Data Scientist Mental Health

Sidy Danioko, Ph.D.;

*** ***** ****** ******, ******, Ca, 92868. 901-***-****. ********@****.*******.*** Summary: Data Scientist with strong background in mathematics, physics (particle physics, quantum physics, and biophysics), statistics, biostatistics, finance, medicine, and healthcare. Have a knack for deploying mathematically and sounded responses to bioinformatics, public health, and medical related questions for better decisions. Experience building statistical models. Five plus (5 +) years of experience deploying predictive models, analyzing data mining algorithms to provide robust and efficient data-driven solutions to very challenging business problems a singularity combined with my personality and ability to work individually and with others, portend my success. Four plus (4 +) years of experience constructing deep learning models with Keras, Tennsorflow and Pytorch. More than six (6) years of experience with signal processing skills and tools. Extensive Hands-on experience with R, R-packages, and R-Shiny.

INDUSTRY WORK EXPERIENCE

Orange County Health Agency

California Santa Ana

Data Research Scientist IV June

2022-

Present

• Develop on the daily basis scripts using R and Python to accomplish data engineering tasks. In this position, I have been extensively processing data for different departments including Outreach and Engagement (OAE), Children and Youth Services (CYS), Adult Mental Health, and Quality Management Services (QMS).

• Analyze, on the daily basis, large datasets from various sources, such as electronic health records, surveys, and social media, to identify trends, correlations, and insights related to mental health.

• Collaborate on the weekly basis with different teams within the Orange County Health Care Agency to design and improve care management software platform.

• Extensively use of statistical machine learning to identify informative patterns to create predictive models to assess numerous risk factors, treatment efficacy, or patient outcomes.

• Participate in designing and analyzing studies aimed at understanding mental health issues, such as the effectiveness of therapies or the impact of social determinants.

• Setting Data Visualization workflow in Tableau, and Tableau Prep

• Extensively using Pyspark to analyze complex text data

• Investigated and Presented Orange County population health trends and insights to C-level executive leadership, Directors and clients.

• Constantly data engineering ETL’s from EHR production database for robust data analytics and modeling.

• Building from simple to advanced statistical Models and Machine Learning Models rooted into electronic health records to predict medical/operational outcomes to improve patient care, streamline CM processes, and minimize health insurance provider costs.

• Currently managing a group of data science interns to optimize hospital operations and predict patient outcomes via an extensive use of machine learning and statistical models on electronic health records.

• Write on the weekly basis R and or Python scripts to automate operational workflows and reports. DevMasters Irvine,

California

Data Scientist/Machine Learner and Statistical Researcher June 2017- Present

• Actively participated in the construction of a Learning Paradigm System (EdTech AI) to help some high schools overcome specific learning challenges that students are facing. The proposed solutions to the highlighted problems were provided by using an array of deep learning algorithms and architectures. This data driven intelligent platform allows us to assess emotional intelligence, conduct social behavior analyses, and quantify intelligence quotients. It also allows us to identify the degree of the learner’s attention, distraction, and directed attention fatigue (DAF) in the process of learning. The platform also enables the customer to predict the learner’s fallout rate, as well as their detention score and engagement rate.

• Actively participated in the construction of Inlook (Fashion AI) for the fashion & retail industry. We built a facial recognition system with the integration of AI. The final intelligent product identifies human facial features like chicks, eyebrows, lipstick so that appropriate products could be recommended for specific looks. This AI-based platform also helps to identify customer happiness, and satisfaction level based on the recommended looks. In this project, I utilized enterprise design frameworks and patterns for web development. I also maintained technical documentation and generated business presentations. I Participated in code reviews as per quality principles.

• Led a study that provided data-driven solutions to a US-based sports analytics company by using optimization techniques, big data, and machine learning models.

• Developed advanced statistical modeling techniques and machine learning models to get insights into the state of the Asian real estate market. The aim of this investigation is to construct an AI driven platform, which will be specialized in predicting house prices in the Asian market, providing robust appraisal services, conducting comparative market analysis, modeling risks inherent to investor’s portfolios. In this process, we intend to build a robust mathematical formula of different points of interests in the Asian housing market. In doing so, we will be able to determine important factors that could impact house prices. This will mainly be based on human sentiments about area, market analytics and social emotional value to property.

• Took positions as a Data Scientist and Project Manager within the Z1data (PropTech Company). In this project, I used PostGres Database and Passing to Data Science Team. Created API For Logs and Model integration. Created web for visualization. I also developed and implemented robust architecture systems for client sites. Plus, I formulated technical specifications by translation of product needs.

• Worked on the use of machine learning on toponyms in mapping African ethnic groups.

• Currently investigating the application of Reinforcement Learning in hedging different types of risks in the face of transaction costs. The aim of this project is to be able to derive optimal hedging strategies for derivatives while incorporating the trading costs in the hedging decisions.

• Currently developing methods to calculate the liability of a portfolio using conditional generative adversarial networks (CGANs) and Bayesian analysis for a comparative study.

• Supervising a large health related project named SoberBuddy (Digital Healthcare Agent). This project aims at constructing a Healthcare platform for drug addiction via an extensive use of AI. The final product will try to identify the user’s sober’s level. It will also enable the customer to understand the user’s quest and direction they would need as help to keep. Furthermore, the obtained AI engine will further be able to periodically follow them and check their sober level. This very smart system will maintain all these activities via deep learning. Xaragon, LLC Sacramento,

California

Data Scientist March 2020 -

June 2022

• Brought my quantitative and computational skills and sciences to help inform numerous companies’ new strategies, and machine learning efforts to improve their performance, reduce the existing risks, identify opportunities, and make more informed decisions.

• Conducted quantitative and qualitative analyses of large and complex data with respect to different project requirements, needs and perspectives.

• In Python, used NLP to perform keywords extraction and custom Name Entity Recognition, Detected anomalies in medical device adverse events through statistical analysis, machine learning model, etc.

• Wrote very complex SQL queries to merge data from multiple tables to obtain relationships between questionnaire data and participant response data.

• Consulted services to help develop, maintain, and monitor robust statistical, machine learning and data driven solutions to numerous complex problems across different domains such as engineering, finance, health, and medicine.

• Conducted finance and academic research to create and refine investment strategies via deep cleaning, the use of mathematical transformations, and analyses of large datasets.

• Accomplished numerous job duties programming in R, Python, Pytorch, SAS, C, C++, MATLAB, SQL, JavaScript and Julia

• Participated in the exploration, conception, and the construction of cloud oriented and scalable platforms for real estate companies.

• Extensively used statistical models, machine learning algorithms, causal modeling, and visualization technologies such as Tableau, Spotfire and PowerBI to efficiently investigate large and high dimensional data for anomaly detection.

• Developed and automated reports and Power Point presentations decreased operation time by 50% using Python.

• Consulted for Company Twilight AI LMS (Url https://twilightai.org/) as a software specialist. My role and responsibilities were to conduct Analyses, design specifications, and manage multiple teams. Cassandra Capital Management LP, Hawaii Hawaii,

Honolulu

Statistical Researcher September 2011 to

August 2012

• Integrated data coming from multiple sources into a space shared by different departments.

• Deployed advanced statistical models to help improve an already existing trading architecture.

• Examined large trading data to increase profitability.

• Constructed new trading strategies by putting at work different time series and volatility models

• Participated in developing solutions to complex problems in quantitative portfolio management, attribution, and optimization.

• Completed a list of end-to-end projects with minimal to no supervision. EDUCATION

Munster Technological Institute Cork,

Cork and Kerry – Ireland

Master of Science in Artificial Intelligence (Online version) 2025 May

• Courses Taken/In Progress -- Fall, 2021/Fall 2024. Practical Machine Learning, Deep Learning, Natural Language, and AI Research Ethics, Computer Vision, Decision Analytics, Big Data Processing, Metaheuristic Optimization, and Knowledge Representation

Chapman University Orange,

California

Ph.D, Computational and Data Science July

2020

Thesis: A novel Correction for the Adjusted Box-Pierce Test -- New Risk Factors for Emergency Department Return Visits within 72 hours with Respiratory Conditions – General Pediatric Model for Understanding and Predicting Prolonged Length of Stay.

• Constructed a novel model diagnosis test that attained the best type I error rates of all goodness-fit- statistics

• Developed investment strategies rooted in the use of some parametric and non-parametric statistical models, time series analysis (univariate and multivariate) machine learning algorithms, advanced deep learning, and stock selection.

• Conducted an extensive simulation study to investigate the effects of imputation methods on the accuracy of predictive models

• Built complex mixed effects models and fifteen (15) advanced machine learning models to discover unprecedented risk factors to prolonged length of stay in pediatric settings. The data used for this study was extracted from the Cerner Electronic Health Records deidentified Respiratory Conditions dataset.

• Conducted a multicenter study of risk factors and prediction of children with high odds of returning to the emergency departments within 72 hours after being discharged. I analyzed the data from 54 health care facilities using the Cerner Electronic Health Records deidentified emergency dataset.

• Participated in constructing very robust statistical models for predicting the mortality rate in intensive care units.

• Was involved in investigating Cardiovascular and other circulatory system diseases that have been implicated in the severity of COVID-19 in adults. A study provides a super learner ensemble of models for predicting COVID- 19 severity among these patients.

University of Hawaii Honolulu,

Hawaii

Master of Science, Financial Engineering August

2012

Thesis: Pricing Oil Price with the incorporation of the fear index (VIX) as a human factor

• Catastrophe Bond Pricing Model with forecasting Hurricane and Tornado University of Memphis Memphis,

Tennessee

Master of Science, Physics December

2008

Thesis: Dissipative Particle Dynamics Simulations of Polymer Solutions in Nano-Channels Undergoing Planar Couette Flow

University of Memphis Memphis,

Tennessee

Master of Science, Mathematics August

2006

PROFFESIONNAL CERTIFICATIONS/ SKILLS/COMPETENCIES

• Currently preparing for Certification in Quantitative Finance (CQF)

• Currently pursuing the finance micro-master’s from MIT

• Have solid theoretical and applied experience with machine learning models and algorithms, deep learning, natural language processing, computer vision, optimization, and multivariable adjustments.

• Have extensive experience with Regression (linear, multi, lasso, ridge, elastic, isotonic, logistic, Poisson) models, Supervised and Unsupervised Learning, Tree-based Methods (Random Forest, Conditional Random Forest, Extreme Gradient Boosting, Extra Trees), Support Vector Machine, Stacking Ensemble Methods (Voting, Weighted Average, Blending, Stacking, Super Learner), Bootstrapping

• Worked on Deep Learning Algorithms: Multi-Layer-Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Deep Residuals, Autoencoder, Generative Adversarial Networks (GAN), Improved GANs, and many others

• Experienced at structuring and analyzing large volumes of data coming from different fields of research such as medicine, finance, and physics for prediction, pattern recognition, and drawing useful conclusions.

• Proficient programming with much software including R, Python, Pytorch, SAS, C, C++, Matlab, SQL, and Julia

• Experienced at high performance computing and parallel computing

• Strong analytical, quantitative, and problem-solving skills, experimental design development, and multiple hypothesis-testing

• In-depth knowledge of parametric and non-parametric statistical models, time series, power, survival, longitudinal data, Bayesian analyses, and causal inference

• Skilled in building forecasting models, estimating probability distributions, volatilities, and correlations

• Working knowledge of one or more of survival analysis, causal and Bayesian inferences.

• Strong communication skills, both oral and in written, with a sounded aptitude of building datasets and provide reports

Contact this candidate