Professional Profile - All About Me
I am Dhanush Balakrishna, a driven Machine Learning Engineer pursuing a Master of Science in Electrical and Computer Engineering at Northeastern University, specializing in Computer Vision and Natural Language Processing. At SolBid Inc., I developed the innovative SolBrain API to provide solar conversion cost estimates and led the creation of the Hyperion AI engine, enhancing rooftop solar installations by identifying obstacles through satellite imagery analysis.
Professionally, I've contributed to ML teams at companies like SolBid Inc., British Airways and SandLogic Technologies, where I refined my expertise in building and deploying training and inference pipelines for applications including Rooftop and Obstacle Detection, Segmentation, Face Recognition, and GANs across various computing platforms. I am well-versed in Deep Learning libraries and toolkits such as PyTorch, TensorFlow, Keras, OpenCV, and PIL, and proficient in programming with Python, C/C++, SQL, R, PHP Laravel, MATLAB, and ROS. My project portfolio, including Neural Machine Translation and Domain Adaptation, showcases my ability to tackle complex AI challenges.
Outside the tech world, I am passionate about photography and music production, which enhance my creative outlook. As a friendly and enthusiastic professional, I welcome discussions and collaborations on topics like machine learning, computer vision, deep learning, and advanced algorithms. If you share these interests, I encourage you to reach out so we can explore innovative solutions together.
See My Work - Projects
This project employs domain adaptation techniques to enhance a deep learning model trained on the SVHN dataset for digit recognition. By adapting the model to the MNIST dataset, it improves performance on the target domain with limited labeled data.
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Detecting motion using Simple Image Filtering between sequential video frames using Gaussian, 3x3 Box, and 5x5 Box filters.
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Focused on neural machine translation (NMT), this project utilizes Bahdanau attention, encoder-decoder architecture, and deep learning. Trained on Stanford datasets for English to Vietnamese and English to Dutch, the NMT system generates accurate translations preserving context and structure.
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This project employs SIFT for keypoint detection and matching between two images using the FLANN matcher and Lowe's ratio test. It provides visual outputs of keypoints, transformations, and depth perception through a disparity map.
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A learning project to implement a basic voice assistant using Python, nothing complicated.
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This C++ script implements a task scheduling algorithm for Mobile Cloud Computing (MCC), optimizing energy usage and meeting deadlines by dynamically allocating tasks between local devices and the cloud. Based on research by Xue Lin et al., it enhances performance in MCC environments through task migration and dynamic prioritization.
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This Personal Assistant Chatbot project leverages Streamlit, OpenAI's GPT-3.5, and various APIs to provide an intelligent conversational assistant. Key features include document processing, web search, weather updates, and the implementation of Retrieval-Augmented Generation (RAG) for enhanced response accuracy and relevance.
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This project uses reinforcement learning to train an agent to play Flappy Bird using the Deep Q-Network (DQN) algorithm with the Stable Baselines3 library. The environment is built with OpenAI Gym and Pygame.
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Boston, MA, USA
+1 (617) 817-6535
dhanushbalakrishna45@gmail.com