Home | Gopikrishna Pavuluri

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About Me

My journey through software has followed the arc Andrej Karpathy famously described. I started in Software 1.0 — hand-coding full-stack applications where every behavior was an explicit rule written by a human. Then I crossed into Software 2.0: instead of writing logic directly, I wrote datasets and let gradient descent find the program — training neural networks that outperformed anything I could have coded by hand. Now I’m deep in Software 3.0, learning and building with large language models, where intelligence is summoned through language itself and the model weights are the program.

Currently, as a Sr Data Scientist IT Analytics at American Airlines, I work on the schedule reliability team predicting precise flight block times — reducing excess flight hours and enabling high-utilization scheduling. With over five years of AI/ML industry experience, I’ve shipped production systems for forecasting, risk management, and personalized recommendations. I hold a Master’s in Computer Science (4.0 GPA) from UT Arlington and am advancing through Stanford University’s Artificial Intelligence Professional Program.

The most exciting frontier is ahead — where software is something you describe rather than something you write.

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Education

Artificial Intelligence Professional Program, Stanford University
- CS224N: Natural Language Processing with Deep Learning
- CS234: Reinforcement Learning
Master of Science in Computer Science, University of Texas at Arlington
- 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
- Highlights: Neural Networks, Computer Vision, Data Analysis & Modeling Techniques
B.Tech in Electronics & Communication Engineering, K L University

Professional Experience

Sr Data Scientist IT Analytics, American Airlines
Software Engineer - Machine Learning, Trustt
Data Scientist, Tracxn
Applied AI/ML Analyst, Charles Schwab
Machine Learning Research Intern, Qutrix Solutions
Machine Learning Intern, Electronics Corporation of India Limited (ECIL)

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%).