Indu Panigrahi

Hi! I'm a Master's student in the Department of Computer Science at Princeton University where I also completed my B.S.E. in Computer Science in 2023 (along with a minor in statistics and machine learning). I currently work with Prof. Szymon Rusinkiewicz, Dr. Ruth Fong, and Prof. Parastoo Abtahi.

My research interests generally lie in machine learning, explainable AI, and human-computer interaction (within AI/ML). During my Master's, I have primarily focused on explainability in computer vision and robotics from a human-centered perspective. Under the co-advisorship of Dr. Ruth Fong and Prof. Adam Maloof (Department of Geosciences), I worked on developing data-efficient computer vision techniques for analyzing a rock sample dataset during my undergrad.

Email  /  GitHub  /  LinkedIn

Explored how end-users leverage and perceive interactive computer vision explanations.

Extended a model-editing technique and conducted extensive experiments to investigate and mitigate systematic mistakes made by a model for fossil segmentation. Originally for my Spring 2022 Junior Independent Work.

Developed a data-efficient computer vision method to detect hazardous, snow-covered sidewalks in images from bus routes by combining Structure from Motion with a segmentation model.

This presentation covered work in the Maloof Research Group on using petrographic imaging and computer vision to bring high-throughput, fine-scale, geophysical data to the study of carbonate outcrops and Earth history.

In addition to the experiences that led to the publications/preprints above, I've had the opportunity for other (related) research experiences!

Created a data-efficient and generalizable computer vision model that leverages self-supervised learning and curriculum learning to improve the segmentation of fossils from an impactful rock sample dataset.

Designed and implemented an inter-image communication mechanism that manipulates the Region Proposal Network of a Mask R-CNN to improve the segmentation consistency of a serial image dataset.

Using a Mask R-CNN, we segmented an image stack of cross sections of a rock sample encasing extinct reef-building organisms and stacked the segmented images to form a three-dimensional model of the embedded specimens.

Through my coursework, I've had the opportunity to work across several topics in addition to AI/ML.

We compared different tokenization techniques for protein sequences to combine amino acid-level information with sequence-level motifs.

Using Planet's Amazon Rainforest dataset, we investigate and compare the effectiveness of three techniques for mitigating data imbalance that derive from importance sampling: loss reweighting, undersampling, and oversampling.

We implemented several methods for detecting wash trading and built a user interface that utilized these methods to flag user-specified NFTs.
DeCenter Spring Conference Outstanding Poster Prize

We worked in a team to implement a vision-based controller on a quadrotor using concepts from motion planning, control, localization, and computer vision.

Reproduced a Visual Dialog model from an EMNLP 2021 paper and performed novel ablations and experiments to analyze and improve the input embedding.

We enhanced pedestrian detection with Faster R-CNN by modifying the loss function to upweight images that lack visual cues, such as crosswalks.

This application allows Princeton University users to find amenities using a map of campus and provide feedback on those amenities. By Indu Panigrahi '23, Raymond Liu '23, and Adam Rebei '23.
Now hosted by TigerApps.

Mentorship is very important to me, having been a mentee when starting research in the first place. In particular, I strive to encourage students, especially early-stage undergrads, to get started in research even if it seems intimidating or if they have no prior research experience.

• Graduate TA: COS 217 Introduction to Programming Systems (Spring 2025, Fall 2024, Spring 2024, Fall 2023)
  
• Undergraduate TA: COS 302 Mathematics for Numerical Computing and Machine Learning (Fall 2022, Spring 2022)
  
• Undergraduate Grader: COS 217 Introduction to Programming Systems (Spring 2023, Fall 2021, Spring 2021)
  

As a ReMatch+ alumna, I often volunteer for OUR as a

• Mentor for the ReMatch+ and OURSIP programs: Hosted weekly support sessions for summer interns at Princeton to discuss directions for progressing their research and ideas for making their concluding presentations accessible to a general audience. (Summer 2023, Summer 2024)
  
• Judge for Princeton Research Day: Evaluated video submissions to PRD based on clarity and accessibility to non-expert audiences. (2023, 2024)
• Student Outreach Volunteer: Help with ad-hoc information sessions and outreach events.

• Volunteer Research Instructor: Taught and helped develop an AI/ML curriculum for high school students underrepresented in AI research. (Summer 2023, Summer 2024)
  
  In 2023, my co-instructors and I designed and mentored a project on exploring the effect of data on ML models by applying computer vision to satellite images to track deforestation (Media Coverage). In 2024, I played a supporting instructor role and helped guide students through the results and final presentation portion of a project that tackled data imbalance in medical imaging.
  

• Workshop Co-organizer: Co-organized an introductory workshop on PID control for a robotics outreach initiative while interning at CMU. (Summer 2022, Recording)
  

• Workshop Organizer

  CVPR 2025 Explainable AI for Computer Vision (XAI4CV) Workshop
  CVPR 2024 XAI4CV Workshop (Lead Organizer, Recording)

• Reviewer
  

  CVPR 2025 Workshops (PixFoundation, XAI4CV)
  ICLR 2025 SCSL Workshop
  IUI 2025
  CVPR 2024 Workshops (L3D-IVU, XAI4CV, WiCV)
  ECCV 2024 Workshops (AI4VA, OOD-CV)
  CVPR 2023 XAI4CV Workshop

• Princeton University Graduate COS Council
  

  Communications Co-chair