Find Academic Supervisors

Tommi JaakkolaView profile

Professor

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +7 more

Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.

• Machine Learning
• Visual Computing
• Image Analysis
• +6 more

Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.

Kyle C. HaleView profile

Associate Professor

• Operating Systems
• High-Performance Computing
• Computer Architecture
• +4 more

Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.

Elias JarlebringView profile

Professor

• Numerical Linear Algebra
• Matrix computations
• Nonlinear eigenvalue problems
• +10 more

Elias Jarlebring is a Professor in Numerical Linear Algebra at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. He has held the position of Full Professor since 2021, following his tenure as Associate Professor (2013-2021) and Dahlquist Research Fellow (2011-2013). His research focuses on numerical analysis, numerical linear algebra, matrix computations, and scientific computing. Jarlebring develops linear algebra algorithms to solve problems from various fields including systems and control, acoustics, electromagnetics, data science, quantum mechanics, and quantum chemistry. He is a core developer of NEP-PACK, a scientific computing software package for nonlinear eigenproblems. His recent publications demonstrate significant contributions to computational methods for nonlinear eigenvalue problems, matrix functions, and parameterized linear systems. The research shows a clear trajectory toward increasingly complex applications in quantum computing, data science, and wave propagation problems. Project grant, Swedish research council (2019) Ruth och Nils-Erik Stenbäcks foundation, junior grant (2019) Göran Gustafsson Prize for junior researchers (2014) Project grant for junior researchers, Swedish research council (2014-2018) Professor Jarlebring has supervised numerous PhD students including Vilhelm Peterson Lithell, Gustaf Lorentzon, Siobhán Correnty, Parikshit Upadhyaya, Emil Ringh, Antti Koskela, and Giampaolo Mele. He has received multiple research grants from the Swedish Research Council and serves as editor for BIT Numerical Mathematics, Linear and Multilinear Algebra, NACO Numerical Algebra Control and Optimization, and CALCOLO. He is actively involved in the numerical linear algebra community as a member of ILAS (International Linear Algebra Society), GAMM Activity Group on Numerical Linear Algebra, and the Nordic Numerical Linear Algebra Association. He also contributes to open source projects, particularly in the Julia programming language ecosystem.

• Spoken Language Processing
• Natural Language Processing (NLP)
• Paralinguistics
• +6 more

Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.

Julia KempeView profile

Professor

• Data Science
• Machine Learning
• Quantum Computing
• +3 more

Julia Kempe is a Silver Professor of Computer Science, Mathematics, and Data Science at New York University (NYU), holding joint appointments at the Courant Institute and the Center for Data Science (CDS). She serves as Director of the CDS and is on research leave at the CSD, ENS, Paris (2023–24). Her expertise spans interdisciplinary research in quantum computing, machine learning, and data science. She holds PhDs in Mathematics (UC Berkeley, 2001) and Computer Science (École Nationale Supérieure des Télécommunications, Paris, 2001), alongside advanced degrees in theoretical physics and mathematics from prestigious institutions in France and Austria. Research Interests: Data Science, Machine Learning (theoretical foundations and applications to physics), and past contributions to quantum computing. She focuses on robustness in machine learning models, adversarial examples, and interdisciplinary applications of physics-informed AI. Awards and Honors: Knight of the National Order of Merit (France, 2010), Femme en Or de la Recherche (France, 2010), ERC Starting Grant (2007, top-ranked in Europe), and numerous academic fellowships. She is a member of Academia Europaea (2018) and a Fellow of the Asia-Pacific Artificial Intelligence Association (2022). Grants and Leadership: Principal investigator of NSF NRT grants for CDS PhD programs, co-PI on NASA TCAN grants, and leader in NYU’s Senior Leadership Team. She designed NYU’s Data Science undergraduate programs and expanded interdisciplinary collaborations in machine learning and quantum computing. Labs and Teams: Directs the CDS, collaborates with the Courant Institute, and leads research initiatives in Paris. Her work bridges theoretical computer science, physics, and applied data science, emphasizing interdisciplinary innovation.

• Artificial Intelligence
• Optimization
• Machine Learning
• +7 more

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.

Chinmay KulkarniView profile

Assistant Professor

• Human Computer Interaction
• Large-scale Learning
• Automation
• +5 more

Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.

• Control Technology
• Distributed Optimization
• Machine Learning
• +4 more

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Julia ChangView profile

Associate Professor

• Modern and Contemporary Iberian Literature and Culture
• Feminist and Queer Theory
• Disability Studies
• +3 more

Julia Chang is an Associate Professor of Hispanic Studies at Cornell University's College of Arts and Sciences, with affiliations in Feminist, Gender, and Sexuality Studies and the Southeast Asia Program. Her interdisciplinary work bridges Spanish literature, feminist theory, and Philippine studies. PhD in Hispanic Language and Literatures with a Designated Emphasis in Women, Gender, and Sexuality Studies (University of California, Berkeley) Previously taught at Brown University and San Quentin State Prison Research spans 19th-century Spanish realist novels , Philippine colonial literature , and contemporary Spanish visual arts , emphasizing race, disability, gender, and queer theory . Her 2022 book Blood Novels examines racial hierarchies in Spanish realism, while recent publications explore game studies, anti-racism, and decolonial frameworks. In 2024, her work on realism and race in the Hispanic Empire continues this trajectory. Awards include the 'Harriet S. Turner Beca' for scholarship and the Stephen and Margery Russell Distinguished Teaching Award (2021). She contributes to collective projects like the CIVIC Research Collaborative ('Interactive Media and Games') and the TRECE research group on race and ethnicity in Spain.

• Social Physics
• Graph Convolutional Neural Networks
• Large Language Models (LLMs)
• +4 more

Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.

Nazli GoharianView profile

Clinical Professor

• Information Retrieval
• Text Mining
• Natural Language Processing
• +1 more

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

• Family
• Gender
• Demography
• +5 more

Julia Behrman serves as an Associate Professor of Sociology at Northwestern University, where she conducts internationally recognized research on family change in global contexts. Her work examines how family structures interact with major societal forces including educational expansion, environmental change, women's labor force participation, and migration patterns, with particular attention to power dynamics within households and their implications for social inequality. Dr. Behrman earned her PhD from New York University in 2017 following a Postdoctoral Prize Research Fellowship at Nuffield College, University of Oxford. Her methodological expertise combines advanced quantitative techniques with comparative demographic analysis across diverse geographical settings. Her research program centers on family sociology, gender studies, and demography, with significant contributions to understanding how events like natural disasters, migration, and policy changes alter family power structures. She investigates whether evolving family forms mitigate or reinforce inequalities both between and within households, addressing critical questions about who holds decision-making authority in families and how crises reshape these dynamics. Analysis of her recent publications reveals a strong methodological emphasis on quantitative analysis of large-scale datasets, with thematic focus on reproductive health preferences, migration impacts, and family size norms across the United States, France, and Sub-Saharan Africa. Her work consistently bridges demographic trends with sociological theory to explain household-level responses to macro-level social changes. Her scholarly excellence has been recognized through multiple prestigious awards: American Sociological Association Section on Education Award American Sociological Association Section on Population Award American Sociological Association Section on Development Award Society for the Study of Social Problems Award Population Association of America Award Sociologist AIDS Network Award Dr. Behrman has secured competitive research funding from the National Science Foundation and South African Medical Research Council to support her investigations into family and demographic change. As an educator, she teaches undergraduate courses including "Gender and Society" and graduate-level courses in "Research Design" and "Regression II," while actively mentoring PhD students through a structured advising process that emphasizes methodological rigor and theoretical innovation.

Julia SchlüterView profile

Professor

• Corpus Linguistics
• Language Variation and Change
• Historical Linguistics
• +7 more

Professor Julia Schlüter is a distinguished academic in the Chair of English Linguistics within the Humanities Faculty at the University of Bamberg, Germany. She has served as Senior Lecturer at the Chair of English Linguistics and Language History under Prof. Manfred Krug since June 2008, holding the title of Professor following her habilitation in 2008. Her institutional profile demonstrates deep commitment to both research and teaching innovation, particularly through her leadership of the KorPLUS project and development of open educational resources for corpus linguistics. Her research interests span corpus linguistics for English language learners, empirical methods for studying language variation and change, and the application of corpus methods to teaching. She specializes in examining grammatical, phonological, and lexical differences between British and American English across historical periods from Middle English to present-day usage. Her work investigates phonological variation (particularly phonotactically controlled alternations), morphological change, and syntactic variation through corpus analysis, with special attention to functional grammar and grammaticalization theory. Professor Schlüter's recent publications (2022-2025) reveal a strategic evolution in her research focus, with increasing emphasis on the intersection of corpus linguistics and digital education. While maintaining her foundational work in historical English linguistics, she has developed significant expertise in applying corpus methods to language teacher education and evaluating AI writing tools. Her work demonstrates consistent methodological innovation, moving from traditional corpus analysis to blended learning approaches and digital educational resource development. Her scientific recognition includes: Winner of the 2025 Teaching Innovation Prize from the International Society for the Linguistics of English for the KorPLUS project University of Bamberg Prize for outstanding habilitation (2009) Lise Meitner Programme post-doctoral scholarship (2005-2006) Rectorate Prize from University of Paderborn for outstanding Ph.D. thesis (2005) Multiple DAAD scholarships for international study Professor Schlüter actively supervises doctoral research, currently guiding three Ph.D. candidates (Katharina Deckert, Aklima Nahar, and Nikolai Beland) while having successfully completed supervision for several others. She leads the KorPLUS project (2021-2025), funded by the Stiftung Innovation in der Hochschullehre, which develops open educational resources for corpus linguistics. Her research has been consistently supported by the German Research Foundation (DFG) and other funding bodies throughout her career. She heads the KorPLUS team (with Carina Großmann and Katharina Deckert) which develops the interactive Open Educational Resource platform for corpus linguistics. Her YouTube channel offers video tutorials on corpus basics, and she has created the Video Podcast Series "How to Update your Grammar" for English teachers. She regularly organizes in-service teacher trainings and collaborates with the Virtual Linguistics Campus at RWTH Aachen University to deliver her educational materials globally.

Joseph WoodView profile

Professor

• Visual Analytics
• Data Visualization
• Geographic Information Science
• +5 more

Professor Joseph Wood is a Professor of Visual Analytics at City St George's, University of London, where he serves as a founding member of the giCentre. His academic career spans over three decades, with continuous contributions to Geographic Information Science and visualization since 1990. He previously served as Head of Department for Computer Science at City University between 2014 and 2017. Professor Wood's educational background includes a PhD in Geographical Information Science from the University of Leicester (1996), an MSc in the same field from the University of Leicester (1990), and a BSc in Physical Geography & Geology from the University of Sheffield (1989). His academic progression shows steady advancement from Research Scholar at the University of Leicester (1990-1992) through various lecturer and senior positions to his current professorship. His research interests center on visual analytics and data visualization, with particular expertise in geographic information science and terrain analysis. Professor Wood has developed innovative methods bridging GI Science, Data Visualization, and education domains. His specific interests include narrative of visual analytic design, computational thinking in pedagogy, and novel visualization design for geographic data. His work demonstrates a consistent focus on making complex spatial data understandable through innovative visualization techniques. Analysis of Professor Wood's recent publications reveals a strong emphasis on practical applications of visualization techniques across diverse domains including transportation, epidemiology, sports analytics, and historical migration patterns. His work shows an evolution from foundational geographic information science toward broader applications in visual analytics, with increasing focus on narrative structures, responsive design, and accessibility considerations in visualization. The interdisciplinary nature of his research is evident in collaborations spanning computer science, geography, urban planning, and public health domains. Professor Wood has been actively involved in the academic community, serving on organizing and program committees for major international conferences including IEEE Infovis and VAST, Eurovis, GIScience, Spatial Accuracy, and Geomorphometry. His contributions to the field have been recognized through invitations to deliver keynote talks at prestigious venues ranging from GeoComputation to TEDx, where he presented on topics such as visualizing movement behavior of cyclists. As an advisor, Professor Wood has supervised numerous PhD and Master's students, with current supervision of Julia Crossley (Student conceptualisation of abstraction in computer science) and Jude Nzemeke (Understanding student misconception in recursive algorithmic thinking). His extensive supervision history includes completed PhDs on topics ranging from cycling behavior to spatio-social relations in photographic archives. His academic leadership extends to software development, with contributions to tools like litvis, elm-vega/el-vegalite, giCentre Utils, handy, and LandSerf GIS. Professor Wood is an active member of professional organizations including IEEE (2007-present), Association of Computing Machinery (ACM) (2007-present), and Association of Geographic Information (AGI) (1997-present), demonstrating his commitment to interdisciplinary collaboration across computer science and geographic information domains.