Hi, my name is Dheeraj Kallakuri. I completed my Masters in Robotics and Autonomous Systems with specialization in Artificial Intelligence from Arizona State University in May 2024. I am an enthusiastic software developer with over two years of experience at Zeus Learning. At Zeus, I enhanced user engagement by significantly 40% through the deployment of interactive learning modules. At ASU, my passion for robotics is demonstrated by my work at the Battery Electric & Intelligent Vehicle lab. Where I engineered advanced sensing and perception modules for autonomous vehicles. With my proficiency in Python and strong skillset in ROS, Sensor Fusion, and Computer Vision have successfully implemented solutions using TensorFlow and Pytorch. My technical expertise encompasses various programming languages, front-end and back-end development, data engineering, and embedded systems.
From my first year of undergraduate studies, I have been deeply involved in robotics, tackling projects that integrate both hardware and software. I am confident that my contributions often exceed the standard requirements, and I have a strong track record of completing prototypes for various proof-of-concept projects. Throughout my academic career, I approached each project with a product-oriented mindset, handling everything from brainstorming and idea development to marketing. In addition to my coding and algorithmic expertise, I excel in planning, execution, and development. My experience in competitive robotics, coupled with my master's degree in Robotics and AI, has equipped me with advanced skills and knowledge of the latest technologies. I strive to make a meaningful impact by addressing real-world problems.
The quality of instruments used in the manufacturing industry is crucial. Ensuing fatigue and inattentiveness limit the reliability of manual inspection. M-Lens is a hand-held device that automatically classifies defective parts even if the defects are novel and previously uncategorized. This allows inspection workers to quickly identify not only previously known defects with great accuracy but also novel defects by adding new/custom defects to the cloud-based defect repository and retraining the deep learning model on the server. The concept of transfer learning is used to enable a shorter training time. The device is made using off-the-shelf Raspberry Pi and camera extensions. It is a beneficial solution where production ecosystems are constantly changing, and time is invaluable to the business. This paper discusses the major features of implementation, working, and analytical results of the final device.
Auxiliary transformers are used extensively in railway systems. Indian Railways use them primarily to convert the 25-kilo volt AC supply into 230-240-volt AC supply as required for various applications. Due to their varied use, many of these auxiliary transformers are often positioned in remote locations. In case of failure of AT supply, no proper feedback system exists as of now. Due to this the lead time on failure attention increases rapidly depending on the location of the transformer. Sometimes this may result in signal failure leading to an increase in train traffic. To cut down on human effort and cost involved, as well as provide a system that continuously monitors these auxiliary transformers, an automated system based on GSM technology is suggested. This system periodically provides updates and generates immediate alerts in case of the occurrence of the failure of an auxiliary transformer, hence bringing a drastic reduction on lead time for failure attention where a failure escalates into costly service losses.
The electrical traction system of railways is a combination of physical upright structures and OCL(Overhead Contact Lines). The horizontal distance from the center of the track to the OHE mast is called implantation, the horizontal displacement of overhead contact wire concerning the center of the railway track called stagger, and the perpendicular height of overhead contact wire from the ground is periodically checked by a lineman to ensure a safe distance from the railway track. In this paper, we have put forth the idea of building a distance-measuring device to measure the implantation and stagger without touching the objects using Open CV on Raspberry Pi with a camera module which will be placed a the center of the track. The system will have two features. To measure the distance of the nearest poles, the camera has to be placed facing the mast perpendicular to a circle of diameter appropriate which is placed on the pole for measurement purposes. To measure the stagger, the camera has to be placed facing the overhead wire from the center of the track.
Dynamic Navigation to the nearest parking spot from a current location.
Python, Computer Vision, Jetson Orin, MongoDB
Study and analyze the ADS and ADAS Level 2 collision and summarize the trends.
Python, Data Visualization, Predictive Modeling
Chair providing real-time feedback for users to maintain good posture.
Python, C++, Arduino, Reinforcement Learning
One Stop Shop: Hand gesture recognition apps.
Python, Mediapipe, Raspberry Pi, Machine Learning
Real-time audio analysis embedded system to detect specific absolutist keywords.
C++, Arduino, Tensorflow Lite
Handheld defect detection device for Airplane Industry.
Python, Computer Vision, Transfer Learning, Raspberry Pi, AWS
Autonomously navigate by following a line and responding to specific traffic signs.
ROS2, Jetson, Open CV, Classification
A server controlled Search and Rescue (SAR) operations robot.
Python, Sensor Fusion, Raspberry Pi, Web App
An autonomous robot capable of detecting and avoiding obstacles.
C++, Arduino, Sensor Fusion
The system logs all the obstacles in the 180-degree range.
Matlab, Simulink, Arduino, Data Processing
An interactive assistant that educates users about various tools & components.
MultiModal LLM, Raspberry Pi, Speech Recognition, Yolov8
An AI assistant for Liminal Works Organization to understand concepts of Theories of Change (ToC).
LLM API, Chatbot,PDF Generation, MongoDB, RAG, Flask, Streamlit
Developed a tool to detect gender bias in Vietnamese and Hindi text inputs.
Transformers, Cosine Similarity, Tokenization & Embedding, Streamlit
Developed a web app using Colorful Image Colorization technique to convert B/W to Color images.
Python, CNN, Unsplash API, Streamlit
Tool that provides detailed insights of YouTubers using Quantised Llama and Youtube API.
Gen AI, Quantization, Llama, Hugging face, Langchain
Fine-tuned BERT(NLP model) for sentiment analysis to classify movie reviews as bad or good.
Gen AI, Open AI, Chatbot, GUI, LLM, Python
Developed a Q/A chatbot using RAG architecture to answer questions from analyzed PDF content.
GenAI, RAG, Chatbot, LLM, Langchain, Python
Built an Image Caption model that summarizes the given input image.
ResNet-50, LSTM, GPT Encoder, Kaggle, Python
Single destination for all photo filters, including iPhone, Instagram, and basic options.
OpenCV, Image processing, Image Filters, GUI
Developed a decoder-only transformer LLM from scratch by following Andrej Karpathy's tutorial.
Gen AI, LLM, Transformers, GPT-2 Architecture, Python
Real-time chatbot, allowing users to interact with GPT-3.5 Turbo.
Gen AI, Open AI, Chatbot, GUI, LLM, Python
Created a python app that combines keyword-specific images to PDF document.
API, Document Conversion, Threading, GUI, Python
Designed a QR code generator app that creates customizable QR codes for validated URLs.
Validation, Python, QR Code, Custom, GUI
Implemented 6 mini projects during course work.
C++, Arduino, Neural Networks, Sensor Fusion
Deployed 3 POC projects for Safety and Maintenance Department.
C++, Arduino, Image Processing, Sensor Fusion
Executed 3 specific tasks using UR robots and Transporter Networks.
UR programming, Python, Simulation, CLIPort
Launched 2 innovative projects using educational robots.
C++, Arduino, Sensor Fusion, Communication