NASA/NIA Big Data Analytics and Machine Intelligence Seminar #8:
BUILDING PREDICTIVE MODELS FROM LARGE REPOSITORIES OF SIGNALS DATA
Dr. Kalyan Veeramachaneni, Research Scientist, Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT
July 8, 2015, 10:00 am, NASA Langley, Pearl Young Theater, Bldg 1202A, Rm 160
Hosts: Manjula Ambur (NASA) and Lise Schioler (NIA)
Abstract: This seminar is focused on the methods and technologies to answer the question ‘Why does it take a long time to process, analyze and derive insights from the data?’ Dr. Veeramachaneni is leading the ‘Human Data Interaction’ Project to develop methods that are at the intersection of data science, machine learning, and large scale interactive systems. With significant achievements in processing, storing , retrieval, and analytics, the answer to this question now lies in developing technologies and new methodologies that are based on intricately understanding the complexities in how scientists, researchers, analysts interact with data to analyze, interpret, and derive models from it. In this talk, Dr. Veeramachaneni will present how they are building systems to transform this interaction for the signals domain using an example of physiological signals. Prediction studies on physiological signals are time-consuming: a typical study, even with a modest number of patients, usually takes from 6 to 12 months.
In this talk, he will describe a large-scale machine learning and analytics framework, BeatDB, to scale and speed up mining predictive models from these waveforms. BeatDB radically shrinks the time an investigation takes by: (a) supporting fast, flexible investigations by offering a multi-level parameterization, (b) allowing the user to define the condition to predict, the features, and many other investigation parameters (c) pre-computing beat-level features that are likely to be frequently used while computing on-the-fly less used features and statistical aggregates. Dr. Veeramachaneni will also discuss potential of these techniques to signal data in NASA domains.
Dr. Kalyan Veeramachaneni is a research scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, where he co-heads a group called ‘Anyscale Learning For All.’ He works at the intersection of big data, machine learning, and data science. His recent work focuses on making human interactions with data (HDI) seamless and efficient. Kalyan is a published scholar with 80 publications in international conferences and journals. Three of his papers have won best paper awards at international conferences. Dr. Kalyan Veeramachaneni received his Masters’ in Computer Engineering and Ph.D. in Electrical engineering in 2009, both from Syracuse University.