Vincent Tran

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Introduction

Vincent Tran is a valued member of Accenture Inc., serving as a Cloud Native Architect. Before joining the Accenture CIO Architecture team, he gained extensive experience working on various projects spanning Network, Security, and Machine Learning. Furthermore, Vincent has made significant contributions to the field, publishing multiple research papers in IEEE and presenting his findings at global conferences. Within Accenture, Vincent's role encompasses a wide range of responsibilities, including analyzing, designing, implementing, testing, and certifying multiple products. These products involve cutting-edge technologies such as Machine Learning, Generative AI, and Migration using automation in conjunction with DevOpsSec practices to effectively meet the dynamic needs of our customers.

Education

  • Degree: B.S Computer Science, and Mathematics
  • City: Austin, Texas, USA
  • Graduated: August 2020
  • Email: vincent.cs.icn@gmail.com

Techical Skills

  • Cloud Provider: GCP, Azure, AWS, Vsphere, NSXT
  • DevOps: Azure DevSecOps
  • Programming Languages: Java, Terraform, Python, TypeScript, C#, BASH, Powershell, Matlab, R, MSSQL., C++
  • Systems: Linux (Debian, Kali), Red Hat Enterprise Linux, Window, Mac.
  • Web Development: HTML, CSS, JavaScript.
  • Enviroment Software: Oracle Virtual Box, VMWare.
  • Microsoft Office: Power Point, Word, Excel, Access, Publisher, Project, Visio.
  • Framework: ASP.NET, Java Spring Boot.
  • Cloud Native: GKE, AKE, EKS, Docker Container, kubernetes.

Resume

Professional Experiences

Cloud Native Architecture

Accenture, CIO Office
  • Collect the requirements from customer or client.
  • Design blueprint based on the customer requirements of cloud service products in GCP, Azure, AWS.
  • Develop IaC codified modules based on standards and templates in Terraform and in Azure DevOps.
  • Implement the automation process for customer to use the cloud products.
  • Enforce security controls into the codified module and remediate security vulnerabilities.
  • Approve the enhancements from IaC Developer whether the product enhancements meet the customer requirements.
  • Publish IaC modules to code repos and conduct quality assurance reviews.
  • Decide whether the product is released for customer uses

Cloud Infrastructure Engineer

Accenture, Austin TX
  • Developing consumable IaC cloud products (GCP and Azure) and services based on technical requirements from customer delivery managers to ensure a satisfying customer experience.
  • Create architecture blueprint deliverables and design Infrastructure as code modules based on blueprint.
  • Develop IaC codified modules based on standards and templates in Terraform and in Azure DevOps.
  • Enforce security controls into the codified module and remediate security vulnerabilities.
  • Publish IaC modules to code repos and conduct quality assurance reviews.
  • Provide incident support and resolution of IaC codified modules.
  • Build IaC module enhancements based on customer delivery managers.

Professional I

El Camino College, Torrance, CA
  • Supervise the Computer Science and Math Study Center room where students need helps in Computer Science and Mathematics.
  • Oversee peer computer science and math tutors when they help students on projects, or challenge questions.
  • Explain and guide students on Computer Science projects in multiple programming languages such as C++, Java, Python.
  • Tutor many math levels from college algebra to Calculus, differential Equation, and Linear Algebra.

Research Assistant (October 2017 - January 2021)

CSU Dominguez Hills Foundation
  • Desisgned the algorithm to reduce the total cost of virtual machine migration in datacenter.
  • Implemented the simulation to evaluate the effectiveness of our algorithm when the virtual machines were migrated in the data center.

Software Development Internship (May 2019 - August 2019)

TechMahindra Inc., Los Angeles, CA
  • Used the data fusion technique to build a recommendation application when the customers click on specific product.
  • Cleaned up the data and made the cross references between multiple different datasets.
  • Used the decision tree to make the recommendation with small datasets. Python were used to implemente all applications.

Courses Taken

Computer Science

  • Computer Networking, Advance Computer Network, Design of Operating System, Advance Operating Systems, Clould Computing and Clould Networking.
  • Computer System Security, Cryptography, Network Security and Hacking Prevention, Cryptography.
  • Artificial Intelligent, Machine Learning, Finite Automata, Fundamental Database, Database Management using MSSQL, Analysis Algorithm, Data Structure, Probability and Statistics Software Engineering.

Mathematics

  • Discrete Mathematics, Linear Algebra, Differential Equations, Calculus, Complex Analysis, Real Analysis, Graph Theory, Abstract Algebra.

Awards

DH Proud Scholar Award

National Science Foundation, March 2021

IEEE Student Grant Award

IEEE INFOCOM Conference, July, 2020, Toronto, Canada

Applied and Computational Mathematics Award

Mathematics Department at CSUDH, 2020

First Place in Student Research Presentation

California State University Dominguez Hills, April 2019

Presented the Research Paper

International Simulation Society Conference, July 2018, Bordeaux, France

Symposium Undergraduate Research Award, (REU)

Computer Science Department of Texas A & M University Corpus Christi, 2018

Projects

Hotel Management Web Application using ASP.NET Framework with MSSQL

Built website for hotel to manage the customers, staffs, and inventories. In the application, customers can do some performances such as create the account, booking room, and cancel reservation. Staffs can register for a new customer, edit reservations, and update bookings for inquiring customers. I built the front and back end website as well as created and managed the database.

Virtual Network Functions Migration in Dynamic Cloud Data Center using Java

Created the new algorithm to migrate the Network Functions and Virtual Machine in Data Center to reduce the communication and migration cost. In this project, I built the Fat Tree simulation to show our outperformances were better than other proposed algorithms.[document]

Protecting the UAS against Collisions and Cyberattack

Used the probabilistic formulas to showthat by crossing multiple di erence data sets, we were able to identify personal data. In the project, I implemented the simulation as well as computing results for small cases to verify the correctness of the mathematical formula.

Maintaining Anonymity in Online Record (using Java)

We analyzed and penestrated the communication between the drone and controller using tools in Kali linux. In this project, I cracked the password, forced the drone to land in specificc location, and used reverse engineering to extract the software in the controller and proposed the suggestion to fix the issues.

Encryption and Decryption (using Python)

I implemented some basic encryptions and decryptions schema such as substitution cipher, Advanced Encryption Standard, One-Time Pad, Block Cipher, Vigenere Cipher.

Unikernel - ran OSv on KVM and Firecracker

In the project, multiple difference application ran on OSv where KVM and FireCracker were used as hypervisor. I compared the booting time and throughput when we run specific application on the OSv. The FireCracker hypervisor work better performance than KVM hypervisor.

Health Insurance Cross Sale Prediction

Vehicle insurance is one of the most common necessities of car ownership in the USA. Each citizen has at least one car. Hence, predicting the satisfaction of customers is an important role from a business point of view. In this paper, we use three different supervised learning classification techniques in machine learning to predict whether the customers are interested in a company’s Vehicle Insurance Policy. With the given information or features of the customers from the past, we use Naive Bayes Algorithm, Decision Trees, and K-Nearest Neighbors to predict the satisfaction of customers and determine which machine learning method is the most appropriate to use.

LOAD VALUE PREDICTION

In our model, we split the data set by training set and test set. The PC address of load instruction memory, memory address, and value in the training set are used to build the model. (We only fed the Load values in the model) Then we feed the load instruction memory and memory address into the model to get the predicted value. We compare the predicted value and actual value based on the confidence value(use Mean Squared Errors as the threshold) to decide whether or not we should choose the data.If the square of the difference between actual and predicted value is less than the Mean Square Errors, we choose the predicted value, otherwise we don’t. If the predicted value is a bit off, we update the model by changing the model parameters such as epochs and the size of training set, training window. If the prediction is close to actual, we keep our model. Our prediction model based on the recent instances.

Publications

  1. Vincent Tran, Jingsong Sun, Bin Tang, and Deng Pan, "Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers", Proceedings of the 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022).
  2. Vincent Tran, Jean Tourrilhes, K.K. Ramakrishnan, Puneet Sharma, "Accurate Available Bandwdith Measurement with Packet Batching Mitigation for High Speed Networks", LANMAN 2021, Virtual Conference, July 12th, 13th, 2021.
  3. Vincent Tran, Jingsong Sun, Bin Tang, Deng Pan, "Cost-Effective Virtual Network Function Placement and Migration for Dynamic Cloud Data Centers", IEEE ICNP 2021, Virtual Event, November 2-5, 2021. (Processing)
  4. Hugo Flores, Vincent Tran, Bin Tang, "PAM AND PAL: POLICY-AWARE VIRTUAL MACHINE MIGRATION AND PLACEMENT IN DYNAMIC CLOUD DATA", IEEE INFOCOM 2020 Technical Program, Virtual Conference, 27-30, April 2020, [github, paper, API , Powerpoint]
  5. Jesus Nunez, Vincent Tran, Ajay Katangur, "PROTECTING THE UNMANNED AERIAL VEHICLE FROM CYBERATTACKS", SAM19, Las Vegas, Nevada. [paper]
  6. Vinent Tran, Alexander Stanoyevitch, "MAINTAINING ANONYMITY IN PUBLIC DATABASES", SIM18, 13-17 July, 2018, Bordeaux, France