Dr. Kalyan Veeramachaneni

During the past three years Dr. Veeramachaneni has set out to answer a seemingly simple question: Why does it take so much time to process, analyze and derive insights from data? Veeramachaneni has ventured out into a number of domains and identified critical issues at the foundation of the way we interact with, work around barriers and materialize insights from data. Consequently, he has founded multiple long term projects with a vision of making human interaction with data easier so insights can be derived faster. In addition to simply scaling machine learning approaches, novel approaches, systems were required. These novel methods include scaling of processes that have "human-in-the-loop," identification of intermediary pre-processed data structures for re-use, and the creation of interfaces to exploit such intermediate structures. Ultimately, this has led Veeramachaneni to design approaches and methods for automating much of the data science endeavor

Dr. Veeramachaneni leads a group called Data to AI. The group is interested in Big data science and Machine learning, and is comprised of 20 members: postdoctoral fellows, graduate and undergraduate students. Current projects include:

(*) Deep Mining: large scale self learning data science platform

(*) Gigabeats: A large scale machine learning platform for physiological data mining

(*) MOOCdb: Advancing MOOC data science through collaborative frameworks

(*) Feature factory: Crowdsourcing feature discovery

(*) Bring your own learner(BYOL): Democratizing cloud use for machine learning

(*) MOINC: Machine learning on BOINC


(summary updated 7/2017)