Abhishek Chandramouli Sharma
(+1) 858-319-5518
La Jolla, California
Education
MS Computer Science and Engineering, September 2021 - Present
University of California, San Diego
B.Tech Computer Science and Engineering, August 2014 - May 2018
PES University, Bangalore
Work Experience
Software Engineer, July 2018 - July 2021
Citrix R&D India, Bangalore
- Worked in the Citrix Gateway Services (CGS) team.
- Played a part in the design, development, testing and deployment of micro-services in the data and control path that are part of a global multi-region deployment across Azure and AWS.
- Major contributor to Citrix Cloud Connector which acts as the single entry point to the customer data-center for proxying VPN, Authentication, ICA traffic between the cloud and servers in the customer data-center.
- Sole developer of a performance/stress testing tool that is used to validate performance of multiple key services of CGS.
- Involved in the designing and development of Citrix Adaptive Authentication Service which provides REST APIs that allows customers to spawn managed single tenant Citrix Gateway instances using Terraform on demand.
Technologies Used: Go, Docker, Kubernetes, Terraform, Azure, MongoDB
Software Engineering Intern, January 2018 - June 2018
Citrix R&D India, Bangalore
- Worked in the Citrix Gateway (NetScaler) SSLVPN Portal team.
- Implemented server-side and client-side customer bug fixes and feature enhancements related to multi-factor authentication flows involving LDAP, RADIUS, SAML, EPA factors.
- Involved in interactions with customers and escalation engineers to help triage issues in a timely manner.
Technologies Used: C, JavaScript, CSS
Software Engineering Intern, June 2017 - July 2017
Pulse Secure, Bangalore
- Worked in the Pulse Cloud Secure (PCS) development team.
- Created a SaaS Platform agnostic web dashboard to summarize of users' activity within a tenant.
Technologies Used: Python, JavaScript, HTML, CSS
Projects
Barebones PaaS Implementation, Jan 2017 - Apr 2017
Cloud Computing Course Project
- Built a basic Platform as a Service on top of Openstack as the underlying Infrastructure provider.
- Enabled users to deploy NodeJS applications and multi-tenant MongoDB database on VMs that were dynamically spawned.
Technologies Used: OpenStack, NodeJS, MongoDB
Projects
Compiler Frontend for a Subset of the C Language, Feb 2017 - Apr 2017
Compiler design project where the stages from lexing through to intermediate code generation were implemented from scratch. The compiler used a hand coded recursive decent parser and syntax directed translation to generate a three address intermediate code.
Technologies Used: C++
Implementation and Defenses against Rootkits, Feb 2017 - Apr 2017
Project to study and experiment with the implementation and operation of modern Rootkits targeted at the Linux Operating System. Involved working with low level APIs and techniques available to Linux Kernel Modules and used by rootkits to mask their existence.
Technologies Used: C
Barebones PaaS Implementation, Feb 2017 - Apr 2017
Cloud Computing project to create Platform as a Service abstraction over Openstack. The final application allowed multiple users to deploy simple NodeJS applications on VMs that were dynamically created on demand. The PaaS also provided the functionality to use a multi tenant MongoDB Database as a Service.
Technologies Used: Node.js, Openstack
Research
GitHub Repository Domain Prediction, Sept 2016 - Nov 2016
Data Analytics project to use unsupervised learning techniques on the recently release BigQuery GitHub repository data to identify repositories belonging to similar domains. The work done included an exposition of the relative effectiveness of various similarity measures for repositories to aid in the clustering. A similar feature was later independently developed and released by GitHub.
Technologies Used: R, SQL
Data Centric Malware Detection in Android , Jan 2018 - May 2018
Final semester research project to explore a data centric approach to static and dynamic feature extraction from Android applications to aid in malware detection. The result was a comparison of the performance of malware classifiers based on the type of features and the classification algorithm used.
Technologies Used: Python, NodeJS, Android, Keras
