Ananna Biswas

Hello, I am Ananna, I am pursuing my Ph.D. studies in Computational Science and Engineering at Michigan Technological University where I am fortunate to work under the supervision of Dr. Guy Hembroff . My research focuses on biomedical signal classification and AI-driven health diagnostics, with a particular emphasis on EEG-based prediction of anxiety, stress, and depression in pediatric populations. I earned my B.Sc. degree in Electronics and Communication Engineering (ECE) from Khulna University of Engineering and Technology (KUET), Khulna, Bangladesh in 2019.

I served as a visiting Ph.D. student at the University of Southern California (USC) under a NSF AccelNet NeuroPAC Fellowship for the project titled “Microelectrode Array Implantation and Neural Signal Acquisition for Neuromorphic Closed-loop Neural Prostheses” in fall 2024. As a part of my skill development, I participated in NeuroAI course in 2025 and Computational Neuroscience course in 2022 organized by Neuromatch Academy . During the latter course, I completed the project (Activity in Mouse Visual Cortex Predicts Spontaneous Running Behaviour) successfully. Our abstract regarding the project work (Exploring the Mouse Visual Cortex) had been accepted as a flash talk in neuromatch conference 2022. In addition, I also attended BCI & Neurotechnology Spring School 2022 , organized by g.tec medical engineering GmbH.

In 2021, I worked as a Research Engineer in the AimsLab, at United International University (UIU), Dhaka. I predicted consumers’ preference, their purchase intention, and attention for specific marketing stimuli through machine learning models using EEG signals and Eye-tracker. My study on neuromarketing led to a conference paper, An Overview of Neuromarketing Research in Developing Countries which was published in ACM. Prior to this, I hold a position as a Research Assistant in an AI-based software company, named Brainekt where I conducted research on breast-cancer image classification, ECG signal analysis, and monitoring wheelchair patients using EEG signals and published two conference paper, namely Invasive Ductal Carcinoma Detection by A Gated Recurrent Unit Network with Self Attention, and Classification of ECG signals by dot Residual LSTM Network with Data Augmentation for Anomaly Detection.

Research Interest

Machine Learning, Deep Learning

Computer Vision, Biomedical Image Processing

Signal Processing, Biomedical Signal Processing

Computational Neuroscience, Statistics

Human-Computer Interaction (HCI), Robotics

Neural Engineering, Brain-Computer Interface (BCI)