Data Scientist
Resume:
EDUCATION Ph.D. Computer Science
Florida Atlantic University,
Boca Raton, FL
GPA: 4.0
M.S. Computer Science
Science and Research University,
Tehran
GPA: 3.8
B.S. Computer Science
Azad University,
Tehran
GPA: 3.6
Similarity Measurement
- Proposed a new similarity measurement to eliminate the problem of Cosine Similarity in high dimensional data.
- Researched K-means, PCA, Symmetric NMF, Normalized cuts, and SVM to compare the performance of our measure with that of the cosine similarity.
- End result shows based on ANOVA and Tukey’s test results, our measure outperforms cosine similarity (https://journalofbigdata.springeropen.com/articles/10.1186/s40537-017-0083-6). Sentiment Analysis
- Conducted Sentiment analysis on financial data to evaluate the impact of investor sentiment on stock market price.
- Investigated Doc2Vec (using Genism), long short term memory: LSTM (using Theano) and Convolutional Neural Network: CNN (using Tensorflow).
- Predicted investor’s sentiment to ~31% of transactions with more than 90% accuracy. Predicting Top Authors
- Examined the word usage of investors who can predict stock prices correctly.
- Studied Doc2Vec and Convolutional Neural Network (CNN).
- Predicted top authors to ~26% of transactions with more than 80% accuracy. JM Family Enterprises, Inc.
Data Scientist
2017-Present
Florida Atlantic University
Python, R, Machine Learning
2015-Present
Recommendation System Based on User Behavior
- Predicted the likeliness that a new user would buy a product based on the behavior of other users. Used Locality Sensitive Hashing and Cosine Similarity to design and implement a collaborative filtering engine.
- The engine is able to make recommendations to ~32% of transitions with more than 85% accuracy.
Recommendation System Based on Product Features
- Probed Singular Value Decomposition to recommend products to a new customer based on a product-feature matrix.
- Recommended products to ~29% of new users with more than 80% accuracy using content- based filtering.
Predicting Claim Amount
- The goal was to predict claim payments based on the characteristics of the customers’ vehicle. Investigated Gradient Boosted Decision Trees to predict the amount of a claim and utilized the Random Forest Information Gain, to obtain the most relevant features.
- Predicted the claim amount for ~30% of transactions with less than 10% root mean square error.
Finding Common Problems
- Assisted the business department in finding common product part failures by exploring association rules to discover the relation between products and their features.
- Used measures of significance and interest, including support, confidence, and lift, to select interesting rules based on business criteria from the set of all possible rules. WORK EXPERIENCE
Sahar Sohangir
*****.********@*****.***
Software Architecture
Auction theory
Matlab
2010-2011
Conducted Research on Service Assignment Methods
- Conducted research on service-oriented architecture to effectively assign service providers to service consumers.
- Investigated auction algorithms and game theory. Implemented service assignments based on auction algorithms in Matlab.
Azad University
Lecturer
2011-2013
MACHINE LEARNING
AND DATA MINING
ALGORITHM
EXPERIENCE
TECHNICAL EXPERTISE
PUBLICATIONS
Freelance Technician
Senior Software Engineer
2007-2009
- Deep Learning for Financial Sentiment Analysis. Knowledge Discovery and data Mining
(KDD) 2016. Sahar Sohangir, Dingding Wang, Anna Pomeranets.
- Document Understanding Using Improved Sqrt Cosine Similarity. International Conference on Semantic Computing (ICSC) 2017. Sahar Sohangir, Dingding Wang.
- Update Summarization using Semi-Supervised Learning Based on Hellinger Distance. Conference of Information and knowledge Management (CIKM) 2015. Dingding Wang, Sahar Sohangir, Tao Li.
- Finding Expert Authors in Financial Forum Using Deep Learning Methods (ICSC) 2018. Sahar Sohangir, Dingding Wang.
- Financial Sentiment Lexicon Analysis (ICSC) 2018. Sahar Sohangir, Nicholas Petty, Dingding Wang.
- A new Method for service Binding in service Oriented Architecture. Advance in Information Science and Service Science 2010. Sahar Sohangir and MirAli Seyyedi.
- A Service Binding Method using Forward Auction. International Journal of Information Processing and Management 2011. Sahar Sohangir and MirAli Seyyedi. Proficient in Programming and Scripting Languages: Python, R, MATLAB, Weka Java, C/C++, C#
MySQL Unix Shell Scripts
- Random Forest, Boosted Trees, Support Vector Machine, Logistic Regression, Naïve Bayes, Feature Selection, and Dimensionality Reduction
- Deep Learning and Neural Networks
- Bagging and Boosting
- Ensemble and Stacking Methods
- Natural Language Processing
Adjunct Professor
- Taught Database Systems, Data Structure, Analysis of Algorithms, and programming in C++ at Azad University.
- Managed a freelance team to implement Oracle Server Database through development and extension of logical and physical data models.
- Evaluated business performance utilizing different key performance indicators. Matin System Rahnegar
Software Engineer
2006-2008
- Designed and implemented Integrated Software Management systems for various companies.
- Implemented an online test for new users to measure their speed and ability to work with our ISM system.
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