Xinghua Mindy Shi
Postdoctoral Fellow in Machine Learning and Privacy
One postdoctoral position in Machine Learning and Privacy is available in the Department of Computer and Information Sciences at Temple University. The appointee will support and complement ongoing projects in developing new algorithms for machine learning and privacy-preserving modeling. The appointee will work closely with scientists in the Institute for Genomics and Evolutionary Medicine (iGEM) and Center for Data Analytics and Biomedical Informatics at Temple, other institutions in Philadelphia (e.g. Penn Medicine) and beyond (e.g. Stevens Institute of Technology) to develop privacy preserving machine learning methods.
Candidate must have or be close to obtaining a Ph.D. in quantitative fields (include but not limited to Computer Science, Statistics, Mathematics, and Physics) and have demonstrated a high level of research productivity through publication in peer-reviewed conferences and journals. Experiences with machine learning or data privacy is required. As a team player working closely with cross-institutional scientists with broad areas of expertise, strong interpersonal and communication skills are essential.
Postdoctoral Fellow in Computational Biology / Bioinformatics
One postdoctoral position in computational biology and bioinformatics is available in the Department of Computer and Information Sciences at Temple University. The appointee will support and complement ongoing projects in developing computational approaches/pipelines to integrate and analyze large-scale (epi)genomic, expression and interaction data sets for scientific discovery and validation. The appointee will work closely with scientists in the Institute for Genomics and Evolutionary Medicine (iGEM) and Center for Data Analytics and Biomedical Informatics at Temple, other institutions in Philadelphia (e.g. Penn Medicine) and beyond (e.g. Centro Infantil Boldrini in Brazil) to develop new machine learning methods toward precision medicine.
Candidate must have or be close to obtaining a Ph.D. in quantitative fields (include but not limited to Bioinformatics, Computational Biology, Computer Science, Statistics, Mathematics, and Physics) and have demonstrated a high level of research productivity through publication in peer-reviewed conferences and journals. Extensive experiences using scripting languages (e.g. Perl, Python) and statistical/mathematical toolkits (e.g. R, SAS, Matlab, Mathematica) are required. Knowledge of machine/statistical learning and mathematical modeling techniques is desirable. As a team player working closely with cross-institutional scientists with broad areas of expertise, strong interpersonal and communication skills are essential.
Recently celebrating its 50th year anniversary, CIS@Temple is one of the oldest computer science departments in the country and is experiencing tremendous growth in its research and academic programs. Temple University is a Carnegie R1 institution that serves more than 40,000 students and is ranked #44 among top public universities by the U.S. News & World Report. Located in the heart of Philadelphia (the 5th largest city in the United States, known for its arts, culture, history and affordable living), Temple University is in close proximity to many outstanding research centers and industry partners in information technology, healthcare, biotechnology, and finance.
We offer a collegial environment, excellent facilities, and a competitive salary commensurate with experience. Applicants should email a single PDF file including cover letter, CV, brief research statement and list of 3 references to Dr. Mindy Shi (email@example.com).
I am seeking for highly motivated graduate students with undergraduate training in computer science, statistics, mathematics, physics or quantitative biological sciences. Full assistantship will be provided upon admission. Please check out the admission information for CIS PhD and Bioinformatics PhD respectively. Please contact Dr. Mindy Shi (firstname.lastname@example.org) if you are interested.
Dr. Junjie Chen (postdoctoral researcher, 2018-)
Chong Li (Bioinformatics PhD student)
Sky Gao (Bioinformatics PhD student)
Yichen Du (Data Science undergraduate student)
Mohammad Erfan Molaei (CIS PhD student)
Chen Song (CIS PhD student)
Bin Li (CIS PhD student)
We work at the intersection of data science, computer science and life sciences, focusing on the development of statistical and machine learning methods for biomedical research.
Ph.D. and M.S. Department of Computer Science , University of Chicago M.Eng. and B. Eng. Department of Computer Science and Technology, Beijing Institute of Technology
Associate Professor, Department of Computer & Information Sciences, Temple University, (2019-)
Assistant Professor, Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, (2013-19)
Postdoctoral Research Fellow, Brigham and Women's Hospital, Harvard Medical School,(2009-2012)
Visiting Research Fellow, Broad Institute of MIT and Harvard, (2009-2012)
Associate, Program in Quantitative Genomics , Harvard School of Public Health, (2011-2012)
NIH T32 Genetics Fellow, Harvard Medical School,(2010-2012)
Research Assistant, University of Chicago, Argonne National Laboratory, (2005-2008)
Research Assistant, University of Chicago, (2002-2005)
Research Assistant, Beijing Institute of Technology, (1998-2001)
Selected Publications (Full publications available at Google Scholar )(* These authors contribute equally to the work.)
- "Differential Privacy Protection against Membership Inference Attack on Genomic Data",Chen J, Wang WH, and Shi X, In the Proceedings of the 26th Pacific Symposium on Biocomputing (PSB 2021) , January 3-7, 2021, The Big Island of Hawaii. (Code for MIA-GAN is at GitHub)
- "Population-scale Genomic Data Augmentation Based on Conditional Generative Adversarial Networks",Chen J and Shi X, In the Proceedings of The 11th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2020), September 21-24, 2020, Virtual. (Code for PG-cGAN is available at GitHub)
- "A Parallelized Strategy for Epistasis Analysis Based on Empirical Bayesian Elastic Net Models", Wen J*, Ford CT*, Janies D, and Shi X, Bioinformatics, 2020, 36(12), Pages 3803–3810. (Code for parEBEN is available at GitHub)
- "Association of CNVs with Methylation Variation", Shi X, Radhakrishnan S, Wen J, Chen JY, Chen J, Lam BA, Mills RE, Stranger BE, Lee C, and Setlur SR, npj Genomic Medicine, 5, 41 (2020).
- "Sparse Convolutional Denoising Autoencoders for Genotype Imputation", Chen J and Shi X, Genes, 2019, 10(9), 652. (Code for SCDA is available at GitHub)
- "A Sparse Convolutional Predictor with Denoising Autoencoders for Phenotype Prediction",Chen J and Shi X , In the Proceedings of The 10th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2019), September 7-10, 2019, Niagara Falls, NY. (Code for SCP_DAE is available at GitHub)
- "Multi-platform Discovery of Haplotype-resolved Structural Variation in Human Genomes",Chaisson MJP, Sanders A.D, Zhao X, Malhotra A, Porubsky D, Rausch T, Gardner EJ, Rodriguez O, Guo L, Collins RL, Fan X, Wen, J, Handsaker RE, Fairley S, Kronenberg ZN, Kong X, Hormozdiari F, Lee D, Wenger AM, Hastie A, Antaki D, Audano P, Brand H, Cantsilieris S, Cao H, Cerveira E, Chen C, Chen X, Chin C-S, Chong Z, Chuang NT, Church DM, Clarke L, Farrell A, Flores J, Galeev T, David G, Gujral M, Guryev V, Haynes-Heaton W, Korlach J, Kumar, S, Kwon JY, Lee JE, Lee J, Lee W-P, Lee SP, Marks P, Valud-Martinez K, Meiers S, Munson KM, Navarro F, Nelson BJ, Nodzak, C, Noor A, Kyriazopoulou-Panagiotopoulou S, Pang A, Qiu Y, Rosanio G, Ryan M, Stutz A, Spierings DCJ, Ward A, Welsch AE, Xiao M, Xu W, Zhang C, Zhu Q, Zheng-Bradley X, Jun G, Ding L, Koh CL, Ren B, Flicek P, Chen K, Gerstein MB, Kwok P-Y, Lansdorp PM, Marth G, Sebat J, Shi X, Bashir A, Ye K, Devine SE, Talkowski M, Mills RE, Marschall T, Korbel J, Eichler EE and Lee C, Nature Communications, 2019, 10:1784
- "A Deep Auto-encoder Model for Gene Expression Prediction", Xie R, Wen J, Quitadamo A, Cheng J, and Shi X, BMC Genomics, 2017, 18(Suppl 9):845.
- "Bayesian Hyperparameter Optimization for Machine Learning Based eQTL Analysis", Quitadamo A, Johnson J, and Shi X, in Proceedings of the 8th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB 2017), pp 98-106, Boston, MA, August 20-23, 2017.
- "An Overview of Human Genetic Privacy", Shi X and Wu X, Annals of NY Academy of Sciences, 2016, DOI: 10.1111/nyas.13211.
- “An Integrated Map of Structural Variation in 2,504 Human Genomes”, Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz M, Konkel MK, Malhotra A, Stütz AM, Shi X, Casale FP, Chen J, Hormozdiari F, Dayama G, Chen K, Malig M, Chaisson M, Walter K, Meiers S, Kashin S, Garrison E, Auton A, Lam H, Mu XJ, Alkan C, Antaki D, Bae T, Chines P, Chong Z, Clarke L, Dal E, Ding L, Emery S, Fan X, Gujral M, Kahveci F, Kidd JM, Kong Y, Lameijer E-W, McCarthy S, Flicek P, Gibbs RA, Marth G, Menelaou A, Muzny DM, Nelson BJ, Noor A, Parrish NF, Quitadamo A, Raeder B, Schadt E, Schlattl A, Shabalin AA, Untergasser A, Walker JA, Wang M, Yu Y, Zhang C, Zhang J, Zheng-Bradley X, Zhou W, Zichner T, Sebat J, Batzer MA, McCarroll SA, The 1000 Genomes Project Consortium, Mills RE, Gerstein MB, Bashir A, Stegle O, Devine SE, Lee C, Eichler EE, Korbel JO, Nature, 2015. 562(7571): 75-81.
- "A Global Reference for Human Genetic Variation", The 1000 Genomes Project Consortium, Nature, 2015. 562(7571): 68-74.
- "An Integrated Map of Genetic Variation from 1,092 Human Genomes", The 1000 Genomes Project Consortium, Nature, 2012, 491: 56-65.
- "A Two-Graph Guided Multi-task Lasso Approach for eQTL Mapping", Chen X*, Shi X*, Xu X, Wang Z, Mills R, Lee C, Xu J, In Proceeding of the fifteenth international conference on Artificial Intelligence and Statistics (AISTATS 2012). JMLR W&CP, 2012, 22: 208-217.
|Last Modified: Oct 6, 2020|