Sunanda Das
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Graduate Research Assistant
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Sunanda Das's research focuses on the application of machine learning and deep learning techniques to address challenges in various domains, including healthcare, cloud computing, intelligent vehicles, and financial markets. Her work has explored the impact of missing value imputation on machine learning model performance and developed hybrid approaches for complex tasks. Das has investigated the prediction of CPU workload in cloud virtual machines using Bi-LSTM based recurrent neural networks and the estimation of road boundaries for intelligent vehicles using DeepLabV3+ architecture. Her publications also include stock price prediction models utilizing Bi-LSTM and GRU, and flood prediction in Bangladesh using the k-Nearest Neighbors algorithm. Additionally, she has examined the association of obesity with hypertension and diabetes in adults in Bangladesh and explored smartphone-based non-invasive hemoglobin level estimation. Das holds a h-index of 14 with 77 total publications and 1,303 total citations.
Metrics
- h-index: 14
- Publications: 77
- Citations: 1,303
Positions
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Graduate Research Assistant 2024–presentUniversity of Arkansas at Fayetteville EECS ORCID
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Assistant Professor 2022–2024Khulna University of Engineering and Technology Computer Science and Engineering ORCID
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Lecturer 2018–2022Khulna University of Engineering & Technology Computer Science and Engineering ORCID
Selected Publications
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DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning (2026)
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DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning (2026)
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DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models (2025)
Collaboration Network
Top Collaborators
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DL$^3$M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Models
- DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning
- DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning
- DiSec: Mitigating Backdoors in Pre-trained Language Models via Disentanglement of Adversarial Weights for Secure Fine-Tuning
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