Profile
Hello World!
I’m Aviel Stern and this is my blog in hopes for you to get to know me and to connect with fellow scientists.
I am a Data Scientist located in Toronto, Ontario. I am originally from San Jose, CA and a 90’s kid which means I grew up going to the mall and eating glorious burritos. Growing up in California, I was always mesmerized by earthquakes and my mom’s tale of the Loma Prieta 1989 earthquake.
Due to my magnetic energy towards earthquakes, natural disasters, and of course beautiful ROCKS! I attended University of California, Santa Cruz for a Bachelor of Science in Earth Science. There I gained lots of geology experience in field mapping, analysis through LiDAR Imagery, use of ArcGIS, measuring the hydraulic flow, and my personal favorite: identifying faults and analyzing their mechanical properties! My senior year, I worked with Brodsky Seismology Lab where ran lab experiments on angular Santa Cruz beach sand to mimic loose particle weakening within fault zones (also known as fault gouge). These experiments show that 1) angular grains experience shear stress and force chain weakening in the presence of increase acoustic vibrations during intermediate slip-rates. 2) These experiments increase our understanding of potential reasons for why fault weakening and strengthening occurs within specific faults zones. After playing with beach sand, I went on to working with wet kaolin clay and boundary element method models! Yay!
I joined Michele Cook’s Geomechanics Lab at University of Massachusetts, Amherst where I received a Master of Science degree in Geoscience with a focus in Geomechanics (Rock Mechanics/Fault and Fracture Mechanics). I ran 3-D computational modelling to obtain stresses along a fault system to 1) predict fault growth and prove that fault interaction influences fault evolution, and 2) estimate absolute shear tractions along the San Andreas fault. There I gained experience in MATLAB, Perl, Poly3D (boundary element method for fracture modelling), 3DMove (Structural Model Building Software), Ubuntu, Linux, Adobe Illustrator, 3D laser scans, experimental design, model setup, technical writing, and presenting work professionally. At UMass Amherst, I gained the analytical and technical skills needed for me to be able to pursue a career in Data Science. I as well gained fundamental knowledge into developing a robust model through validating and verifying model results and ensuring that the boundary conditions are met correctly.
After graduating at UMass Amherst, I realized my passion for data and running computational models. I decided to work at OriginLab, a graphing data analyst software company, where I worked with customer’s data to ensure that they are utilizing the data manipulation, grpahing, and fitting tools properly in the software. To further my career path towarads Data Science, I decided to attend BrainStation Data Science diploma program in Toronto. At BrainStation I fell in love with Data even more than before! I worked on many hands-on projects in SQL, Tableau, Python and AWS. For my final project I created a predictive model for wine scores by using geography, vintage, variety, description, and price, and found that logistic regression performed the best compared to random forest and XGBoost models.
Currently, I am a Data Science Fellow with SharpestMinds where I am collecting twitter API data on the US 2020 elections in order to identify and analyze major topics from tweets. My approach is to apply a variety of topic modelling and sentiment analysis methods to 1) identify the best approach 2) identify sentiment and topic trends.
I am interested in Data Science because I am excited to apply scientific methods and analysis within real-world applications in order to gain insights through data findings and help businesses grow. My aptitude for research, analytics, and communicating data to a non-scientific audience makes data science an inherent career path for me.
Things that I love to do are swimming, hiking, dancing, and baking the best oatmeal chocolate chip cookies. I as well co-created Tasty Chicks Comedy.
Skills
Data Science Techniques
𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: NLP, Decision Trees, Random Forest, Boosting, KNN, Logistic Regression, K-Means, NLP, Model Evaluation, Preprocessing Data, Feature Engineering
Programming Languages
Technical Skills
Education
BrainStation | Data Science Diploma
March 2020, Toronto, Ontario
University of Massachusetts, Amherst | Master in Geoscience
September 2016, Amherst, MA
University of California, Santa Cruz | Bachelors in Earth Science
September 2012, Santa Cruz, CA
Projects
Use of NLP and Supervised Learning to target wine scores