1. Chicago, IL 60607
SREEJITH MENON
312-975-3677
http://www.linkedin.com/in/smenon8 smenon8@uic.edu
http://compbio.cs.uic.edu/~sreejith https://github.com/smenon8
EDUCATION
University of Illinois at Chicago – GPA: 4.00/4.00
Master of Science in Computer Science with Thesis
Chicago, IL
May 2017
Mumbai University – AggregatePercentage: 70%
Bachelor of Engineering in Computer Engineering
Mumbai, India
May 2013
TECHNICAL SKILLS
Languages: Python, Bash Shell Scripting, Java, C++, PL/SQL, HTML/CSS, Matlab/Octave.
Tools/Platform: Jupyter, Pandas, Plot.ly, Folium, GitHub, Amazon Mechanical Turk (AWS), Abinitio, Oracle,
Teradata, MySQL, Tivoli Workload Scheduler, Jira, Business Objects, Informatica, Weka.
PROFESSIONAL EXPERIENCE
University of Illinois at Chicago Chicago, IL
Graduate Research Assistant May 2016 – Present
Performed research to identify a predictive population estimation model using photos shared on social media
under the collective advice of Prof. Tanya Berger-Wolf (UIC) and Emre Kiciman (Microsoft Research).
Mentored 3 undergraduate students from UIC in a project to infer if stripe-patterns in zebras are inherited.
University of Illinois at Chicago Chicago, IL
Graduate Teaching Assistant January 2016 – May 2016
Assisted Prof. Douglas Hoganfor Introduction toComputing and Programming(C++) course in the CS department.
J. P. Morgan Chase & Co., Consumer& Community Banking Mumbai, India
Technology Analyst July 2013 – July 2015
Built high performance real-time data-warehousing solutions using Abinitio in conjunction with Bash scripting.
Pioneered in remodeling a load strategy that conserved over 2000 CPU and 100 human hours per month.
Awarded the title of Subject Matter Expert for enabling the team to achieve tight service level agreements by
formulating solutions that required a deep understanding of both functional and technical aspects of the project.
THESIS
Animal Wildlife Estimator using SocialMedia (GitHub Page)
Funded by Microsoft Research.
Technologies: Python3, Folium, sklearn,
urllib, plot.ly, Amazon Mechanical Turk
Formulated a population estimation model for animals in certain geographic regions using pictures shared on
various social media platforms.
Designed a crowd sourcing environment on Amazon Mechanical Turk for simulating the sharing of pictures on
social media using API’s built on Python that interfaces with AWS for creating jobs and parsing results of the turk.
Designed and implemented middleware in Python to extract public animal images from Flickr and Twitter.
Implemented and evaluated different machine learning algorithms in Python3 to predict the likelihood of a
picture being shared on social media based on various ecological as well biological features.
Interpreted, quantified and incorporated different levels of self-reporting or behavioral bias towards sharing
photographs of species of animals with unique patterns in the proposed population estimation model.
ACADEMIC PROJECTS
SF Park Assist Simulator (GitHub Link) Technologies: Python3, Google Maps API
Engineered real-time algorithms for searching available parking spots in San Francisco bay area using Python
that adapt to both deterministic and probabilistic environments.
Boosted the worst-case performance of the algorithms by 34% compared to baseline algorithms.
SantanderCustomerSatisfaction (GitHub Link) Technologies: Python3, sklearn, matplotlib
Explored different techniques of principal component analysis and built an API in Python to compute
information gain to extract meaningful attributes from over 570 anonymized attributes.
Implemented and evaluated metrics for different classification algorithms using Python for forecasting if a
Santander’s customer is likely to maintain the relationship with the bank.
Ad-Non Ad Image Classifier for HTML pages (GitHub Link) Technologies: Python3, sklearn, matplotlib
Designed and engineered different learning techniques for ImageClassification.
Incorporated various data cleansing techniques and k-NN algorithm for imputing missing data.
Delivered an implementation of Logistic Regression that yielded 96% accuracy in prediction.