Research

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.

News

Latest

1. Our paper titled Use of Artificial Intelligence in Regulatory Decision-Making has been published in the Oct 2021 issue of the Journal of Nursing Regulation.
2. Our paper titled Interpretability in Gated Modular Neural Networks has been accepted at the eXplainable AI Approaches for Debugging and Diagnosis Workshop on 14 Dec 2021 at NeurIPS2021 conference.
3. Our demo paper titled Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies was accepted at the ACL-IJCNLP 2021 conference.
4. Our workshop Parallel, Distributed and Federated Learning (PDFL) has been accepted at ECML PKDD 2021. This is the 4th year of the workshop.

Past

1. 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.

2. Co-chaired the ECML PKDD 2020 workshop on Parallel, Distributed and Federated Learning.

3. My poster, AlphaD3M: Machine Learning Pipeline Synthesis, was selected for presentation at the Deep Learning and Reinforcement Learning Summer School 2019.

4. 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.

5. Our paper, Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar, was accepted to AutoML workshop at ICML 2019.

6. Selected for and attended the Deep Learning and Reinforcement Learning Summer School 2019 in Edmonton, Alberta, Canada in summer 2019.

7. Co-chaired the ECML PKDD 2019 workshop on Decentralized Machine Learning at the Edge.

8. Our paper, AlphaD3M: Machine Learning Pipeline Synthesis, was accepted to AutoML workshop at ICML 2018

9. Co-chaired the ECML PKDD 2018 workshop on Decentralized Machine Learning at the Edge.

10. Received 3rd Prize (among 70 teams) at NYU Tandon School of Engineering Research Expo 2017 for presenting our DARPA Memex work. It was covered by the local Technical.ly, Brooklyn.

11. 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.

Selected Publications

• 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
  

  ↓ Paper

• 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
  

  ↓ Paper

• 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
  

  ↓ Paper

• 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
  

  ↓ Paper

• 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
  

  ↓ Paper

• 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

  ↓ Paper

Contact

• Email: c o n t a c t [ at ] y a m u n a k . c o m
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