Nidhi Parikh Phone: (540)-449-4634

Email: nidhip@vt.edu

Summary

6+ years of combined research and development experience in agent-based modeling, data mining, machine learning, and network science with applications in social computational science and urban computing.

Extensive hands-on experience in working with structured and unstructured data including surveys (e.g., US Census, NHTS), geo data, and social media (e.g., Twitter) to extract quantitative,

meaningful, and actionable information.

Very familiar with databases, software engineering, machine learning, and statistical analysis.

Education

Ph.D. in Computer Science (GPA 3.78) anticipated Dec 2016 Virginia Tech, Blacksburg, VA

Thesis: Augmenting Synthetic Population Activities and Behaviors for Disaster Modeling

Masters in Computer Science (GPA 3.7) May, 2011

Virginia Tech, Blacksburg, VA Thesis: Generating Random Graphs with Tunable Clustering Coefficient

B.E. in Electrical Engineering (73.3%, Top 5%) Jun, 2005 Nirma Institute of Technology, Gujarat University, Ahmedabad, India

Experience

Graduate Research Assistant Network Dynamics and Simulation Science Lab, Virginia Tech Jan 2010 – Present

Research Topics: Data mining, Machine learning, Social media analytics, Behavior modeling,

Social simulation, Simulation analytics, Synthetic populations

Simulation Summarization

Developed methods to summarize large-scale simulation results using causally-relevant states (states that change the probability distribution over the final outcomes significantly) and

compress agent trajectories.

Developed a method to summarize large-scale simulation results by capturing frequent temporal patterns of agent state trajectories.

Behavior Modeling

Modeled human behavior and its dependencies with infrastructural systems (based on decentralized semi-Markov decision process with communication) in the aftermath of a large

scale disaster in a major urban area.

It simulates human behavior such as family reconstitution, sheltering, evacuation based on the individual circumstances like location, health status, knowledge about the status of other family members, etc. and used for planning the response to the disaster.

Learning Activity Patterns from Twitter

Coupled hidden Markov model, consisting of activity and burstiness channels, was used to learn daily activity (work, school, shop, etc) schedules using data from Twitter.

Relevant tweets were extracted by classification and then topic modeling was used to get a discrete set of observations.

Tweets from Foursquare were filtered separately and the venue search APIs were used to identify the categories of tweet locations.

Modeling the Effect of Transient Population on Epidemics

Extended synthetic population model of Washington DC metro area to include leisure and business travelers by combining data from the Destination DC, the Smithsonian Institution

and other geo-spatial data sources.

Evaluated the effects of transients on epidemics and compared various intervention strategies like closing museums, promoting healthy behavior, and vaccination using an agent based

simulation software EpiSimdemics.

Updating Synthetic Population for US with ACS 2009 data

Updated synthetic population for the United States with the latest data from the American

Community Survey (ACS).

Synthetic population is created by combining data from the US Census/ACS, activity survey and other geo-spatial data using statistical methods (e.g., IPF, CART, gravity model) to model

households and individuals within these households. Each individual is represented by

demographics and activity related information (including activity location) which is then used

to create a social contact network.

Data Scientist - Intern E8 Security Inc., Palo Alto, USA. May 2014 – Aug 2014

Designed and developed stochastic simulations of user access patterns using ensemble and global checkpoint models for risk modeling.

Graduate Teaching Assistant Department of Computer Science, Virginia Tech. Jan 2011 – Dec 2011

Graded assignments and projects, and advised students during the office hours for “Data structures and Algorithms”, “Comparative Languages”, and “Software Design and Data

Structures” courses.

Supervised labs for “Software Design and Data Structures” course.

Software Engineer Infosys Technologies Limited, India. Nov 2005 – Apr 2008

Identified defects in the code and thus provided suitable workaround for the frontend application users.

Extracted information from multiple data sources.

Class Projects Keyword Trend Analysis using Twitter

Designed and developed a web-based visualization tool for time-series analysis of Twitter data based on user selected keyword.

It allows generic (overall weekly trend by hour and monthly trend by date) to specific (weekly trend by hour for a selected week) time-series analysis.

User can also select a specific date to see tweets for the specified keyword.

QBank -- A Parameterized Question Bank

Designed and developed a web-based question authoring tool with a user friendly interface for generating dynamic questions.

It reduces the time required to develop multiple set of questions by supporting parameterization and random value generation at run time.

Questions are stored in a generic format which could be converted to any other question formats. Also, developed a converter for the format supported by Khan Academy.

Continuous Hidden Process Model for rack sensor relationship mining

Implemented Continuous Hidden Process Model (CHPM) to mine relationships between rack sensor temperature levels and various hidden processes and estimate number of processes and their

activity levels over a time series.

Crime Report Search Engine

Designed and developed a domain specific search engine to search local crime related information in Washington DC metro area and implemented using Java based open source softwares, Nutch

and Solr.

Extended Nutch parser to accommodate date-range, crime type and geo-spatial (city, zip code and radius) based search criteria in addition to general keyword based search.

Journal Papers

1. N. Parikh, H. Hayatnagarkar, R. Backman, M. V. Marathe, and S. Swarup, „A comparison of

multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation‟, The Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS) special issue on Autonomous Agents for Agent-Based Modeling, doi:10.1007/s10458-016-9331-y.

2. N. Parikh, M. Youssef, S. Swarup, and S. Eubank, „Modeling the effect of transient populations on epidemics in Washington DC‟, Scientific Reports 3, article number 3152, Nov 2013.

3. L. S. Heath and N. Parikh, „Generating random graphs with tunable clustering coefficient‟, Physica A 390, pp 4577-4587, Nov 2011.

Book Chapters 4. M. V. Marathe, H. S. Mortveit, N. Parikh, and S. Swarup, „Prescriptive Analysis Using Synthetic

Information‟, In William H. Hsu (Ed), Emerging Trends in Predictive Analysis: Risk Management and Decision Making, IGI Global, Jan 2014.

Refereed Conference and Workshop Papers 5. N. Parikh, M. Marathe, S. Swarup, „Summarizing Simulation Results using Causally-relevant

States‟, The 17th International Workshop on Multi-Agent-Based Simulation (MABS 2016) held in conjunction with the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016). Selected as the Visionary Paper

6. N. Parikh, M. Marathe, S. Swarup, „Integrating Behavior and Micro-simulation Models‟, The 1st Workshop on Agent Based Modelling of Urban Systems (ABMUS 2016) held in conjunction with the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016).

7. N. Parikh, M. Youssef, S. Swarup, S. Eubank, and Y. Chungbaek, „Cover Your Cough! Quantifying the Benefits of a Localised Healthy Behavior Intevention on Flu Epidemics in Washington DC‟, International Social Computing, Behavioral-Cultural Modeling and Prediction (SBP) Conference, Apr

2014. Winner of Best Student Paper Award

8. C. Barrett, K. Bisset, S. Chandan, J. Chen, U. Chungbaek, S. Eubank, Y. Evrenosoglu, B. Lewis, K.

Lum, A. Marathe, M. Marathe, H. Mortveit, N. Parikh, A. Phadke, J. Reed, C. Rivers, S. Saha, P.

Stretz, S. Swarup, J. Thorp, A. Vullikanti, and D. Xie, „Planning and Response in the Aftermath of a Large Crisis: An Agent-based Informatics Framework‟, The Winter Simulation Conference (WSC),

Dec 2013.

9. N. Parikh, S. Swarup, P. Stretz, C. Rivers, B. Lewis, M. Marathe, S. Eubank, C. Barret, K. Lum,

and Y. Chungbaek, „Modeling Human Behavior in the Aftermath of a Hypothetical Improvised Nuclear Detonation‟, The twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.

10. N. Parikh, „Towards “Live” Synthetic Populations for Large-scale Realistic Multiagent Simulations‟, In Doctoral Consortium at the Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Saint Paul, MN, USA, May 2013.

11. N. Parikh, S Shirole, and S. Swarup, „Modeling the Effects of Transient Populations on Epidemics,‟ The AAAI Fall Symposium on Social Networks and Social Contagion, Nov 2012.

Technical Skills

Languages: C, Java, Python, SQL, Shell scripting

Math/stat. softwares: R, Mathematica

Databases: Oracle, Sybase, Postgres Machine learning tools: Weka, scikit-learn, Stanford Classifier and Topic Modeling Tools, tensor

toolbox, nway toolbox, javaPlex

Network analysis tools: Twitter4j, Foursquare API, NetworkX, GaLib, Gephi

Big data tools: Apache Spark Front-end: PHP, JavaScript, D3.js

Awards and Honors

Selected as the visionary paper at the International Workshop on Multi-Agent-Based Simulation (MABS) 2016.

Best student paper award at Social Computing, Behavioral-Cultural Modeling and Prediction (SBP) 2014.

Travel award from Social Computing, Behavioral-Cultural Modeling and Prediction (SBP) 2014.

Travel award from Autonomous Agents and Multiagent Systems (AAMAS) 2013.