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

About Me

I am Rizal Fathony [full name: Rizal Zaini Ahmad Fathony], a fourth 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

Research interest:

surrogate loss, statistical consistency, multivariate performance metrics, robust adversarial formulation, structured prediction, graphical models, approximate inference, causal machine learning, deep learning, Bayesian machine learning, multi-tasks learning, learning under constraints.

 

Ongoing Research: Performance-Aligned Learning Algorithms with Statistical Guarantees

The goal of many prediction tasks in machine learning is to learn a prediction function that minimizes certain loss functions (e.g. zero-one, and ordinal loss) or maximizes certain performance metrics (e.g. accuracy, precision, recall, F-score, and ROC curve). The prevalent techniques in machine learning, probabilistic and large-margin approaches, suffer from the inability to easily incorporate the metrics into the learning process or the lack of statistical guarantee of Fisher consistency. My research focuses on designing learning algorithms 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. My approach in constructing learning algorithms is based on the robust adversarial formulation, i.e. what predictor best maximizes the performance matrix (or minimizes the loss function) in the worst case given the statistical summaries of the empirical distributions. I have designed learning algorithms for many machine learning tasks such as multiclass classification, ordinal regression, weighted bipartite matching, structured prediction, and graphical models.

My future research directions focus on investigating the statistical properties of loss functions, such as stronger statistical guarantees, and the Fisher consistency of structured loss functions, as well as developing learning algorithms for various machine learning tasks, such as structured prediction, graphical models, multi-tasks learning and combining the robust adversarial approach with deep learning.

Publications

Rizal Fathony, Ashkan Rezaei, Mohammad Bashiri, Xinhua Zhang, Brian D. Ziebart. “Distributionally Robust Graphical Models”. Submitted to NIPS 2018.


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]


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]


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]


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

Awards

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 2016 - 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.


Template Design by W3layouts