About

Kanika Jindal is a hardworking and astute learner with a passion for artificial intelligence. She is currently pursuing a Master of Science in Computer Science at the University of Southern California, with a focus on AI. Kanika has over 3 years of experience as a machine learning scientist, working with supervised, unsupervised, and reinforcement learning. She is proficient in Python and has hands-on experience with Flask, MongoDB, Gensim, Scikit-Learn, Tensorflow, and other platforms such as Amazon SageMaker, AWS Lambda, AWS S3, and API Gateway. Kanika's expertise lies in natural language processing, time series forecasting and predictions, and computer vision applications. She has a keen interest in mathematical algorithms and hard calculus, and enjoys solving complex problems using machine learning. Kanika is also a trained Indian classical vocalist, with a degree from Prayag Sangeet Samiti. She has performed on over two dozen stages, winning prizes and gaining valuable experiences. Kanika has experience in team management and leadership, having served as the president of the college music society and an active organizer of events in and out of college. She is currently working as a course grader and evaluator for BUAD-312 under Professor Jacob Bien at USC Marshall School of Business. Previously, she worked as a machine learning R&D engineer at Sopra Steria, where she built data stories, predictive models, and worked with machine learning algorithms to improve the organization's services and identify markets. Kanika was also a course grader for DSCI-558 at Information Sciences Institute under Professor Jay Pujara and Professor Craig Knoblock. Kanika holds a Bachelor of Technology in Computer Engineering from Guru Gobind Singh Indraprastha University and a Machine Learning Engineer Nanodegree from Udacity. She is proficient in ML, AI, AWS, reinforcement learning, Java, supervised learning, Seaborn, Pandas, and other relevant tech stacks. Kanika is a vGHC'21 attendee and has achieved several accomplishments, including a team excellence award in June 2020 and developing two python libraries that reduced man efforts by 90% for preprocessing in NLP and forecasting for time series and similar applications.