About Me

Research Interests

1. Robotics
   • Kinematics, Control systems, Reinforcement larning, Imitation learning, Path planning, Human-robot interaction, Sensor integration (LiDAR, cameras, IMUs), ROS (Robot Operating System) development, Simulation software, Sim-to-real
2. Reinforcement Learning
   • Deep RL algorithms (DQN, DDPG, TD3, SAC, PPO, A3C), Policy gradient methods and actor-critic architectures, Inverse reinforcement learning and imitation learning, PyBullet, Custom reward functions
3. Computer Vision
   • CNN architectures (ResNet, DenseNet, EfficientNet, Vision Transformer), Object detection (YOLO v3-v7), Segmentation (U-Net, Mask R-CNN, DeepLab), 3D vision (PointNet++, VoxelNet, point cloud processing), SLAM, Pose estimation, Multi-View Geometry
4. Signal Processing
   • Time series analysis (ARIMA, LSTM, GRU- RCNN), EEG signal processing, Spectral analysis, Time-frequency analysis (STFT, wavelets, Hilbert-Huang transform), Signal filtering and enhancement (Kalman filters, FIR/IIR), Feature extraction methods (FFT, wavelets), Denoising, Blind source separation (ICA, NMF)
5. Generative AI
   • GAN architectures (DCGAN, WGAN, StyleGAN, VAE), Diffusion Models (DDPM, Latent Diffusion), Transformers for generative tasks (GPT, Vision Transformers for image synthesis), Autoregressive models (PixelCNN, PixelSNAIL), and Neural Radiance Fields (NeRF).
6. Natural Language Processing (NLP)
   • Text preprocessing and feature extraction (TF-IDF, word embeddings), Transformer architectures (BERT, RoBERTa, GPT), Attention mechanisms, Sentiment analysis, language modeling, and transformer architectures.

Technical Skills

• Programming: Python, C/C++, MATLAB, SQL
• Machine Learning & AI: TensorFlow, PyTorch, torchvision, Scikit-learn, Keras, OpenAI Gym, OpenCV, PIL
• Robotics: ROS, MoveIt, PyBullet, Gazebo
• Data Analysis & Visualization: NumPy, Pandas, Matplotlib, Seaborn, Plotly, SKLearn, SciPy
• Tools: Git, GitHub, Docker, Kubernetes, Streamlit, Hugging Face, AWS
• Hardware: Sensor fusion, Microcontroller programming (Arduino, Raspberry Pi)

Selected Projects

Kinova RL: a ROS package for Shared Control Deep Reinforcement Learning

This ROS package implements a shared control Deep Reinforcement Learning (DRL) system for robotic manipulation using a Kinova robotic arm. The system integrates visual input from a camera, head motion data from an IMU sensor, and a trained DQN (Deep Q-Network) agent to control the robot’s actions. GitHub Link

Adaptive Robotic Control for Users with Severe Impairments using DRL

A system that optimizes mapping from low-DoF inputs to high-dimensional robotic actions, enabling intuitive control for users with severe impairments. It uses adaptive goal prediction and reinforcement learning to guide actions in real-time, seamlessly blending user input with autonomous assistance. GitHub Link

Dual-Mode Robotic Arm Control with GUI and IMU Integration

This project integrates a graphical user interface (GUI) with an inertial measurement unit (IMU) sensor to provide dual-mode control of a robotic arm. Users can choose to control the robotic arm by clicking on cursor buttons in the GUI or by using the IMU sensor to move the cursor for button selection. GitHub Link

Text-to-Speech App

This is a Streamlit-based web application that converts text to speech. It supports text extraction from uploaded Word and PDF files, manual text input, and text summarization using an LLM model. GitHub Link

LLM-based Autonomous Sorter

A simulation of an autonomous robot that can sort objects based on natural language commands. The robot uses a zero-shot classification model to interpret commands and can sort objects based on their shape or color. GitHub Link

Panorama Creation via Harris Corner Detection and Image Stitching

This project involves the creation of panoramic images by detecting key interest points in multiple images using the Harris corner detector. After identifying these points, the project matches them between images and computes the homography to accurately align and stitch the images together, resulting in panoramic views. GitHub Link

EEG Data Analysis

Developed a Python-based toolkit for processing and analyzing complex EEG datasets (64 channels, 640 time points, 99 trials). Implemented advanced signal processing techniques including epoch extraction, ERP computation, peak time identification, topographical mapping, and Laplacian filtering for enhanced spatial resolution. GitHub Link

IMDB Sentiment Analysis

Implemented a sentiment analysis pipeline on the IMDB dataset, employing various text preprocessing techniques (stop word removal, stemming, lemmatization). Developed and compared multiple classification models for sentiment prediction, and evaluated different clustering methods to uncover patterns in the dataset. GitHub Link

GAN and DCGAN Implementation for Synthetic Digit Generation

Implemented GAN and DCGAN to synthesize realistic handwritten digits. Trained models on the MNIST dataset, comparing the performance and quality of generated images between standard GANs and DCGANs. Demonstrated proficiency in deep learning architectures, image generation techniques, and evaluating generative model outputs. GitHub Link