Julia has over 20 years working experience in the areas of Artificial Intelligence including Machine Learning, Deep Learning, OCR, Image Processing, Speech Recognition and Analysis, Natural Language Processing. E.g., Julia's work includes NLP-based “Did you mean” functionality for Wikipedia's 300+ languages.

Julia Komissarchik is a CEO & co-founder of Glendor, Inc. The company’s focus is on solving the challenge of enabling worldwide multimodal medical big data access, sharing and aggregation while protecting patients’ privacy. Julia has over 20 years working experience in the areas of Artificial Intelligence including Machine Learning, Deep Learning, OCR, Image Processing, Speech Recognition and Analysis, Natural Language Processing. E.g., Julia's work includes NLP-based “Did you mean” functionality for Wikipedia's 300+ languages. Julia has degrees from UC Berkeley (Mathematics) and Cornell University (Computer Science). She is a co-founder of several startups and holds 10 US Patents in AI. Reference: Healthcare systems worldwide are facing a critical challenge with a shrinking pool of doctors and a rapidly growing and aging patient population. LLMs and other self-supervised models can change the situation but require access to a large volume of data to be generalizable and less biased. Without large amount of data, the models are not generalizable and biased. ChatGPT is trained on publicly available internet data, and even they report that it is no longer enough. Imagine how dire the situation is in AI in Healthcare that requires access to Personal Medical records. These records are not just bits and bytes, but somebody's very personal very private data. E.g., on the dark web price for a person's healthcare record is 500x the price for a credit report. To give AI in Healthcare access to the personal medical data, the data must be deidentified. Local large volume deidentified multimodal medical data lakes offer a feasible solution that empowers hospitals and individual patients to make a significant impact, while monetizing their data.