publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2025

Uncertainty-Aware Knowledge Tracing Models

Joshua Mitton^*, Prarthana Bhattacharyya^*, Ralph Abboud, and 1 more author

Pre-print, Under review, 2025

TL;DR arXiv

New uncertainty-based approach that identifies struggling students where traditional metrics fail, enabling EdTech platforms to intervene at critical moments.
Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables

Prarthana Bhattacharyya^*, Joshua Mitton^*, Ryan Page^*, and 8 more authors

Pre-print, Under review, 2025

TL;DR arXiv PDF Video

Architected mobile-optimized models for smart glasses achieving 25x power reduction with 20% accuracy gain (F1>80%). Profiled inference bottlenecks, implemented quantization and efficient architectures. Deployed at CES-2025 in commercial development kit.

2024

Helios: An extremely low power event-based gesture recognition for always-on smart eyewear

Prarthana Bhattacharyya^*, Joshua Mitton^*, Ryan Page^*, and 8 more authors

Best Paper, Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2024

TL;DR arXiv PDF Video

Pioneered low-power low-latency ML models, representations and custom loss functions enabling micro-gesture detection for Meta-Raybans. Rapidly delivered a prototype within 6 months, publicly demonstrated at AWE-USA 2024 and featured by CNET. Won Best Paper award at ECCV-2024 Workshop.
SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction

Prarthana Bhattacharyya, Chengjie Huang, and Krzysztof Czarnecki

In 2024 IEEE Intelligent Vehicles Symposium (IV), 2024

TL;DR arXiv PDF

Self-supervised pretext tasks boost multi-agent interaction prediction by 8% in complex driving scenarios.

2023

Perception and Prediction in Multi-Agent Urban Traffic Scenarios for Autonomous Driving

Prarthana Bhattacharyya

University of Waterloo, 2023

HTML PDF Video

2022

SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

Prarthana Bhattacharyya, Chengjie Huang, and Krzysztof Czarnecki

In Conference on Robot Learning (CoRL), 2022

TL;DR arXiv PDF Code

Pioneered SSL methods for autonomous vehicle motion forecasting (80+ citations, 120+ GitHub stars, presented to Gatik AI). Achieves state-of-the-art prediction accuracy while maintaining lower computational complexity than transformer-based approaches.
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime

Prarthana Bhattacharyya, Chenge Li, Xiaonan Zhao, and 2 more authors

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

TL;DR arXiv PDF Code

Developed self-supervised transformer framework reducing manual data annotation requirements. Achieved 4-11% improvement over SOTA on five benchmarks (ICASSP-2022).

2021

Sa-Det3D: Self-Attention Based Context-Aware 3D Object Detection

Prarthana Bhattacharyya, Chengjie Huang, and Krzysztof Czarnecki

In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops , 2021

TL;DR arXiv PDF Video Code

First method to combine self-attention with CNNs for 3D point cloud detection (90+ citations, 150+ GitHub stars). 1.5 AP accuracy gain while slashing model size by 15-80% and compute by 30-50%. Proven across KITTI, nuScenes, and Waymo datasets.

2020

Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

Prarthana Bhattacharyya and Krzysztof Czarnecki

In Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2020

TL;DR arXiv Code

First method to integrate deformable convolutions in 3D object detection (40+ citations, 100+ GitHub stars).

2019

Fantrack: 3d multi-object tracking with feature association network

Erkan Baser, Venkateshwaran Balasubramanian^*, Prarthana Bhattacharyya^*, and 1 more author

In 2019 IEEE Intelligent Vehicles Symposium (IV), 2019

TL;DR arXiv PDF Code

NeurIPS 2019 3D tracking competition winner (150+ citations). Replaces hand-crafted cost functions and complex optimization with a CNN that learns end-to-end. Competitive multi-object tracking by automatically learning optimal assignments.

2017

3D scene understanding at urban intersection using stereo vision and digital map

Prarthana Bhattacharyya, Yanlei Gu, Jiali Bao, and 2 more authors

In IEEE 85th Vehicular Technology Conference (VTC Spring), 2017

TL;DR arXiv PDF

Proposed a novel method for 3D scene understanding at urban intersections by integrating stereo vision with digital map data. Tested on real-data collected from urban intersections in Tokyo.