About

I studied electrical engineering at Shahid Beheshti University, where I focused on control systems and optimization. For my bachelor’s thesis, I designed a smart-grid control system that used PID controllers and KNN algorithms to manage different energy sources efficiently.

I moved into Information and Communication Technology (ICT) for my master’s studies. During this time, I worked on topics such as smart network optimization, machine learning, deep learning, and IoT. I completed my thesis at the University of Cologne, where I worked on MRI calcification detection methods. This project introduced me to medical imaging and showed me how learning-based methods can support diagnosis and treatment.

I am now a Ph.D. student working on deep learning for medical and biomedical imaging. My research involves 3D data and microscopic images, with a focus on finding complex patterns and making the models more explainable and robust. I am especially interested in developing reliable AI tools that can be used in real clinical settings.

Education

  • Ph.D., Computer Science & Engineering — University of Connecticut, USA (2023–Present)
  • M.Sc., Information & Communication Technology — Politecnico di Torino, Italy (2019–2022)
  • B.Sc., Electrical Engineering — Shahid Beheshti University, Iran (2013–2018)

Research Experience

Graduate Research Assistant — University of Connecticut

Storrs, CT, USA · 2023 – Present

  • Designing a Bayesian Transformer + higher-order graph matching pipeline for cell tracking in serial tissue sections, supporting accurate 3D reconstructions for ultraplex imaging.
  • Developing CAPTURE and ImageReg — feature-based registration frameworks that combine SIFT/SURF-style keypoints with ResNet50 embeddings to analyze distortion and robustly align large biological image stacks.
  • Building reproducible evaluation pipelines, emphasizing uncertainty estimates, efficient GPU pipelines, and integration into downstream spatial analysis workflows.

Research Assistant — University of Cologne

Cologne, Germany · 2022 – 2023

  • Applied ML to radiomic MRI features to predict MGMT promoter methylation in glioblastoma, using 1153 Pyradiomics descriptors (including LoG and wavelet features).
  • Used XGBoost for feature selection and trained LR, SVM, and MLP models with nested cross-validation across FLAIR, T1w, T1Gd, and T2 sequences.
  • Managed end-to-end pipelines (data curation, preprocessing, documentation, GitHub) to ensure reproducible radiogenomics experiments.

Graduate Research Assistant — Politecnico di Torino

Turin, Italy · 2021 – 2022

  • Modeled relationships between particulate matter exposure and CNS disease mortality (Alzheimer’s, Parkinson’s) using regression and time-series forecasting.
  • Processed large environmental and epidemiological datasets to estimate region-level risk and identify spatiotemporal patterns.
  • Developed interactive dashboards that expose model outputs to clinical and public-health collaborators.

Publications

Selected peer-reviewed publications and submissions.

  • 2025 — Robust Training with Data Augmentation for Medical Imaging Classification.
  • 2025 — Longitudinal Tumor Generation in Mammograms via Dual Encoder GAN and Learnable Blending.
  • 2025 — Segmentation for Early Tumor Detection in Mammograms via Temporal Discrepancy Analysis and Dynamic Loss Weighting.
  • 2025 — Bayesian Transformers and Higher-Order Graph Matching for Cell Tracking in Serial Tissue Sections.
  • 2025 — ImageReg: Modular, Open-Source Toolkit for Image Registration and Alignment.
  • 2024 — Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-Resolution Histology Images.
  • 2024 — DCCNV: Enhanced CNV Detection in Single-Cell Sequencing Using Diffusion Process and Contrastive Learning.
  • 2024 — CAPTURE: A Clustered Adaptive Patchwork Technique for Unified Registration Enhancement in Biological Imaging.
  • 2023 — Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost.
  • 2023 — Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson’s Disease Through Voice Signals.
  • 2022 — Analyzing the Network System Performance Based on the Queuing Theory Concept.
  • 2022 — Multicast Optimization: Operational Research Theory and Applications.
  • 2022 — Machine Learning Algorithms for Radiogenomics: Application to Prediction of the MGMT Promoter Methylation Status in mpMRI Scans.
  • 2018 — Spam Detection from Big Data Based on Evolutionary Data Mining Systems.

Projects

Overview of the longitudinal tumor generation framework (SynGAN)

Longitudinal Tumor Generation in Mammograms (SynGAN)

We introduce an end-to-end generative framework that synthesizes realistic tumor development in full-field digital mammograms by conditioning on prior and current normal exams. The model encodes each timepoint with Transformer-based feature extractors, samples diverse tumor appearances from a variational latent space, and reconstructs context-aware tumors using attention-based decoding. A differentiable soft-mask blending module inserts the synthesized tumor into the current mammogram to support longitudinal simulation and data augmentation.

Citation: A. Ahsan Jeny, S. Hamzehei, M. Karami, et al., “Longitudinal Tumor Generation in Mammograms via Dual Encoder GAN and Learnable Blending.” ACM BCB, 2025.

Code: NabaviLab/SynGAN

Overview of the Bayesian Transformer cell tracking framework

Bayesian Transformer Cell Tracking for Serial Tissue Sections

We propose a Bayesian Transformer tracking framework for highly noise-prone serial 2D multiplex tissue sections, where ultrathin slicing and alignment artifacts break conventional tracking. The approach uses uncertainty-aware feature embeddings (morphology, texture descriptors, and CNN features) and links cells across sections via higher-order graph matching with belief propagation. This enables more consistent cell trajectories and supports reliable 3D reconstruction of tissue organization, with validation on private multiplex data and public time-lapse microscopy.

Citation: M. Karami, S. Hamzehei, D. Arce, G. Raimondi, L. Ostroff, S. Nabavi, “Bayesian Transformers and Higher-Order Graph Matching for Cell Tracking in Serial Tissue Sections.” MICCAI, 2025.

Code: NabaviLab/bayesian-transformer-cell-tracking

Skills

  • Programming: Python, C/C++, MATLAB, JS, R, SQL.
  • Frameworks: PyTorch, TensorFlow, scikit-learn, Keras.
  • Tools: Git, VS Code, Docker, LaTeX.

Let’s Work Together

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