Research projects, collaborations, and independent work.
Full-Waveform Inversion for Seismic Velocity Prediction
Ongoing
Research Supervisors: Naveen Gupta & Divakar Vashisth
Extending advisor’s ICLR 2025 latent-space-translation framework for subsurface forward/inverse problem to using Diffusion priors and flow matching
Working on optimal-transport-based misfit functions (Wasserstein distance, unbalanced OT) as an alternative for L2 loss to mitigate cycle-skipping on Marmousi/Overthrust benchmarks
Literature survey on diffusion- and flow-matching-based generative priors as regularizers for Bayesian FWI
CSD722 Project: Depth-Conditioned Video Generation using ControlNet & AnimateDiff
Jan – May 2026
Research Supervisors: Dr. Sumit Shekhar & Dr. Saurabh Shigwan (CSE Dept, SNU)
Extended ControlNet to text-to-video diffusion models using AnimateDiff and Motion LoRA
Modified model architecture to prevent training collapse where generated videos ignored depth-map conditioning; stabilized training by adding auxiliary supervision losses
Conducted ablation experiments on prompts, depth conditioning, and text guidance
Achieved 98% accuracy by developing a ResNet, CNNs to classify surface electromyography (sEMG) stress measurements.
Improved classification performance from 91% to 97.98% through architecture redesign and training, showing raw sEMG inputs outperform PyEMGPipeline preprocessing.
Implemented AlexNet alongside ResNet experiments to further refine accuracy and generalization across sEMG datasets.
Selected to present this research on stress-level classification at the SUMMIT 2.0 Conference.
Conducted time series pattern recognition and sentiment analysis in US financial markets using 7 years of daily data across 31 financial indicators to model 2, 5, and 10-year bond yields
Applied PCA and Mutual Information (MI), identifying 10 key indicators explaining 95% of variance; optimised cluster timelines using rolling windows to identify dates with maximum regime separation
Best Silhouette Score: 0.45; ANOVA p-value: 1.21 × 10⁻¹⁵⁴, Kruskal-Wallis p-value: 1.25 × 10⁻²¹¹, confirming significant differences across clusters
Research Assistant: Investor Behavior Analysis using Deep Learning
2024–2025
Research Supervisor: Dr. Ashish Vazirani
Developed an unsupervised multimodal model to analyze investor emotions and decision-making patterns in Shark Tank videos
Combined facial expression recognition, RoBERTa-based transcript analysis, and tabular neural networks for financial characteristics, with cross-modal attention to model investor behavior
Fine-tuned EmotioNet for facial expression recognition and RoBERTa for transcript analysis
Generated time-series investment likelihood scores (–1 to 1) for each second, validated through accuracy of final decision