About

Aman Khurana is an Applied Scientist and Machine Learning Engineer with over 3 years of experience in Natural Language Processing and Reinforcement Learning. He is currently conducting research on making Bayesian optimization cost-aware and scalable, which has use cases that could save millions of dollars in performing black-box function optimizations. Aman is a skilled software engineer with a solid understanding of algorithms and data structures and can build and maintain efficient machine learning systems. Aman has experience in building datasets by fetching, cleaning, and preprocessing raw data, distributed data processing and analysis using tools like Spark, data analysis, and effective data visualization. He can understand and reproduce state-of-the-art machine learning models in domains such as Natural Language Processing and Reinforcement Learning, evaluate and tune machine learning models for the business problem, and deploy and maintain models in production. He has also built data pipelines using Airflow, containerized Machine Learning applications using Docker, and deployed them using FastAPI. Aman can manage containerized applications using container orchestration tools like Kubernetes and automate hyperparameter optimization in production using AutoML packages. He has worked with different cloud providers such as AWS and GCP to use their services and build solutions. Aman's skillset includes languages like Python, Java, SQL, and C++, frameworks and libraries like Pytorch, TensorFlow, Spark, Numpy, Pandas, Scikit-Learn, XGBoost, and Pyspark, and tools like Docker, Git, Kubernetes, AWS, and GCP. He has experience working with databases like MongoDB and PostgresSQL. Aman has worked as a Research Assistant at the University at Buffalo, where he worked on a novel method for cost-aware and scalable Bayesian Optimization on large-scale distributed systems under Dr. Jaroslaw Zola. He has also worked as a Java developer, built and deployed multi-robot swarm intelligence missions on physical robots for the DARPA OFFSET program, and designed and developed the interface for Raytheon's simulator using Java. Aman has a Master's degree in Computer Science from the University at Buffalo and has completed his All India Senior School Certificate Examination from Delhi Public School and Council for the Indian School Certificate Examinations from St. Mary's Convent School. He is proficient in ML, AWS, Reinforcement Learning, Java, NLP, Spark, Kubernetes, Docker, SQL, GCP, XGBoost, Software Engineering, Hyperparameter, Apache Airflow, Scikit, TensorFlow, Pandas, MongoDB, Pytorch