Asif Ahmed Neloy is a Faculty Member at the Department of Computing Studies and Information Systems (CSIS) at Douglas College in New Westminster, British Columbia. He also holds an adjunct Faculty position at the University of British Columbia (UBC), Faculty of Land and Food Systems, as well as in the Department of Computing and Academic Studies at the British Columbia Institute of Technology (BCIT). Currently, he is teaching courses on Advanced Databases, System Analysis and Design, Data Analytics, and Fundamental Machine Learning. Aside from teaching, he is actively pursuing theoretical and applied research related to Probabilistic and Bayesian Modeling, Anomaly Detection, Dimension Reduction, and interdisciplinary applications of Auto-Encoders. Previously, he taught undergraduate and graduate courses at Vancouver Island University, University of Manitoba, and North South University.
He obtained his MSc in Computer Science from University of Manitoba, supervised by Dr. Maxime Turgeon and Dr. Cüneyt Akçora, focusing on Dimension Reduction and Anomaly Detection using Unsupervised Machine Learning. Along with Unsupervised settings, he has researched various Data Analytics methods, including Feature Extraction, Two-staged Modeling approaches, and Statistical Modeling under Dimension Reduction Lab and NSERC CREATE fund on The Visual and Automated Disease Analytics (VADA) Graduate Training program. Prior to that, he worked with Dr. Shahnewaz Siddique on interdisciplinary research topics, including Robotics, Recommender Systems, Health Informatics, and Computer Vision.
His research interests lie in the intersection of Supervised and Unsupervised Machine Learning, with a specific focus on Probabilistic and Bayesian Modeling, Anomaly Detection, and Dimension Reduction. Currently, he is exploring the intricacies of Auto-Encoders and their applications in Variational and Gaussian modeling. His work delves into the statistical interpretation and visualization of Unsupervised Machine Learning algorithms, emphasizing dimension reduction and anomaly detection. Additionally, he contributes to Data Engineering by developing interactive Python packages for tasks such as Data Cleaning, Visualization, Model Interpretation, Data Scaler Selection, and Statistical Analysis. Explore some of his Python packages on PyPI.
- [November 2024] My paper titled "Disentangled Conditional Variational Autoencoder for Unsupervised Anomaly Detection" has been accepted at the IEEE Big Data Conference (IEEE BigData 2024) in Washington, D.C., taking place from December 15-18, 2024.
- [July 2024] My paper titled "A Comprehensive Study of Auto-Encoders for Anomaly Detection: Efficiency and Trade-offs," published in the Machine Learning with Applications Journal, DOI: https://doi.org/10.1016/j.mlwa.2024.100572
- [June 2024] Received Research Dissemination Present and Research Dissemination Publish Grant from Douglas College
- [December 2023] Joined Douglas College, New Westminster Campus as a Teaching Professor.
- [August 2023] Started my new journey as a Faculty Member, at the Vancouver Island University.
- [May 2023] Promoted to Senior ML Engineer, Forum Inc
- [February 2023] Lastest Published Conference Paper - Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling
- [January 2023] My Msc dissertation, Dimension Reduction and Anomaly Detection using Unsupervised Machine is now online
- [November 2022] Guest Lecture, Introduction to Python and Numpy, STAT-447: Statistical Machine Learning for Data Science, Department of Mathematics and Statistics, University of Saskatchewan
- [September 2022] Received Graduate Travel Award from University of Manitoba, NSERC CREATE VADA Program
