Home | Gopikrishna Pavuluri


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About Me

Embarking on my tech journey at the age of 16 with my first line of C code—without having access to mobile phone or computer prior—I’ve traversed an extraordinary path over the past 12 years. Today, I leverage advanced machine learning technologies, training sophisticated transformer networks on GPUs. This voyage from my initial curiosity in programming to the forefront of artificial intelligence reflects my adaptability and passion for continuous learning in the ever-evolving tech landscape.

With a solid foundation in programming, starting with two years of experience in C, followed by four years in C++, and 7 years mastering Java, I have now expanded my expertise to include Python. In this realm, I specialize in crafting models with PyTorch and TensorFlow. My transition from procedural programming to artificial intelligence demonstrates a broad spectrum of expertise, making me well-suited for roles in Software Engineering, Machine Learning, and as an Applied Scientist.

My engagement in programming competitions—solving over 1,300 algorithmic problems on platforms like LeetCode, Codeforces, and transitioning my focus to Kaggle—has sharpened my problem-solving abilities, competitive spirit, and creative approach to complex challenges. This competitive advantage enhances my technical proficiency, presenting me as a comprehensive candidate for forefront tech positions.

Leveraging my background in cutting-edge technologies, I aim to empower teams, bolster technological capabilities, and drive the creation of transformative solutions that propel companies forward. My commitment to collaboration, continuous learning, and pushing the boundaries of possibility equips me to make a substantial impact in my forthcoming roles.

Email  /  Google Scholar  /  Github  /  LinkedIn  /  LeetCode


  • Master of Science. in Computer Science, University of Texas at Arlington, 2023
    • Key Courses: Advanced Machine Learning, Data Structures & Algorithms, Big Data, AI, Cloud Computing, Neural Networks.
  • Graduate Diploma in Deep Learning, University of Texas at Arlington, 2023
    • Highlights: Neural Networks, Computer Vision, Data Analysis & Modeling Techniques.
  • B.Tech in Electronics & Communication Engineering, K L University, 2017

Professional Experience

Research Papers

  • A Bidirectional People Counting Algorithm in Crowded Areas Arxiv Paper
    • Proposed and implemented a new algorithm to count the people in crowded areas and achieved an accuracy of 96%.
  • A Deep Learning Approach to Video Anomaly Detection using Convolutional Autoencoders Arxiv Paper
    • Proposed an algorithm for detecting anomalies in videos using convolutional autoencoders and decoders on the UCSD dataset(99%).


Teaching Assistant for the following courses at UTA:

  • CSE5311: Design and Analysis of Algorithms, Fall’22
  • CSE3313: Theory of Computation, Summer’22
  • CSE5306: Distributed Systems, Spring’22



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