Work

• Took 12 lectures on JAVA and Software Testing to the undergrad students at NUS computer science.

• Assisted the students on projects and theories.

• I guide and mentor working professionals and students in data science and machine learning on Kaggle.

• Visiting faculty for Machine Learning in the Electronics and Communication Engineering department at L. J. University.

• Visiting faculty of Business Analytics with Python for Business School of L. J. University.

• Started a new department called "AI Gharana" at L. J. University focused on AI practice and research. 

• Part of the core team that designs courses on artificial intelligence, and data science for  Apple.

• Took live lectures in the USA, China, and Europe.

• Was a part of the Deep Learning Team for Research and Development of algorithms.

• Worked on the production of a human segmentation algorithm for the autonomous delivery robot using PyTorch, YOLACT, TensorRT, ONNX, and NVIDIA-DeepStream.

• Worked on an ongoing research “Machine Learning Techniques for Hardware Assisted Anomaly Detection” under the guidance of Prof. Thambipillai Srikanthan and Kalindu Herath.

• Developed an algorithm using ensemble methods similar to Random Forest with probabilistic learning on the sequential data of system calls.

Wobot is globally renowned for providing computer vision solutions to enterprises.

Worked on Gun Threat Detection model on CCTV live feed on Masked-RCNN on Python and C++ language with PyTorch to train the model and deployed it on Docker.

• Mentored USA, Canadian, and Australian students pursuing their postgrad in Data Science and Business Analytics.

• Live teaching on topics including Data Engineering, Machine Learning Models, Cross-Validation, AutoML, and Basics of Neural Networks(Dense, CNN, and RNN).

• Worked on the blood collection data from 33 districts of Gujarat.

• Prepared software that real-time visualisation, and an ML model to predict the best location to conduct the next blood collection camp according to the preliminary data.

• The language used was Python and the tools used Numpy, Pandas, Matplotlib, Seaborn, Plotly, and ScikitLearn.