Data Scientist

Technical Skills: Python, SQL, AWS, Snowflake, MATLAB

Education

• Ph.D., Physics | The University of Texas at Dallas (May 2022)
• M.S., Physics | The University of Texas at Dallas (December 2019)
• B.S., Physics | The University of Texas at Dallas (May 2017)

Work Experience

Data Scientist @ Toyota Financial Services (June 2022 - Present)

• Uncovered and corrected missing step in production data pipeline which impacted over 70% of active accounts
• Redeveloped loan originations model which resulted in 50% improvement in model performance and saving 1 million dollars in potential losses

Data Science Consultant @ Shawhin Talebi Ventures LLC (December 2020 - Present)

• Conducted data collection, processing, and analysis for novel study evaluating the impact of over 300 biometrics variables on human performance in hyper-realistic, live-fire training scenarios
• Applied unsupervised deep learning approaches to longitudinal ICU data to discover novel sepsis sub-phenotypes

Projects

Data-Driven EEG Band Discovery with Decision Trees

Publication

Developed objective strategy for discovering optimal EEG bands based on signal power spectra using Python. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.

Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales

Publication

Used Matlab to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.

Talks & Lectures

• Causality: The new science of an old question - GSP Seminar, Fall 2021

• Guest Lecture: Dimensionality Reduction - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2021

• Guest Lecture: Fourier and Wavelet Transforms - Scientific Computing (PHYS 5315), Fall 2020

• A Brief Introduction to Optimization - GSP Seminar, Fall 2019

• Weeks of Welcome Poster Competition - UTD, Fall 2019

• A Brief Introduction to Networks - GSP Seminar, Spring 2019

• Data Science YouTube

Publications

1. Talebi S., Lary D.J., Wijeratne L. OH., and Lary, T. Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning (2019). DOI: 10.26717/BJSTR.2019.20.003446
2. Wijeratne, L.O.; Kiv, D.R.; Aker, A.R.; Talebi, S.; Lary, D.J. Using Machine Learning for the Calibration of Airborne Particulate Sensors. Sensors 2020, 20, 99.
3. Lary, D.J.; Schaefer, D.; Waczak, J.; Aker, A.; Barbosa, A.; Wijeratne, L.O.H.; Talebi, S.; Fernando, B.; Sadler, J.; Lary, T.; Lary, M.D. Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning. Sensors 2021, 21, 2240. https://doi.org/10.3390/s21062240
4. Zhang, Y.; Wijeratne, L.O.H.; Talebi, S.; Lary, D.J. Machine Learning for Light Sensor Calibration. Sensors 2021, 21, 6259. https://doi.org/10.3390/s21186259
5. Talebi, S.; Waczak, J.; Fernando, B.; Sridhar, A.; Lary, D.J. Data-Driven EEG Band Discovery with Decision Trees. Preprints 2022, 2022030145 (doi: 10.20944/preprints202203.0145.v1).
6. Fernando, B.A.; Sridhar, A.; Talebi, S.; Waczak, J.; Lary, D.J. Unsupervised Blink Detection Using Eye Aspect Ratio Values. Preprints 2022, 2022030200 (doi: 10.20944/preprints202203.0200.v1).
7. Talebi, S. et al. Decoding Physical and Cognitive Impacts of PM Concentrations at Ultra-fine Scales, 29 March 2022, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-1499191/v1]
8. Lary, D.J. et al. (2022). Machine Learning, Big Data, and Spatial Tools: A Combination to Reveal Complex Facts That Impact Environmental Health. In: Faruque, F.S. (eds) Geospatial Technology for Human Well-Being and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-71377-5_12
9. Wijerante, L.O.H. et al. (2022). Advancement in Airborne Particulate Estimation Using Machine Learning. In: Faruque, F.S. (eds) Geospatial Technology for Human Well-Being and Health. Springer, Cham. https://doi.org/10.1007/978-3-030-71377-5_13

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