Prior to this I was a research associate in VIDA Lab at NYU Tandon School of Engineering where I worked with Professor Juliana Freire on DARPA Memex and D3M projects. Before joining NYU, I did a summer internship with Microsoft Research and Cloud AI Group in Redmond.
My areas of interest include machine learning, deep learning and reinforcement learning. More specifically my current research is in modular deep learning with goals of learning from less data, ensuring explainability and enabling transfer learning while constantly striving towards the holy grail of artificial general intelligence.
Joined the interdisciplinary project, between computer science and law and criminology departments, on applying AI to nurse regulatory decision making in complaints about nurses in the US, UK and Australia. Further details of the project can be found HERE.
My poster, AlphaD3M: Machine Learning Pipeline Synthesis, was selected for presentation at the Deep Learning and Reinforcement Learning Summer School 2019.
Our poster, Agent-based Modelling of Collective Algorithms Implementable by T Cells, was selected for presentation at the Mathematics in Life Sciences (MiLS) Meeting on Modelling Challenges in Cancer and Immunology at King's College London in summer 2019.
Selected for and attended the Deep Learning and Reinforcement Learning Summer School 2019 in Edmonton, Alberta, Canada in summer 2019.
Mentored the 2nd Place winning team in End Human Trafficking Hackathon, 2016, organized by Manhattan District Attorney’s (DANY) office in partnership with Cornell Tech.
Interpretability in Gated Modular Neural Networks
Yamuna Krishnamurthy and Chris Watkins
In Explainable AI approaches for debugging and diagnosis Workshop at Neural Information Processing (NeurIPS), Dec 2021
Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies
Piyawat Lertvittayakumjorn, Ivan Petej, Yang Gao, Yamuna Krishnamurthy, Anna Van Der Gaag, Robert Jago, and Kostas Stathis.
In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 81–91, Virtual, August 2021
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Iddo Drori, Yamuna Krishnamurthy, Raoni de Paula Lourenco, Remi Rampin, Kyunghyun Cho, Claudio Silva, Juliana Freire.
International Workshop on Automatic Machine Learning 2019, International Conference on Machine Learning (ICML), Long Beach, USA, June 2019
AlphaD3M: Machine Learning Pipeline Synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva, Juliana Freire.
International Workshop on Automatic Machine Learning 2018, International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018
Interactive Exploration for Domain Discovery on the Web
Yamuna Krishnamurthy, Kien Pham, Aecio Santos, Juliana Freire.
Workshop on Interactive Data Exploration and Analytics (IDEA) 2016, Knowledge Discovery and Data Mining (KDD), San Francisco, Aug 2016
Bayesian Optimal Active Search and Surveying
Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff Schneider, Richard P Mann.
29th International Conference on Machine Learning (ICML) 2012, Madison, WI, USA, June 2012
Bayesian Optimal Active Search on Graphs
Roman Garnett, Yamuna Krishnamurthy, Donghan Wang, Jeff Schneider, and Richard Mann
Ninth Workshop on Mining and Learning with Graphs (MLG ’11), Knowledge Discovery and Data Mining (KDD), San Diego, Aug 2011