Help & Supportcareers@gravityer.com
AI at LinkedIn
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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.