Designed custom GAN and Transformer-based Latent Diffusion architectures to generate high-fidelity synthetic ECG signals for medical data augmentation. Achieved 42% signal similarity improvement over classical GANs and outperformed U-Net LDMs by up to 95% across 8 evaluation metrics. Integrated the synthetic data into a downstream classification pipeline, improving heart disease detection accuracy by 20%.

Developed a real-time AI system combining emotion recognition, RAG-based LLM inference, and generative audio-visual media. Integrated memory-augmented feedback for dynamic emotional adaptation. Improved user emotional resonance by 56% and satisfaction by 67% in pilot studies.

Designed a custom game environment in Python using PyGame and implemented Deep RL and Adversarial RL agents to dynamically adapt game level difficulty based on player skill. Demonstrated the potential of reinforcement learning for immersive, user-driven game experience tuning.

Built an advanced image processing tool in Java using the MVC design pattern. Applied object-oriented principles to build a scalable and extensible framework for applying filters, transformations, and enhancements.

Designed a compact neural network architecture using MATLAB that accurately models both linear and non-linear logic gates. The architecture demonstrated robust performance on high-dimensional datasets with significantly fewer neurons.

Developed an efficient solver for Wordle using concepts from information theory and probability. The system selects guesses that maximize expected information gain, reliably solving Wordle puzzles in an average of 3–4 attempts.

Focused on classifying urban built-up areas using neural networks trained on labeled ground truth datasets derived from K-means and Maximum Likelihood classifications. Applied on Google Earth Engine datasets.

Built a music recommendation system driven by real-time emotion detection using machine learning and OpenCV. The system captures facial expressions and classifies emotional states, recommending mood-aligned songs accordingly.

Developed an IoT-enabled wearable wristband capable of detecting user stress levels by analyzing biometric signals in real time, using neural networks to classify physiological data.

Designed and implemented an embedded system to detect vehicular accidents and trigger real-time alerts using the AT89C51 microcontroller and Raspberry Pi, serving as a low-cost prototype for emergency response systems.