Asif Ahmed Neloy

I'm a Teaching Professor at the Department of Computing Studies and Information Systems, Douglas College, New Westminster, British Columbia. I also hold an Adjunct Faculty Position at the School of Computing and Academic Studies, British Columbia Institute of Technology (BCIT). 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

• [July 2024] Latest Publication - "A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs" at the journal of Machine Learning with Applications, DOI: https://doi.org/10.1016/j.mlwa.2024.100572
• [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

Research

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.

This paper explores the efficiency and trade-offs among various Auto-Encoder architectures for anomaly detection. It categorizes 11 Auto-Encoder architectures and ranks them based on F1-score and ROC analysis. The study addresses reproducibility challenges and parameter tuning, offering comprehensive insights into the applications of Auto-Encoders in unsupervised anomaly detection using FMNIST and MNIST datasets.

A novel architecture of generative autoencoder by combining the frameworks of β-VAE, conditional variational autoencoder (CVAE), and the principle of total correlation (TC). This architecture improves the disentanglement of latent features and optimizes TC loss more efficiently, enhancing the detection of anomalies in high-dimensional instances, such as imaging datasets.

This work presents a combination of a classical statistical model using a stacked ensemble classifier and a deep learning (DL) framework incorporating CNN and Bi-RNN, with a focus on efficiently modeling complex combined text and survey data.

This paper focuses on analyzing various features of rental apartments to predict their rental prices using ensemble learning models. It utilizes categorical features and deep learning approaches to enhance prediction accuracy.

Teaching

• Douglas College
  
  • Summer 2024:
    • CSIS 2300: Database I
    • CSIS 3300: Database II
    • CSIS 3360: Fundamentals of Data Analytics
  • Winter 2024:
    • CSIS 2200: Systems Analysis & Design
    • CSIS 2300: Database I
    • CSIS 3290: Fundamentals of Machine Learning in Data Science
• 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

• Invited Sessions:
  • ICSA-Canada Chapter 2022 Symposium, Banff Center, Banff, Alberta, Canada.
    Topic: Auto-encoders for Anomaly Detection: Efficiency and Trade-Offs.
• Lectures:
  • Introduction to Machine Learning, North South University, Dhaka, Bangladesh.
  • Courses: STAT 447: Statistical Machine Learning for Data Science

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!
Last updated: September 04, 2024.