Stanislav Fort

I am a PhD student at Stanford University. My research spans theoretical physics, AI, and deep learning. I am excited about applications of artificial intelligence and machine learning in physics, emergent phenomena, and the role of complexity in physical systems.

I completed my Bachelors and Masters (Part III of the Tripos) at Trinity College, University of Cambridge.

I worked at Institute of Astronomy on galaxy clusters in X-ray, Albert Einstein Institute on large scale data mining for pulsar discovery, Perimeter Institute for Theoretical Physics on perturbative approaches to black hole formation in AdS-like geometries, and DAMTP on cross-correlations of gamma-rays and the CMB in the sky. At Stanford, I have worked on quantum gravity, theoretical neuroscience, computer vision, cosmology, and astrophysics.

I actively co-organize and coach at the Czech Astronomy Olympiad, setting problems and preparing students for the IOAA. I sometimes lecture at the Czech Physics Olympiad and prepare students for IPhO. I co-organized the 1st and 2nd International Workshop on Astronomy and Astrophysics in Estonia and the Czech Republic. I am also an amateur astrophotographer.

On top of my research, I work on a number of side projects in mathematics, physics, and CS. They usually involve coding in Python, NumPy, and TensorFlow.

Twitter  /  GitHub  /  LinkedIn

Research

I'm interested in physics, emergence, and AI. My current focus is on applying deep learning methods to physical sciences.

Towards understanding feedback from supermassive black holes using convolutional neural networks
Stanislav Fort

A novel approach to detection of X-ray cavities in clusters of galaxies using convolutional neural architectures.

Accepted at the Deep Learning for Physical Sciences workshop at NIPS 2017.

Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
Stanislav Fort

An architecture capable of dealing with uncertainties for few-shot learning on the Omniglot dataset.

Accepted and presented at BayLearn 2017.
Accepted at the Bayesian Deep Learning workshop at NIPS 2017.

Essential code available on GitHub.

Discovery of Gamma-ray Pulsations from the Transitional Redback PSR J1227-4853
T. J. Johnson, P. S. Ray, J. Roy, C. C. Cheung, A. K. Harding, H. J. Pletsch, S. Fort, F. Camilo, J. Deneva, B. Bhattacharyya, B. W. Stappers, M. Kerr

A pulsar detection in gamma-ray.

Class projects

At Stanford, I worked on the following class projects:

Fun side projects

I work on a number of side projects and fun problems in mathematics, physics, and CS. Some of them are shown here.

Drawing an envelope/barn without lifting one's pen - all 88 (44 unique and their mirrors) solutions at once.

Continuous deformation of one face to another using key facial points.


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