Technical Electives at MIT:
- Probability and Random Variables - 18.440
- Foundations of Computational and Systems Biology - 20.390
- Introduction to Computational Neuroscience - 9.29
- Neurotechnology Ventures - 9.455
- Quantitative Physiology: Organ Transport Systems - 20.371
- Design Medical Devices and Implants - 20.451
- Mechanisms of Drug Actions - 20.201
- Cellular & Tissue Biomechanics - 20.310
- Introduction to MATLAB - 6.094
- Neuropharmacology - 9.98
Bachelor of Science in Biological Engineering, MIT, 2013
Data Science Consultant
- Slalom Consulting
- Designed and developed statistical approaches and machine learning models for clients.
Harvard School of Public Health
- Bioinformatics Analyst
- Predicted functional annotations of unknown proteins by designing and developing Python pipeline.
- Parkland Center for Clinical Innovation
- Developed computational tool to predict corrections to errors in EMR data resulting in improved disease predictions.
- University of California San Diego
- Automated cardiac capacity prediction from CT images using Active Shape Model in MATLAB.
- Instructor: Instructed high school students in calculus, arithmetic, geometry and statistics for SAT Preparation.
- MIT Information and Systems Technology – Technical Consultant
- MIT TechFair – Executive Member
Participation in Programs:
- MIT Educational Studies Program
Undergraduate Research Experiences:
- Brigham and Women’s Hospital: Developed computational tools to integrate and validate epidemiological data for genetic disorders from various sources
- Sasisekharan Lab: Developed a series of quality-control tests in Python to ensure workflow validity for antigen-antibody binding prediction
- Fraenkel Lab: Quantified and compared zinc finger binding motif predictions using EM algorithm as compared to motifs in TRANSFAC using divergence metrics in Python
- Bathe Lab: Developed automated method to extract chromosome tracks from 3D movies using MATLAB
- Zifan A., Shafquat A., Chapman B.E., ‘Automatic Ventricle Chamber Segmentation Using a Regression Neural Network Initialization Based Active Shape Model,’ 2013 AMIA Clinical Research.
- Shafquat A., Joice R., Simmons S. L. Huttenhower C., ‘Functional and phylogenetic assembly of microbial communities in the human microbiome’, (2014) Trends in Microbiology Vol. 22, Issue 5, Pages 261-66
- Joice R., Yasuda K., Shafquat A., Morgan X., Huttenhower C., ‘Determining microbial products and identifying molecular targets in the human microbiome’, (2014) Cell Metabolism Vol. 20, Issue 5, Pages 731-741
- Frazonsa E., Hsu T., Sirota-Madi A., Shafquat A., Abu-Ali G., Morgan X., Huttenhower C., ‘Sequencing and beyond: integrating molecular 'omics' for microbial community profiling’, (2015) Nature Methods Vol 13, Issue 6, Pages 360-372
- Boernigen D., Moon Y.S., Waldron L., Shafquat A., Franzosa E., Sweeney C., Morgan X., Garrett W., Huttenhower C., ‘A reproducible approach to high-throughput biological data acquisition and integration’, (2015) PeerJ e79
- Hsu T., Joice R., Morgan X., Vallarino J., Baranowski C., Shafquat A., Gevers D., Spengler J., Huttenhower C. ‘Microbial communities in an urban mass transit system’ (2016) MSystems
- Shafquat A., Mezey J., ‘Identification of novel disease loci by Bayesian latent variable recoding of phenotypes in GWAS,’ 2017 Biology of Genomes, Cold Spring Harbor Laboratory.
- Meyers-Wallen VN, Boyko AR, Danko CG, Grenier JK, Mezey JG, Hayward JJ, et al. (2017) XX Disorder of Sex Development is associated with an insertion on chromosome 9 and downregulation of RSPO1 in dogs (Canis lupus familiaris). PLoS ONE 12(10): e0186331. https://doi.org/10.1371/journal.pone.0186331
Activities, interests and hobbies:
- MIT Addir Fellows Interfaith Program - Fellow
- MIT Alumni Association – Tech Callers
- Pakistani Students @ MIT – Vice President
- MIT Multicultural Conference
- Interfaith Youth Core Leadership Conference
Cornell University, Ph.D. in Computational Biology (Expected: 2020)