Niccolò Dalmasso
Vice President at JPMorgan Chase & Co.
New York, New York, United States
Summary
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Senior🎓
Top SchoolNiccolò Dalmasso is an AI research lead and Vice President at J.P. Morgan Chase with a Ph.D. in Statistics from Carnegie Mellon and a decade of experience applying statistical machine learning to finance. He designs and deploys advanced models—ranging from deep generative models and temporal point processes to uncertainty quantification and fairness-aware algorithms—to tackle real-world financial problems and publishes regularly at top AI venues. Comfortable bridging research and production, he has a strong background in time series, optimization, and LLMs, with prior internships at IBM Research and hands-on R&D as a summer associate at J.P. Morgan. His teaching pedigree includes leading graduate and undergraduate courses at Carnegie Mellon, reflecting a talent for explaining complex methods to diverse audiences. Based in New York, he combines rigorous academic training with practical product impact, often focusing on reproducible synthetic data and high-fidelity modeling that quietly underpin safer, more auditable financial systems.
10 years of coding experience
4 years of employment as a software developer
Master of Science (MS) Statistics, Master of Science (MS) Statistics at Imperial College LondonScientific High School "Giuseppe Peano", CuneoBachelor of Science (BS) Mathematics, Bachelor of Science (BS) Mathematics at Università degli Studi di TorinoCertificate of High Qualification Government and Human Sciences, Certificate of High Qualification Government and Human Sciences at Scuola di Studi Superiori di TorinoDoctor of Philosophy (Ph.D.) Statistics, Doctor of Philosophy (Ph.D.) Statistics at Carnegie Mellon University
Github Skills (17)
approximate9
hierarchical9
prediction-model9
sampling9
density-estimation9
data-storage8
statistical-inference8
computation8
statistics7
regularization7
julia7
hypothesis-testing6
classification6
inference5
machine-learning5
Programming languages (3)
Github contributions (5)
lee-group-cmu/DeepCDE
May 2019 - May 2020
Neural networks for conditional density estimation
Contributions:20 commits, 6 PRs, 9 pushes in 11 months
conditionalneural-networksdensity-estimationestimationneural-network
Mr8ND/academic-kickstart
Jan 2019 - Sep 2024
Contributions:73 pushes, 1 branch in 5 years 9 months
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