Aditya Grover
Ph.D. Candidate, Computer Science
Stanford Artificial Intelligence Laboratory
Statistical Machine Learning Group
Email: adityag at cs.stanford.edu

I am on the job market this year.

I am a fifth year Ph.D. candidate in Computer Science at Stanford University advised by Stefano Ermon. My research focusses broadly on developing algorithms for probabilistic machine learning in high dimensions, including topics in generative modeling, approximate inference, and deep learning. I am particularly excited in grounding my research in applications relating to accelerating science and engineering. My research is supported by a Microsoft Research Ph.D. Fellowship, a Lieberman Fellowship, and a Data Science Scholarship.

In 2018, I created a new course on Deep Generative Models at Stanford with my advisor and have been a course instructor for the last two years. During my Ph.D., I have spent wonderful summers interning at Google Brain (2019), Microsoft Research (2018), and OpenAI (2017). Before joining graduate school, I obtained my bachelors in Computer Science and Engineering from IIT Delhi (2015), where I was advised by Mausam and Parag Singla and received a best undergraduate thesis award.

In my free time, I like playing tennis, hiking, and social dancing.

Recent

• Feb 2020: Our paper on using machine learning for optimizing charging of electric batteries appeared in Nature!
• Fall 2019: Presented our recent work on mitigating bias in generative modeling (NeurIPS 2019) at Caltech, CMU, Harvard, Google Brain, MIT, UCLA, UMass Amherst, and USC.
• Fall 2019: Enjoyed teaching CS 236: Deep Generative Models for the second consecutive time. Check out our updated course materials as well as students' course projects here.
• Fall 2019-2020: Honored to have received the Lieberman Fellowship, recognizing "students who have demonstrated broad potential for leadership in academia through their research accomplishments, teaching and university service".

Peer-Reviewed Journal and Conference Publications

1. Closed-loop optimization of fast-charging protocols for batteries with machine learning
   Peter Attia*, Aditya Grover*, Norman Jin, Kristen Severson, Todor Markov, Yang-Hung Liao, Michael Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, William Chueh
   Nature, 2020.
   [paper][code][press release][selected media]


2. AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
   Aditya Grover*, Christopher Chute*, Rui Shu, Zhangjie Cao, Stefano Ermon
   AAAI Conference on Artificial Intelligence (AAAI), 2020.
   [paper][code]


3. Permutation Invariant Graph Generation via Score-Based Generative Modeling
   Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
   To appear in International Conference on Artificial Intelligence and Statistics (AISTATS), June 2020.
   [paper and code coming soon]


4. Bias Correction of Learned Generative Models using Likelihood-free Importance Weighting
   Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
   Advances in Neural Information Processing Systems (NeurIPS), 2019.
   [paper][code][blog]


5. Graphite: Iterative Generative Modeling of Graphs
   Aditya Grover, Aaron Zweig, Stefano Ermon
   International Conference on Machine Learning (ICML), 2019.
   [paper][code]
   


6. Stochastic Optimization of Sorting Networks via Continuous Relaxations
   Aditya Grover*, Eric Wang*, Aaron Zweig, Stefano Ermon
   International Conference on Learning Representations (ICLR), 2019.
   [paper][code]
   


7. Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
   Aditya Grover, Stefano Ermon
   International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
   [paper][code][blog]
   


8. Learning Controllable Fair Representations
   Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
   International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
   [paper][code][blog]
   


9. Neural Joint Source-Channel Coding
   Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon
   International Conference on Machine Learning (ICML), 2019.
   [paper][code]
   Full Oral Presentation


10. Streamlining Variational Inference for Constraint Satisfaction Problems
    Aditya Grover, Tudor Achim, Stefano Ermon
    Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Extended version appears as invited paper in the special issue of Journal of Statistical Mechanics: Theory and Experiment on Machine Learning, 2019.
    [paper][code]
    


11. Learning Policy Representations in Multiagent Systems
    Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
    International Conference on Machine Learning (ICML), 2018.
    [paper]
    Full Oral Presentation [acceptance rate: 212/2473 (8.6%)]


12. Modeling Sparse Deviations for Compressed Sensing using Generative Models
    Manik Dhar, Aditya Grover, Stefano Ermon
    International Conference on Machine Learning (ICML), 2018.
    [paper] [code]
    Full Oral Presentation [acceptance rate: 212/2473 (8.6%)]


13. Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs (short)
    Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018.
    [paper]


14. Variational Rejection Sampling
    Aditya Grover*, Ramki Gummadi*, Miguel Lazaro-Gredilla, Dale Schuurmans, Stefano Ermon
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
    [paper][blog]


15. Best arm identification in multi-armed bandits with delayed feedback
    Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicholas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, Stefano Ermon
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
    [paper][code]


16. Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models
    Aditya Grover, Manik Dhar, Stefano Ermon
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
    [paper][code][blog]
    Full Oral Presentation [acceptance rate: 417/3800 (10.9%)]


17. Boosted Generative Models
    Aditya Grover, Stefano Ermon
    AAAI Conference on Artificial Intelligence (AAAI), 2018.
    [paper][code]


18. Variational Bayes on Monte Carlo Steroids
    Aditya Grover, Stefano Ermon
    Advances in Neural Information Processing Systems (NIPS), 2016.
    [paper][spotlight video]


19. node2vec: Scalable Feature Learning for Networks
    Aditya Grover, Jure Leskovec
    Knowledge Discovery and Data Mining (KDD), 2016.
    [paper][code]
    Oral Plenary Presentation [acceptance rate: 70/784 (8.9%)]


20. Contextual Symmetries in Probabilistic Graphical Models
    Ankit Anand, Aditya Grover, Mausam, Parag Singla
    International Joint Conference on Artificial Intelligence (IJCAI), 2016.
    [paper][code]
    Best Paper Award at the International Workshop on Statistical Relational AI (StarAI), 2016.


21. A Deep Hybrid Model for Weather Forecasting
    Aditya Grover, Ashish Kapoor, Eric Horvitz
    Knowledge Discovery and Data Mining (KDD), 2015.
    [paper][press Microsoft , Gizmodo]


22. ASAP-UCT: Abstraction of State-Action Pairs in UCT
    Ankit Anand, Aditya Grover, Mausam, Parag Singla
    International Joint Conference on Artificial Intelligence (IJCAI), 2015.
    [paper][code]


23. A Novel Abstraction Framework for Online Planning (short)
    Ankit Anand, Aditya Grover, Mausam, Parag Singla
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015.
    [paper]
    

Preprints

1. Fair Generative Modeling via Weak Supervision
   Aditya Grover*, Kristy Choi*, Rui Shu, Stefano Ermon
   NeurIPS Workshop on Human-Centric Machine Learning, 2019.
   [paper]

Teaching

• Fall 2019: Instructor for CS 236: Deep Generative Models at Stanford
  
• Fall 2018: Instructor for CS 236: Deep Generative Models at Stanford
  
• Winter 2017: Head Course Assistant for CS 228: Probabilistic Graphical Models at Stanford
  
• Spring 2015: Teaching Assistant for CSL 333: Artificial Intelligence at IIT Delhi

Service

• Journal Reviewer: Nature, Journal of Machine Learning Research, Machine Learning Journal, Transactions on Knowledge Discovery from Data, Transactions on Networking, Transactions on Pattern Analysis and Machine Intelligence
• Conference Program Committee: AAAI (2020, 2019), KDD (2019), UAI (2020, 2019)
• Conference Reviewer: AISTATS (2020), ICML (2020, 2019), ICLR (2020, 2019), NeurIPS (2019, 2018, 2016)
• Student Representative in the Committee on Research within the Stanford University Academic Council (2016-17)

Outreach

• At NeurIPS 2018, Paroma Varma and I led the organization for a workshop on Relational Representation Learning.
• At IJCAI-ECAI 2018, Stefano Ermon and I presented a tutorial on Deep Generative Models.