Machine Learning Researcher & Lecturer at
Starting October 2016, I will be a Team Leader in Tokyo at
's newly established
Center for Advanced Integrated Intelligence Research (CAIIR)
A new paper
A new paper
the teaching award
My talk at AI-Stats 2014
Goal 1: To design
to learn from data that are unreliable, noisy, high-dimensional, heterogenous, missing, and large.
Topics : Bayesian Models, Probabilistic Graphical Models, and Latent Variable Models.
Goal 2: To design
that are accurate, fast, scalable, and easy to use, all at the same time.
Topics: Variational Inference, Stochastic non-convex optimization, Bayesian Optimization.
List of current projects
Stochastic-approximation variational inference.
Variance reduction for stochastic variational inference.
Application of variational inference to deep learning.
Scalable inference for Gaussian process models.
Distributed optimization for variational inference.
UAVs doing Bayesian optimization to track humans.
Online collaborative prediction of vote outcomes.
Predicting success of crowfunded projects.
Personalization and breaking the filter bubble.
Home monitoring of patients.
Pattern Classification and Machine Learning
(Aug. 25, 2015)
Short course on "Fundamentals of ML"
Pattern Classification and Machine Learning, EPFL.