diff --git a/README.md b/README.md index 56902f8d..e7b93340 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,12 @@ -# Data Scientist +# ML Enthusiast -#### Technical Skills: Python, SQL, AWS, Snowflake, MATLAB +#### Technical Skills: Python (Pandas, NumPy, Scikit-learn), SQL, Git, Matplotlib/Seaborn: ## 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 +B.S. in Computer Science — Yerevan State University, (_September 2024 - Present_) ## Projects -### Data-Driven EEG Band Discovery with Decision Trees -[Publication](https://www.mdpi.com/1424-8220/22/8/3048) - -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. - -![EEG Band Discovery](/assets/img/eeg_band_discovery.jpeg) - -### Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales -[Publication](https://www.mdpi.com/1424-8220/22/11/4240) - -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. - -![Bike Study](/assets/img/bike_study.jpeg) - -## 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](https://www.youtube.com/channel/UCa9gErQ9AE5jT2DZLjXBIdA) - -## 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 - -- [Data Science Blog](https://medium.com/@shawhin) +- Performed data preprocessing and feature engineering for predictive modeling using **Python**. +- Built and validated ML models (Linear Regression) to solve regression tasks. +- Collaborated with other students/engineers to test and refine model performance. +- Documented experiment results and metrics (Accuracy, F1-score) in **Jupyter Notebooks**. \ No newline at end of file