Developed a multi-step, no-external-API document extraction and chunking pipeline for ixi-RAG, the in-house retrieval-augmented generation(RAG) system for proprietary knowledge- Improved correct passage retrieval (defined as top-3 recall) from 0.633 to 0.907(+0.274) and response correctness from 0.547 to 0.753(+0.206) on the Allganize RAG benchmark- Optimized average per-page processing times by 56%, to slightly over 3 seconds

Improved the performance and analyzable range of 3Cnet, an RNN-based deep learning model that predicts the pathogenicity of amino acid mutations- Raised the top-5 recall of pathogenic protein variants from 0.430 to 0.548 (+0.118, identifying one or two pathogenic variants amongst 10K+ other variants)- Broadened the types of analyzable protein variants from four to fourteenPrototyped a system for identifying promising drug target candidates- Integrated multiple disease-drug-target databases- Added feature to filter targets with known severe adverse side effectsMaintained on-premise AI R&D servers- Introduced containerization for environment isolation as the number of users increased from three to eight- Introduced monitoring of core server metrics- Handled matters related to server networking, storage and backups

Developed Poolda, a Speech-to-Text(STT) SaaS specialized for psychotherapy- Searched for and implemented the STT model best suited to the task at hand- Developed voice activity detection and emotional expression detection models in response to domain-specific demand- Conducted user interviews and collected feature requests- Developed the service payment model and pricing

Continued the research and development of CNN-based deep learning models for the identification of prostate cancer using scanned microscopy images (multi-class semantic segmentation model, later named DeepDx-Prostate)- Curated training, validation, and test datasets- Created pre- and post-processing data pipelines (image thresholding, data augmentation, etc.)- Performed quality control of incoming data and annotations under the guidance of a licensed pathologist- Developed, tested, and distributed data deidentification software used by external research partners (Python, PyQt)- Designed experiments and evaluated model performance (PyTorch, Scikit-learn)Developed CNN-based deep learning models for the calculation of the Ki-67 index, a measure of cellular proliferation used to assess cancer prognosis and make treatment decisions (multi-class density estimation model)- Curated and processed training, validation, and test datasets- Created pre- and post-processing data pipelines (image thresholding, data augmentation, etc.)- Designed experiments and evaluated model performanceSupported the in-house global business development unit at US-based academic and industrial conferences(six times per year, such as those hosted by the Digital Pathology Assoc., US and Canadian Academy of Pathology, American Assoc. for Cancer Research, and American Urological Assoc.)- Led product demonstrations to conference participants- Provided on-site hardware and software technical support- Gathered and relayed product feedback to R&D colleagues

Assisted in the development of a CNN-based deep learning model for the identification of prostate cancer using scanned microscopy images- Curated and processed training, validation, and test datasets- Designed experiments and evaluated model performance