Senior Research Scientist
Oct 2025 - Present
Sirona Medical
Evaluation and measurement infrastructure
- Led the migration of the production ASR provider and built provider trialing, evaluation, and management directly into the application. Converted the incumbent single-vendor transcription service into a multiplexer that runs several providers against the same audio and returns shadow results, making head-to-head comparison of latency and WER possible on live production traffic rather than offline samples. Cut word-by-word latency by 50% and reduced WER by 20%.
- Designed and shipped end-to-end evaluation infrastructure covering every production AI feature: LLM-as-judge scoring, continuous sampling of production traces, and direct dev-to-prod comparison. Validated in production, with judge metrics tracking dev eval scores and no significant drift.
- Introduced a hallucination-detection metric into the evaluation pipeline and used it as the optimization target for prompt and model iteration; auto-impression hallucination rate fell from 25% to 9%.
- Led an observability program consolidating evaluation, monitoring, and usage tracking across all AI features, enabling apples-to-apples comparison of foundation models and prompts on fixed datasets, turning model selection into a measured decision rather than a judgment call and accelerating release cadence.
- Prototyped and benchmarked three ASR accuracy-improvement approaches against the same evaluation harness on proprietary medical datasets (denoising BERT, GPT-style post-correction, classical language-model rescoring).
- Built Datadog dashboards tracking AI output quality and aggregate feature usage, giving the team its first real-time view of production model behavior.
Systems and agentic infrastructure
- Cut auto-impression inference latency ~87% at p50 (8.0s to 1.0s) and ~61% at p95 (15.6s to 6.09s) through prompt and model optimization.
- Shipped cross-stack contributions spanning frontend, LLM inference infrastructure (llm-plex), ASR infrastructure (asr-plex), and agentic services (rhino-api).
- Stood up Epic EHR sandbox integration with an internal agent, validating APIs and mapping accessible data to expand the agent's tool surface; scoped a second integration against Cerner.
Senior Machine Learning Scientist
Sep 2024 - Oct 2025
Altea Healthcare
- Led a team of 6 ML engineers across four production generative AI systems, defining architecture and product direction while remaining hands-on.
- Built a medical information retrieval agent on LangGraph orchestration with LangSmith evaluation and RAG over vector databases; 3.5x improvement in customer satisfaction and 2x usage growth over the prior version.
- Established end-to-end Azure MLOps: CI/CD, model monitoring, and A/B prompt testing for compliant, repeatable deployment.
- Built a transformer speech-to-text and LLM structured-generation pipeline producing encounter documentation, reducing encounter duration by 50%.
- Built a real-time note classifier (embeddings + transformer heads) surfacing critical patient conditions, and an automated RAG ingestion pipeline over patient documents.
Advanced Machine Learning Engineer
May 2023 - Sep 2024
CACI Inc.
- Collaborated on research in deepfake detection and AI-generated-text detection supporting trust and safety initiatives.
- Built and productionized variational autoencoder models for anomaly detection in network traffic, improving detection accuracy while reducing false positives.
- Designed and deployed an LLM-based RAG pipeline for automated sanitization of sensitive documents, cutting manual review time by more than 70%.
Student Informatics Assistant II
Nov 2018 - Jun 2021
Children's Hospital of Philadelphia
- Implemented influence functions in PyTorch for model interpretability and stability analysis; this became Revisiting the Fragility of Influence Functions.
- Built and validated an ICU early-warning system predicting mortality risk from patient vital signs, and a pipeline for early detection of acute respiratory distress events in children.