Debanna Das

Advanced Cardiovascular Imaging Lab, University of Pennsylvania

SUPERVISOR: Prof. Walter Witschey

Hemothorax—internal chest bleeding from trauma—requires rapid diagnosis, but manual CT analysis is slow and subject to human variability. I developed an AI-assisted detection system at Penn Medicine to help radiologists identify these critical bleeds more quickly and consistently. The system combines multiple deep learning architectures working together: 2D models analyze slice-by-slice patterns while 3D models capture full volumetric context. By merging their predictions, this ensemble approach outperformed both individual models and fine-tuned versions of existing medical imaging systems. The work shows that architectural diversity, not just more data, can drive better clinical AI performance. This reduces diagnostic delays in emergency settings and gives radiologists a reliable AI assistant for detecting these life-threatening bleeds.

Xu Lab, Carnegie Mellon University

SUPERVISOR: Prof. Min Xu

Cryo-electron tomography generates millions of 3D images of molecular structures inside cells, but manually labeling these volumes is prohibitively slow which creates a bottleneck for biological discovery. I developed and compared two unsupervised discovery pipelines to automatically identify and group similar molecular structures without requiring human annotations. The first approach used transfer learning: a pretrained 3D ResNet-18 from video recognition extracted volumetric features, followed by t-SNE for dimensionality reduction and K-Means clustering to discover structural groups. The second approach trained a custom 3D SimCLR encoder from scratch using contrastive self-supervised learning on the cryo-ET data itself, then applied UMAP for manifold learning and HDBSCAN for density-based clustering that automatically handles noise and determines cluster counts. The self-supervised approach outperformed the transfer learning approach, nearly tripling cluster coherence and producing substantially better-separated molecular groupings. This enables high-throughput structural discovery in areas where expert annotation is the rate-limiting factor for scientific progress.

Mit Media Lab

Optimized the Self-Organizing Network of Aggregated Representations (SONAR) stack by refactoring critical services, adding automated linting, and streamlining deployment workflows. The work reduced code complexity, hardened CI/CD, and ensured the federated perception system stayed maintainable as contributions scaled.

Genie Ai

Designed a modular AI system architecture with optimized data pipelines and workflow automation that improved processing efficiency and supported high concurrent user loads. Focused on making evaluation, observability, and deployment loops as rigorous as the underlying models.

Routechef

Built a multimodal travel assistant atop LangGraph and LLMs, integrating multiple travel APIs, Redis caching, and WhatsApp/web delivery channels; also shipped ML-powered chat analytics that reached 92% accuracy and improved user engagement.