Skilled professional with overall 3 years of experience. Well-informed on latest machine learning and deep learning advancements. Advanced understanding of predictive ,statistical, and other analytical techniques. Highly organized, motivated and diligent with significant background in computer science and programming. Ready to combine tireless hunger for new skills with desire to exploit cutting-edge data science technology.
Graduate Research Assistant, 10/2019 to 06/2020 The New School – New York, NY, USA
Conducted meticulous research for Text Retrieval Conference – Incident Streams (TREC-IS) tasks to identify information and answer multifaceted questions.
Evaluated and validated models(naive bayes and random forest)to identify gaps, proposed and implemented ideas to proliferate metrics measures.
Achieved considerable F score and accuracy as 0.92 and 96% by boosting performance of existing models majorly with optimized textual embeddings using NLP libraries(NLTK, fastText, BERT, RoBERTa) and textual augmentation.
Developed image pipeline from scratch to extract images from tweets and implemented pre-trained model VGG16 with augmentation to address class imbalance and categorized tweets using Linear SVC.
Escalated F score metrics significantly by 6% , resulted in overall improvement by combining text and image pipeline results.
Data Science Intern, 07/2019 to 08/2019 Motorola Solutions – Plano, TX, USA
Applied extensive data preprocessing methods on DICOM images of various modularity (X-RAY,MR, CT Scan) to reduce into low memory format images and produced trainable data.
Improvised healthcare practices by developing CNN (VNET) using transfer learning/ keras, to anticipate development of diseases by performing segmentation and achieved accuracy of 93 % on test/validation set.
Created data visualization graphics, translating complex data sets and results into comprehensive visual representations.
Presented and discussed latest research paper on volumetric medical imaging in deep learning at weekly talks with team.
Associate Software Engineer, 08/2015 to 05/2017 Assurant – Farmington, CT, India
Designed and implemented real-time software for ‘Toshiba Automotive display controllers’ aimed at providing capability to display instrument clusters, navigation systems and other in-vehicle applications.
Implemented Interfaces and test cases to add new features for environment sensing device ’Black box logger’ aimed at improving logistic operations and evaluated performance of sensors by analyzing parameters.
Managed design and implementation of software for embedded devices and systems, right from requirements to production and commercial deployment.
Collaborated with cross-functional development team members to analyze potential system solutions based on evolving client requirements.
Monitored debugging process results and investigated causes of non-conforming software.
Education
Master of Science: Data Science, Expected in 05/2020 New Jersey Institute of Technology - Newark, NJ, GPA:
Majored in [Data Science]
Received [ EduCo Graduate Scholarship]
Graduated with [3.9/4.0] GPA
Coursework in [Medical AI], [Data Analytics with R] , [Data Analytics with Info System], [Machine Learning], [Deep Learning ] ,[Applied Statistics],[ Database System Design], [Big Data] and [Data Mining and Analysis for Managers]
Bachelor’s: computer science And Engineering, Expected in 05/2015 Chitkara Institute of Technology - India, GPA:
Coursework in [Data Structures and Algorithms], [Database Management] and [Numerical Methods and Statistics]
Majored in [Computer Science and Technology]
Graduated with [7.75/10] GPA
Accomplishments
ASHRE- Great Energy Predictor 3| github.com/Jessica24/ASHRE-Great-Energy-Predictor-3 Python, Pandas Analysed data and applied data pre-processing methods to produce trainable data.
Implemented models (Scikit-learn); KNN, Decision Tree and Random Forest, to predict energy consumptions for meteredbuildings and evaluated models by calculating Root Mean Squared Logarithmic Error score.
Image Classification Using Deep Learning Methods |github.com/Jessica24/Deep-Learning-Tasks Python, Keras, Linux OS Development of Convolutional Neural Network from Scratch using optimizers SGD, Adagrad, Adam.
Application of pre-defined models (ResNet, VGG Net, Alex Net), applied concept of transfer learning on fruits, flower andChernobyl image data set to identify categories.
Classification of SNP Data |github.com/Jessica24/Dimensionality-Reduction-and-Classification-On-SNP-Dataset Python Performed feature selection using univariate technique Chi-square on SNP data and performed classification using SupportVector Machine (SVM) and achieved accuracy of 90%.
Predictive and Statistical Analysis on Food Joint |github.com/Jessica24/Predictive-and-Stastical-Analysis-using-JMP-SASJMP, SAS Ran regression analysis and performed predictive modelling on dataset and used statistical output of the model to make decisionon how to adjust operational activities to best manage and operate a fast food restaurant.
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