Hi, I'm Syed Zami-Ul-Haque Navid

A graduate student passionate about solving real-world problems

About

I completed my BSc in Computer Science and Engineering from Bangladesh University of Engineering and Technology. Since my graduation in February 2021, I had been working at Enosis Solutions as a Software Engineer. I worked there until June 2022.

Between January 2023 and July 2023, I was an SDET at Populate, a US-based health-tech startup.

I joined as a PhD student at Arizona State University in August 2023. Besides conducting research in Privacy of EdTech apps, I have been a GTA at the School of Computing and Augmented Intelligence.

I have undertaken several research projects during my undergrad (and shortly after as well), mostly involving Artificial Intelligence. As I have stated earlier, lately I have ventured into privacy and security of mobile applications.


Skillset

  • Languages: C#, Python, C/C++, Java, VB.NET, working knowledge in R
  • Scripting Languages: Bash, JavaScript
  • Databases: MSSQL, Oracle SQL
  • App Analysis Tools: Objection, Frida, MITMProxy, adb
  • Machine Learning Frameworks and Libraries: PyTorch, Scikit-Learn, Numpy, Pandas, HuggingFace, OpenCV, OpenPose
  • Development Frameworks: .NET, Angular, NodeJS
  • Network Simulator: Cisco Packet Tracer, Wireshark, NS2
  • Microcontroller Programming: Atmel Studio
  • Ontology Tool: Protege
  • Version Control: git, SourceTree, GitKraken
  • Task Tracking: Jira, Confluence


I am also well versed in both written and verbal communication; be it with the team members or the clients. I am an active participant in idea generation sessions.

Experience

Graduate Teaching Assistant
  • I have taught CSE 110, An Introduction to Java Programming
August 2023 - Present
Software Development Engineer in Test
  • Performed Manual testing of both Development and Production environment
  • Implemented Unit Testing
  • Developed SDET handbook
  • Technology: .NET
January 2023 - July 2023
Software Engineer
  • Developed features for a California-based business management system
  • Implemented functionalities on both front-end and back-end
  • Wrote performant backend code following onion architecture
  • Optimized Stored Procedures
  • Designed and developed reports employing SSRS
  • Technology: .NET, MSSQL, Angular, SSRS, SSDT, VB.NET
March 2021 - February 2022 | Software Engineer Level 1
March 2022 - June 2022 | Software Engineer Level 2

Research

EdTech App Analysis
EdTech App Analysis

Investigating privacy breaching behaviors of EdTech apps

Accomplishments
  • We want to find out if EdTech applications breach user privacy
  • We have been analyzing network traffic
  • We have also been collecting the system logs
  • We want to learn if apps transmit more user data than they need

Malicious Code Detection
Static Detection of Malicious Code in Programs Using Semantic Techniques

Malicious Code Detection Using Semantic Techniques

Accomplishments
  • Our study concentrates on unraveling the traits of malware written in Java programming language
  • We have studied the source code of several malware and identified their characteristics
  • Then we expressed the aforementioned characteristics of malicious source code through Code-Ontology

Fake News Detection
Are You Misinformed?

Fake News Detection from Bengali Facebook Posts about Covid-19

Accomplishments
  • We created a dataset containing Bengali Facebook posts regarding Covid-19 using CrowdTangle
  • We trained Transformer based models on our dataset
  • Having identified the best model, we applied that model on one year's worth (March, 2020 - June, 2021) of Facebook posts
  • Presented analysis about the types and prevalence of Fake posts
  • This paper was submitted to CHI 2022

Violence Detection
Violence Detection from Surveillance Videos

Anomaly Detection from Videos

Accomplishments
  • We extracted two kinds of deep feature represtation from surveillance videos using I3D and OpenPose
  • We proposed heirarchical Multiple Instance Learning to perform detection
  • We emphasized on interpretability as well as accuracy of our system
  • We used CRCV dataset for our purpose. We also intended to use their model for baseline comparison

Warning Classification
Warning Classification Raised by Static Analysis Tools

Classification of Warnings Based on Source Code Features

Accomplishments
  • Our intension was to classify the warnings that static analysis tools provide after inspecting source code
  • For features, we utilized several engineered properties of source code
  • We applied state-of-the-art tree classifiers such as XGBoost and LightGBM
  • Moreover, we used Decision Tree, SVM, Linear Regression and LSTM-based classifiers

Projects

Vasha Sikkha
Vasha Sikkha

An immersive game for the users to learn English language

Accomplishments
  • A user learns about English language while playing games
  • This mobile application was built on top of the Flutter framework
  • For backend, Laravel framework was used
  • Technology: Flutter, SQLlite, Laravel

TCP Session Hijacking
TCP Session Hijacking

Script for Session Hijacking

Accomplishments
  • Python script for launching TCP session hijacking attack on the Ethernet in a lab environment

TCP Session Hijacking
PII Redaction from Classroom Audio

An AI-based project that removes PII

Accomplishments
  • Given an audio as input the pipeline generates transcript with Conformer-2 model
  • Then BERT-base-NER model identifies PIIs from the transcript
  • PIIs are then replaced with dummy data
  • Finally, the altered transcript is converted back to audio

Tour Planner
Tour Planner

An Itinerary Management App

Accomplishments
  • A webapp that helps tourists plan their itineraries
  • This app would help tourists in choosing accommodation options as well as transports
  • Every suggestion will be made keeping the tourist's budget in consideration
  • This app was built with Java Server Faces (JSF) and Oracle RDMS
  • Technology: JSF, Oracle RDMS
Spotify Music Recommender
Spotify Music Recommender

Clustering of similar music based on Collaborative filtering

Accomplishments
  • Given a spotify playlist, this system will recommend songs based on the perceived taste
  • Similar songs were found by using K-Means algorithm and Eucledian distance
  • The resultant clusters were illustrated using tSNE algorithm
  • Moreover, the efficacy of the K-Means algorithm was determined with Silhouette method

Spotify Music Recommender
Conversational AI

Conversational AI trained on Facebook conversations

Accomplishments
  • To make a conversational bot, the OpenAI's GPT model was trained on a dataset of conversations
  • The said dataset consists of Banglish conversations from Facebook Messenger
  • Banglish is simply Bengali written with English Alphabet
  • It was an experiment to see if the insights gained from pre-training on English can be transferred to Banglish

Education

Bangladesh University of Engineering and Technology

Dhaka, Bangladesh

Degree: Bachelor of Science in Computer Science and Engineering

    Noteworthy Courseworks:

    • Operating Systems
    • Computer Security
    • High Performance Database Systems
    • Computer Architecture
    • Simulation and Modeling
    • Fault Tolerant Systems
    • Microcontroller and Microprocessors

Arizona State University

Tempe, Arizona

Degree: PhD in Computer Science
(Ongoing)

    Noteworthy Courseworks:

    • Advanced Data and Information Privacy

    Research:

    • EdTech App Analysis

Extra-curricular Certificates

Interests

  • I am a beginner ukulele player. I suppose the guitar was way too big for me! So I wanted to start small. Literally!
  • I would like people to believe that I can sing too! Watch me destroy two popular songs!
  • I am also trying to learn how to play chess. It does seem daunting. Wish me luck!
  • I hope to travel more

Contact