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Work Background
AbbVieAssociate Director
Apr. 2024Irvine, California, United States
AbbViePrincipal Data Scientist I, R&D
Sep. 2022 - Apr. 2024Irvine, California, United StatesLead a team of 12 software engineers, data scientists and data analysts. A suite of new imaging tools capable of capturing aesthetic features.
Robust frameworks to quantify, analyze, and share imaging data and methods.
Direct all strategies, planning, coordination and fiscal management of the Clintech Platform of products. Interview, hire, train and retain staff. Implemented a successful Intern Program. Drive the definition, design, implementation and FDA required validation of cutting-edge algorithms to analyze disparate data sources. Work directly with product management teams for compliance with Good Clinical Practices (GCP’s), SOP’s and ICH guidelines.
AbbVieSr Data Scientist II, Clinical Technology DS Lead, R&D
Apr. 2021 - Sep. 2022Irvine, California, United StatesProvided concept, vision and organizational planning to drive strategies for data foundation, MLOps, QA and analytics. Defined processes to achieve optimal results across the entire AI platform for all stages from concept, design, data collection and deployment. Performed Data Scientist functions while also managing staff.
VITRU.aiFounder
Feb. 2021
AbbVieSr Data scientist I
Mar. 2020 - Apr. 2021Irvine, California, United StatesML engineering:
working with latest models involving Computer vision, supervised(Semantic-segmentation, Gans, ...), unsupervised(SVD,....) , semi-supervised( Automatic semantic segmentation labeling with few tagged images) DB engineering: Created and taught curriculum on SQL/NOSQL PostGRES/MongoDB. AI engineering:
In charge of converting medical aesthetics and medical Vision POC's into actionable Data Science Work-streams. Software Engineering:
Setting up Devops environment, Data Science Life Cycle, Code modularity, API creation. Engineering:
Infrastructure, Vm(Data Science vm's), Data( Storage, retrieval, manipulation), Tools(Update environments with latest tools, versions),Report(Creating tools for management to be able to retrieve and create reports form data or Machine Learning work streams) Management: In charge of the VISI(Vision) and IMAGINE(aesthetics) Data science team.
University of California, Irvine Division of Continuing EducationData Science professor | 2U
Feb. 2019 - Mar. 2023Irvine, CaliforniaTopics Covered: VBA, Python, Pandas, visualization (Matplotlib, Plotly, D3, Tableau) , API, SQL, HTML, CSS, Bootstrap, Dashboard, Web Scraping, ETL, JavaScript (API, Web Charting), Geomapping, R, Machine learning, Classification, Clustering, Neural Network, Deep learning, Hadoop, Spark, NLP. Based on this topics multiple projects were presented.
Galaxy.AIData Scientist|Machine Learning Engineer
Feb. 2019 - Mar. 2020GANs, RNN, CNN, Data Generation, Code Generalization, Code modularization, Project Management.
Code Optimization for training extremely large Image Datasets. Data Engineering pipelines to put algorithm into production.
Galaxy.AIData Scientist
Aug. 2018 - Jan. 2019Project 3: Multilabel classification of Big Data. •Performed the complete ETL process for multiple data sources from the client and using NLP, Random forest to create a classifier for the required target.
•Improved upon previous results using a machine learning approach. Project 2: Semantic segmentation of a multi class images and videos. •Created and implemented pre-processing and post-processing procedures to improve results. From research using the latest emerging AI concepts. Project 1: Built an image classifier using CNN with a very small data set. •Used an external data set and Multi-task Learning to improve results and exceeded expected baseline accuracy.
UCSF Medical CenterData Science researcher
Oct. 2017 - Jul. 2018San Francisco Bay AreaProject 1: Build an ETL pipeline on billions of patient meta data. •The client required extraction of patient data into a SQL format for easy access •Automated data ingestion, converted and cleaned excel files into PostgreSQL. •Improved search time for a patient data from hours and days into seconds. The organization of the data also now allows for further research using ML. Project 2: Predict local failure and grade of a tumor with structured, supervised data using tree-based models. •Goal of this paper was to replicate and surpass the metrics of an already published paper. •Created an Experiment and used Replication, Blocking and randomization to minimize the effects of nuisance factors and allowed to vary factors. •Predicted on the data using feature engineering, imputation and machine learning. •Used random sampling techniques to assure the validity of the results. •Met and exceeded results in previously published papers using the accuracy metric. Project 3: Unstructured Data: Predict local failure and grade of a tumor with unstructured, supervised data using deep learning models •Transformed medical images into machine learning ready data structures. •Implemented prediction model using 2D and 3D Convolutional Neural Networks in Pytorch. •Improved upon the results of the tree based model approach.
Golden West CollegeMathematics Professor
Aug. 2014 - Jun. 2017Huntington Beach, CA• Taught the entire spectrum of lower division math classes, such as multivariate calculus, statistics, and finite math. • Spearheaded On-Course workshop strategies to empower students and create a learning path.
Avalon Risk ManagementQuantitative Research Analyst
Aug. 2008 - Jul. 2010Irvine, CAProject 1: Build an options calculator to simulate option prices in a given time based on market condition. • Used VBA to create the options calculator. • This allowed brokers to quickly input different market conditions and get a price for the option. Project 2: Build a monitoring system that calculated the risk associated with trades made by brokers. • Used statistical techniques such as Anova, t-test, z-test to assess if a trade was considered risky based on a given risk tolerance.
• Helped the CTO to assess the risk on all brokers positions much more quickly.
Dyslexia Foundation (ADA)Mathematics Teacher (Mcnichols Learning)
Nov. 2005 - Sep. 2013Newport Beach, CA• Used multi-sensory techniques and alternative approaches to strengthen student’s mastery of subjects: Statistics, Accounting, Calculus and Economics.
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