Venkataramanan Kumar

***** ******** ***** **., *****, FL 33613 206-***-**** adlovr@r.postjobfree.com www.linkedin.com/in/venkataramanan-kumar-913387187/ www.github.com/Venkat-2709

Objective

Seeking an entry-level position to begin my career in a high-level professional environment to expand my learnings, knowledge, and skills while making a significant contribution to the success of the company. A highly motivated and positive individual with great organizational and communication skills. An efficient problem solver with in-depth analytical skills.

Education

BACHELOR’S IN COMPUTER SCIENCE AND ENGINEERING AUG 2015 - MAY 2019 RAJALAKSHMI ENGINEERING COLLEGE, THANDALAM, INDIA GPA – 7.5 / 10

MASTER’S IN COMPUTER SCIENCE AUG 2019 - MAY 2021 UNIVERSITY OF SOUTH FLORIDA, TAMPA, FLORIDA GPA – 3.4 / 4.0

Projects

CHARACTERIZING COMMENTS USING COARSE DISCOURSE SEQUENCES OCT 2019 – DEC 2019

·Classifying the online discussion on Reddit and characterizing the comments based on their nature.

·Preprocessing is done by tokenizing the comments and embedding the data using Google’s word2Vec.

·Embedded training data is used to train the Bi-Directional LSTM model to predict the output.

PAIN CLASSIFICATION WITH PHYSIOLOGICAL SIGNALS FEB 2021 – MAR 2021

·Classification of physiological data such as Blood Pressure, EDA (Electrodermal Activity) and Respiration rate are taken from the subject to predict whether there is pain or no pain.

·The data from subjects are preprocessed by converting the data in form of Mean, Variance, Entropy, Max and Min values of the data and trained using K-Fold cross validation of Random Forest Classifier and obtained accuracy of 89%.

EMOTION RECOGNITION MAR 2021 – APR 2021

·Images are used to classify pain in subjects using OpenCV ‘s Cascade Classifier to detect the face of the subjects and then the face was cropped from the original images.

·Using Transfer Learning VGG19 Model from Keras application the model was created, then after training, our model was able to perform quite good on test data.

STUDENT PERFORMANCE ANALYSIS FEB 2019 – MAY 2019

·Created a Website for Student Management System using Django and Firebase Database as a Backend to store all the information of the students in organization.

·Data is used to predict the performance of the students using Linear Regression model.

Technical Skills

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·Programming Languages – Python, C/C++, Go, Java, JavaScript, HTML, CSS, CUDA.

·Database – SQL, MySQL, PostgreSQL.

·Python Skills – Django, TensorFlow, Keras, NumPy, NLTK, Sci-Kit Learn, Pandas, OpenCV.

·Technology - Machine Learning, Deep Learning, Natural Language Processing, Octave, Tableau.

Certifications and Publications

Presented a paper on Student Performance Analysis using Linear Regression in the National Conference on Data Science and Intelligent Information System (NCDSIIS’19), India.

Deep Learning Specialization – Specialization has 5 courses taught by DeepLearning.AI on Coursera.

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