I am one of the founding faculty members of the newly minted Department of Data Science at NJIT and I direct the M.S. Data Science program.
I will be recruiting 1-2 new PhD students in Spring/Fall 2024 working on human-centered data science topics funded by NSF and DOE.
Please apply here.
My research interests lie at the intersection of information visualization and human-centered data science. I use visualization as a communication medium between data-driven computational models and human reasoning about information.
I direct NJIT’s Intelligible Information Visualization Lab (NiiV), where we pursue intelligibility as the foundational principle for making information more accessible, meaningful, and actionable to experts (e.g., doctors, climate scientists) and non-experts alike.
Grant
Funding (* indicates active funding)
*September 2023-Aug 2027, PI
National Science Foundation (Future of Work), $460K
Trapeze: Responsible AI-assisted Talent Acquisition for HR Specialists
Collaborators: Prof. Julia Stoyanovich (NYU), Prof. Fred Oswald (Rice),
& Prof. Ann Marie Ryan (Michigan State)
*September 2023-Aug 2027, PI
National Science Foundation (Core Program, III) $400K
Responsible Design and Validation of Algorithmic Rankers
Collaborators: Prof. Julia Stoyanovich (NYU) & Prof. H.V. Jagadish (Umich)
*October 2023-Sep 2026, PI
Department of Energy (CESER) $450K,
(PROTECT) Proactive Human-Machine Teaming Enabled Cybersecure Technologies"
Collaborators: Dr. Soumya Kundu and Dr. Seemita Pal (PNNL)
*June 2023-Sep 2024, PI
Department of Energy (HELM), $70K,
Interactive Visual Analytics for Explainable AI/ML in Grid Sensing
Collaborators: Dr. Soumya Kundu (PNNL)
*Sep 2022-May 2024, PI
Department of Energy (RDPP), $145K,
A Scientist-in-the-Loop Data Analytics Framework for Intelligent Simulation Model Tuning and Validation
Collaborators: Prof. Chase Wu (Co-PI, NJIT)
Sep 2021-June 2022 PI, NJIT Seed Grant, $7.5K, Analyzing Communicative Visualizations
April 2020- June 2020 PI, Hearst Media, $23K Data Visualization Education
Oct 2019-Sep 2023 Co-PI, NSF Future of Work, $849K (responsible for $220K) Augmenting Social Media Content Moderation
Previous: (at PNNL)
October 2016-October 2018 PI, Department of Energy-Lab Directed Research and Development, $329K,Data-Driven Reasoning for Climate Model Predictions
October 2015-April 2018 PI, Department of Energy-Lab Directed Research and Development, $662K, Transpire: Transparent Model-Driven DIscovery of Streaming Patterns
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Research
Themes
Visualization+Explainable AI for Algorithmic Rankers
Design and evaluate explanation methods and interactive visualization techniques for probing score-based and learned algorithmic rankers.
[VisualComputer23]
[HILDA23]
[Nature23]
ML Model Explainability and
Human-Machine Interaction
Provide domain experts and model developers with tools
that explain the decisions of machine learning models and
help them semantically validate models.
[HILDA17]
[VAST17] [UIST18]
[TREX20] [TREX21] [VIS21]
Privacy-Aware Data Discovery
Adapt visualizations to prevent disclosure of sensitive
information by developing information loss metrics that
can help address the trade-off between privacy gain and
loss of utility due to anonymization.
[InfoVis11] [CGF12] [CGF13] [EHRVis14] [VizSec19]
[CGF20]
Visualization for Simulation Model Comparison
Let domain scientists reason about computational model
behavior and help them select the most accurate models by
interactively comparing multiple facets of model
performance.
[EuroVis14] [TVCG14] [CISE15] [TVCG20]
User Studies on Trust, Preference,
& Familiarity
Conduct user studies with experts from biology and climate
science domains to evaluate if and how optimal
visualization design can overcome potential biases due to
familiarity.
[TVCG17]
[CHI17]
[Chapter
6,Cognitive Biases Book 18]
[TVCG20]
Visualization Perception &
Design Analysis
Study and survey of the visualization design space for
devising classification schemes that bridge human
perception with visual encodings.
[TVCG15]
[CGF17]
[CGF18]
[VisComm18] [VIS20]
Optimizing
Pattern Search in High-Dimensional Data Spaces
Provide guidance to analysts for finding
patterns in high-dimensional subspaces by devising
metrics that quantify salient patterns.
[InfoVis10] [LDAV12] [CGF2015] [LDAV2016]
Ph.D. Students
Jun Yuan 2021- 2024 (expected)
Kaustav Bhattacharjee 2019- 2024 (expected)
Vrushali Koli 2022- 2026 (expected)
Research Assistants
Sravya Aare (MS DS), Tejaswini Rao (MS DS), Aravind Krishnan (MS DS),
Manikanta Rayala (MS CS)
Previous research assistants:
M.S. Data Science
Parvathy Neelkanta Sharma, Sasikala Vasudevan, Likhitha Musku Divya Chandana, Yaohua Zhao
B.S. IT/CS
Gabriel Aquende, Tanvirul Islam
Parth
Merchant
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