Assistant Data

CHAITANYA KULKARNI

adjnks@r.postjobfree.com j 540-***-**** j Blacksburg, VA j www.linkedin.com/in/chaitanyakulkarni-csk EDUCATION

Master of Science: Industrial Engineering(Machine Learning Track) Expected May 2021 Virginia Tech GPA: 3.94/4

Relevant Coursework: Data Analytics, Probability Distribution Theory, Advanced Machine Learning, Deep Learning, Ad- vanced Regression Methods, Optimization, Advanced Supply Chain Management. Bachelor of Technology: Mechanical Engineering, 2015 - 2019 VIT Vellore GPA: 9.0/10

SKILLS

Programming Python(Numpy, Pandas, scikit-learn,SciPy,),R, MATLAB,SQL Frameworks Pytorch, Keras, Tensor ow,OpenCV,Git,caret Data Analysis and Visualization Tableau,Excel,Matplotlib,Seaborn,Minitab EXPERIENCE

Graduate Research Assistant Sept 2019 - Present

Virginia Cognitive Systems Engineering Lab, Virginia Tech Blacksburg, VA Surgical Skill Assessment using Machine Learning

Working on building an classi cation model that predicts expertise in surgical tasks.

Discovered and analyzed novel predictive metrics from eye-tracking video data.

Trained a Heatmap regression model that detects and localizes tools in surgical videos. Conference Paper

Designed a Augmented Reality based platform that leverages a CNN architecture aimed at reducing accidents in factory settings. Published and presented the work in HFES conference. Graduate Teaching Assistant Jan 2020 - May 2020

Virginia Tech Statistics Department Blacksburg, VA

Provided assignment and project guidance involving grading and proctoring for undergraduate stat majors.

Worked at Statistics lab guiding non-stats majors in basic statistics and data analysis. PROJECTS

M5-Forecasting.

Appeared in top 1% rank in annually held forecasting competition.

Analyzed hierarchical time series data of sales of over 30,000 products.

Tested models like ARIMA, N-beats, LGBM and optimal reconciliation methods for forecasting sales over a 28 day horizon.

Proposed a novel ensemble model for sales prediction in data of 12 hierarchical levels by enforcing aggregation coherence. Egocentric eye-gaze prediction.

Built a model that predicts gaze point in videos by employing a two-stage 3D CNN referring Zhang et al paper.

Leveraged transfer learning and reduced Average Angular Error by 40%.

Deployed the model as an expert gaze-imitator for building a platform geared towards training novice surgeons. Generating SQL queries from natural language(Deep Learning Project).

Implemented a Bi-directional LSTM and GRU based model referring to SQLNET for converting natural language to an SQL query.

Boosted the execution accuracy by 2% by proposing a new attention model. Vehicle Localization for self-driving cars(Advanced Machine Learning Project).

Deployed deep convolutional neural network (CentreNet architecture) from scratch to detect vehicles.

Performed a comparative study of ResNet and VGGNet as backbone architectures.

Achieved an F1-score of 0.6 on test set.

Predicting a Company’s Success.

Implemented predictive classi cation models on Chrunchbase’ data to determine probability of success of a start-up.

Tested various models like Regularized Logistic regression, XGBoost and KNN. EXTRA-CURRICULAR ACTIVITIES

Served as a session chair for HFES 2020 conference.

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