About

Sidharth Prasad is a highly skilled software engineer and research scientist with a strong background in engineering, programming, computer systems, probability theory, stochastic modeling, statistics, and linear algebra. He is currently working as a Software Engineer at Google in New York City, where he is responsible for building scalable, maintainable, and robust software systems and pipelines for various applications such as personalization, anomaly detection, data ingestion, and signal-mixing, employing state-of-the-art technology, algorithms, and machine learning techniques. Sidharth holds a Bachelor's and Master's degree in Electrical Engineering from IIT Bombay, where he conducted research in Neuromorphic Architectures for ML and published three papers in IJCNN '18, Brazil, and ICANN '17, Italy. He also obtained a Master's degree in Computer Science with a concentration in Machine Learning from Columbia University in the City of New York. Sidharth has worked as a software engineer at Volant Trading and quantPORT (hedge fund), as a visiting research scholar at the Singapore Institute of Neurotechnology, and as a financial analyst at the Indian Institute of Management. He has also worked as a software engineer intern at Google, where he explored various representation learning techniques for Message Passing Algorithms for collective classification in graphs, worked on enhancing message aggregation inside a tool for semi-supervised classification in multi-graphs via histograms, and experimented with embedding the aggregated features in a supervised manner using deep neural networks and autoencoders to obtain their low dimensional embedding. Sidharth is skilled in a wide range of technologies, including Kubernetes, Apache Airflow, Azure, Terraform, CI/CD, Spring Boot, Spring, Jenkins, Microservices, Flask, Python, and Java. He is primarily interested in roles that allow him to leverage his skills in the industry and build scalable, maintainable, and robust software systems and pipelines for various applications employing state-of-the-art technology, algorithms, and machine learning techniques.