About me

I am a Postdoctoral Fellow advised by Finale Doshi-Velez at Harvard University, working on probabilistic models, inference, interpretable machine learning, and healthcare applications. I currently hold a Harvard Data Science Initiative fellowship, co-funded by the Center of Research on Computation and Society institute.

I completed my Ph.D. "Bayesian nonparametrics for data exploration" advised by Fernando Perez-cruz from University Carlos III in Madrid, and M.Sc. from Stuttgart University. Before my PhD, I spent two years in the industry, working at Sony EU Research Center in Stuttgart and Sony Corporation R&D in Tokyo.

My research lies at the intersection of impactful healthcare applications and probabilistic machine learning. Recently, I am interested in personalizing mental healthcare and advances in Bayesian neural networks. See my CV for further information about me.


  • 2020-05: Our paper on generating interpretable predictions for antidepressant treatment stability just got published to JAMA Network Open!
  • 2020-04: How to incorporate functional prior knowledge in Bayesian RBF NNs? Our work just got accepted to UAI Conference! Check it out here: Poisson Process Radial Basis Function Networks.

  • 2020-02: Who gets an initial antidepressant prescription and then never follows up? Check out our latest publication in Nature Translational Psychiatry.

  • 2019-12: We will present a poster at the AABI Workshop on "Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences".

  • 2019-11: I am happy to announce that our work "General Latent Feature Models for Heterogeneous Datasets" has been accepted for publication at the Journal of Machine Learning Research!

  • 2019-06: We will be teaching a new master course "Introduction to Machine Learning and Statistics`` and Workshop on Data Science at Kigali, Rwanda. Thanks to the African Center of Excelence in Data Science (ACE-DS)'' for the opportunity!

  • 2019-05: Check out our recent work on Output-Constrained Bayesian Neural Networks, to be presented at ICML2019 Workshop on Generalization.

  • 2019-03: I will be talking as a panelist member at the event ``Data Science Everywhere'' organized by Harvard Big Data Club.

  • 2019-03: I will be a mentor at the ``Women in Data Science Cambridge'' organized by the Institute of Applied Sciences at Harvard and Microsoft Research.

  • 2019-02: We have been awarded a HDSI competitive research fund for our project ``Personalizing mental health care: Bringing machine learning support into the clinic through user-centered design''.

  • 2019-01: Our new work on Hierarchical Stick-breaking Feature Paintbox was presented at the NeurIPS'18 workshop on Bayesian Nonparametrics as a spotlight talk.

  • 2018-12: Check-out our new work on projected Bayesian neural networks. A shorter version appeared at the NeurIPS'18 workshop on Bayesian deep learning.

  • 2018-11: I recently gave a talk "Towards better uncertainty in Bayesian Neural Networks" at IBM Research in Cambridge, and University Carlos III in Madrid.