Himangi Mittal

I am a first year Ph.D. student in the Robotics Institute (RI) at Carnegie Mellon University (CMU), working with Prof. Shubham Tulsiani. I graduated with a Master of Science in Robotics (MSR) from the Robotics Institute (RI) at Carnegie Mellon University (CMU) where I worked with Prof. Abhinav Gupta and collaborated with Prof. Pedro Morgado at UW-Madison. Previously, I worked as a Research Assistant at CMU with Prof. David Held at the R-Pad Lab, in collaboration with Pittsburgh-based autonomous driving company, Argo AI.

During my Masters at CMU, I am working on self-supervised representation learning methods for multimodal audio-visual videos. Previously, as a RA at CMU, I worked on self-supervised algorithms for 3D LiDAR point clouds. I did my bachelors from Jaypee Institute of Information Technology, Noida, India where I worked with Dr. Anuja Arora.

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• August 2023 : Started my Ph.D. in the Robotics Institute (RI) at Carnegie Mellon University (CMU).
• May 2023 : Started research internship at Honda Research Institute (HRI), San Jose, California.
• Jan 2023 : Teaching Assistant for 16-825: Learning for 3D Vision.
• Sep 2022 : Paper accepted at NeurIPS 2022.
• Oct 2021 : Paper accepted at BMVC 2021 (Oral).
• Apr 2021 - Dec 2021: I will be serving as a reviewer for ICCV 2021, AAAI 2022, WACV 2022, and CVPR 2022.
• Aug 2021: Journal paper accepted in PAA (in collaboration with Robert Bosch, India).
• Feb 2021: Accepted as a Master of Science in Robotics (MSR) student at Carnegie Mellon University for Fall 2021.
• July 2020: Presented a short paper at RSS Workshop on Self-Supervised Robot Learning 2020.
• Feb 2020: Paper accepted at CVPR 2020 (Oral).

I am interested in self-supervised learning, multi-modal machine learning, video representation learning, generative models, point clouds and autonomous driving.

We propose a self-supervised algorithm to learn representations from untrimmed, egocentric videos containing audible interactions. Our method uses the audio signals in two unique ways: (1) to identify moments in time that are conducive to better self-supervised learning and (2) to learn representations that focus on the visual state changes caused by audible interactions.

A self-supervised method to complete the incomplete, partial point clouds for real-world settings like LiDAR where ground truth complete point cloud annotations are unavailable. We achieve this via inpainting where a region of the point cloud is removed and the network is trained to complete this removed region.

A method for multi-modal depression detection using audio, video, and textual modalities using LSTMs. This work leverages emotions to detect an early indication of depression.

A method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets.

Predicted action and spatial relationships in images between objects detected by YOLO, then combining VGG-Net based visual features and Word2Vec based semantic features.

A method to capture the anomalous behavior in a social network based on degree, betweenness, and closeness of graph nodes using Graph Neural Networks (GNN) in Keras.

Presents trajectory analysis of spatio-temporal graph nodes using DeepWalk algorithm in NetworkX (Python) for classification and detecting changing points of interest using SVMs.

• Teaching Assistant for 16-825: Learning for 3D Vision (Spring 2023).
• Reviewer Service: ICCV 2021, AAAI 2022, WACV 2022, CVPR 2022, CVPR 2023 (+ Emergency reviewer), ICCV 2023, NeurIPS 2023, Pattern Recognition Journal, WACV 2024 (+ Emergency reviewer).
• Volunteer at NeurIPS 2022 High School Outreach Program.
• Mentor at CMU AI Undergraduate Mentoring Program (Fall 2022, Spring 2023, Fall 2023).
• Mentor at Spring 2023 CMU Research Mixer for undergraduate students organized by DPAC Undergraduate Research Working Group.

Source Code