Computer Science Design

Mukesh Nathan

*** * ****** ** ***

Chicago, IL **611

763-***-****

******@**.***.***

mukeshnathan.net

University of Minnesota, Minneapolis, MN

Education

PhD Computer Science, GPA: 3.7/4, Expected Spring 2012

MS Computer Science, GPA: 3.8/4, Human Computer Interaction, Aug 04 -May 07

Bharathiar University, India

MS Computer Technology, GPA: 9.4/10, July 00 - Mar. 02

BS Computer Technology, GPA: 8.8/10, Sep. 97 - June 00

Research: Task Analysis, Diary Studies, Field Studies, Lab Experiments, Usability Testing, Online

Skills

Surveys, User Interviews, Thinkalouds

Design: Persona Design, Scenarios, Low/Hi-Fi Prototyping, Heuristic Evaluation, Cognitive Walk-

through, User Task Flows

Tools: OmniGra e, Illustrator, HTML, CSS, Javascript, Java, Python, ActionScript/Flash, SQL/MySQL,

XML, Apache, Tomcat, Hibernate, CVS, SVN

Selected Research Intern, IBM T. J. Watson Research Center

Experience Summer 10, Hawthorne, NY

Conceived, designed, and implemented a social feature for the meeting recording system of LotusLive.

Feature allowed attendees to create and review shareable notes time-aligned to the video recordings.

Surveyed IBM employees to inform the design of the social feature. Evaluated the feature design using a

lab study (A/B test) with 40 participants.

Research Intern, AT&T Research Labs, Inc.

Summers 07, 08 & 09, Florham Park, NJ

Built a Flash-based web interface for a social television application. Remote TV viewers could interact

with each other, in real-time or o ine modes, using on-screen avatars. Evaluated the live and o ine

viewer interaction modes using a longitudinal eld study. Filed three patents related to the application

interface and novel viewer interaction modes.

Designed a novel method for automatically creating TV show recaps using viewer comments. Evalu-

ated such automated recaps against expert-generated recaps with 100 users from Amazon Mechanical

Turk.

Research Assistant, GroupLens Research Lab, University of Minnesota

Feb 06 - Present, Minneapolis, MN

Conference-related Twitter Activity:

Analyzed the relationship between Tweets made during paper presentations at technical conferences

(e.g. CHI, CSCW) and the citation/download activity of these papers after the conference.

Recommender Systems:

Compared human vs. machine recommendations of movies using a controlled lab study to identify types

of recommendation tasks each method could be better for.

Mobile Applications:

o Designed a location-aware meeting reminder application for Pocket PC mobile devices. Evaluated the

application using a longitudinal diary study.

o Built a companion mobile application for the MovieLens movie recommender website

(http://movielens.com). Conducted user interviews to identify key tasks, explored multiple designs using

paper prototypes and nally evaluated a functional prototype with target users.

// For a portfolio of my work, visit my website at mukeshnathan.net

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