Debasish Das is an accomplished computer engineer and machine learning expert with an extensive background in large-scale machine and deep learning, convex modeling, automatic differentiation, graph algorithms, and network flow. With an impressive education track record of a PhD in Computer Engineering from Northwestern University and a BTech in Computer Science and Engg from the Indian Institute of Technology, Kharagpur, Das has worked in several organizations including Credit Karma, Verizon, Synopsys, Magma Design Automation, and Intel Corporation.

As a Senior Engineering Manager at Credit Karma, Das leads data and machine learning engineering teams while collaborating with data science and analytics teams to deliver business solutions. During his time at Credit Karma, he developed skills in predictive operators for OLAP datasets, recommendation system simulation, and quadratic programming. Prior to that, he worked for Verizon as a Distinguished Engineer, where he was responsible for developing large-scale timing-driven analytical global placement algorithms.

Das has also contributed to the open-source community by working on projects such as TensorFlow, Apache Spark, ScalaNLP Breeze, Verizon Trapezium, and Interior Point Method for JVM. Overall, he is an accomplished and highly skilled computer engineer who has made commendable contributions to the development of machine learning algorithms and frameworks.