Senior Statistician and Data Scientist with 9+ Years Experience

Curriculum Vitae

Yousif Alyousifi, Ph.D.

Address: Glencoe Hills Dr, Ann Arbor, MI 48108

Mobile: 313-***-****

Email: ************@*****.*** ********@***.*****.*** Professional Summary

Senior Statistician and Data Scientist with over 9 years of academic and research experience in statistical modeling, machine learning, and deep learning. Demonstrated expertise in developing and validating non-invasive predictive models for liver disease (MASLD, MASH, cirrhosis, and liver- related mortality) across large-scale cohorts, including UK Biobank, Framingham, MGI, and AllofUs. Skilled in regulatory-grade statistical methods, survival analysis, and advanced machine learning techniques.

Professional Experience

University of Michigan Medical School – Ann Arbor, MI Postdoctoral Research Fellow (Senior Statistician) 2023 – Present

• Designed and validated statistical and ML models predicting fatty liver disease outcomes, outperforming current indices.

• Applied R, Python, SQL, and Spark for data cleaning, modeling, and visualization on biobank and clinical datasets.

• Conducted survival/time-to-event modeling (Cox, AFT, C-index, bootstrap CI) for liver outcomes and validated predictive performance.

• Developed risk prediction scores (ESI, MASH-CB, MASLD-PRS), improving early detection and stratification, and implemented transfer learning approaches for non-IID datasets

• Collaborated cross-functionally with clinicians, geneticists, and data scientists to deliver actionable insights for translational medicine.

Universiti Putra Malaysia (UPM) – Selangor, Malaysia Assistant Professor (Statistics) 2021 – 2023

• Taught Statistical Modeling, Statistical Inference, and Data Analytics for undergraduate

/graduate students.

• PI of a statistical machine learning grant in air pollution modeling; successfully secured funding.

• Supervised MSc/PhD students; published research in environmental analytics. Universiti Teknologi Malaysia (UTM) – Johor, Malaysia Postdoctoral Researcher (Statistics) May-September, 2021

• Built forecasting and Bayesian models for environmental and health risk monitoring.

• Delivered cross-disciplinary insights for policy-driven research. Universiti Teknologi PETRONAS (UTP) – Seri Iskander, Malaysia Instructor / Data Analyst 2018 – 2020

• Taut and developed curriculum in data analytics & applied mathematics using R/Python.

• Applied ML methods to energy/environmental datasets, publishing in peer-reviewed outlets. Additional Teaching & Analyst Roles 2007 – 2018

• Math & Statistics educator and institutional data analyst at IMAS (Malaysia, 2016-2018).

• Lecturer at University of Science & Technology (Yemen, 2008-2012).

• Lecturer at Thamar University (Yemen, 2012-2013). Education

• Ph.D. Applied Statistics – Universiti Kebangsaan Malaysia (UKM), 2021

• M.Sc. Statistics – UKM, 2017

• B.Sc. Mathematics (Honors) – Thamar University, Yemen, 2007 Selected Achievements

• Developed ML-derived non-invasive scores for MASLD, MASH, and liver mortality.

• Secured two competitive research grants (UPM, UKM).

• Published 20+ peer-reviewed papers (Springer, Elsevier, IEEE).

• Presented at AASLD, ASHG, MyState, and global conferences.

• Recipient of UKM Excellent Thesis Award (2021) & Graduate on Time Award. Technical Skills

• Statistical Tools: R, Python, SQL, Spark, Shiny, SPSS

• Libraries: Scikit-learn, Lifelines, Optuna, Tidyverse, CART, TensorFlow, PyTorch

• ML/AI Expertise: Penalized regression, Random Forest, XGBoost, CatBoost, LightGBM, CNNs. Training and inference of large-scale AI models, including Large Language Models

(LLMs), multimodal models, and reasoning models.

• Statistical Methods: Bayesian inference, MCMC, GLMMs, time-series, survival analysis, causal inference, multivariate analysis.

• Clinical/Regulatory Experience: Biobank analyses, risk stratification, biomarker evaluation, longitudinal modeling

• Visualization: ggplot2, matplotlib, seaborn, plotly, tidyplot

• Methods: Cross-validation, bootstrapping, decision curve analysis, SHAP, feature engineering.

• Collaboration: Statistical consulting, cross-functional teamwork, mentoring diverse trainees Key Projects

Clinical & Genetic Risk Modeling for Liver Disease

• Built multi-cohort ML models for MASLD/MASH detection, fibrosis prediction, and mortality risk.

• Applied transfer learning across UKBB, FHS, MGI, and AllofUS cohorts.

• Delivered results at AASLD/ASHG (2023–2025); manuscripts under review at Journal of Hepatology and Liver International.

Stochastic & Bayesian Models for Air Pollution

• Developed spatio-temporal Markov chain and Bayesian frameworks for air quality forecasting.

• Published in Q1 journals (Springer, IEEE Access, Elsevier). Publications & Presentations

• 20+ peer-reviewed ISI Q1/Q2 publications in applied statistics and machine learning (Full publication list available on Google Scholar)

• Presenter at AASLD, ASHG, UMCGR GI Retreats, and Internal Medicine Research Day.

• Referee for leading journals: IJFS, Knowledge-Based Systems, IEEE Access, Environmental Science & Pollution Research.

Honors & Awards

• Putra Research Grant (PI), Universiti Putra Malaysia, 2022

• Excellent Thesis Award, Universiti Kebangsaan Malaysia(UKM), 2021

• Graduate on Time Award, University Kebangsaan Malaysia (UKM)

• Yemeni President’s Award for Distinguished Students, Yemen, 2008

• Graduate Research Assistant Fellowship, UKM, 2017 Memberships

• American Association for the Study of Liver Diseases (AASLD)

• American Society of Human Genetics (ASHG)

• American Statistical Association (ASA)

References contact: Upon request

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