Aadit Kapoor
I am Aadit Kapoor. I love to think of new innovative ideas and most importantly try hard to make them a reality. In addition to that, I am extremely curious.
I am interested in solving technically difficult, real world AI problems. I enjoy solving applied A.I problems that are value-driven and can fundamentally change the way humans live. Additionaly, I love reading and analyzing businesses and companies.
My research areas of interests include: Artificial Intelligence , Machine Learning (A.I/M.L in Healthcare), Data Science, Biomedical Informatics/NLP.
I believe Data Science allows me to express my curiosity in ways I'd never imagine. The coolest thing in Data Science is that I see data not as numbers but as an opportunity (business problem), insights(predictive modelling\stats and data wrangling), and improvement (metrics).
Sessions
Lightning Talks are short 5-10 minute sessions presented by community members on a variety of interesting topics.
In this proposal, we talk about how we utilized data science and machine learning to analyze, prevent and predict system errors in a dental milling device. The total market size estimated for the dental milling industry is close to $3billion+. Based on estimates by the World Health Organization, oral diseases affect close to 3.5 billion people worldwide. Often times these machines have a high cost of manufacturing and the unfavorable reimbursement policy hinders the customer experience. In this scenario, using data science/machine learning/artificial intelligence for predictive maintenance is absolutely critical. Based on years of data accumulated, we built systems that are capable of analyzing, preventing, and predicting system errors commonly found in a dental lab milling workflow. We show how we utilized state-of-the-art Natural Language Processing techniques and Classification algorithms to build a system that is able to analyze, prevent and predict system errors. We show how we efficiently applied data science techniques to aid in value generation and how we utilized state-of-the-art techniques in a manner that was applicable to a business problem. This talk is suitable for the general data science community and particularly data scientists/machine learning engineers interested to see how data science can efficiently be applied in a Computed Aided Manufacturing industry to yield favorable business outcomes.