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Xin Deng
Ph.D, Machine Learning Scientist
Microsoft Corporation
Research
I am currently a Machine Learning Scientist at Microsoft. My Current work is involved in processing various data warehouse sources, building models in customer behavior, user targeting, personalized user recommendation and feedback topics, and shipping intelligent product features through data mining, machine learning and NLP techniques. Before joining Microsoft, I had been a research scientist with LexisNexis Healthcare for over one year. My previous work focused on healthcare risk analytics, with medical, pharmaceutical claims and public record data by utilizing statistical analysis, computational intelligence, data mining and machine learning techniques. I graduated from University of Missouri-Columbia with a Ph.D in computer science, after five plus years’ research in Machine Learning and AI.
I published twenty papers and book chapters in premium journals and conferences, and I also gave invited talks and presentations at many conferences and universities. I was the Program Chair of 2014 KDD Workshop: Big Data Analytic Technology For Bioinformatics and Health Informatics (KDDBHI), Chair of the special session in 2014 IEEE Symposium Series on Computational Intelligence (SSCI), Chair of 2015 ACM-BCB Workshop: Big Data Analytic Technology For Bioinformatics and Health Informatics (KDDBHI), and Co-Chair of IEEE BigData Workshop: Big Data Analytic Technology For Bioinformatics and Health Informatics (KDDBHI) from 2016 to 2021, and also Co-Chair of IEEE BigData Workshop: Big Data Technology and Ethics Considerations in Customer Behavior and Customer Feedback Mining (BEBF) from 2016 to 2018. I was also the chair of Young Professional Group of IEEE Orlando Section.
Education
PhD student of Computer Science, 2008-2013
University of missouri-columbia, Columbia, MO, US
One year master experience of Computer Science, 2007-2008
Wuhan Univerisity, Hubei Province, Wuhan, China
Bachelor Degree of Computer Science, 2003-2007
Wuhan Univerisity, Hubei Province, Wuhan, China
Experiences
Rewiew of submitted papers for (2014)
Rewiew of submitted paper for (2013)
MCBIOS 2013, April, 2013
Pacific Symposium on Biocomputing, Big Island of Hawaii, January, 2013
Poster: Predicting Protein Model Quality from Sequence Alignment by Support Vector Machines
BIBM 2011, Atlanta, November, 2011
Rewiew of submitted paper for (July, 2012)
Rewiew of submitted paper for (March, 2012)
Proteome Science & Computational Biology
Rewiew of submitted paper for (Aug, 2011)
Rewiew of submitted paper for (Aug, 2011)
2012 Pacific Symposium on Biocomputing (PSB)
Rewiew of submitted paper for (Sep, 2010)
Rewiew of submitted paper for (Sep, 2010)
27th Annual IPG Symposium: Plant Protein Phosphorylation, May, 2010
Missouri Life Sciences Week@MU, April 12th to 17th, 2010
Web-based Tools, softwares and Services
MSACompro:a novel and practical multiple protein sequence alignment algorithm based on secondary structure, solvent accessibility, and contact map information
PreDisorder: Sequence-Based Protein Disordered Region Prediction Using Neural Networks
MotifMatcher: a tool to Search for matched Transcription Factors in Jaspar Database for predicted TFBS motifs
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