Asif Ahmed Neloy

I'm a Teaching Professor at the Department of Computing Studies and Information Systems, Douglas College, New Westminster, British Columbia. Currently, I am teaching courses on Advanced Databases, System Analysis and Design, Data Analytics, and Fundamentals of Machine Learning. Aside from my teaching, I am actively pursuing theoretical and applied research related to Probabilistic and Bayesian Modeling, Anomaly Detection, Dimension Reduction, and interdisciplinary applications of Auto-Encoders. Previously, I taught undergraduate and graduate courses at Vancouver Island University, University of Manitoba and North South University.

I obtained my Msc in Computer Science Degree from University of Manitoba, supervised by Dr. Maxime Turgeon and Dr. Cüneyt Akçora where I focused on Dimension Reduction and Anomaly Detection using Unsupervised Machine Learning. Along with Unsupervised settings, I researched on various Data Analytics methods including Feature Extraction, Two-staged Modeling approach, Statistical Modeling under Dimension Reduction Lab and NSERC CREATE fund on The Visual and Automated Disease Analytics (VADA) graduate training program. Prior to that, I worked with Dr. Shahnewaz Siddique and Zunayeed Bin Zahir on interdisciplinary research topics including Robotics, Recommender Systems, Health Informatics and Computer Vision.

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Recent News

  • [January 2024] 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
  • [December 2022] Successfully defended my MSc thesis.
  • [November 2022] Manuscript in Preparation - A Comprehensive Study of Auto-Encoders for Anomaly Detection: Efficiency and Trade-Offs
  • [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


My 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. I am currently exploring the intricacies of Auto-Encoders and their applications in Variational and Gaussian modeling. My work delves into the statistical interpretation and visualization of Unsupervised Machine Learning algorithms, emphasizing dimension reduction and anomaly detection. Additionally, I contribute to the field of 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 my Python packages on PyPI. Also, representative papers are highlighted.

 See my Google Scholar profile for the most recent publications.

Disentangled Conditional Variational Autoencoder for Unsupervised Anomaly Detection
Asif Ahmed Neloy*, Maxime Turgeon,
UManitoba UMSpace, 2022
project page / UMSpace

A novel architecture of generative autoencoder by combining the frameworks of β-VAE, conditional variational autoencoder (CVAE), and the principle of total correlation (TC). We show that our architecture improves the disentanglement of latent features, optimizes TC loss more efficiently, and improves the ability to detect anomalies in an unsupervised manner with respect to high-dimensional instances, such as in imaging datasets

Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling
Asif Ahmed Neloy*, Maxime Turgeon,
ICDM, 2022
project page / IEEE

We effectively layout a combination of the classical statistical model incorporating a stacked ensemble classifier and a DL framework of convolutional neural network (CNN) and Bidirectional Recurrent Neural Networks (Bi-RNN) to structure a more decomposed architecture with lower computational complexity.

Ensemble learning based rental apartment price prediction model by categorical features factoring
Asif Ahmed Neloy*, HM Sadman Haque, Md Mahmud Ul Islam,
ICMLC, 2021

The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors.


  • Douglas College
  • Vancouver Island University
    • Fall 2023:
      • CSCI 159: Computer Science I
      • CSCI 112: Applications Programming
  • University of Manitoba
    • Winter 2023:
      • DATA 2010: Tools and Techniques for Data Science

Guest Lectures and Seminar Presentations

Python Packages

  • Data Scaler Selector: Data Scaler is an open-source python library to select the appropriate data scaler for your Machine Learning model.
  • Image to Sketch: Python open-source library to convert color/ B&W image to pencil sketch.
  • Data Preparer (On-Progress): Data Preparer is an open-source Python package to Clean and Prepare your dataset before applying Machine Learning Model.

Template credit-Jon Barron!
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