Ishani Vyas

Ishani Vyas has had a diverse work experience that spans over a decade. Most recently, since 2021, they have been working as a Computer Vision | Machine Learning Engineer at Passio AI. Prior to that, Ishani worked as a Graduate Researcher at the Video and Image Processing Lab in UC Berkeley from 2019, where they were involved in research projects related to SLAM, Camera Calibration, and 3D Reconstruction for Indoor Mapping. Their responsibilities included obstacle detection, depth-estimation for drones, indoor mapping, and asset tagging using various technologies and devices.

Before their time at UC Berkeley, Ishani worked at UCLA Health, the David Geffen School of Medicine at UCLA, Computing Technology Research Lab from 2010 to 2017 as a Programmer/Analyst-III. Their role involved software requirements elicitation, architecture, design, and implementation using technologies such as Objective C, iOS, Java, and more.

From 2009 to 2010, Ishani worked at the David Geffen School of Medicine at UCLA, Department of Neurology as a Programmer/Analyst-I. They were responsible for developing web applications to support Medical Residents, Clinical Research, Room Reservation, and faculty performance analysis. Their skills included technologies like Tomcat Apache, Microsoft SQL Server, Java, and more.

In 2008, Ishani briefly worked as a Web Developer for the City of Los Angeles. Their role focused on web development tasks.

Overall, Ishani Vyas has gained experience in computer vision, machine learning, research, web development, and software architecture throughout their career.

Ishani Vyas completed a Graduate Certification in Ph.D/Graduate degree in Machine learning/Data Science from UC Berkeley College of Engineering from 2017 to 2020. Prior to that, they obtained an M.S degree in Computer Science from California State University, Los Angeles. Ishani also holds a Bachelor's degree in Engineering in Computer Engineering from Savitribai Phule Pune University. Additionally, they completed a course in iPhone and iPad Application Programming from UCLA Extension. Furthermore, they have obtained various certifications including Convolutional Neural Networks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Full Stack Deep Learning, and Neural Networks and Deep Learning.