Skip to content

Upcoming Seminar

Wildlife Seminar Fall ‘26 Jesse Andrews, Screenwriter and Novelist How to Write a Top Grossing Movie About Riparian Ecosystem Restoration Talk Description: On the one hand, commercial storytelling—e.g., big-budget animated movies—can reach enormous global audiences. On the other hand, commercial storytellers are under relentless pressure from their corporate overlords to oversimplify reality and provide emotional uplift at the expense of complexity, nuance, and even truth itself. Is it possible to entertain millions and at the same time expand their understanding of the world? The answer is: Maybe! Sometimes! Sort of!! Bio: Jesse Andrews is a novelist, filmmaker, and former German youth hostel receptionist. Most recently, he is the screenwriter of Pixar’s HOPPERS. Other movies he has written include LUCA and ME AND EARL AND THE DYING GIRL, which he adapted from his own bestselling novel of the same name. Together with his friend Trevor Jimenez, he is currently co-directing and co-writing GHOST MARKET, another feature film for Pixar. He was born in Pittsburgh, Pennsylvania, and is a graduate of Schenley High School and Harvard University. He lives in the Bay Area with his family. Friday 9/18/26, 12noon-1pm, Mulford Room 36 or https://berkeley.zoom.us/j/99556744478 https://wildlife.berkeley.edu

Wildlife Seminar
9/18/26, 12noon-1pm

Jesse Andrews


Screenwriter and Novelist


How to Write a Top Grossing Movie About Riparian Ecosystem Restoration


Mulford Room 36 or https://berkeley.zoom.us/j/99556744478

To be added to our list, email us at berkeleywildlife@berkeley.edu

Fall ’26 Schedule

-9/4, Yun Sing Koh, Director of the Centre of Machine Learning for Social Good, Te Kura Matai Rorohiko School of Computer Science, University of Auckland AI for Wildlife Monitoring and Ecological Intelligence: A New Zealand Perspective -9/18, Jesse Andrews, Screenwriter/Novelist, How to Write a Top Grossing Movie About Riparian Ecosystem Restoration -10/9, Stratton Hatfield and Claire Okell Southern Kenya: Biodiversity and Communities Across a Connected Conservation Landscape -10/16, Lyman and Safari Award Recipient Speed Talks 10/23, Sharon Zou, Cooperative Extension Specialist in Community Economic Development/Outdoor Recreation and Tourism, University of California Agriculture and Natural Resources Conservation Finance at the Intersection of Wildlife, Recreation, and Tourism -10/30, John P. Casellas Connors, Associate Professor of Geography, Assistant Director of Environmental Programs, Texas A&M University The Conservation of Guns: How Wildlife Management Has Been Shaped by Gun Politics -11/13, Carl Boettiger, Associate Professor ESPM AI for decision support in land and wildlife conservation -11/20, Tucker Russell, Tribal Liaison, Stone Center for Environmental Stewardship Disease in a Fragmented Landscape: Movement, Habitat Use, and Survival of Mule Deer with a High Prevalence of Chronic Wasting Disease

Previous Seminars

Flyer with the following text: Wildlife Seminar Fall ‘26 Professor Yun Sing Koh, Director of the Centre of Machine Learning for Social Good, Te Kura Matai Rorohiko School of Computer Science, The University of Auckland AI for Wildlife Monitoring and Ecological Intelligence: A New Zealand Perspective Artificial intelligence has achieved remarkable advances in perception and recognition, yet many AI systems continue to struggle in dynamic, open-world environments where data distributions evolve, labelled examples are limited, and new concepts emerge over time. Wildlife conservation presents precisely these challenges. Building AI that can operate reliably in natural ecosystems therefore requires advances beyond conventional supervised learning, spanning continual learning, open-world recognition, multimodal foundation models, uncertainty estimation, and trustworthy AI. New Zealand provides a unique testbed for this research. Its distinctive biodiversity, together with ambitious conservation programmes targeting introduced predators and endangered native species, creates opportunities to develop AI methods that must adapt to changing environments while supporting real-world conservation decisions. In this talk, I will present recent AI research from the Centre for Machine Learning for Social Good at the University of Auckland, where we are developing intelligent systems for long-term wildlife monitoring in dynamic, open-world environments. Our work spans computer vision, multimodal foundation models, continual learning, open-world recognition, and trustworthy AI to enable the identification of individual animals, adaptation to changing ecosystems, and learning from limited and evolving data. These challenges are motivating new advances in representation learning, robust multimodal reasoning, and AI systems capable of reliable long-term deployment beyond benchmark datasets. The talk will conclude by examining how wildlife conservation is driving advances in artificial intelligence. The need for AI systems that can recognise new individuals, adapt to changing environments, learn continuously, and make reliable decisions under uncertainty is motivating research that extends beyond conservation and contributes to the development of more general, robust, and trustworthy intelligent systems. Artificial intelligence has achieved remarkable advances in perception and recognition, yet many AI systems continue to struggle in dynamic, open-world environments where data distributions evolve, labelled examples are limited, and new concepts emerge over time. Wildlife conservation presents precisely these challenges. Building AI that can operate reliably in natural ecosystems therefore requires advances beyond conventional supervised learning, spanning continual learning, open-world recognition, multimodal foundation models, uncertainty estimation, and trustworthy AI. New Zealand provides a unique testbed for this research. Its distinctive biodiversity, together with ambitious conservation programmes targeting introduced predators and endangered native species, creates opportunities to develop AI methods that must adapt to changing environments while supporting real-world conservation decisions. In this talk, I will present recent AI research from the Centre for Machine Learning for Social Good at the University of Auckland, where we are developing intelligent systems for long-term wildlife monitoring in dynamic, open-world environments. Our work spans computer vision, multimodal foundation models, continual learning, open-world recognition, and trustworthy AI to enable the identification of individual animals, adaptation to changing ecosystems, and learning from limited and evolving data. These challenges are motivating new advances in representation learning, robust multimodal reasoning, and AI systems capable of reliable long-term deployment beyond benchmark datasets. The talk will conclude by examining how wildlife conservation is driving advances in artificial intelligence. The need for AI systems that can recognise new individuals, adapt to changing environments, learn continuously, and make reliable decisions under uncertainty is motivating research that extends beyond conservation and contributes to the development of more general, robust, and trustworthy intelligent systems. Friday 9/4/26, 12noon-1pm, Mulford Room 36 or https://berkeley.zoom.us/j/94180561590 info: wildlife.berkeley.edu

Wildlife Seminar
9/4/26, 12noon-1pm

Professor Yun Sing Koh


Director of the Centre of Machine Learning for Social Good, Te Kura Matai Rorohiko School of Computer Science, The University of Auckland


AI for Wildlife Monitoring and Ecological Intelligence: A New Zealand Perspective


Mulford Room 36 or https://berkeley.zoom.us/j/94180561590

To be added to our list, email us at berkeleywildlife@berkeley.edu