Jack Bodine

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For my master’s thesis, I created computational models for processing neuroscience data, specifically the massive amount of video data generated by two-photon calcium imaging (2PCI). Neuroscientists use 2PCI to record neuron activity, but manually mapping out individual neurons across terabytes of video makes scalable studies impossible. Existing automated tools either only segment the main body of the cell, or struggle to process lab-grown neural organoids.

To solve this, I built an end-to-end deep learning pipeline, focusing on U-Net segmentation models that I trained to map both the cell bodies and their processes. The biggest hurdle was an extreme lack of training data, as I only had 32 manually annotated images to work with. To get the model to perform, I implemented synthetic image generation (GANs and heuristics), data augmentation, and domain transferability to investigate if a model trained on one type of brain tissue could generalize to another.

I validated the pipeline on two very different datasets provided by the University of Copenhagen. The first involved neural organoids, comparing samples from healthy patients to those with schizophrenia, which allowed me to extract and analyze specific signal features like burst rates and synchronicity. The second dataset contained in vivo recordings of mice to observe how different medications affected their neuronal behavior. The results derived from my models are currently being worked into neuroscience publications.

Machine Learning, Research, Python, Project Management

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As a final assignment in Advanced Topics in Deep Learning, I worked with a team to write a research report testing how different Graph Neural Network (GNN) architectures scale under computational constraints. We compared conventional GCNs, JKNet, and DropEdge, examining their performance while measuring FLOPs and MACs during inference. By varying hidden dimensions and layer counts, we assessed both accuracy and scalability across multiple graph datasets (Cora, Citeseer, PubMed).

Machine Learning, Research

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Advanced Topics in Human Centered Computing focused on the latest research in the field of HCI. We read several papers per class session and were tasked with writing three papers to demonstrate our understanding. I wrote a literature review on the current state of typing-based input methods for Virtual and Augmented Reality. Additionally, I designed an experiment proposal to investigate how people interact differently with anthropomorphic interfaces and wrote technical evaluations of several recently published papers related to ubiquitous design in computing.

Research, Human Computer Interaction, UI/UX Design