I am a PhD candidate at the Electrical and Computer Engineering department, UCSD. My research interests are data mining, machine learning, predictive analytics and related applications. I have been a part of several long research internships - Symantec Research, Yahoo Labs, and Samsung Research - during the course of my PhD. My previous projects have focused on multimedia retrieval, text classification and enterprise information systems. Note: I am currently on the job market, looking for full time positions. Please check out the details of my previous projects and publications and drop me a mail at cverma@ucsd.edu if you have relevant openings.

Graduate Student Researcher @ My research interests are data mining, machine learning, predictive analytics and related applications. I have been a part of several long research internships - Symantec Research, Yahoo Labs, and Samsung Research - during the course of my PhD. My previous projects have focused on multimedia retrieval, text classification and enterprise information systems. From January 2010 to Present (5 years 11 months) La JollaResearch intern @ The data which knowledge workers need to conduct their work is stored across an increasing number of repositories and grows annually at a significant rate. It is therefore unreasonable to expect that knowledge workers can efficiently search and identify what they need across a myriad of locations where upwards of hundreds of thousands of items can be created daily. This paper describes a system which can observe user activity and train models to predict which items a user will access in order to help knowledge workers discover content. We specifically investigate network file systems and determine how well we can predict future access to newly created or modified content. Utilizing file metadata to construct access prediction models, we show how the performance of these models can be improved for shares demonstrating high collaboration among its users. Experiments on eight enterprise shares reveal that models based on file metadata can achieve F scores upwards of 99%. Furthermore, on an average, collaboration aware models can correctly predict nearly half of new file accesses by users while ensuring a precision of 75%, thus validating that the proposed system can be utilized to help knowledge workers discover new or modified content. From June 2014 to March 2015 (10 months) Mountain view, CAResearch Intern @ Multimedia retrieval, Flickr image classification, tag similarity From June 2013 to December 2013 (7 months) Bengaluru Area, IndiaIntern @ text mining, NLP, twitter, recommendation system From June 2012 to August 2012 (3 months) IrvineAssociate Engineer @ Developed voice processing modules using C language From July 2008 to July 2009 (1 year 1 month) Intern @ Channel coding for H.264 video stream. Experimented with CRC, RS-code and convolution codes. From May 2007 to July 2007 (3 months) Intern @ Developed a hand-held device to calculate the Voltage Standing Wave Ratio (VSWR) for a given antenna. Product made its way into the market. From May 2006 to July 2006 (3 months)

Doctor of Philosophy (PhD), Multimedia retrieval and Enterprise information systems @ University of California, San Diego From 2011 to 2014 Master of Science (MS), Computer Engineering @ University of California, San Diego From 2009 to 2011 BTech, Electrical Engineering @ Indian Institute of Technology, Madras From 2004 to 2008 Chetan Verma is skilled in: Electrical Engineering, DSP, OFDM, Verilog, C, Data Mining, SQL, Machine Learning, Computer Vision, Text Classification, Artificial Intelligence, Clustering, Python, R, Perl, C++, Java, Digital Signal..., Algorithms, Optimization, Matlab, Data Management