Our Team

The WildFire Risk Assessment project team is composed of the following people.

Cassie Cassie Seney leveraged her a technical know-how to help the team on two critical fronts - weather data and the Tableau dashboards. When she’s not demonstrating her Data Science skills or Tableau prowess, Cassie spends her time with her family in the lovely Washington town of Bellevue.

Nick Nick Conidas led our modeling efforts and he’s put knowledge about using GCP Compute Engines to good use. He is currently a strategist at Google. The team has never seen Nick outside the Google offices and there is suspicion that that’s where he lives. Supposedly this is disputed by a recent vacation to New Hampshire, however, this has not yet been verified.

Yulia The only representative from the east coast, Yulia Zamriy brought the New York energy and spunk to the team. She led multiple fronts on the data side, from wildfire history to transmission lines to pioneering S2 technology which proved critical to coalescing our different information. When she’s not working on the project or studying MIDS, she spends time powerlifting and being mistaken for Scarlett Johanssen.

Scott Scott George led our efforts to identify, consume and extract relevant information from satellite data. He is currently a data scientist at DNV GL, a Norwegian company that specializes in global quality assurance and risk management. Scott has recently been convinced to stay on with the company by being given two large cabinets - what he uses them for is still a mystery. The WildFire Risk Assessment system came from Scott’s proposal. Why Scott cares about Wildfires so much is still under debate although the team believes Scott lives in a cabin in the forest….beside a transmission line.

Jay Jay Zuniga is currently a BI manager with Pinterest. Aside from some cat herding, Jay helped with integrating the different data components together using Google’s Cloud Platform. We’re still looking for something interesting to say about Jay…that’s currently in our Kanban backlog.

Thank you

Our team would like to acknowledge all the help and assistance we got along the way, without which the project would not be possible.

Elizaveta Malashenko, Koko Tomassian Sr and Anthony Noll of the California Public Utility Commission for providing actionable information on the state of Wildfire prediction, key developments in that area and providing ideas on other contacts.

Researchers at UC San Diego for letting us pick their brains on their Wildfire research and providing us feedback on our methodology and direction.

Our classmates at UC Berkeley W210 Section 2 for their support, encouragement and for their suggestions on how to improve our tool. We thank them as well for the patience as they sat through several presentations and numerous project updates.

The folks who create the S2 Geometry Library for providing us an easy and flexible way to coalesce our spatial information together. Thank you for making such a cool tool and for making it available.

Our data providers who provided the raw material for our project. These people, companies and agencies who generously go through the time and effort of maintaining weather, wildfire historical data, satellite and fuel moisture available and updated so that projects such as ours may benefit. We appreciate the people at Synoptic Data, Copernicus Global Land Service, US Forest Service, The California Department of Forestry and Fire Protection (CalFire), California Energy Commission.

The volunteers who maintain Jekyll which we used to quickly put together our website. You made deployment of our website so much easier!

Tableau Software for their student version and free public web hosting which we relied on heavily for our dashboards.

Google Cloud for our cloud-based tools: Cloud Storage, BigQuery and Compute Engine.

Last but definitely not the least, our teachers Joyce Shen and David Steier. Your support, suggestions, directions, feedback and advice have been critical in the project. It’s no exaggeration to say without you, none of this would have happened. Thank you so much.