Syed Mostofa Monsur
I am a 1st Year PhD Student in the Computer Science Department at Stony Brook University.
Previously, I led the AI/ML Team at Celloscope, a Bangladeshi Fintech. I have 5+ years of experience working in the industry
where I developed multiple user-facing systems powered by NLP and Speech-based UX. I developed a number of Real-World Applications with Natural Language Interfaces
thus making services accessible to hundreds of thousands of users who have limited access to information.
I received my Bachelor of Science in Computer Science and Engineering from CSE BUET. There I worked with
Professor Muhammad Abdullah Adnan on
Active Learning for Distributed Systems.
Email /
CV /
gScholar /
LinkedIn
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Research Interests
I'm interested in NLP, Conversational AI, Human-Centered Design etc.
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[4] SynthNID: Synthetic Data to Improve End-to-end Bangla Document Key Information Extraction
Syed Mostofa Monsur,
Shariar Kabir,
Sakib Chowdhury
BLP Workshop at EMNLP, 2023
[paper]
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[3] Grid-Coding: An Accessible, Efficient, and Structured Coding Paradigm for Blind and Low-Vision Programmers
Md Ehtesham-Ul-Haque,
Syed Mostofa Monsur,
Syed Masum Billah
UIST, 2022 (Best Paper Award)
[project page]
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[video]
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[paper]
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[2] SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus
Syed Mostofa Monsur,
Sakib Chowdhury,
Md Shahrar Fatemi,
Shafayat Ahmed
LREC, 2022
[poster]
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[paper]
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[1] Distributing Active Learning Algorithms
Syed Mostofa Monsur, Muhammad Abdullah Adnan
NSysS, 2020
[video]
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[slides]
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[paper]
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Agrani Voice Banking
Leaded Speech and NLU Team at Celloscope
Agrani Bank is
Bangladesh's one of the largest state-owned banks with a huge number of customers
who have very little access to information. Agrani Voice Banking makes
banking services accessible to everyone. It is powered by Bengali ASR
and a finetuned NLU engine for natural language-driven
fund transfers and inquiries.
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National ID Information Extraction using Document Transformers
Leaded NLP Team at Celloscope
slides
After fine-tuning pretrained document transformers,
it achieves significantly good performance on extracting
NID information. We treated the NID extraction problem
as a document question-answering problem – querying on
key fields of the NID image document. The model is fine-tuned
with real user data and synthetic data as well.
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License-Plate Extraction from Very Noisy Real-World Deployment
Leaded NLP Team at Celloscope and ML Team at Spectrum
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License-Plate extraction task in very noisy real-world setting. Fine-tuning
end-to-end sequence extraction models on real and synthetic data for
better performance. System deployed in several toll booths in Bangladesh for
reporting analytics.
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Industry-Grade ASR, TTS and Speaker Verification for Bengali Speech-Driven Systems
Leaded NLP Team at Celloscope
Collected and pre-processed 400+ hrs of Bengali audio and
transcription. Trained end-to-end high-quality ASR models.
Trained industry-grade TTS for Bengali language with 40+ hours of
curated data and improved generated audio quality with Vocoders (naturalizing audio)
Integrated with Natural Language driven User Interfaces
including speech-driven chatbots. Developed industry-grade speaker
verification system using ensemble of pre-trained
unispeech-sat, wavlm and ecapa-tdnn.
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