Kirankumar Ashokkumar Email & Phone Number

Fremont, California, United States

San Mateo County, California, United States

• Reduced network traffic by utilizing an additive regression model to predict anomalies in time seriesdata (0.88 RMSE)• Built data collector and processor for event streams and worked on various data transformations• Built an anomaly detector that identifies anomalies from the given 100+ features using a univariantand multivariant isolation forest

Merced, California, United States

• Collaborated with 4 other colleagues to develop a web application using the MERN stack that allows truckers to share relative information to the admin at the storage facility to increase efficiency• Utilized a google maps API for truckers to send in real-time location to the admin's UI

Merced, California, United States

• Received funding to construct and analyze CNN models for detecting COVID-19.• Compared AlexNet, ResNet, and a novel prediction-based model using Keras and Python• Demonstrated that the proposed prediction-based model outperforms alternatives• Presented findings at the annual UC Merced UROC symposium

Merced, California, United States

• Learnt the mathematical side of machine learning topics such as CNN and LSTM• Created scripts to generate additional image data using image processing techniques (rotation, zooming, translating) to increase dataset size• Tuned hyperparameters for deep learning models that classified items from Columbia Object Image Library, using Bayesian Optimization• Explored various deep-learning techniques that can be used for stock market predictions• Implemented and trained Lasso, Linear, Ridge, Net Elastic regressions, and LSTMs to predict the closing price of different companies using Python (0.90 RMSE)• Compared R-squared and Mean Squared Error for each model to identify better performing model• Created a pipeline using Google Finance API to check the predictive power of these algorithms with real-time data• Presented research progress at weekly laboratory meetings

Merced, California, United States

• Developed a machine learning pipeline to detect asteroids from raw observatory data to aid national defense with natural disaster detections• Built using an architecture that combined CNN models (LesNet & Average Pooling) in PyTorch• Demonstrated high performance of the architecture (97% precision and recall)