A PhD holder, Machine Learning Researcher & Scientist, and I'm building smart solutions and sharing insights.
With a strong foundation in data science and a drive for continuous learning, I believe that technology should empower both its creators and users.
Sklearn
1 Years
MLFlow
1 Years
Software Engineering
12 Years
RecSys
5 Years
Python
5 Years
Spark
1 Years
Machine Learning
5 Years
ETL
5 Years
TensorFlow
5 Year
NumPy
5 Years
Pandas
5 Years
Pytorch
2 Years
Databricks
1 Year
Deep Learning
5 Years
Docker
2 Years
Dive into my collection of research contributions in the fields of machine learning, deep learning, recommendation systems, fairness, and Explainable AI. Each publication explores key advancements and insights aimed at pushing the boundaries of AI and ensuring transparency and equity in intelligent systems.
Recommender systems face challenges in ensuring fairness, and this work introduces Adding-based Counterfactual Fairness Reasoning (ACFR) to address fairness issues by preserving user-item relational structures, showing significant improvements over traditional methods in benchmark tests.
Ranking algorithms in recommender systems often prioritize utility, leading to fairness issues. To address this, we propose FairGAN, a GAN-based approach that dynamically generates fairness signals, balancing exposure fairness with user utility without treating unobserved interactions as negative.
Factorization models are vital for CF, but intermediate data from their training is often overlooked. We introduce Convergence Pattern to enhance prediction reliability and recommendation quality, showing effectiveness in benchmark experiments.
This paper explores using Quantum Annealers for feature selection in recommendation algorithms, framing it as a QUBO problem. By integrating Counterfactual Analysis, we enhance the performance of the item-based KNN algorithm, with experiments showing promising results.
We propose CAQUBO for feature selection in recommender systems, leveraging counterfactual analysis with quantum annealing to optimize feature combinations and improve recommendation performance, outperforming existing quantum methods.
Over the past 12+ years, I've worked on a lot of cool projects with great people.
Dabble
Machine Learning Engineer
March 2024 ~ Present
Building and fine-tuning machine learning models for sports betting, managing ETL pipelines, experimenting with algorithms, evaluating models, troubleshooting issues, and documenting processes using tools like Databricks and Spark.
RMIT University
Machine Learning Researcher
April 2023 ~ Present
Assisted in research projects, mentored 3 PhD students, managed project timelines, conducted data analysis using Python and TensorFlow, provided administrative support, presented findings, and contributed to problem-solving.
The Little Office
Lead Full-Stack Engineer
May 2021 ~ March 2024
Led full-stack development of a job platform using React, PostgreSQL, and GraphQL. Managed database design, cross-team collaboration, API integration, performance optimization, security, and ongoing maintenance. Mentored junior developers throughout the project.
Loyalty Corp
Full-Stack Engineer
November 2018 ~ January 2019
Researching, testing, and implementing loyalty system features, refactoring functionalities, designing and restructuring databases, and maintaining deployment pipelines.
Qihoo 360
Software Engineer
July 2015 ~ March 2017
Assisted in research projects, mentored 3 PhD students, managed project timelines, conducted data analysis using Python and TensorFlow, provided administrative support, presented findings, and contributed to problem-solving.