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April Yi WangView profile

Assistant Professor

• Human-Computer Interaction
• Educational Technology
• Programming Support
• +4 more

April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.

Andrew HeadView profile

Assistant Professor

• Human-Computer Interaction
• Programming
• Reading
• +13 more

Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.

Aditya ParameswaranView profile

Associate Professor

• Database Systems
• Human-Computer Interaction
• Data Analytics
• +3 more

Aditya Parameswaran is an Associate Professor in the Electrical Engineering and Computer Sciences (EECS) department at the University of California, Berkeley. He co-directs the EPIC Data Lab and the Police Records Access project, focusing on simplifying data science at scale through human-in-the-loop systems, LLM-powered tools, and scalable data systems. His research spans database systems, human-computer interaction, and machine learning, with notable contributions in tools like Lux, Modin, and DataSpread. Education : PhD in Computer Science from Stanford University (2013) BTech in Computer Science and Engineering from IIT Bombay (2007) Research Interests : Parameswaran's work centers on empowering end-users with intuitive data tools. Recent projects include LLM-powered systems for document processing (DocETL, TWIX), proactive data systems, and benchmarking frameworks. He emphasizes democratizing data science through low/no-code solutions and improving production ML workflows. Articles Trends : His recent work (2023–2025) prioritizes LLM integration into data systems, focusing on robust pipelines, assertion generation (SPADE), and debugging tools (RAGGY). Earlier contributions include visualization recommendation (Lux), scalable dataframes (Modin), and spreadsheet optimization (DataSpread). Awards : Recipient of the VLDB Early Career Award (2019), Sloan Research Fellowship (2020), NSF CAREER Award (2017), and multiple best paper/demonstration awards at top venues like SIGMOD and VLDB. Advising & Grants : Guides over 20 PhD/postdoc alumni, many now in academia (e.g., Madelon Hulsebos at CWI) and industry leadership roles. Active in securing grants (e.g., NSF, Army Research Office) and industry partnerships (e.g., Snowflake, LangChain). Labs/Teams : Leads the EPIC Data Lab, focusing on agentic data systems, and co-founded Ponder (acquired by Snowflake). Collaborates on the Police Records Access initiative, building transparency tools for public records.

• Educational Technology
• Human-Computer Interaction
• Data Science
• +4 more

Prof. April Wang is an Assistant Professor in the Department of Computer Science at ETH Zurich. Her work focuses on Educational Technology, Human-Computer Interaction, and AI-driven tools for programming education. She is affiliated with the Professur Educational Technology group and teaches courses such as Human Computer Interaction and Ethics in Computing. Her research explores interactive learning environments, collaborative data science workflows, and AI-augmented educational systems. Her research interests span computational notebooks, live coding interfaces, and human-AI collaboration in software development. Notable contributions include frameworks like DBox for algorithm learning and EDBooks for interactive programming narratives. She also investigates challenges in real-time collaboration tools and automated documentation systems for data scientists. Recent work emphasizes understanding user needs in automated copilot systems and resolving conflicts in collaborative notebook environments. Her projects often bridge theoretical HCI principles with practical educational tools, aiming to enhance both instructor efficacy and student engagement. Prof. Wang’s teaching portfolio includes courses on user-centered interface design and ethical scientific practices. She actively contributes to advancing interdisciplinary computational literacy through initiatives like datAR, which uses everyday objects to teach data concepts.

• Data Systems for AI
• Retrieval-Augmented Generation (RAG)
• Database Systems
• +4 more

Yongjoo Park is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. He leads research in data-intensive AI systems as a member of the Data and Information Systems (DAIS) lab, focusing on novel data systems that bridge database theory and practical AI applications. His work emphasizes open-source contributions through GitHub and direct societal impact. Research interests center on systems for data-intensive AI , particularly efficient Retrieval-Augmented Generation (RAG) systems for exploratory AI, data science versioning, and in-storage computing. Key projects include Kishu (the world's first undoable Jupyter notebook with time-travel capabilities), CARE (a causal-relational system for structured/unstructured data), and AirDB/AirIndex (serverless transactions and automatic index optimization). His group develops tools enabling scalable, optimized AI workflows from storage layers to LLM inference. Recent publications reveal a strong focus on interactive data systems (85% of recent work), with significant contributions to notebook environments (Kishu), vector databases (ISCA'25), and RAG optimization. Awards highlight technical innovation, including SIGMOD 2025 Best Demo Award and NSF CAREER funding. His open-source philosophy drives GitHub releases of all major systems. SIGMOD 2025 Best Demo Award (Kishu) NSF CAREER Award (Novel data science systems) SIGMOD'23 Best Artifact Award Honorable Mention (DeepOLA) IBM-Illinois Project Selection (VectorDB/RAG) Mentorship spans 12 current PhD/MS students and 6 graduated advisees, including Supawit Chockchowwat (now Postdoc at Google, future Assistant Professor at CMKL University). He teaches advanced courses like CS511 (Advanced Data Management) and recruits 1-2 new PhD students annually, prioritizing data systems research. His lab emphasizes diversity, individual respect, and concrete outcomes in a collaborative workspace.

• Medieval English Literature
• Middle English Religious Prose
• Vernacular Religious Writings
• +12 more

Niamh Pattwell is Associate Professor and Head of School at the School of English, Drama and Film, University College Dublin. She holds a PhD from Trinity College Dublin and has been a faculty member at UCD since 2004, progressing from Lecturer in Old and Middle English to her current role. Her academic leadership extends to curriculum development, postgraduate teaching, and serving on university committees, including the Board of Trustees for UCD Volunteer Overseas. Her educational background includes a BA, H.Dip.Ed., H.Dip.T.Rel.Ed., and MA from University College Cork, and a PhD from Trinity College Dublin. She taught secondary school in Co. Wexford and Co. Cork before transitioning to higher education, holding teaching fellowships at Queen’s University Belfast and Trinity College Dublin. Niamh Pattwell's research focuses on the circulation and transmission of vernacular religious prose in the late Middle Ages. She has developed expertise in codicology, palaeography, and manuscript fragment studies, particularly through her work with Marsh's Library and the Index of Middle English Prose at Trinity College Dublin. Her scholarship explores the production, readership, and theological context of both orthodox and Wycliffite texts. Her recent publications show a consistent focus on Middle English religious prose, manuscript provenance, and the material culture of books. She has contributed to the Year's Work in English Studies and published on fragments, personal notebooks, and devotional texts, reflecting a deep engagement with the material and spiritual dimensions of medieval manuscripts. National Digital Learning Resources Award (2012) Broad Curriculum Post-doctoral Teaching Fellow, Trinity College Dublin (2003–2004) Irish Research Council for the Humanities and Social Sciences Postgraduate Scholarship (1999–2002) Niamh Pattwell has successfully supervised postgraduate students to MA and PhD completion and has led curriculum innovation at UCD, including the development of research-based modules on medieval manuscript fragments. She has secured internal research grants such as the HoS Research Support Scheme and has been actively involved in organizing major academic events, including the Early Book Society Biennial Conference in 2019 and 2023. Her teaching includes modules on King Arthur, medieval literature, and animal narratives, emphasizing student engagement with primary texts. She collaborates with institutions such as Marsh's Library and Trinity College Dublin and has contributed to public engagement through radio appearances and public lectures. Her work bridges academic research, teaching innovation, and cultural heritage preservation.

Ke ZhangView profile

Professor

• Dynamical Systems
• Hamiltonian Dynamics
• Weak KAM Theory
• +3 more

Ke Zhang is a Professor in the Department of Mathematics at the University of Toronto. His research focuses on dynamical systems, particularly Hamiltonian dynamics, weak KAM theory, Arnold diffusion, and smooth dynamics. He also works on thermodynamic formalism and variational methods in Hamiltonian systems. B.S. in Mathematics from Tsinghua University Ph.D. in Mathematics from Pennsylvania State University under Yakov Pesin Postdoctoral research at University of Maryland and Fields Institute Ke Zhang's research explores instability phenomena in Hamiltonian systems (e.g., Arnold diffusion), viscosity solutions of Hamilton-Jacobi equations in weak KAM theory, and normal form/averaging theory. He has also contributed to vector calculus education through interactive Jupyter notebooks and teaching real analysis courses. He teaches courses such as MAT334 (Complex Variables), MAT1000/457 (Real Analysis I), MAT1001/458 (Real Analysis II), and MAT1845 (Dynamical Systems). The recent trends in his work align with geometric and analytical methods in dynamical systems, with applications to physics and nonlinear modeling. Ke Zhang maintains affiliations with institutions including the University of Toronto, University of Maryland, and Fields Institute. His educational background and postdoctoral training reflect a strong foundation in mathematical analysis and dynamical systems.

Gert-Ludwig IngoldView profile

Professor

• Casimir effect
• Dissipative quantum systems
• Mesoscopic physics
• +2 more

Prof. Dr. Gert-Ludwig Ingold is a Professor of Theoretical Physics at the Institute of Physics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg. His research spans multiple areas of theoretical physics with emphasis on quantum phenomena in nanoscale systems. His work on the Casimir effect explores interactions between various geometries including spheres, plates, and dielectric materials, with applications in nanotechnology and biophysics. His research on dissipative quantum systems investigates thermodynamic anomalies and quantum Brownian motion. In mesoscopic physics, he studies charge transport through nanoscale structures and quantum interference effects. His work on quantum systems in phase space connects classical and quantum dynamics, while his semiclassical research examines quantum revival patterns and phase-space trajectories. Prof. Ingold's publications reveal a strong focus on the Casimir effect, with numerous papers examining interactions between different geometries and materials. His work demonstrates expertise in both theoretical modeling and numerical methods, as evidenced by his development of the CaPS software for Casimir effect calculations. He frequently collaborates with international researchers, particularly with Paulo A. Maia Neto, Tanja Schoger, and Benjamin Spreng. Prof. Ingold has made significant contributions to physics education through textbooks and popular science books, including Quantentheorie: Grundlagen der modernen Physik and Die 101 wichtigsten Fragen: Moderne Physik . He has co-authored the Python-based educational resource Numerische Physik mit Python and maintains extensive online teaching materials including lecture notes and video tutorials.

Dongwook YoonView profile

Associate Professor

• Human-Computer Interaction
• Human-AI Interaction
• Virtual and Augmented Reality
• +3 more

Dongwook Yoon is an Associate Professor at the Department of Computer Science , University of British Columbia , and serves as Director of the SOCIUS Lab . He actively contributes to research in Human-Computer Interaction, Human-AI Interaction, and Virtual/Augmented Reality as a member of the Designing for People (DFP) and CAIDA research clusters. Education : PhD in Computer Science from Cornell University (2017), MS (2009) and BS (2007) in Computer Science from Seoul National University Research Focus : Designing socio-technical systems that bridge the gap between technology and human social processes, with innovations in AR/VR, multimodal interaction, and inclusive design Article Trends show his work spans: Temporal and bichronous learning environments AI self-clones and ethical implications Income inequality in virtual platforms Enhanced multimodal collaboration in VR Eyes-reduced interfaces for situational impairments Speculative participatory design for gig economy challenges Scientific Awards include: Google Academic Research Award (2024) Best Paper Award at CHI 2024 High Impact Award in Educational Technology (2024) CHCCS/SCDHM Graphics Interface Early Career Award (2023) Multiple Honorable Mentions at CHI, DIS, and CSCW Students & Collaborators range from active PhD candidates (Anika Sayara, Yuri Kim) to notable alumni (Thitaree Tanprasert, Ashish Chopra) across his SOCIUS Lab projects. His research receives funding from NSERC , KIST , Adobe , Microsoft , and Google grants.

Andrew HeadView profile

Assistant Professor

• Human-computer interaction
• Programming
• Reading
• +5 more

Andrew Head is an Assistant Professor at the University of Pennsylvania's Department of Computer Science, specializing in Human-Computer Interaction (HCI) and Programming. His work bridges interactive reading , math notation accessibility , and AI-assisted code comprehension . Affiliated with Penn HCI, PLClub, and MindCORE, he co-leads research with Danaé Metaxa and Benjamin Pierce. University of Pennsylvania Assistant Professor, Computer Science Affiliations: Penn HCI, PLClub, MindCORE His research focuses on interactive reading interfaces , AI-powered programming tools , and math notation analysis . Recent projects include: FreeForm : Interactive math notation editor Tyche : Property-based testing tools Explainable Notes : Medical note interpretation systems Publications in CHI , UIST , and ICSE demonstrate his systems-centric approach combining user studies with working prototypes. Notable awards include Best Paper at UIST 2024 and CHI 2022. Advising: Ph.D. Students: Alyssa Hwang, Litao Yan, Hita Kambhamettu, Jeff Tao, Jessica Shi Grants: $1M NSF grant for Property-based Testing Tools (2024) Teaching: Spring 2025: CIS 4120/5120 - Human-Computer Interaction Fall 2024: CIS 7000 - Interactive Reading

Muhammad Ali GulzarView profile

Assistant Professor

• Fault Localization
• Big Data systems
• Data Intensive scalable computing
• +5 more

Muhammad Ali Gulzar is an Assistant Professor in the Computer Science Department at Virginia Tech and an Amazon Scholar at Amazon Web Services. His research focuses on improving developer productivity through automated debugging and testing for applications in emerging domains, including data-intensive software such as dataflow programs, ML/AI applications, and computational notebooks. Education Ph.D. in Computer Science from University of California, Los Angeles (Google Ph.D. Fellow 2017-2020) Research Interests Gulzar's research spans three primary areas: (1) automated tracking-code localization techniques in web applications, (2) re-engineering testing and debugging for data-intensive applications, and (3) advancing current testing and debugging practices in Federated Learning Applications. His work addresses the challenges of debugging in complex systems where traditional approaches fail due to the scale and distributed nature of modern applications. His research has significant implications for improving software quality, developer productivity, and accessibility in web applications. Research Trends Recent publications demonstrate a strong focus on debugging and testing challenges in emerging application domains. His work bridges traditional software engineering with machine learning, data-intensive systems, and web technologies. Notably, he has made significant contributions to Federated Learning debugging (FedDebug), accessibility challenges in ad-driven web applications, and semantic caching for Large Language Models. His approach often combines novel algorithmic insights with practical implementations that address real-world challenges in software development and maintenance. Scientific Awards Google Ph.D. Fellow (2017-2020) $1.1 million NSF award for Federated Learning research ACM CCS 2024 Distinguished Artifact Award Advising and Grants Gulzar leads a productive research group with multiple students contributing to publications in top-tier venues. His NSF-funded research on Federated Learning demonstrates his ability to secure competitive funding for innovative projects. His advising style appears to emphasize practical impact alongside theoretical contributions, with students often taking lead roles in publications. Current research directions include debugging techniques for Large Language Models, accessibility challenges in modern web applications, and novel testing approaches for distributed data processing systems.

Jian ZhaoView profile

Associate Professor

• Information Visualization (InfoVis)
• Human-Computer Interaction (HCI)
• Data Science
• +6 more

Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.

Jan WoudstraView profile

Professor

• History and theory of the modern movement in landscape architecture
• Landscape conservation
• Planting design
• +7 more

Dr Jan Woudstra is an Honorary Professor at the University of Sheffield's School of Architecture and Landscape. With a PhD in Landscape Architecture from University College London and a background in landscape design, horticulture, and conservation, he has taught landscape architecture and history since 1995 and part-time at the Architectural Association School of Architecture from 1988-2005. Education: PhD, MA (Conservation), Dip Hort (Kew) Research Focus: History and theory of modern movement landscape architecture, planting design, hard landscape detailing, and international design trends Supervision: Claudia Leticia Martínez Velarde, Lei Gao, Kairan Li, Sang Jung Yoon, Vivienne Rose Parrott, Jijun Zhao, Manoochehr Salahi Moghadam, Hossein Donyavi, Sally O’Halloran Contact: j.woudstra@sheffield.ac.uk | Arts Tower, Floor 12, Western Bank, Sheffield, S10 2TN His research emphasizes landscape conservation, sustainability, and social equitability, with case studies on designers like Christopher Tunnard, Peter Shepheard, and Robert Marnock. He explores the tension between memory and innovation in historic landscapes. Recent publications address Buddhist pilgrimage sites, Scandinavian design, and historical planting techniques. Collaborations with Peter Blundell Jones and others analyze the interplay between architecture and landscape.

Tengfei MaView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Biomedical Informatics
• +6 more

Tengfei Ma is an Assistant Professor in the Department of Biomedical Informatics at Stony Brook University, with affiliations to Computer Science and Applied Mathematics & Statistics. He holds a Ph.D. from The University of Tokyo, M.S. from Peking University, and B.E. from Tsinghua University. Previously, he was a Research Scientist at IBM T.J. Watson Research Center. His research focuses on machine learning, natural language processing (NLP), and biomedical informatics, particularly deep graph learning, scalable graph methods, and healthcare applications. He has contributed to frameworks like EvolveGCN for dynamic graphs and IGB datasets for graph benchmarks. Key awards include ISWC 2021 Best Paper (Research Track) and IBM Outstanding Research Accomplishments (2019, 2022). His work bridges theory and practice, addressing challenges like over-dilution in GNNs and interpretable time series analysis. Collaborations span interdisciplinary areas, such as AI for wound monitoring and code summarization. He teaches BMI530: Software Development for Biomedical Informatics and is open to graduate students from CS, BMI, and AMS departments. Research highlights include: Deep Graph Learning: Scalability (FastGCN, IGB), dynamic graphs (EvolveGCN), and topology-enhanced GNNs. Healthcare: Models for EHR analysis, medication recommendation (GAMENet), and wearable wound monitoring. NLP: Document summarization, code summarization (CP-BCS), and commonsense generation via knowledge graph compression. Recent projects include AI tools like Influencer for promotional content creation and neural-symbolic models for interpretable time series analysis. His lab explores foundational AI for healthcare, code analysis, and graph systems.

Radu JianuView profile

Lecturer

• Data Visualisation
• Visual Analytics
• Human Computer Interaction
• +7 more

Dr. Radu Jianu is a Lecturer in the Department of Computer Science at City, University of London , where he has been a faculty member since 2016. He is affiliated with the giCentre , a leading research group in information visualization. He earned his PhD and MSc in Computer Science from Brown University, USA, and a Diploma in Engineering from the Polytechnic University of Timisoara, Romania. His academic career includes a previous role as Assistant Professor at Florida International University (2012–2016). His research focuses on Data Visualisation, Visual Analytics, and Human-Computer Interaction . He conducts interdisciplinary collaborations with domains such as biology, food policy, and energy decarbonisation, aiming to develop interactive visual tools that enhance data understanding and decision-making. His methodological approach includes user studies, eye-tracking, and the design of novel visualization techniques. Dr. Jianu teaches Programming in Java and Cognition and Technologies , and he coordinates the Programming Bootcamp. He also holds administrative responsibilities as the Progression and Support Director in the Computer Science Department and is a member of its Executive Committee (ExCo). His recent publications reflect a growing interest in LLM-assisted visual analytics, gaze-aware systems, and collaborative human-AI analytical frameworks . He has published in top venues such as IEEE TVCG, CHI, EuroVis, and Nature Immunology, with several best paper awards. His work on the RAMPVIS project highlights his contributions to visualization in public health emergencies. Scientific Awards: Best Paper Award, Symposium on Graph Drawing (2018) Best Short Paper Award, EuroVis (2020) Advising and Grants: Dr. Jianu supervises multiple PhD and MSc students, including Dany Laksono (Energy Decarbonisation) and Maeve Hutchinson (NLP-mediated Visualization). His students have co-authored high-impact, award-winning papers. He has been involved in funded research initiatives such as RAMPVIS, which received support from UKRI/EPSRC for developing visual analytics infrastructure during the COVID-19 pandemic. Labs and Teams: He is an active member of the giCentre at City, University of London, a hub for visualization research. He also collaborates with interdisciplinary teams in epidemiology, immunology, and computer science, contributing to large-scale projects like the Immunological Genome Project and RAMPVIS.