Principal Research Scientist
Kalyan Veeramachaneni is a principal research scientist at MIT’s Institute for Data, Systems, and Society’s Laboratory for Information and Decision Systems, where he heads the Data-to-AI Group. His group focuses on building large-scale AI systems that work alongside humans, continuously learning from data, generating predictions and integrating those predictions into human decision-making. The group develops foundational algorithms, abstractions, and systems to enable these three tasks at scale. Algorithms, systems and open-source software developed by the group are deployed for applications in the financial, medical, and education sectors.
Veeramachaneni is the co-founder of PatternEx, a cybersecurity company that adapts machine learning models based on real-time analyst feedback. He is also the co-founder of Feature Labs, a data science automation company. He was an adviser to EverVest, a company that provided advanced software for analyzing, valuing, and financing renewable energy projects. Veeramachaneni earned an MS in computer engineering and a PhD in philosophy and electrical engineering from Syracuse University.
- Arnaldo, I., Veeramachaneni, K. (2019). The Holy Grail of” Systems for Machine Learning” Teaming humans and machine learning for detecting cyber threats. ACM SIGKDD Explorations Newsletter, 21 (2), 39-47.
- Santu, S. K. K., Veeramachaneni, K., Zhai, C. X. (2019). TILM: Neural Language Models with Evolving Topical Influence. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pp. 778-788.
- Xu, L., Skoularidou, M., Cuesta-Infante, A., Veeramachaneni, K. (2019). Modeling tabular data using conditional gan. Advances in Neural Information Processing Systems (NeurIPS), 7333-7343.
- Wang, Q., Ming, Y., Jin, Z., Shen, Q., Liu, D., Smith, M. J., Veeramachaneni, K., Qu, H. (2019). ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning. ACM Conference on Human Factors in Computing Systems (CHI), Glasgow, Scotland UK.
- May 31, 2019: MIT News, Cracking open the black box of automated machine learning.
- March 6, 2018: MIT News, ML 2.0: Machine learning for many.
- March 6, 2018: Harvard Business Review, Getting value from machine learning isn’t about fancier algorithms — it’s about making it easier to use.
- April 10, 2017: TechRepublic, Artificial data reduces privacy concerns and helps with big data analysis.
- March 7, 2017: Popular Mechanics, Scientists can now get real results from fake data.