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Dhagash Mehta, Ph.D.

Head of Applied Artificial Intelligence Research for Investment Management

Dr. Dhagash Mehta is a prominent figure in the field of Applied Machine Learning Research within the Investment Management sector.

With a Ph.D. background, Dr. Mehta has held key positions at reputable institutions like BlackRock Inc., Vanguard Group, and United Technologies Research Center.

His expertise spans Artificial Intelligence (AI), Generative AI, machine/deep learning, quantitative finance, and computational mathematics, science, and engineering.

Dr. Mehta's educational journey includes a Doctor of Philosophy (PhD) in Computational and Applied Mathematics and Theoretical Physics from the University of Adelaide, among other degrees from prestigious institutions globally.

He has contributed significantly to scholarly research initiatives and has been associated with renowned journals such as the Journal of Financial Data Science and the Journal of Impact and ESG Investing.

Dr. Mehta's involvement in various research and academic roles at leading universities and research centers worldwide showcases his diverse and extensive experience within the fields of AI, quantitative finance, and computational mathematics.

Highlights

Aug 2 · linkedin

A full-fledged version of my paper, titled 'Enhanced Local Explainability and Trust Scores with Random Forest Proximities', with Joshua Rosaler Dhruv D. Dimitris Vamvourellis Bhaskarjit Sarmah Deran Onay and Stefano Pasquali is out on arXiv now: https://lnkd.in/eY3V96yV

Here, we have used proximities extracted from the trained Random Forests for local explanability for the predictions of the Random Forest.

The tree-based methods are still some of the most used machine learning methods especially for the tabular data. This paper is a part of a series of papers on case-based explanability of the tree-based methods I am working on with my team. A few more to come out soon, stay tuned :-)

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Dhagash Mehta, Ph.D.
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Greater Philadelphia