Prachi Jhanwar

University of Southern California

Master of Science - MS, Computer Science

January 1, 2017 – January 1, 2019

SYMBIOSIS INTERNATIONAL UNIVERSITY

Bachelor of Technology - BTech, Computer Science

January 1, 2011 – January 1, 2015

Microsoft

Engineering Manager

January 1, 2021 – Present

San Francisco Bay Area

Amazon Web Services (AWS)

Software Development Engineer

July 1, 2019 – January 1, 2021

Greater Seattle Area · On-site

Symantec

Associate Threat Analysis Engineer

June 1, 2015 – July 1, 2017

Pune/Pimpri-Chinchwad Area

Amazon

Software Development Internship

January 1, 2014 – July 1, 2014

Hyderabad , India

AI with Game Theory for Public Security and Safety

November 1, 2017 – April 1, 2018

This project is a development of an AI game to test how humans respond to deception. A scenario is setup in which the user plays an inside attacker at a company and needs to access different computers to get points. Before accessing each computer, the user has a chance to check it for a "Monitored" alert. This alert states that a security analyst is monitoring who accesses the computer, but it might not always be true. That is, the analysts may send the message even if they aren't actually monitoring the computer, and this deception is based on underlying optimal probabilities from a machine learning algorithm developed by CAIS group. Technologies used are Machine Learning, Data Mining, JavaScript, HTML, CSS, JAVA.

CNF Resolution

November 1, 2017 – December 1, 2017

Resolution is a method of inference leading to a refutation theorem-proving technique for sentences in propositional logic and first-order logic(FOL). Iteratively applying the resolution rule along with unification in a suitable way allows for inferencing whether a CNF statement is satisfiable or unsatisfiable. Attempting to prove a satisfiable FOL statement as unsatisfiable may result in a nonterminating computation. Resolution works on statements in Conjunctive Normal Form(CNF). Resolution is a complete and sound inference procedure because it works on CNF which is universal. Implemented inference Engine for Resolution, a proof by contradiction for Conjunctive Normal Form in pure python.

The Fruit Rage - an AI Game

October 1, 2017 – November 1, 2017

Implemented time constrained AI game using minimax algorithm with alpha beta pruning in pure python. Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player games. It stops completely evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move.

N Queens with Obstacles

August 1, 2017 – September 1, 2017

The code is an implementation of traditional N-Queens problem with obstacles using 3 algorithms namely Depth First Search, Breadth First Search & Simulated Annealing. If an obstacle is present in a row of the board, the queen can be placed at either side of the obstacle without attacking each other. The code is a pure python implementation of basic search algorithms in Artificial Intelligence.

A new technique for detecting and blocking the installation of malicious software based on the reputation of the loadpoint n-grams

April 1, 2017 – June 1, 2017

Deploying a loadpoint entry is an integral part of installation for every malicious payload. It enables the payload to launch and execute every time the system boots. However, the loadpoint entries are not used as standalone detection entities. Instead, they are only cleaned up by anti-virus software, if the associated files are detected, either in a static scan or based on their behaviour. By identifying unique loadpoint trigrams from an internal telemetry collected over a predefined period and studying their associations with Ground Truth Good and Bad files, low confidence Good and Bad files and Unknown files, as well as honouring their prevalence and age, we were able to successfully validate the idea. Even in its most restricted form, based on the confidence for the disposition for the trigram, the technology is successfully used to either block an attack, prompt the user, or silently submit files and associated telemetry for backend validations. This research work is published in Virus Bulletin Conference 2017.

Implementation of Automated, Elastic resource provisioning for NoSQL clusters using Tiramola Framework

March 1, 2015 – Present

TIRAMOLA is a cloud-enabled, open-source framework to perform automatic resizing of NoSQL clusters according to user-defined policies. Decisions on adding or removing worker VMs from a cluster are modeled as a Markov Decision Process and taken in real-time. The system automatically decides on the most advantageous cluster size according to user-defined policies, it then proceeds on requesting/releasing VM resources from the provider and orchestrating them inside a NoSQL cluster. As a part of this project deployed private cloud at home network using eucalyptus 3.5 on CentOS 6.0. Installed multi-node Hadoop and HBase cluster over it. Also installed Ganglia for monitoring. TPCC benchmark was set for all the transactions. Neo load was used for load generation and distribution. Implemented Markov's Decision Process to decide at run time whether to add the VM, remove the VM or no change depending upon the load. Technologies used are JAVA, Cloud services, Hadoop, HBase, Machine learning algorithms.