Biswajit Paria

PhD student
Machine Learning Department
Carnegie Mellon University

Resume | Google Scholar | Twitter

I am a fourth year PhD student in the Machine Learning Department at Carnegie Mellon University. I am fortunate to be advised by Barnabás Póczos and Jeff Schneider. I am broadly interested in statistical machine learning and some aspects of deep learning. More specifically, I am interested in Bayesian optimization, bandits, and sequential decision making. I have also spent time at Snap Research (2018), and Google Research (2020) as a research intern.

Prior to starting my PhD, I graduated from the Indian Institute of Technology Kharagpur with a 5-year bachelors + masters in Computer Science and Engineering. I was advised by Pabitra Mitra for my undergraduate thesis.

Outside of work, I am a regular boulderer (≤ V3), and sometimes paint on my iPad.


  1. Hierarchically Regularized Deep Forecasting
    Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das
    Pre-print, 2021 [arxiv]

  2. Cost-Aware Bayesian Optimization via Information Directed Sampling
    Biswajit Paria, Willie Neiswanger, Ramina Ghods, Jeff Schneider, Barnabás Póczos
    ICML Workshop on Real World Experiment Design and Active Learning, 2020

  3. Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning
    Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Han Wang, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan
    Cell Reports Physical Science, 2020

  4. Minimizing FLOPs to Learn Efficient Sparse Representations
    Biswajit Paria, Chih-Kuan Yeh, Ian E.H. Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos
    International Conference on Learning Representations (ICLR), 2020
    [paper] [arxiv] [code]

  5. Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
    Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing
    Journal of Machine Learning Research (JMLR), 2020
    [paper] [arxiv] [github]

  6. A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
    Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
    Uncertainty in Artificial Intelligence (UAI), 2019 (oral)
    [paper] [arxiv]

  7. Analytic Connectivity in General Hypergraphs
    Ashwin Guha, Muni Sreenivas Pydi, Biswajit Paria, Ambedkar Dukkipati
    Pre-print, 2017 [arxiv]

  8. Forward Stagewise Additive Model for Collaborative Multiview Boosting
    Avisek Lahiri, Biswajit Paria, Prabir Kumar Biswas
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2016
    [paper] [arxiv]

  9. A Neural Architecture Mimicking Humans End-to-End for Natural Language Inference
    Biswajit Paria, K. M. Annervaz, Ambedkar Dukkipati, Ankush Chatterjee, Sanjay Podder
    Pre-print, 2016 [arxiv]