher (Directeur de Recherche) Education Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Computer Science, Université Rennes 1 (2006) Engineering Degree in Computer Science, INSA Rennes (2002) Research Interests Perret's research focuses on urban dynamics through computational approaches, including agent-based modeling and 3D urban simulation . He investigates historical cartographic data like the Napoleonic land registry and Cassini Carte de France to understand long-term urban evolution. His work integrates geospatial data with epidemiological modeling using digital twins, particularly for pandemic simulations. Scientific Contributions Key publications include: 2025: Building change models for urban densification studies 2024: Historical map vectorization benchmarks 2017: Scalable point cloud management systems 2015: 3D analysis of urban regulation impact 2005: Procedural geometry modeling with FL-systems Advising and Collaborations Perret has supervised multiple PhD students and collaborated on projects like SoDUCo (1789-1950 Paris urban dynamics) and iSpace&Time (4D GIS for city modeling). He contributes to open-source GIS tools like GeOxygene .

Marc ChaumontView profile

Associate Professor

• Visual Data Analysis
• Remote Sensing
• Steganography
• +10 more

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Expressive Speech Processing
• Speech Synthesis
• Emotion Recognition
• +3 more

Marie Tahon is a Professor at Le Mans University and Director of the LST (Langage, Signal et Texte) team at LIUM (Laboratoire d'Informatique de l'Université du Maine). Her research spans expressive speech processing with applications in speech synthesis, emotion recognition, and speaker identification, complemented by expertise in musical acoustics for automatic song analysis and organology. Education : Engineering degree from École Centrale de Lyon (2007), M.S. in Acoustics from École Centrale & INSA Lyon (2007), and Ph.D. in Computer Science from University of Paris-Sud (Orsay, 2012). Postdoctoral positions at LIMSI-CNRS (affective computing), LMSSC CNAM (acoustics), and IRISA (Expression team). Research Focus : Tahon's work centers on developing interpretable systems for expressive speech processing. Key contributions include the ALLIES corpus for speech segmentation/diarization and AlloSat for call-center emotion analysis. Her recent publications demonstrate strong emphasis on low-resource speech translation (e.g., Kurdish), speaker verification after resynthesis, and turn-taking analysis in French media using explainable AI techniques. She integrates acoustic and linguistic features for continuous emotion prediction and develops noise-robust models for digital holography. Collaborations & Infrastructure : Leads the COMMUTE and ESPERANTO projects while directing LIUM's LST team. Her work leverages specialized resources like the ALLIES corpus (segmentation, diarization, recognition) and AlloSat (satisfaction/frustration analysis). Current efforts focus on lifelong learning for MOS prediction, multilingual speech translation, and perceptual evaluation of turn-taking phenomena in broadcast media.

Pradip K SrimaniView profile

Professor

• Parallel and Distributed Computing
• Interconnection Networks
• Self-Stabilizing Systems
• +2 more

Pradip K Srimani is a Professor at the School of Computing , Clemson University , with research focus on Parallel and Distributed Computing , Self-Stabilizing Systems , and Graph Theory Applications . He is an IEEE Life Fellow and ACM Distinguished Scientist with over 250 publications. Research Interests Self-stabilizing algorithms for network graphs Ontology-based biomedical information retrieval High-performance computing resource management Biologically inspired distributed algorithms Network topology computation Recent publications demonstrate expertise in token circulation protocols, genome assembly frameworks, and semantic similarity measurement. He has served on program committees for APDCM , GPC-2018 , and IEEE BigMM conferences, while maintaining editorial roles at International Journal of High Performance Computing and Journal of Big Data . Scientific Awards IEEE Life Fellow ACM Distinguished Scientist

Nicole Li-JessenView profile

Associate Professor

• upper airway health and diseases
• computational medicine
• tissue engineering
• +5 more

Nicole Li-Jessen, PhD is an Associate Professor in the Department of Surgery at McGill University's Faculty of Medicine and Health Sciences and holds the Canada Research Chair (Tier 2) in Personalized Medicine of Upper Airway Health and Diseases. She serves as Graduate Program Director for the School of Communication Sciences and Disorders and is an Associate Investigator at The Research Institute of the McGill University Health Centre (RI-MUHC). Dr. Li-Jessen maintains additional affiliations with the Departments of Otolaryngology and Biomedical Engineering, and is a Research Member of Quantitative Life Sciences and McGill Regenerative Medicine Network. Dr. Li-Jessen's research laboratory integrates in vitro, in vivo, and in silico (computational) approaches to study vocal fold biology and wound healing, with the goal of generating computational platforms that guide surgeons and speech pathologists in voice repair methods. Her key research areas include: Non-invasive assessment of airway reflux Designing 3D printable biomaterial for laryngeal tissue and muscle reconstruction Creating wearable e-health devices for upper airway monitoring Agent-based modeling of vocal trauma and repair response Development of tumor-on-a-chip platforms for studying laryngeal cancer chemoresistance Her work is funded by a Canada Research Chair, the CIHR, NSERC, SSHRC, FRQS, and NIH in the United States. Dr. Li-Jessen has published extensively in high-impact journals across biomedical engineering, otolaryngology, and regenerative medicine fields, with recent publications focusing on vocal fold biomaterials, computational modeling of vocal pathology, and audio-based digital biomarkers for respiratory diseases. Dr. Li-Jessen has received several prestigious awards including the Canada Research Chair (2021), Principal's Prize for Excellence in Teaching (2018), and the Rosemary Wedderburn Brown Prize (2018). She serves as Section Editor of PLOS Digital Health and is actively involved in science policy as a Delegate for Science Meets Parliament. As Graduate Program Director, Dr. Li-Jessen oversees graduate thesis programs in the School of Communication Sciences and Disorders and has mentored numerous PhD, Master's, and undergraduate students. She leads the Voice and Upper Airway Research Lab (VUA Lab), which advocates for EDI principles in STEM and currently has open positions for PhD and post-doctoral researchers in computer modeling and digital health projects.

• Logic and Philosophy of Logic
• Linguistic Communication
• 19th Century German Philosophy
• +8 more

Jens Lemanski is an Associate Professor at the Philosophical Seminar of the University of Tübingen, with part-time roles as an Adjunct Professor at the University of Münster and a Researcher at Fernuniversität Hagen. His research spans Logic and Philosophy of Logic , Linguistic Communication , 19th Century German Philosophy , and Visualization in Mathematics . His work focuses on the historical and philosophical foundations of logic diagrams, including projects like Logic Diagrams in Kantianism (Fritz-Thyssen-Stiftung) and Gestures and Diagrams in Visual-Spatial Communications (DFG-priority programme). He has contributed extensively to understanding the evolution of logic from antiquity to modernity, emphasizing the role of diagrams in Euler-type systems , Byzantine logic , and Kantian thought . Lemanski co-edits Historia Logicae (College Publications) and serves as a Book Review Editor for History and Philosophy of Logic . His recent publications analyze the interplay between transcendental philosophy , formal logic , and AI-driven multimodal communication . He holds a PhD from Johannes Gutenberg University Mainz (2011) and contributes to conferences like Diagrammatic Representation and Inference (2024).

Juli HuangView profile

Senior Lecturer

• Economic Anthropology
• Social Enterprise
• Technology and Development
• +8 more

Dr. Juli Huang is a Senior Lecturer in Anthropology of Development at the University of Edinburgh's School of Social and Political Science. She serves as Founding Programme co-Director of the MSc Data, Inequality & Society at the Edinburgh Futures Institute and holds a British Academy/Wolfson Foundation Fellowship (2022-2025). Research Focus Her research examines social enterprise through the lens of economic anthropology, focusing on: Technology and data practices in development contexts Moral logics of money circulation and alternative currencies Gendered dimensions of entrepreneurship in South Asia Digital transformation of social welfare systems Circular economy models in creative industries Her fieldwork spans Bangladesh and Iran, employing ethnographic methods to study development interventions. Publication Trends Recent publications demonstrate strong focus on social enterprise dynamics, digital transformation in Global South contexts, and critical examinations of development paradigms. Her work consistently employs ethnographic methodologies to analyze power relations in economic systems, with regional specialization in South Asia. Awards and Recognition Latifeh Yarshater Award for best book on women in Iran (2009) British Academy / Wolfson Foundation Fellowship (2022-2025) Research Leadership Currently supervising PhD candidates researching: AI training and data annotation labor in China Data journalism development narratives in Malaysia Energy access for displaced populations in Northeast India Secured significant funding including: British Academy/Wolfson Fellowship for comparative study of social enterprise data practices in Bangladesh/Scotland Creative Horizons (AHRC) grant for 'Creative Coin' alternative currency project Collaborative Projects Leads interdisciplinary teams including the 'Creative Coin' initiative with Edinburgh Napier University, designing special-purpose currencies to measure environmental/societal value in creative industries. Her work bridges anthropology with data science through the Edinburgh Futures Institute.

• Game Technology
• Game Design
• Serious Games
• +7 more

Professor Remco Veltkamp holds a faculty position at Utrecht University's Faculty of Science with a focus on Game and Media Technology . As Scientific Director of AI Labs and coordinator of the Utrecht Center for Game Research , his work bridges serious games, virtual reality, and human-centered AI applications. He leads the Dynamics of Youth Hub 'Healthy Play, Better Coping' exploring gaming's role in pediatric chronic illness management. Academic leadership in gaming technology Director of Utrecht's AI Labs Founder of Serious Game Society Editor of International Journal of Serious Games Research spans game design, AR/VR interaction, 3D object recognition, and multimedia systems with applications in: Healthcare gamification Energy conservation Bioinformatics Social behavior analysis Computer vision Recent work includes: 2025: Developing fatigue management therapy games 2024: Analyzing protest dynamics through social media 2023: Creating equine pain assessment systems 2022: Gamification in food sustainability As educator, he teaches Game Programming and Small Project Game and Media Technology , while pioneering applications of gaming in healthcare and education sectors.

Sara MostafaviView profile

Associate Professor

• Computational Biology
• Machine Learning
• Artificial Intelligence
• +4 more

Sara Mostafavi is an Associate Professor at the Paul Allen School of Computer Science & Engineering at the University of Washington (UW), with a focus on Artificial Intelligence and Computational Biology. She is currently on leave (2024-2025) leading the Computational Biology and Translation Organization at Genentech. Mostafavi previously held faculty roles at the University of British Columbia (UBC) and the Vector Institute, where she received prestigious Canada Research Chair (CRC II) and Canada CIFAR Chair in AI (CIFAR-AI) awards. PhD in Computer Science, University of Toronto (2011) Postdoctoral Researcher, Stanford University Her research develops machine learning and statistical methods to study gene regulation, disease susceptibility, and molecular networks, with applications in immunology, genetics, and neuroscience. She has pioneered sequence-to-function models for understanding gene regulatory grammar and predicting disease mechanisms. Her publications highlight expertise in genomic data integration, interpretable AI, and cross-modality learning. Recent work includes analyzing mosaic chromosome loss in aging microglia, immune cell differentiation, and Alzheimer's disease networks. Scientific Awards Canada Research Chair (CRC II) in Computational Biology (2015–2020) Canada CIFAR Chair in Artificial Intelligence (CIFAR-AI) Co-founder of the Machine Learning for Computational Biology (MLCB) Conference Mostafavi has mentored numerous PhD and Master’s students, including Xinming Tu, Anna Spiro, Gherman Novakovsky (PhD 2023), and Elijah Willie (MSc 2020). Her lab collaborates with institutions like the Immunological Genome (ImmGen) Consortium and Canadian Institute for Advanced Research (CIFAR). She leads the Mostafavi Lab, which specializes in combining association evidence across genomics datasets and modeling biological pathways to disentangle spurious correlations. Her team develops tools like CEWAS, AI-TAC, and Brain xQTL web server.

Karin OrtmayrView profile

Assistant Professor

• Phytochemistry
• Biodiscovery
• Metabolomics
• +4 more

Karin Ortmayr serves as an Assistant Professor at the University of Vienna within the Faculty of Life Sciences, Department of Pharmaceutical Sciences, specifically in the Division of Pharmacognosy. Her research laboratory is located in room 2G 154 (floor 1) at Josef-Holaubek-Platz 2, A-1090 Vienna, Austria. Her primary research interests focus on Phytochemistry & Biodiscovery , Molecular Targets , and MS-based metabolome profiling . Dr. Ortmayr investigates the impact of cellular plasticity on cellular metabolism and responses to stress & bioactive compounds. Her work addresses fundamental questions on the interplay between different cell states, cellular metabolism, and the action of bioactive compounds, with particular emphasis on (1) harnessing cell state-specific vulnerabilities to target disease-relevant cell subpopulations like quiescent cancer cells (cancer recurrence), and (2) considering cell state switches to improve cell-based systems for drug discovery. Her research employs advanced analytical tools in mass spectrometry-based metabolomics for systematic studies in human health and disease. She utilizes combined metabolic- and phenotypic profiling approaches, with technologies including mass spectrometry-based metabolomics (non-targeted and targeted), in vitro cell cultivation and multiparallel perturbation studies (96-well format), live-cell microscopy imaging, and integrative computational data analysis. Her publication record demonstrates consistent research output from 2014 through 2025, with a recent focus on metabolic mapping of pharmacological perturbations, biopharmaceutical profiling of natural compounds, and the relationship between cellular plasticity and metabolism in disease contexts. Her work spans microbiology, cancer research, metabolomics, and phytochemistry, reflecting interdisciplinary approaches to understanding metabolic pathways and their therapeutic applications. Dr. Ortmayr utilizes mass spectrometry-based metabolomics as her primary analytical approach, with applications ranging from antibacterial compound discovery to cancer cell metabolism and natural product analysis. Her research bridges fundamental metabolic understanding with practical applications in drug discovery and disease treatment. Her laboratory work involves in vitro cell cultivation, high-throughput perturbation studies, live-cell imaging, and computational data analysis to investigate metabolic responses to various stressors and bioactive compounds. This integrated approach allows for comprehensive analysis of cellular metabolic states and their implications for disease mechanisms and therapeutic interventions.

• Electrophysiology
• Neuroethology
• Sensory Information Processing
• +6 more

Dr. Jan Grewe is a Senior scientific employee at the Institute for Neurobiology, Department of Biology, Faculty of Science at Eberhard-Karls University Tübingen. His research focuses on neuroethology and electrophysiology, particularly studying sensory information processing in weakly electric fish. He is affiliated with the Neuroethology research group led by Prof. Dr. Jan Benda. Dr. Grewe's primary research interests include neural coding, electrosensory processing, and computational neuroscience. He investigates how sensory information is processed by the nervous system to guide behavior, with a specific focus on weakly electric fish as a model system. His work combines electrophysiological recordings with computational approaches to understand neural mechanisms. He is also a strong advocate for open science and open data practices. His recent publications demonstrate expertise in neural coding, electrosensory systems, and data management. The research spans from basic neural mechanisms in weakly electric fish to developing computational models and data standards. His work shows a consistent focus on understanding how neural systems process information efficiently, with applications to both biological and artificial systems. Dr. Grewe has been actively involved in teaching and mentoring, including organizing the G-Node Course on Neural Data Analysis and mentoring in the Google Summer of Code program. His GitHub profile shows active contributions to neuroscience software projects like NIX and odML, which facilitate data sharing and reproducibility in neuroscience research.

• Artificial Intelligence
• Autonomous Agent Systems
• Natural Language Processing
• +6 more

Dirk K.J. Heylen serves as Full Professor at the Digital Society Institute within the University of Twente, holding a dual appointment in the Human Media Interaction department. His academic profile demonstrates extensive engagement in artificial intelligence research with particular focus on human-centered applications. Research interests span Artificial Intelligence, Autonomous Agent Systems, Natural Language Processing , and Knowledge Representation , with significant applications in healthcare, well-being, and social robotics. His work bridges technical AI development with societal context considerations, particularly examining human-AI interaction patterns in daily life scenarios. Recent publications reveal strong emphasis on conversational agents, virtual human interaction, and predictive health technologies. Notable scientific recognition includes the Best Student Paper award at the 22nd ACM International Conference on Multimodal Interaction (2018). His research activities demonstrate consistent output across conferences, journals, and datasets, with particular growth in healthcare applications since 2020. Professional activities include editorial work for major conferences (ICMI 2020), organization of specialized events (Narrative Matters 2018), and hosting academic visitors. His supervised work encompasses 22 research projects and multiple datasets supporting human-AI interaction studies.

Teague HenryView profile

Assistant Professor

• Network Science
• Quantitative Psychology
• Neuroscience
• +7 more

Teague Henry is an Assistant Professor at the University of Virginia with joint appointments in the Department of Psychology (Quantitative Psychology program) and the School of Data Science . His work bridges network science, dynamical systems modeling, and Bayesian estimation to analyze complex data in neuroscience, clinical science, and natural language processing. Methodological expertise: Graph theory, exponential random graph models, functional neuroimaging Substantive applications: Autism, ADHD, semantic networks, social media topic analysis His research spans functional connectivity, network psychometrics, and language networks, with a focus on cross-disciplinary similarities in network data. Recent publications emphasize time-series modeling, imputation methods, and neurodevelopmental disorders. Software development: R packages like gimme and netjack Future student advising: Plans to recruit graduate students starting 2022-2023