01 / Researcher · Engineer · Builder

Zannatul Naim

Generative AI Researcher|

B.Sc. CSE · University of Rajshahi · Working at the intersection of generative modeling, representation learning, and robust ML for real-world BCI and EEG applications.

3.68
CGPA / 4.00
B.Sc. Computer Science
2
Published Papers
International Conferences
600+
Problems Solved
Competitive Programming
95%
Cross-Subject Accuracy
Neural Network (LOSO)

Research Highlights

All Publications →
UCICS 2026
Subject-Independent Event Recognition Using a Lightweight 1D Neural Network
Achieved 95.35% accuracy under leave-one-subject-out validation via efficient cross-subject EEG architecture.
UCICS 2025
Data Augmentation with Mask-Encoding Based Deep Learning
Mask-encoding augmentation approach improving model invariance under noisy time-series EEG conditions.
Undergraduate Thesis
GAN-Based Data Augmentation for SSVEP Recognition
Conditional WGAN-GP synthesizing realistic EEG data for low-resource BCI tasks at the HCI Lab, U. of Rajshahi.

Technical Stack

Full Skills →
PyTorch TensorFlow Generative AI GANs / WGAN-GP Python C / C++ Deep Learning Representation Learning EEG / BCI Linear Algebra Optimization Probability Theory Computer Vision Git Linux

From the Blog

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