• Curriculum Vitae
  • About Me
  • Research
  • Publications
  • Awards
  • Experience

About Me

I am Rizal Fathony [full name: Rizal Zaini Ahmad Fathony], a fifth year PhD student at the Department of Computer Science, University of Illinois at Chicago. I work with my advisor Prof. Brian Ziebart in statistical machine learning theory and applications. Before attending PhD program at UIC, I completed Bachelor of Applied Science degree in Statistical Computing from the Institute of Statistics, Jakarta, Indonesia and Master of Science in Computer Science from the University of Illinois at Chicago.

Research

Performance-Aligned Learning Algorithms with Statistical Guarantees

My research focuses on designing robust adversarial learning algorithms for various tasks that simultaneously align with the learning objective by incorporating the performance metrics or loss functions into the learning process and provide the statistical guarantee of Fisher consistency. In my previous and ongoing works, I design robust adversarial learning algorithms for the following tasks:
- Multiclass classification
- Ordinal classification with absolute loss and squared loss
- Classification with abstention
- Conditional graphical models (chain, tree, and low-treewidth graph structures)
- Bipartite matching in graphs
- Fairness in machine learning

 

Research interest:

Consistent learning algorithms Fairness in machine learning
Multivariate metrics Multi-task learning
Robust adversarial learning Deep learning
Structured prediction Causal machine learning
Approximate inference Bayesian machine learning
Multivariate metrics Multi-modal regression

Publications

In submission/preparation:

[1] Rizal Fathony, Kaiser Asif, Anqi Liu, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Consistent Robust Adversarial Prediction for General Multiclass Classification”. Journal manuscript in preparation.


Conferences:

[2] Rizal Fathony, Ashkan Rezaei, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Distributionally Robust Graphical Models”. Advances in Neural Information Processing Systems 31 (NIPS), 2018. (to appear).


[3] Rizal Fathony*, Sima Behpour*, Xinhua Zhang, Brian D. Ziebart. “Efficient and Consistent Adversarial Bipartite Matching”. International Conference on Machine Learning (ICML), 2018. [pmlr/icml] [pdf] [poster] [code]


[4] Rizal Fathony, Mohammad Bashiri, Brian D. Ziebart. “Adversarial Surrogate Losses for Ordinal Regression”. Advances in Neural Information Processing Systems 30 (NIPS), 2017. [nips] [pdf] [poster] [code]


[5] Rizal Fathony, Anqi Liu, Kaiser Asif, Brian D. Ziebart. “Adversarial Multiclass Classification: A Risk Minimization Perspective”. Advances in Neural Information Processing Systems 29 (NIPS), 2016. [nips] [pdf] [poster] [code]


Workshops:

[6] Anqi Liu, Rizal Fathony, Brian D. Ziebart. “Learning to Explore by Abstaining”. 13th Women in Machine Learning Workshop (WiML), 2018 (to appear).


[7] Rizal Fathony, Naveen Goela. “Discrete Wasserstein GANs”. IEEE Information Theory and Applications Workshop (ITA) 2018.


Preprints:

[8] Anqi Liu, Rizal Fathony, Brian D. Ziebart. “Kernel Robust Bias-Aware Prediction under Covariate Shift”. ArXiv Preprints, 2016. [arXiv]

Awards

NIPS Travel Award 2018

NIPS Travel Award 2017

NIPS Travel Award 2016

Won an International Fulbright Master of Science and Technology Scholarship Award (2012 - 2014)

Runner Up Developer at Indonesia Open Source Festival - Android Apps Competition (2010)

Nominee of Research and Development Category at Asia Pacific Information and Communication Technology Award (APICTA), Melbourne, Australia (2009)

Best Research and Development Category at Indonesia Information and Communication Technology Award, Jakarta, Indonesia (2009)

Grantee of a full scholarship and monthly stipend from the Indonesian government during undergraduate study at the Institute of Statistics, Jakarta, Indonesia (2003 - 2007)

Experience

Research AssistantDecember 2014 - present

DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF ILLINOIS AT CHICAGO

Research Assistant at Prof. Brian Ziebart’s lab.

Research Intern May 201 - August 2017

TECHNICOLOR RESEARCH, ARTIFICIAL INTELLIGENCE LAB, SAN FRANCISCO BAY AREA

Conducted research in deep learning area especially in Generative Adversarial Networks (GAN):
- Developed a network architecture of GAN for discrete distribution
- Applied Conditional Wasserstein GAN to image colorization tasks

Teaching AssistantAugust 2015 - December 2016

DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF ILLINOIS AT CHICAGO

CS 412 Introduction to Machine Learning (Fall 2016).
CS 412 Introduction to Machine Learning (Spring 2016).
CS 491 Introduction to Machine Learning (Fall 2015).

Statistical Dissemination System Developer January 2008 - June 2012

CENTRAL BUREAU OF STATISTICS INDONESIA

Developed web-based statistical data dissemination and visualization systems for major surveys and censuses conducted by Central Bureau of Statistics Indonesia.


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