My Favorite quote:
Only in their dreams can men be truly free,
‘twas always thus and always thus will be!
– John Keating (Dead Poet’s Society)
Some Fun Facts:
Pastimes: I enjoy reading books (Fiction/Sci-Fi/Detective/Classic), cooking my favourite meals, drinking good coffee, writing my crazy thoughts down, taking strolls down the memory lane, wood-crafting (new interest), and participating voluntary religious activities in nearby mosque.
Key Attributes:
MSc in Computer Science (Continuous), 2021
University of British Columbia
BSc in Computer Science & Enginnering, 2018
North South University
Department of Computer Science,
Irving K. Barber Faculty of Science,
Supervisor: Dr. Fatemeh Hendijani Fard
Topics include:
Department of Computer Science,
Irving K. Barber Faculty of Science,
Supervisors: Dr. Fatemeh Fard, Dr. Ramon Lawrence, and Dr. Khalad Hassan
Courses include:
Courses Taught:
Department of Computer Science,
School of Engineering and Physical Sciences
Courses Taught:
Responsibilities:
Responsibilities:
Department of English and Modern Language,
School of Humanities & Social Sciences
Courses Taught:
Responsibilities:
90%
90%
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70%
65%
65%
70%
80%
40%
45%
35%
70%
30%
25%
90%
75%
65%
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55%
30%
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20%
Full Papers in Top Tier Conferences
We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings.
In this paper, we propose Adaptive Online Biterm Topic Model (AOBTM) to model topics in short texts adaptively. AOBTM alleviates the sparsity problem in short-texts and considers the statistical-data for an optimal number of previous time-slices.
Papers and Pre-prints
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering - A Study on Classification of App-Reviews
AOBTM - Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
The interactive visualization tool makes it easier for the developers to traverse through the extensive result set generated by the text classification and topic modeling algorithms. It also helps developers to quickly comprehend the outcomes of implemented model feature combinations.
The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the election data through various techniques, including Geospatial visualization.