Joint work with Pallavi Desai (UT Austin CS); course project for ECE 381K (Networks), December 2025.
Combined graph-theoretic analysis with Llama-3.1-8B agent simulations to model how rumors propagate, mutate, and are contained across synthetic networks (Erdős–Rényi, Barabási–Albert, Watts–Strogatz, SBM) and real Facebook/Twitter ego networks.
Extended SIR/SEIR diffusion with persona-driven decision-making (skeptical, neutral, conformist, sensationalist) and showed that strategically placed fact-checkers on hub nodes meaningfully suppress false-rumor cascades — true content reached ~10% higher saturation across all topologies.
Quantified information distortion via SBERT cosine similarity, GPT-2 perplexity, and Flesch–Kincaid scores; discovered a "Semantic Anchor" effect where clustered networks preserve rumor fidelity 2× better than random networks, and that mutations follow a directed evolution into distinct "generational dialects".
Implemented and compared the performance of RL methods (A2C, DQN, PPO) against conventional Mixed-IP optimized pricing and (S, s) re-order policies for single/multi-echelon environments with stochastic demand.
Achieved a 25% increase in profit with dynamic pricing, and a 1.09× increase in profit compared to a traditional (S, s) order policy.
Seamlessly merged a Stable Diffusion-based text-to-image model with AlignProp (a backpropagation-based approach to align diffusion models with downstream reward functions) and DreamGaussian (a 3D Gaussian Splatting model with mesh extraction and texture refinement).
Demonstrated DreamGaussian’s superiority in generating high-quality textured meshes — a 10× acceleration over existing methods, producing content from a single-view image in just 2 minutes.
Undertaken as a requirement for the PhD-level course Statistical Modeling 1 at UT Austin, supervised by Dr. Abhra Sarkar (Department of Statistics and Data Sciences, UT Austin).
Developed a framework that incorporates a Constraint Satisfaction Problem to reduce the state space of a Hidden Markov model, applied to financial time series. Used the model to predict closing prices of the Dow Jones Industrial Average.
Induced constraints by analyzing Twitter sentiment from Jan 20, 2017 — Jan 20, 2020. The approach reduced MAPE by 0.59%, increased accuracy to 90%, and reduced computational time by 0.02s versus a conventional model.
Part of my undergraduate thesis, supervised by Dr. Dinesh Shankar Reddy (Associate Professor, NIT Andhra Pradesh).
Conducted literature surveys on the sources, types, and effects of medical waste, and on properties associated with characterizing medical waste.
Developed a Deep Learning model in PyTorch to simulate thermodynamic equilibrium modeling of a downdraft gasifier for the conversion of medical biomass to energy.