Welcome

I am currently a PhD student at Donald Bren School of Information and Computer Sciences (UC Irvine, California), under the direction of Prof. Pierre Baldi. My focus is on artificial intelligence and machine learning and their application to life sciences, biological and chemical data, especially in the domain of drug discovery. My current research relates to the prediction of small molecules properties, virtual high-throughput screening, reaction prediction, and docking. My (tentative) PhD dissertation title is Statistical Machine Learning and Data Mining for Chemoinformatics and Drug Discovery.

Latest News

Will present a poster on Virtual High-Throughput Screening and Early Recognition at the Women in Machine Learning 2009 Workshop, co-hosted with NIPS.

Interned at IBM R&D Labs in Israel over the summer of 2009. I was part of the HyperGenes Project, under the direction of Michal Rosen-Zvi.

Awarded an IBM PhD Fellowship that covers my tuition, fees and a stipend for 2009-2010.

Attended the Learning Workshop in Clearwater, FL (April 13-17, 2009) and presented Performance Prediction of the Influence Relevance Voter (Chloé-Agathe Azencott, S. Joshua Swamidass, and Pierre Baldi) as a poster.

Attended the 237th ACS National Meeting in Salt Lake City, UT (March 22-26, 2009) and presented Combining quantitative data and qualitative knowledge to score reaction energies (Chloé-Agathe Azencott, Matthew A. Kayala, and Pierre Baldi). Received a CINF-Symyx Scolarship for Scientific Excellence.

S. Joshua Swamidass, Chloé-Agathe Azencott, Ting-Wan Lin, Hugo Gramajo, Sheryl Tsai, and Pierre Baldi. The Influence Relevance Voter: an Accurate and Interpretable Virtual High Throughput Screening Method, J. Chem. Inf. Model., March 2009. DOI: 10.1021/ci8004379.

Chloé-Agathe Azencott and Pierre Baldi. Virtual High-Throughput Screening With Two-Dimensional Kernels, in Hands-On Pattern Recognition Challenges in Data Representation, Model Selection, and Performance Prediction, I. Guyon, G. Cawley, G. Droor, and A. Saffari Editors, Lulu Press, 2008.(in press)

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October 24th, 2007. Advancement to Candidacy: Learning Molecular Properties: Structural Representation of Molecular Data.

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Presentation at the Agnostic Learning vs. Prior Knowledge Workshop at IJCNN 2007 (Orlando)

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Best Results on the HIVA dataset for the Agnostic Learning vs. Prior Knowledge Challenge.

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