Diana Galvan Sosa is a Researcher at the University of Cambridge, affiliated with the ALTA Institute (Automated Language Teaching and Assessment) and the NLIP group. She holds a doctoral degree in Information Science from Tohoku University (Japan). Her research focuses on Natural Language Processing (NLP), particularly knowledge acquisition and applications of NLP in education, including feedback generation systems for second language (L2) learners. She also contributes to clinical text analysis and hate speech detection research. Education: PhD in Information Science from Tohoku University, Japan. Research Interests: Grammatical Error Correction Educational Technology Clinical NLP Explainability in AI Temporal Relation Extraction Multimodal Reasoning Recent Work Trends: Her most recent publications (2023-2025) emphasize advanced NLP applications like GPT-4 analysis for error correction, hate speech detection leveraging platform guidelines, and evaluation frameworks for AI explanations. Earlier work includes foundational studies on clinical text analysis and addiction treatment populations. Labs/Groups: Active member of the ALTA Institute and NLIP group at Cambridge. Also contributes to open-source projects like the GECToR framework for grammatical error correction, reflected in her GitHub repositories.
Associate Professor Uwe Roehm is a faculty member at the University of Sydney's School of Computer Science, specializing in database systems and big data analytics. He holds a PhD from ETH Zurich and joined the University of Sydney in 2004. His research focuses on distributed data management, integrating machine learning with databases, and scalable data processing for bioinformatics. Education: PhD in Computer Science, ETH Zurich (2002) Diplom-Informatics (MSc equivalent), University of Passau Research Interests: Database Systems and Transaction Processing Human-Centred Data Management Freshness-Aware Scheduling (FAS) Big Data Analytics and Cloud Computing Key Projects and Contributions: Developed Serialisable Snapshot Isolation (SSI), implemented in PostgreSQL Created the Master of Data Science program at the University of Sydney Recipient of the 2018 ACM SIGMOD Test of Time Award Awards: 2018 ACM SIGMOD Test of Time Award 2013 Dean's Award for Outstanding Teaching 2008 ACM SIGMOD Best Paper Award Teaching and Grants: Teaches courses on database systems, data science platforms, and cloud computing Principal Investigator on ARC-funded projects, including Linkage and Discovery grants Labs and Teams: Leads the Database Research Group and contributes to the Human-Centred Technology Research Cluster at the University of Sydney.
Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.
Franziska Sofia Hafner is a Researcher at the Oxford Internet Institute (OII), University of Oxford, focusing on algorithmic fairness, machine learning, and interactive data visualization. She holds an MSc in Social Data Science from the OII and a Bachelor’s degree in Computer Science and Public Policy from the University of Glasgow. Her work bridges technical AI research with societal impacts, particularly addressing bias in language models and healthcare algorithms. Education: MSc in Social Data Science, Oxford Internet Institute (2023–2024) Bachelor of Science in Computer Science and Public Policy, University of Glasgow Her research interests include mitigating gender and ethnicity biases in AI systems, with notable contributions to gender performativity theory in language models and ethnicity-aware algorithm design. She presented her work on gender bias at the 2024 NeurIPS conference and has published in journals like AI & Society and Social Network Analysis and Mining . Research Contributions: Franziska’s articles explore cultural differences in sentiment analysis, bias in healthcare algorithms, and equitable algorithmic design. Her work highlights how AI systems encode societal inequities, such as gender binaries and ethnic disparities in medical diagnostics. She collaborates with the OII’s Equitable Access to Quality Information Lab and Digital Ethics and Defence Technologies group to advance ethical AI practices. Public Engagement: Her research has been featured in press releases discussing AI’s impact on health equity and gender representation, emphasizing the need for inclusive algorithmic frameworks.
Prof. Christoph Kinkeldey is a Lecturer at Hamburg University of Applied Sciences, affiliated with the Department of Information, Media and Communication within the Faculty of Design, Media and Information. He holds a doctorate in Geoinformatics from HafenCity University Hamburg (2015) and has conducted research globally, including at PennState University, University of Melbourne, and Inria. His work focuses on data visualization, visual analytics, and uncertainty visualization, emphasizing how visual tools aid decision-making in complex data environments. Education: PhD in Geoinformatics, HafenCity University Hamburg (2015) Research Interests: Interactive visual data analysis Uncertainty communication in visualizations Blockchain data exploration (e.g., Bitcoin network analysis) Evaluation of visualization techniques His research bridges cartography, computer science, and human-centered design to empower diverse stakeholders in understanding complex information. Publications: Recent work emphasizes uncertainty visualization for data analysts, machine learning interpretability, and blockchain analytics. Key contributions include the BitConduite tool for Bitcoin network analysis and participatory design methods for non-technical users. Awards: 2016 VAST Mini Challenge 2: Honorable Mention for Clear Analysis Strategy Advising & Collaboration: Currently on parental leave until August 2024, he collaborates with the gicentre (City, University of London) and Monash University’s Department of Human-Centered Computing. His research teams focus on interdisciplinary projects merging visualization theory with practical applications. Labs/Teams: Active in the gicentre’s visualization initiatives and Monash’s Human-Centered Computing group, contributing to open-source tools and international research networks.
Jessica Olivares is an Assistant Professor of Supply Chain Management at the Shannon School of Business, Cape Breton University. Her expertise spans supply chain resilience, digital twins, and Industry 5.0, with a focus on mitigating disruptions in global networks. Dr. Olivares contributes to both academic research and practical solutions for sustainable supply chain management. Her academic credentials include: B.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico M.S. in Industrial Engineering, University of the Americas Puebla (UDLAP), Mexico Ph.D. in Industrial and Manufacturing Systems Engineering, University of Windsor, Canada Dr. Olivares' research centers on supply chain management, with specific interests in disruption recovery, digital twin applications, and sustainable design. She explores how Industry 5.0 principles can humanize smart manufacturing while enhancing resilience. Her work addresses critical gaps in perishable food supply chains and resource distribution during crises, integrating risk assessment with technological innovation to build robust systems. Her recent publications (2021-2025) show a strong emphasis on digital twins for supply chain resilience, with increasing attention to sustainability and multi-objective optimization. She has pioneered frameworks for recovery from major disruptions, including pandemic impacts, and investigates energy-aware scheduling in manufacturing. Her scholarship bridges theoretical models with real-world applications in food systems and global networks. No scientific awards were documented in the available sources. Information on graduate student supervision and research funding was not provided, though her active publication record suggests engagement in scholarly mentorship and potential grant-supported projects. No details about laboratories or research teams were mentioned.
Federica Mucci is an Associate Professor of International Law at the University of Rome Tor Vergata, affiliated with the Department of History, Humanities and Society. She specializes in international protection of cultural heritage, European Union law, and treaty law. Her teaching includes courses on cultural heritage protection and EU law for programs in Education and Tourism. Legal Expert: Italian Ministry of Foreign Affairs UNESCO Delegation Member: Contributed to the 2005 UNESCO Convention on cultural diversity and its implementation. Her research focuses on international law frameworks for cultural heritage, maritime law, treaty interpretation, and environmental protection. Key publications include monographs on cultural heritage law (2012) and a PhD thesis on EU copyright law (1998). Publications span interdisciplinary topics such as topological data analysis, though the majority of her work aligns with legal and humanities disciplines. Awards: No specific prizes are mentioned, but her scholarly output includes influential books and articles on international law. Advising/Grants: No formal student advisees or grant details provided in text. Labs/Teams: No specific lab affiliations mentioned, though her role at UNESCO implies collaboration with international bodies.
Ansaf Salleb-Aouissi is a Senior Lecturer in the Department of Computer Science at Columbia University’s Fu Foundation School of Engineering and Applied Science. She holds affiliations with the Foundations of Data Science and Health Analytics centers. With a PhD from the University of Orleans, France (2003), she pursued postdoctoral training at INRIA Rennes before joining Columbia as an Associate Research Scientist in 2006. She transitioned to her current role in 2015 after serving as an adjunct professor in Computer Science and Data Science from 2014–2015. Her research focuses on machine learning applications in healthcare, education, and infrastructure systems. Key areas include medical informatics (e.g., preeclampsia prediction, genetic associations in pregnancy), educational data mining (intelligent tutoring systems, bootcamp design), and power grid reliability. Notable achievements include winning the NIH Maternal Morbidity Data Challenge and developing tools like LogicLearner for logic education. She has contributed to projects such as analyzing CDC pregnancy data and optimizing the New York City power grid. Her work bridges theoretical machine learning with real-world applications, emphasizing interpretability, bias mitigation, and collaboration across disciplines. She has published extensively in venues like JMLR, TPAMI, and ECML, addressing topics from counterfactual explanations to ensemble learning with missing data.
Brian K. Smith is a Professor at the Lynch School of Education and Human Development at Boston College , holding the Honorable David S. Nelson Chair and serving as Associate Dean for Research . His career spans roles at Drexel University, MIT, the National Science Foundation, and Rhode Island School of Design. Education: Ph.D., Learning Sciences, Northwestern University B.A., Computer Science and Engineering, University of California at Los Angeles (1991) Research Interests focus on the design of computer-based learning environments, human-computer interaction, and computational thinking. He leads the Lynch School’s new M.A. program in Learning Engineering , blending learning science with practical design for curricula, museum exhibits, and corporate training. Recent publications highlight trends in AI integration for education , game-based learning platforms , and sociomateriality theory for learning sciences. His work emphasizes equity, particularly for underrepresented groups in STEM. Scientific Awards include the NSF CAREER Award , Apple Distinguished Educator , and TRW Chairman's Award for Innovation . Grants & Collaborations: Technical advisor to the Center for Inclusive Computing at Northeastern University Co-investigator in RISD’s “STEM to STEAM” initiative Labs & Teams: Co-director of Boston College’s M.A. in Learning Engineering program Vice chair of the World Usability Day Design Challenge
Sai Manoj Pudukotai Dinakarrao is an Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering and Computing. He leads the HArt (Hardware and AI Research) Group, focusing on cutting-edge research at the intersection of hardware security and artificial intelligence. His educational journey includes a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University (2010), an MTech in Information Technology from International Institute of Information Technology Bangalore (2012), and a PhD in Electrical Engineering from Nanyang Technological University, Singapore (2015). Following his doctoral studies, he completed post-doctoral research at TU Wien, Vienna (2015-2017) and George Mason University (2017-2018). Dr. Dinakarrao's research spans hardware security, adversarial machine learning, IoT networks, and deep learning in resource-constrained environments. His work integrates hardware design with AI techniques to address security challenges in computing systems, with particular focus on side-channel attack detection, malware detection in IoT networks, on-chip security, and hardware accelerator design for machine learning applications. His research has resulted in numerous publications in top-tier conferences and journals including IEEE Transactions, ACM conferences, and Design Automation Conference. Analysis of his recent publications reveals a strong trend toward hardware security solutions using machine learning techniques. His work increasingly focuses on Processing-in-Memory architectures, energy-efficient security solutions for IoT devices, and innovative approaches to hardware Trojan detection. Many publications demonstrate interdisciplinary collaboration across electrical engineering, computer science, and cybersecurity domains. Young Research Fellow Award at Design Automation Conference (DAC) 2013 Best paper award at International Conference on Data Mining (ICDM) 2019 Best paper award at International Conference on Consumer Electronics (ICCE) 2020 Best paper nomination at International Conference on Computer-Aided Design (ICCAD) 2019 Best paper nomination at Design Automation and Test in Europe (DATE) 2018 Dr. Dinakarrao has successfully mentored numerous PhD and MS students, with alumni securing positions at AMD-Xilinx, US Government agencies, and academic institutions. His research has been supported by significant grants from NSF, DARPA, and Virginia Commonwealth Cyber Initiative. Current projects include securing supply chains with UVA, developing novel architectures for machine learning acceleration, and creating energy-preserving cryptography protocols. The HArt Group maintains active collaborations with industry partners including AMD-Xilinx and government agencies. The lab focuses on practical implementations of theoretical security concepts, with particular emphasis on creating deployable security solutions for real-world hardware systems. Current research directions include intermittent computing with energy harvesting, hardware fuzzing techniques, and robust machine learning models resistant to adversarial attacks.
Regina Kaplan-Rakowski is an Assistant Professor in the Department of Learning Technologies at the University of North Texas. Her research focuses on immersive technologies like virtual reality (VR), artificial intelligence (AI), and computer-assisted language learning. She holds a PhD in Curriculum and Instruction from Southern Illinois University (2016), an MA in Foreign Languages (2006), and an MEd in European Studies (2001) from Pedagogical University, Cracow. Her research interests include VR applications in education, emotional responses to technology, and second language acquisition. Notable projects explore VR for social isolation mitigation, AI-mediated language learning, and accessibility for visually impaired learners. She has co-edited multiple books on educational technology, including pandemic-era teaching strategies and AI-driven innovations. Recent publications highlight VR’s impact on language anxiety reduction, AI integration in teacher training, and the effectiveness of immersive technologies in vocabulary and listening comprehension. Her work bridges theoretical insights with practical applications, influencing both classroom practices and technology design.
Beppe Liotta is a Full Professor at the Department of Engineering, University of Perugia. He serves as Rector Delegate for ICT and Digital Agenda. His research spans network discovery, graph drawing, algorithm engineering, and computational geometry . Laurea in Electrical Engineering (1990), Ph.D. in Computer Engineering (1995), both from University of Rome 'La Sapienza' Post-doc at Brown University (1995-1996) Current teaching: Information Visualization and Database Management Systems Liotta has authored over 170 papers and led projects like VisFAN (financial crime detection), VHyXY (large graph visualization), COWA (web traffic analysis), and WhatsOnWeb (web clustering). His work focuses on hybrid visualizations and network robustness . Recent articles highlight his expertise in biological networks , financial activity networks , and one-to-many matched graph visualizations . He has contributed to journals like IEEE Transactions on Visualization and Computer Graphics and conferences including PacificVis and Graph Drawing . Liotta actively participates in scientific service, including editorial roles for the Journal of Graph Algorithms and Applications and program committees for IEEE PVIS 2019.
Dr. Andrew Hoegh is an Associate Professor of Statistics at Montana State University (MSU), affiliated with the Department of Mathematical Sciences within the College of Letters & Science. He leads the Bozeman Environmental and Ecological Statistics (BEES) research group, focusing on Bayesian computation, spatiotemporal modeling, and ecological applications. His work bridges statistical theory and practical problems in environmental science, epidemiology, and sports analytics. Education: Ph.D. (2016) Virginia Tech; M.S. (2008) Colorado School of Mines; B.A. (2006) Luther College. Research Interests: Bayesian statistics, statistical ecology, computational methods for complex data, and pathogen dynamics in wildlife. His group addresses challenges like bat ecology, zoonotic spillover, and aquatic invasive species using advanced statistical techniques. Awards: Kopriva Faculty Lectureship (2021), multiple nominations for research and advising awards, and grants from institutions like Cornell University and the USGS. Teaching: Courses include Bayesian Statistics, Spatial Data Analysis, and Statistical Computing. Current projects involve bat monitoring, virus spillover modeling, and agent-based movement simulations. Labs/Groups: BEES group meets biweekly, collaborating with USGS scientists. Active projects include bat acoustic data analysis, zebra mussel detection, and radar-based animal movement tracking.
Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.
Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.
