Najibul Haque Sarker
About me
If you are in a hurry
- Hi I am Najib - CS graduate student @ Virginia Tech, MLE @ IQVIA, Research Intern @ Xulab, Fresh Graudate @ BUET CSE.
- Passionate about Deep Learning Research and Competitions. Specifically in the fields of Computer Vision, Natural Language Processing, Video-Language and Multi-Modality.
- Author of 7 papers and counting
- Participated and won in multiple deep learning competitions. Received rank of Kaggle Competitions Master in Kaggle.
If you have some time
Publications
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SONICS: Synthetic Or Not - Identifying Counterfeit Songs
Md Awsafur Rahman*, Zaber Ibn Abdul Hakim*, Najibul Haque Sarker*, Bishmoy Paul, SA Fattah Under Review at ICLR 2025 Same contribution as 1st author Synethetic Song Generation and Detection, Large Audio Models
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ENTER: Event Based Interpretable Reasoning for VideoQA
Hammad Ayyubi, Junzhang Liu, Zhecan James Wang, Hani Alomari, Chia-Wei Tang, Ali Asgarov, Md. Atabuzzaman, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Shih-Fu Chang, Chris Thomas Accepted at Multimodal Algorithmic Reasoning workshop at NeurIPS 2024 Video QA, Vision-Language, Multi Modality reasoning
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Leveraging Generative Language Models for Weakly Supervised Sentence Component Analysis in Video-Language Joint Learning
Zaber Ibn Abdul Hakim*, Najibul Haque Sarker*, Rahul Pratap Singh, Bishmoy Paul, Ali Dabouei, Min Xu Accepted in Multimodal Learning and Applications Workshop at CVPR 2024 Same contribution as 1st author Computer Vision, Vision-Language, Multi ModalitySecond research intern project under the supervision of Dr Min Xu and project leader Ali Dabouei. In this work, we try to enhance video-language joint learning tasks by incorporating comprehension about significance of sentence components in the context of video-text analysis. Specifically, we utilize LLMs to generate component targeted negative samples which we use for contrastive learning along with an additional adaptive negative importance estimation module. This paper was accepted in Multimodal Learning and Applications Workshop at CVPR 2024.
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Forward Diffusion Guided Reconstruction as a Multi-Modal Multi-Task Learning Scheme
NH Sarker, MS Rahman 2023 IEEE International Conference on Image Processing (ICIP), 3180-3184 1st Author Publication Computer Vision, Medical Imaging, DiffusionThis is based on my undergraduate thesis. Worked under the supervision of Dr. M. Sohel Rahman to develop a novel multi-task mechanism utilizing the forward diffusion process for segmenting brain MRI images. The work was accepted for oral presentation in ICIP 2023.
| Article | Presentation | Preprint |
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ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
M. A. Rahman*, B. Paul*, N. H. Sarker*, Z. I. A. Hakim* and S. A. Fattah 2023 IEEE International Conference on Image Processing (ICIP), 2200-2204 Same contribution as 1st author Image Generation, Synthetic Image DetectionThis paper is based on our results of IEEE VIP CUP 2022: Synthetic Image Detection Challenge where my team ranked 1st in LB. In this work, to assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, we presents a large-scale dataset1 named ArtiFact, comprising diverse generators, object categories, and real-world challenges. We propose a multi-class classification scheme combined with a filter stride reduction strategy that addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. This work was done under the supervision of Dr. Shaikh Anowarul Fattah and the paper was accepted for poster presentation in ICIP 2023.
| Article | Github |
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Detecting anomalies from liquid transfer videos in automated laboratory setting
NH Sarker, ZA Hakim, A Dabouei, MR Uddin, Z Freyberg, A MacWilliams, J Kangas, M Xu Frontiers in Molecular Biosciences 10, 1147514 1st Author Publication Video Anomaly Detection, Object TrackingFirst research intern project under the supervision of Dr Min Xu and project leaders Mostafa Rafid Uddin and Ali Dabouei In this work, we address the problem of detecting anomalies in a certain laboratory automation setting through utilizing practical human-engineered feature extraction method to detect anomalies from liquid transfer video images. The paper was accepted in the journal Frontiers in Molecular Biosciences.
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Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown Multi-Class Ensemble of CNNs
M. A. Rahman*, B. Paul*, N. H. Sarker*, Z. I. A. Hakim* and S. A. Fattah Same contribution as 1st author Signal Processing, Synthetic Speech AttributionThis paper is based on our results of IEEE Signal Processing Cup 2022: Synthetic Speech Attribution Challenge where we became the Winners. The challenge was to detect synthetic speech from natural ones and also identify the algorithm behind the fake speech. In this work, a detector network is proposed that transforms the audio into log-mel spectrogram, extracts features using CNN, and classifies it between five known and unknown algorithms, utilizing semi-supervision and ensemble to improve its robustness and generalizability significantly. This work was done under the supervision of Dr. Shaikh Anowarul Fattah.
| Article | Github |
Work Experience
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Graduate Student Researcher
Virginia Tech
Aug 2024 - present -
Machine Learning Engineer
Next Best Action ML Team
IQVIA
June 2023 — Aug 2024 -
Research Internship
Computational Biology Department
Carnegie Mellon University
Jan 2022 — July 2024Working under the supervision of Dr Min Xu on Computer Vision and Vision-Language projects. Previously worked on video anomaly detection, currently working on video grounding and moment retrieval.
Education
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B.Sc. in Computer Science and Engineering
Bangladesh University of Engineering and Technology
April 2018 - May 2023CGPA: 3.95/4.00
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Higher Secondary School Certificate (HSC)
Notre Dame College
2017GPA: 5.00/5.00
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Secondary School Certificate (SSC)
St Joseph Higher Secondary School
2015GPA: 5.00/5.00
Technical Skills
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Programming Languages
Python, C++, JavaScript, Java, C#, SQL, Bash, CSS, Latex
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Frameworks
Pytorch, Tensorflow, Keras, Sklearn, React, Bootstrap, Django
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Tools
AWS Sagemaker, Git, Wandb, Trello, Oracle, PostgreSQL
