Hi there, I'm H M QUAMRAN HASAN.

A
Inquisitive and self motivated researcher with a sharp focus on solving real-world problems leveraging artificial intelligence and data

About

I am a Master's student in Computer Science at the University of Alberta. With a solid foundation in Computer Science and Business Technology Management from KAIST, I bring a rigorous analytical approach and a proven record of leveraging technology and business insights to solve real-world challenges.

My professional experience as an AI Researcher and Data Scientist have sharpened my skills in developing impactful applications, demonstrating my commitment to excellence and a passion for making a tangible difference through technology. Apart from that I also research on the application of Large Language Models in the fields of explainable medical and legal AI, and I am also interested in their performance and evaluation when it comes to low-resource languages.

I am Looking for professional/reseach opportunities that will allow me to leverage my skills in AI and Data Science to solve complex yet interesting real-world problems enabling my work to impact millions of people.

Education

UofA Logo

University of Alberta

Alberta, Canada.

Degree: MSc. Computer Science.

    Relevant Courseworks:

    • Introduction to Natural Language Processing.
    • Foundations of Trustworthy Machine Learning.

KAIST Logo

Korea Advanced Institute of Science and Technology

Daejeon, South Korea.

Degree: BSc. Computer Science, Double Major in Business Technology Management, Semi Minor in Artificial Intelligence.

    Relevant Courseworks:

    • Introduction to Natural Language Processing
    • Introduction to Data Science
    • Data Science Methodology
    • Introduction to Artificial Intelligence
    • Data Structures
    • Introduction to Algorithms
    • Probability and Statistics
    • Principles of Marketing
    • Marketing Research

Experience

Research Assistant
  • Working on the integration of large language models in the fields of explainable medical and legal AI.
  • Supervised by Dr. Randy Goebel and Dr. Mi-Young Kim
  • Tools: Python, PyTorch, Tensorflow, NumPy
Sep 2024 - Present | Edmonton, Canada
Artificial Intelligence Researcher
  • Developed and deployed a novel metric and a model with a precision of 0.83 for cell structure detection for qualitative and quantitative evaluation of NASH.
  • Achieved an FID score of 12.97 and precision of 0.77 after developing a GAN model to generate lung tissue Whole Slide Images.
  • Tools: Python, OpenCV, PyTorch, Tensorflow, NumPy
Feb 2024 - June 2024 | Daejeon, South Korea
Research Intern
  • Contributed to the DB4DL project, focusing on enhancing distributed training efficiency for Transformers.
  • Conducted research under the Undergraduate Research Program on models such as BERT and T5 to identify optimal parallelism strategies.
  • Tools: Python, Numpy, Pytorch
July 2023 - Dec 2023 | Daejeon, South Korea
Research Intern
  • Enhanced ChatGPT's accuracy for reasoning tasks in Bengali by 20% through back translation and CoT reasoning.
  • Established the BEnQA English-Bengali parallel corpus, comprising 5,161 science questions from the Bangladesh national curriculum grade school exams, including many that involve multi-step reasoning.
  • Proposed a prompting strategy that consistently improves performance in Bengali.
  • Tools: Python, Seaborn, Transformers
Mar 2023 - Dec 2023 | Daejeon, South Korea
Data Scientist
  • Led the UX/UI team to optimize service UI based on user behavior analysis using Hotjar and Google Analytics.
  • Designed and implemented a streamlined data collection strategy using Google Tag Manager.
  • Achieved a 90% reduction in data collection time by automating POS data collection.
  • Improved sales by developing a recommendation system from the ground up.
  • Tools: Python, Pandas, Numpy, Scikit-Learn, Seaborn, Selenium, Hotjar, Google Analytics, Google Tag Manager, Google Big Query, Tableau, MySQL
Nov 2022 - Feb 2023 | Seoul, South Korea
Data Analyst
  • Achieved a 150% increase in users by identifying potential markets and improving market-wise user experience.
  • Achieved an 80% increase in user engagement, by leading the content team based on user behavior analysis.
  • Led data collection, storage, and extraction for insights.
  • Guided data-driven go-to-market and artist selection through user location and behavior analysis.
  • Tools: Python, Pandas, Numpy, Scikit-Learn, Seaborn, Google Analytics, Google Tag Manager, Google Big Query, Tableau, MySQL
Jan 2022 - Aug 2022 | Seoul, South Korea

Skills

Programming Languages

Python
SQL
C
Java

Frameworks & Tools

NumPy
Pandas
Scikit-Learn
Seaborn
TensorFlow
PyTorch
Keras
OpenCV
Git
Django ORM
Selenium
Flask
Figma
Hotjar
Redash

Cloud Computing & Data Management

AWS
Google BigQuery
Google Analytics
Google Tag Manager
Tableau
MySQL
PostgreSQL
IBM SPSS Statistics
SAP Analytics Cloud

Publications

Projects

Trashinator
Trashinator

A minimalistic and affordable AI-based trash sorter based on SAP Technologies.

Accomplishments
  • Tools: SAP Analytics Cloud, Arduino, AWS, SAP Hana Database, SAP AppGyver
  • I led the data collection and analysis using SAP services like Hana Cloud and SAP Analytics Cloud.
  • Demoed the project to the senior executives of SAP Korea.
  • My team ranked 1st out of 5 teams.
quiz app
Empathetic Dialog Generation

Reproduction of the paper CEM: Commonsense‑aware Empathetic Response Generation

Accomplishments
  • Tools: Python, Transformers, Deep learning
  • Improved the benchmark perplexity results by conducting successful ablation studies
  • Ranked 1st out of 52 teams
quiz app
QnA Bot

An AI that utilized Retrieval Augmented Generation for QnA on a specific PDF

Accomplishments
  • Tools: Python, Transformers
  • Utilized Retrieval Augmented Generation for QnA on a specific PDF
ZEPademics
ZEPademics

Redefining online learning using ZEP metaverse. Project for JunctionAsia 2022.

Accomplishments
  • Tools: ZEPscript
  • Earned 4th place (honorable mention) out of over 100 participating teams.
  • Participated in the Naver ZEP track, earning the most curious project award.
fake import declarations
Detecting Fake Import Declarations

Using Machine Learning techniques to identify fake import declarations.

Accomplishments
  • Tools: Python, Scikit-Learn, Pandas, Machine Learning
  • Achieved a 91.12% accuracy in detecting fake import declarations
  • Earned 10th position out of 82 participants.

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