Data Science

Aiman Sherani

631-***-**** • ************@*****.*** • https://www.linkedin.com/in/aiman-sherani

SUMMARY

• Strong quantitative background through 5 years research experience in Biophysics using Statistical Modeling, Program- ming (Python), andMachineLearningtoaddressbiologicalproblemsthatwereunsolvablethroughtraditionalexperimental methods.

• AppliedDataMiningandVisualizationtechniquestoparsethroughdata,identifywhichscoringfunctionswouldbestaddress the problem of interest, and weed out irrelevant data.

• Implemented machine-learning methods to reveal trends in large data sets and predict novel solutions.

• Assessed validity of using specific statistical models to the data through ensuring the implicit assumptions of the models held true in our experiment and data.

• Worked collaboratively in large, multidisciplinary research groups with people of different backgrounds.

• Excellent communication skills. Invited presenter at national and international conferences to technical and non-technical audiences

DATA SCIENCE EXPERIENCE

THE UNIVERSITY OF CHICAGO GRADUATE RESEARCHER JULY 2016-JUNE 2018

• Developed advanced computational methodologies to characterize the binding specificity of protein complexes.

• Incorporated both statistics based models (i.e. Mutual Information, Regression, Entropy) from sequence analysis of the potential protein protein complexes and physics based models (i.e. free energy calculations) to differentiate between cognate and noncognate interactions. WELLESLEY COLLEGE UNDERGRADUATE RESEARCHER SEPTEMBER 2013 – MAY 2015

• Computed the electrostatic binding free energy computationally utilizing numerical methods to evaluate the role of physical characteristics of proteins in determining their ability to bind specifically or promiscuously.

• Used data mining and statistical models such as principal component analysis and a two-sample test of proportions to assess significance of the resulting calculations.

• Long term, the resulting models can be applied to understand the molecular mechanisms of protein-protein interactions and for designing novel biomolecular systems. UNIVERSITY OF MICHIGAN NATIONAL SCIENCE FOUNDATION REU FELLOW JUNE 2014 – AUGUST 2014

• Used data mining techniques and structural information available in the protein data bank for well characterized interaction to predict other potential binders.

• Developed a model using clustering (k-means) and optimization (simulated annealing) that was able to recover most of the original interactions without prior knowledge. RELEVANT SKILLS

Programming - Python (NumPy, SciPy, matplotlib, pandas, scikit-learn) • MATLAB • R • Perl • Bash • Tcl • Fortran Machine Learning - Principal Component Analysis • Unsupervised Learning (Clustering) • Optimization Statistical Modeling - Regression • Entropy • Mutual Information • Bayesian networks • Stochastic Models Tools - SQL • iPython • Jupyter Notebook

EDUCATION

UNIVERSITY OF CHICAGO M.SC. IN BIOPHYSICAL SCIENCES JUNE 2018 CHICAGO, IL Coursework includes Statistical Mechanics, Applied Numerical Methods in Molecular Engineering, Simulation, Modeling, and Computation in Biophysics, Machine Learning, Biophysics of Biomolecules. WELLESLEY COLLEGE B.A. IN CHEMICAL PHYSICS MAY 2015 WELLESLEY, MA Institutional Honors: Sigma Xi • Departmental Honors: Honors in Chemical Physics • Hypercube Prize in Chemistry. Coursework includes Statistical Mechanics, Linear Algebra, Differential Equations, Calculus, Computational Chemistry, Physical Chemistry, Organic Chemistry.

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