Pushkal Mishra

Subbareddy Batreddy, Pushkal Mishra, Yaswanth Kakarla et al.

Given partial measurements of a time-varying graph signal, we propose an algorithm to simultaneously estimate both the underlying graph topology and the missing measurements. The proposed algorithm operates by training an interpretable neural network, designed from the unrolling framework. The proposed technique can be used both as a graph learning and a graph signal reconstruction algorithm. This work enhances prior work in graph signal reconstruction by allowing the underlying graph to be unknown; and also builds on prior work in graph learning by tailoring the learned graph to the signal reconstruction task.

Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur

We propose a learning algorithm to overcome the limitations of traditional backpropagation in resource-constrained environments: Solo Pass Embedded Learning Algorithm (SPELA). SPELA operates with local loss functions to update weights, significantly saving on resources allocated to the propagation of gradients and storing computational graphs while being sufficiently accurate. Consequently, SPELA can closely match backpropagation using less memory. Moreover, SPELA can effectively fine-tune pre-trained image recognition models for new tasks. Further, SPELA is extended with significant modifications to train CNN networks, which we evaluate on CIFAR-10, CIFAR-100, and SVHN 10 datasets, showing equivalent performance compared to backpropagation. Our results indicate that SPELA, with its features such as local learning and early exit, is a potential candidate for learning in resource-constrained edge AI applications.

Pushkal Mishra, Kshitiz Bansal, Dinesh Bharadia

Radar sensors provide reliable perception across adverse weather, lighting, and long-range conditions, yet existing machine learning approaches remain fragmented and task-specific, with each downstream task employing distinct architectures and training objectives. We present RadarVLM, a vision-language framework that learns unified scene-level representations through structured spatial language supervision. Leveraging the CARLA simulator with a realistic radar model, we collect over 800k radar-caption pairs across 110+ hours of simulated driving in diverse scenarios. We make two key contributions: (1) a structured caption framework encoding vehicle distributions in the radar's native coordinate system, and (2) Spatially-Grounded CLIP (SG-CLIP) objective that replaces binary matching with continuous scene similarity, enabling fine-grained spatial reasoning. We further propose localization-aware evaluation metrics that directly assess spatial accuracy beyond traditional linguistic similarity measures. Validated on generative captioning and vehicle segmentation, SG-CLIP achieves up to 50\% relative F1-score improvement over vanilla CLIP and a 21\% AP gain on segmentation, demonstrating that language grounding produces spatially structured representations.