About APURVA SUNIL CHAVAN
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About me
An AI & ML enthusiast currently pursuing a Master’s in Robotics from the State University of New York at Buffalo.
I have 2+ years of experience working as a Software Development Engineer (AI/ ML) at a fast-paced ed-tech start-up. I have worked with the development team to develop an algorithm that would provide suitable course and career choices to the user based on their input using Regression models. I have also worked on developing and improving the efficiency of the AI chatbot using the NLTK library to reduce the burden on the faculties in addressing the doubts of the students.
Working in an ed-tech start-up made me realize the interest and the immense potential of the youths of today’s generation in the field of Artificial Intelligence and Machine Learning. so, I volunteered to teach Python programming to kids starting from grade 3 and even mentored them in various Robotics & Coding Competitions and also to experienced professionals from non-technical fields who wanted to put their first step in the world of coding & programming as it is the way of the future.
Languages: C, C++, Python, HTML, CSS, Bootstrap 4.
Programming: MATLAB, SciPy, Scikit-learn, OpenCV, TensorFlow, PyTorch, Robot Operating System (ROS), MongoDB,
ReactJS, Amazon Web Services (AWS), MATLAB, Arduino, Jupyter.
Databases: SQL Server 2008
Education
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2021 - 2023
University at Buffalo, The State University of New York
Master of Science in Robotics
Courses: - Introduction to ML, Reinforcement Learning, Fundamentals of AI, Computer Vision and Image Processing, Robotics Algorithms, Deep Learning.
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2015 - 2019
University of Mumbai, India
Bachelor of Engineering in Electronics and Telecommunication
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2022 - 2022
Revolutionary Integration Group (RIG), Bethany, Connecticut
AI/ ML Software Development Intern
- Building a face detection and recognition module as the first step of the identification process and establishing a trust level between the user and the company. - Performing Detection using Haar cascade classifiers, extracting and storing the image features in an array form. Calculating the Cosine Similarity between the input image array and the image arrays in the Database to perform recognition. - Alternatively, deploying the ML model to perform classification using Random Forest and XGBoost classifiers on the non-linear image array dataset led to a 30% increase in the recognition accuracy than the cosine similarity algorithm and integrating it with the application dashboard of establishing trust created using MongoDB and ReactJS on AWS.
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2019 - 2021
Science Kidz Educare Pvt. Ltd, Mumbai, India
Software Development Engineer
- Leveraged regression methods to predict potential students based on interesting subjects extracting a list of potential customers saving up to 6 hours of manual work per database. - Increased positive reviews of AI chatbot by 32% by using models to correctly predict appropriate lecture parts based on keywords in doubt questions. - Implemented localization algorithms based on LIDAR and developed programming exercises for students based on the algorithms.