About Me

Publications

Invited Talks

Teaching

About Me

My name is Nikola B. Kovachki and I am a third year graduate student at Caltech studying Applied and Computational Mathematics (ACM) under the advisership of Professor Andrew M. Stuart. I was born in 1993 in Sofia, Bulgaria but relocated to the United States in March of 2005. I obtained a B.Sc. in Mathematics from Caltech in 2016.

My research interests lie at the intersection of learning theory and inverse problems. I am keen on the development of the mathematical theory of learning and its implications for advancement of numerical algorithms. Further I am interested in the application of existing learning systems to physical problems arising in the sciences.

I love spending time with my beautiful wife and dog, traveling, hiking, and experiencing art.

In Progress

Kovachki N.B., Stuart A.M. The Continuous Time Limits of Momentum Methods in Machine Learning. 2018.

Kovachki N.B., Stuart A.M. Model Reduction for Input-Output Maps. 2018

Submitted

Kovachki N.B., Stuart A.M. Ensemble Kalman Inversion: A Derivative-Free Technique For Machine Learning Tasks. arXiv:1808.03620. 2018.

Submitted to: Inverse Problems

Upcoming

Applied Inverse Problems (AIP). July 8th-12th 2019. Grenoble, France.

Workshop on Inverse Problems and Machine Learning (IPML). May 27th-29th 2019. Montreal, Quebec, Canada.

SIAM Conference on Applications of Dynamical Systems (DS19). May 19th-23rd 2019. Snowbird, Utah, USA.

SIAM Conference on Computational Science and Engineering (CSE19). February 25th - March 1st 2019. Spokane, Washington, USA.

Center for Materials in Extreme Dynamic Environments Fall Meeting (MEDE-ARL). October 10th 2018. Baltimore, Maryland, USA.

Past

Southern California Applied Mathematics Symposium (SOCAMS). Derivative-Free Ensemble Methods for Machine Learning Tasks. Poster. April 28th 2018. Santa Barbara, California, USA.

UQ for Inverse Problems in Complex Systems (UNQW04). Derivative-Free Ensemble Methods for Machine Learning Tasks. Poster. April 9th-13th 2018. Cambridge, UK.

Workshop on Inverse Problems and Machine Learning (IPML). Derivative-Free Ensemble Methods for Machine Learning Tasks. Talk. February 9th-11th 2018. Pasadena, California, USA.

Teaching Assistantships

Linear Analysis with Applications (CMS/ACM/IDS 107). Computing and Mathematical Sciences/ Applied and Computational Mathematics/ Information and Data Sciences. Fall 2018. California Institute of Technology.

Instructor: Andrew M. Stuart

Linear Analysis with Applications (CMS/ACM 107). Computing and Mathematical Sciences/ Applied and Computational Mathematics. Fall 2017. California Institute of Technology.

Instructor: Andrew M. Stuart

Introduction to Probability Models (ACM/EE 116). Applied and Computational Mathematics/Electrical Engineering. Fall 2016. California Institute of Technology.

Instructor: Konstantin Zuev

Technical Seminar Presentations (E 10). Engineering. Fall 2016. California Institute of Technology. Instructor: Anthony Fender

Technical Seminar Presentations (E 10). Engineering. Spring 2016. California Institute of Technology.

Instructor: Anthony Fender