Computer Science Data

Aditya Krishna Menon

Email: kmenondu sdFedu

University of California, San Diego

Homepage: httpXGGwwwF seFu sdFeduG kmenon

CSE Department, 0404

**** ****** ***** *****: 858-***-****

La Jolla, CA 92093

University of California, San Diego, La Jolla, CA.

EDUCATION

Ph.D. in Computer Science, expected graduation: March 2013.

Thesis title: Latent feature models for dyadic prediction.

C.Phil. in Computer Science: June 2011.

M.S. in Computer Science: June 2009.

University of Sydney, Sydney, Australia.

B.Sc. (Advanced) with Honours in Computer Science, November 2006.

Thesis title: Random projections and applications to dimensionality reduction.

Awards and Scholarships

Jacobs Fellowship, University of California San Diego, 2007 2009. Award given to best

incoming PhD students in the Jacobs School of Engineering.

Allen Bromley prize, The University of Sydney, 2007. Awarded for best Honours thesis in

School of Information Technologies.

University Medal, The University of Sydney, 2007. Selective award given to the top Hon-

ours students in the Faculty of Science.

RESEARCH Collaborative ltering, link prediction, latent feature modelling, probability estimation, im-

INTERESTS balanced classi cation, large-scale learning, random projections.

REFEREED A Machine Learning Framework for Programming by Example. Aditya Krishna Menon, Omer

Tamuz, Sumit Gulwani, Butler Lampson, and Adam Tauman Kalai. To appear in International

PUBLICATIONS

Conference on Machine Learning (ICML) 2013.

Learning and Inference in Probabilistic Classi er Chains with Beam Search. Abhishek Kumar,

Shankar Vembu, Aditya Krishna Menon, and Charles Elkan. In Machine Learning and Knowl-

edge Discovery in Databases - European Conference (ECML-PKDD), 2012.

Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for Joint Op-

timization of Discrimination and Calibration. Xiaoqian Jiang, Aditya Krishna Menon, Shuang

Wang, Jihoon Kim, and Lucila Ohno-Machado. In PLoS ONE 7(11): e48823, 2012.

Predicting accurate probabilities with a ranking loss. Aditya Krishna Menon, Xiaoqian Jiang,

Shankar Vembu, Charles Elkan, and Lucila Ohno-Machado. In International Conference on

Machine Learning (ICML) 2012.

Link prediction via matrix factorization. Aditya Krishna Menon, Charles Elkan. In Machine

Learning and Knowledge Discovery in Databases - European Conference (ECML-PKDD), Proceed-

ings Part II, 2011.

Response prediction using collaborative ltering with hierarchies and side-information. Aditya

Krishna Menon, Krishna-Prasad Chitrapura, Sachin Garg, Deepak Agarwal, and Nagaraj Kota.

In Knowledge Discovery and Data Mining (KDD), San Diego, California, 2011.

Fast algorithms for approximating the singular value decomposition. Aditya Krishna Menon,

Charles Elkan. In Transactions of Knowledge and Data Discovery: Special Issue on Large-Scale

Data Mining (TKDD-LDMTA), Volume 5, Number 2, February 2011.

Aditya Krishna Menon

A log-linear model with latent features for dyadic prediction. Aditya Krishna Menon, Charles

Elkan. In IEEE International Conference on Data Mining (ICDM), Sydney, Australia, 2010.

Predicting labels for dyadic data. Aditya Krishna Menon, Charles Elkan. In Data Mining and

Knowledge Discovery: Special Issue on Papers from ECML-PKDD, Volume 21, Number 2, 2010.

An incremental data-stream sketch using sparse random projections. Aditya Krishna Menon,

Gia Vinh Anh Pham, Sanjay Chawla, and Tasos Viglas. In Proceedings of the 2007 SIAM Inter-

national Conference on Data Mining (SDM), Minnesota, USA.

PAPERS Privacy breach detection using collaborative ltering. Aditya Krishna Menon, Xiaoqian Jiang,

Jihoon Kim, and Jaideep Vaidya. Submission to ML4S 2012.

PENDING

REVIEW

Data Scientist Intern, LinkedIn. June 2012 Sep 2012. Worked with Data Science and

WORK

Search Relevance teams on search log analysis. Helped devise end-to-end system for using

EXPERIENCE

machine learning to help automate analysis, from extracting data via Hadoop to collecting

training labels to performing predictive analytics using machine learning models.

Intern, Microsoft Research New England. June 2011 Sep 2011. Worked on a new ap-

proach to repetitive text processing using programming by example, the paradigm where a

user instructs a machine to perform a task by showing it an example. We showed how to

use machine learning to perform ef cient inference of the user s intent, thus signi cantly ex-

tending the scope of operations over prior systems. We implemented our ideas in a working

prototype of the system, designed to run client-side on a web browser.

Intern, Yahoo! Labs Bangalore. June 2010 Sep 2010. Worked with the advertising sciences

team on estimating the probability of an advertisement being clicked when displayed on a

webpage. We showed how to approach the problem using techniques from the collaborative

ltering literature. We further extended these techniques to exploit hierarchical information

about webpages and advertisement, which led to signi cant performance increases.

Intern, Infosys Bangalore. December 2005 February 2006. Worked in the Grid Computing

group in SETLabs on static analysis of code to detect parallelism opportunities. Refactored

existing Java code base to make use of several design patterns. Developed code in Java,

interfacing with the ANTLR and GraphWiz packages to create a visual display of dependencies

inside Java code to be deployed on a grid.

Programming languages: C, C++, C#, Java, MATLAB, OCaml, Python, Apache Pig.

SKILLS

Other: Working knowledge of HTML, JavaScript and SQL. Developed code with Visual Studio

.Net, Eclipse. Comfortable with Linux and Windows.

Charles Elkan. Professor, Department of Computer Science and Engineering, University of

REFERENCES

California, San Diego. Email: *****@**.****.***.

Sanjay Chawla. Head of School, School of Information Technologies, University of Sydney.

Email: ******@**.****.***.**.

Sachin Garg. VP Big Data Labs, American Express, Bangalore. Email: ******.****@****.***.,

Adam Kalai. Senior Researcher, Microsoft Research New England. Email: ****@*********.***.

Lawrence Saul. Professor, Department of Computer Science and Engineering, University of

California, San Diego. Email: ****@**.****.***.

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