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Case Western Reserve University
Doctor of Philosophy (PhD), Computer Engineering
N/A – Present
Nanjing University of Science and Technology
Bachelor of Science (B.S.), Electrical Engineering
N/A – Present
Tianjin University
Master of Science (M.Sc.), Electrical Engineering
N/A – Present
Staff software engineer, machine learning
March 1, 2022 – Present
Senior software engineer, machine learning
June 1, 2019 – March 1, 2022
Software engineer, machine learning
December 1, 2017 – May 1, 2019
Verisk Analytics
Data Scientist
April 1, 2017 – October 1, 2017
San Francisco Bay Area
Case Western Reserve University
Research Staff Member
October 1, 2015 – March 1, 2017
Case Western Reserve University
Research Assistant
January 1, 2011 – January 1, 2015
Predicting drug toxicity using machine learning approaches
May 1, 2016 – Present
Analyzed heterogeneous features that could contribute in identifying drug toxicity; developed a supervised machine learning model to predict drug toxicity; identified potential toxic cancer targeted drugs.
Understanding gene-environment interactions in human diseases
January 1, 2016 – Present
Constructed a large scale biological network (>20K nodes, >8M edges) to model multi-type gene-environment interactions; developed a random walk model based network analysis approach; identified Alzheimer's disease related food metabolites; results showed indications for Alzheimer's disease prevention.
Predicting precision medicine based drug therapies
August 1, 2015 – Present
(a) Developed a novel unsupervised machine learning approach to predicting potential disease therapies; achieved better performance than state-of-art drug discovery approaches; identified a new candidate drugs for Parkinson’s disease that is supported by animal experiments. (b) Customized the approach to predict drugs for glioblastoma and its four genomic subtypes; results demonstrate the heterogeneity of the genetic basis and treatments for glioblastoma.
Understanding disease genetics towards drug discovery
January 1, 2013 – Present
(a) Developed a novel network integration and analysis approach to identify disease associated genes using disease phenotypic relationships; systematically prioritize associated genes for 5662 diseases, achieved significantly better performance than state-of-art approaches; identified promising drug targets for Parkinson's disease. (b) Developed a context-sensitive model to construct disease phenotype networks; developed a disease gene prediction approach based on the context-sensitive network model; achieved significantly improved performance than traditional approaches. (c) Developed a cross-species genetic network based disease gene prediction approach; identified associated genes and candidate drug targets for malaria.
Understanding disease phenotype-genotype associations
January 1, 2012 – December 1, 2014
(a) Developed a data mining approach to systematically identify comorbidity patterns from 3 million patient records; conducted networks analysis on the comorbidity patterns and demonstrated the effect of age and gender on comorbidity patterns. (b) Developed a comorbidity network analysis approach to detecting the molecular evidence for the link between colorectal cancer and obesity.
Retrieving relevant web images for image library construction
December 1, 2010 – December 1, 2012
- Developed a novel semi-supervised image classification approach. - Designed ontology-guided semantic-level image features. - Reduced manual efforts by 85% and improved the precision by 10% comparing with the standard approach. - Automatically annotated tens of thousands of medical images with an average precision of 80%.
Designing hardware architecture for advanced signal acquisition and processing system
January 1, 2009 – Present
– Designed hardware architecture for compressive sensing based image reconstruction algorithm. – Developed compressive sensing algorithm for acquiring multichannel neurological signals.
Social Network Analysis
Coursera
June 24, 2026 – Present
Data Analysis
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Useful Genetics
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is heavily focused on academic and research-oriented data science and machine learning, particularly in bioinformatics and medical informatics. While this demonstrates strong analytical capabilities, the direct alignment with a typical 'Data Analyst' role, which often involves business intelligence, reporting, and dashboarding, is not explicitly clear. The experience at LinkedIn as a 'Software Engineer, Machine Learning' suggests a more engineering-focused role rather than pure data analysis. The breadth of skills is strong in ML/data science, but less so in traditional data analysis tools and business domain understanding. This suggests a potential mismatch for a standard Data Analyst role, but a strong fit for a Data Scientist or ML Engineer role.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, analytical thinking, and the ability to work on complex, multi-faceted problems. The progression through engineering roles at LinkedIn suggests a capacity for growth and leadership. However, without specific assessment data on communication, teamwork, or stress handling, a definitive statement on operational fit is limited.