├── README.md ├── _config.yml └── assets └── img ├── bike_study.jpeg ├── eeg_band_discovery.jpeg └── headshot_circle.png /README.md: -------------------------------------------------------------------------------- 1 | # Data Scientist 2 | 3 | #### Technical Skills: Python, SQL, AWS, Snowflake, MATLAB 4 | 5 | ## Education 6 | - Ph.D., Physics | The University of Texas at Dallas (_May 2022_) 7 | - M.S., Physics | The University of Texas at Dallas (_December 2019_) 8 | - B.S., Physics | The University of Texas at Dallas (_May 2017_) 9 | 10 | ## Work Experience 11 | **Data Scientist @ Toyota Financial Services (_June 2022 - Present_)** 12 | - Uncovered and corrected missing step in production data pipeline which impacted over 70% of active accounts 13 | - Redeveloped loan originations model which resulted in 50% improvement in model performance and saving 1 million dollars in potential losses 14 | 15 | **Data Science Consultant @ Shawhin Talebi Ventures LLC (_December 2020 - Present_)** 16 | - 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 17 | - Applied unsupervised deep learning approaches to longitudinal ICU data to discover novel sepsis sub-phenotypes 18 | 19 | ## Projects 20 | ### Data-Driven EEG Band Discovery with Decision Trees 21 | [Publication](https://www.mdpi.com/1424-8220/22/8/3048) 22 | 23 | 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. 24 | 25 | ![EEG Band Discovery](/assets/img/eeg_band_discovery.jpeg) 26 | 27 | ### Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales 28 | [Publication](https://www.mdpi.com/1424-8220/22/11/4240) 29 | 30 | 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. 31 | 32 | ![Bike Study](/assets/img/bike_study.jpeg) 33 | 34 | ## Talks & Lectures 35 | - Causality: The new science of an old question - GSP Seminar, Fall 2021 36 | - Guest Lecture: Dimensionality Reduction - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2021 37 | - Guest Lecture: Fourier and Wavelet Transforms - Scientific Computing (PHYS 5315), Fall 2020 38 | - A Brief Introduction to Optimization - GSP Seminar, Fall 2019 39 | - Weeks of Welcome Poster Competition - UTD, Fall 2019 40 | - A Brief Introduction to Networks - GSP Seminar, Spring 2019 41 | 42 | - [Data Science YouTube](https://www.youtube.com/channel/UCa9gErQ9AE5jT2DZLjXBIdA) 43 | 44 | ## Publications 45 | 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 46 | 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. 47 | 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 48 | 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 49 | 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). 50 | 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). 51 | 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] 52 | 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 53 | 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 54 | 55 | - [Data Science Blog](https://medium.com/@shawhin) 56 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | title: Shaw Talebi 2 | logo: /assets/img/headshot_circle.png 3 | #description: text below image 4 | show_downloads: true 5 | theme: jekyll-theme-minimal 6 | -------------------------------------------------------------------------------- /assets/img/bike_study.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShawhinT/example-portfolio/74177a57a46ab009f43d4b55937235adac563e7c/assets/img/bike_study.jpeg -------------------------------------------------------------------------------- /assets/img/eeg_band_discovery.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShawhinT/example-portfolio/74177a57a46ab009f43d4b55937235adac563e7c/assets/img/eeg_band_discovery.jpeg -------------------------------------------------------------------------------- /assets/img/headshot_circle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShawhinT/example-portfolio/74177a57a46ab009f43d4b55937235adac563e7c/assets/img/headshot_circle.png --------------------------------------------------------------------------------