Current Projects

Quantum Machine Learning

We are exploring this field along two main directions. First, we focus on developing ML algorithms that can run on quantum hardware. Because quantum computing is fundamentally different from classical computing, we adapt existing algorithms to the quantum paradigm. For example, we recently worked with 1D quantum convolution, also known as quanvolution [1]. Second, we take a more conceptual approach by designing ML models inspired (either entirely or partially) by quantum mechanics. The idea is that such models may offer advantages over classical approaches in terms of generalizability, robustness, and parameter efficiency. Our systematic review [2] and investigations across several domains [1, 3], including mental health [4], highlight these potential benefits and point to the promise of quantum ML.

1. Orka NA, Haque E, Awal MA, Moni MA. Fully quanvolutional networks for time series classification. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2025. [code][datasets]
2. Orka NA, Awal MA, Liò P, Pogrebna G, Ross AG, Moni MA. Quantum deep learning in neuroinformatics: A systematic review. Artificial Intelligence Review. 2025.
3. Haque E, Orka NA, Moni MA. A linearly scaled quanvolutional neural network for continuous value prediction. In Preparation. [datasets]
4. Orka NA, Haque E, Jannat M, Awal MA, Moni MA. QuanvNeXt: An end-to-end quanvolutional neural network for EEG-based detection of major depressive disorder. Under Review. [dataset1][dataset2]

Neuroinformatics

From a data science perspective, the broad field of neuroinformatics can be divided into several modalities: signals (e.g., EEG, MEG), images (e.g., MRI, CT), tabular data (e.g., cognitive assessments), texts (e.g., EHRs, clinical notes), and omics (e.g., genomics, proteomics). Our goal is to integrate ML with neuroscience to improve understanding of the human brain and its structure, function, and/or abnormalities. Notable works so far include identifying potential biomarkers of depression from EEG signals [1], neural representation learning [2], mapping cognitive function to multimodal MRI data [3–5], and detecting brain tumors [6, 7].

1. Haque E, Orka NA, Jannat M, Moni MA. Generalized EEG representation learning captures deviations linked to major depressive disorder. In Preparation. [dataset1][dataset2][dataset3]
2. Orka NA, Haque E, Rahman MT. A shared neural embedding of human psychological phenotypes. In Preparation. [dataset]
3. Rahman MT, Orka NA, Khan A, Moni MA. A geometric deep learning framework for modeling human intelligence from structural brain organization. Under Review. [dataset]
4. Rahman MT, Orka NA, Khan A, Moni MA. Explainable deep learning on functional connectome reveals candidate biomarkers of intelligence in adolescence. In Preparation. [dataset]
5. Rahman MT, Orka NA, Khan A, Moni MA. Understanding neurocognition with deep learning and MRI: A systematic review. IEEE Transactions on Cognitive and Developmental Systems. 2025.
6. Ali MA, Hossain MA, Rahman MT, Shuva TF, Almoyad MA, Orka NA, Bulbul AAM, Khan RT, Kaiser MS, Moni MA. RGNN3D: A hybrid radiomic graph neural network for 3D MRI glioma grading. Knowledge-Based Systems. 2026. [dataset1][dataset2]
7. Tonni SI, Sheakh MA, Tahosin MS, Hasan MZ, Shuva TF, Bhuiyan T, Almoyad MA, Orka NA, Rahman MT, Khan RT, Kaiser MS. A hybrid transfer learning framework for brain tumor diagnosis. Advanced Intelligent Systems. 2025. [dataset]

Foundation Models

Outside of task-specific applications, I am highly motivated by the development of generalized, domain-flexible models. My goal is to address the challenges of cross-domain scaling and deployability using SSL, transfer learning, and multimodal fusion in zero- or few-shot settings. In collaboration with Rural Health Research Institute, CSU, I work on foundation models for biomedical computer vision and time-series analysis, trained on both open-access and proprietary datasets [1]. Earlier, at BARI, Gazipur, I collected over 13,000 plant leaf images (RAIA), now being used to develop a plant stress foundation model capable of generalizing across species, varieties, and stressors.