
Hariharan Subramonyam
Assistant Professor (Research) of Education and, by courtesy, of Computer Science
Graduate School of Education
Bio
Hari Subramonyam is an Assistant Professor (Research) at the Graduate School of Education and a Faculty Fellow at Stanford's Institute for Human-Centered AI. He is also a member of the HCI Group at Stanford. His research focuses on augmenting critical human tasks (such as learning, creativity, and sensemaking) with AI by incorporating principles from cognitive psychology. He also investigates support tools for multidisciplinary teams to co-design AI experiences. His work has received multiple best paper awards at top human-computer interaction conferences, including CHI and IUI.
Academic Appointments
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Assistant Professor (Research), Graduate School of Education
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Assistant Professor (Research) (By courtesy), Computer Science
Honors & Awards
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Student Design Competition 3rd Place, CHI (05/2015)
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Best Paper Award, CHI (05/2019)
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Best Paper Award, CHI (04/2020)
Professional Education
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Ph.D. Information, University of Michigan, Dissertation: Role of End-User Data in Co-Designing AI-Powered Applications (2021)
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B.E. Telecommunication, CMR Institute of Technology (2008)
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M.S. Information, University of Michigan, Human Computer Interaction (2015)
2022-23 Courses
- Data Visualization
CS 448B (Win) - Designing Explorable Explanations for Learning
EDUC 432 (Win) -
Independent Studies (6)
- Directed Reading
EDUC 480 (Aut, Win, Spr, Sum) - Directed Reading in Education
EDUC 180 (Aut, Win, Spr, Sum) - Directed Research
EDUC 490 (Aut, Win, Spr, Sum) - Directed Research in Education
EDUC 190 (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win) - Senior Project
CS 191 (Win)
- Directed Reading
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Prior Year Courses
2021-22 Courses
- Designing Explorable Explanations for Learning
EDUC 432 (Spr)
- Designing Explorable Explanations for Learning
All Publications
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texSketch: Active Diagramming through Pen-and-Ink Annotations
ASSOC COMPUTING MACHINERY. 2020
View details for DOI 10.1145/3313831.3376155
View details for Web of Science ID 000695432500028
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Explore, Create, Annotate: Designing Digital Drawing Tools with Visually Impaired People
ASSOC COMPUTING MACHINERY. 2020
View details for DOI 10.1145/3313831.3376349
View details for Web of Science ID 000695438100022
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Affinity Lens Data-Assisted Affinity Diagramming with Augmented Reality
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300628
View details for Web of Science ID 000474467905012
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Designing Interactive Intelligent Systems for Human Learning, Creativity, and Sensemaking
ASSOC COMPUTING MACHINERY. 2019: 158-161
View details for DOI 10.1145/3332167.3356878
View details for Web of Science ID 000518192300053
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SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
2019; 25 (1): 597-607
Abstract
Details-on-demand is a crucial feature in the visual information-seeking process but is often only implemented in highly constrained settings. The most common solution, hover queries (i.e., tooltips), are fast and expressive but are usually limited to single mark (e.g., a bar in a bar chart). 'Queries' to retrieve details for more complex sets of objects (e.g., comparisons between pairs of elements, averages across multiple items, trend lines, etc.) are difficult for end-users to invoke explicitly. Further, the output of these queries require complex annotations and overlays which need to be displayed and dismissed on demand to avoid clutter. In this work we introduce SmartCues, a library to support details-on-demand through dynamically computed overlays. For end-users, SmartCues provides multitouch interactions to construct complex queries for a variety of details. For designers, SmartCues offers an interaction library that can be used out-of-the-box, and can be extended for new charts and detail types. We demonstrate how SmartCues can be implemented across a wide array of visualization types and, through a lab study, show that end users can effectively use SmartCues.
View details for DOI 10.1109/TVCG.2018.2865231
View details for Web of Science ID 000452640000057
View details for PubMedID 30136998
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TakeToons: Script-driven Performance Animation
ASSOC COMPUTING MACHINERY. 2018: 663-674
View details for DOI 10.1145/3242587.3242618
View details for Web of Science ID 000494260500056
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The application of ecological momentary assessment and geolocation to a longitudinal twin study of substance use
SPRINGER. 2017: 676-677
View details for Web of Science ID 000415813600114
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Agency in Assistive Technology Adoption: Visual Impairment and Smartphone Use in Bangalore
ASSOC COMPUTING MACHINERY. 2017: 5929-5940
View details for DOI 10.1145/3025453.3025895
View details for Web of Science ID 000426970505065