I am a Sr. Machine Learning Engineer at SDSol Technologies, where
I work on deep learning and computer vision development to solve business problems. I also work on
NLP and Data Science related problems.
From March 2021 to September 2022, I was assosiated with Department of Computer Science @ UET as an AI Instrctor . I
taught AI/ML course and conducted labs. I also conducted research in Medical Image Analysis. Our
research proposal related to Deep Learning and Agriculture won research funding of PKR 3.5 million
from HEC's NRPU
program.
I am broadly interested in learning from limited data for visual recognition problems. My primary
area of interests include transfer learning and self supervised learning with applications to
Medical Image Computing, Agriculture, and Vision based Industrial Defects Detection. I believe
that artifical vision and learning from limited data can help us in improved object recognition,
detection and segmentation to solve real-world problems.
[August 2023] Our paper related to network security and ML,
entitled "Optimization of Predictive Performance of Intrusion Detection System Using Hybrid
Ensemble Model for Secure Systems" has been accepted for publication in "PeerJ Computer
Science".
[April 2023] A paper entitled "Using Data Augmentation and
Bidirectional Encoder Representations
from Transformers for Improving Punjabi Named Entity Recognition" has been accepted for
publication in The ACM Transactions on Asian and Low-Resource Language Information Processing
(TALLIP)
[Novemeber 2022] I have joined SDSol
Technologies as a Machine Learning Engineer. I will work on deep learning and computer
vision projects.
[November 2021] I joined WortelAI as a part time Software Engineer
(Deep Learning and Computer
Vision)
[Mar 2021] I joined Department of Computer Science as a Teaching
Fellow @ UET Lahore
Publications
Optimization of Predictive Performance of Intrusion Detection System Using Hybrid Ensemble
Model for Secure Systems
Qaiser Abbas, Sadaf Hina, Hamza Sajjad, Khurram Shabih Zaidi, Rehan Akbar
PeerJ Computer Science, 2023
Paper | Code | Citation
We experimented with various machine learning models for recent intrusion detection datasets.
NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB-15 datasets were used in the study. We proposed a hybrid, cost
and compute efficient ensemble model for development of secure IDS systems.
Using Data Augmentation and Bidirectional Encoder
Representations from Transformers for
Improving Punjabi Named Entity Recognition
Hamza Khalid, Ghulam Murtaza, Qaiser Abbas ACM Transactions on Asian and Low-Resource Language Information Processing, 2023
Paper | Code | Citation
We worked on data generation for Punjabi (Shahmukhi Script). A multilingual BERT model was trained
for NER task. We developed a large training corpora using a simple and novel PoW augmentation
technique
Detection and Classification of Malignant Melanoma Using Deep Features of NASNet
Qaiser Abbas, Anza Gul
SN Computer Science, 2022
Paper | Code | Citation
We investigtaed a NASNet neural network architecture for Malignant melanoma detection using
dermoscopic images and data augmentation.
Acral melanoma detection using dermoscopic images and convolutional neural networks
Qaiser Abbas, Farheen Ramzan, Muhammad Usman Ghani Khan
Visual Computing for Industry, Biomedicine, and Art, 2021
Paper | Code | Citation
We proposed a neural network architecture for classification of acral lentiginous melanoma using
dermoscopic images.
Detection and Prediction of Acral Lentiginous Melanoma in Dermoscopic Images using Deep
Learning
Qaiser Abbas Masters Thesis, 2020
Thesis PDF
Due to acral melanoma’s infrequent occurrences, limited data is available so its early diagnosis is
hard. To overcome this problem, we applied data centric techniques to develop a large
dataset to train a deep learning models. Our proposed convolutional neural network achieved an
accuracy of 91% on test set.
Special thanks to Jon Barron for the awesome template.