People
Leadership Team
Sara Beery
Sara Beery has always been passionate about the natural world, and she saw a need for technology-based approaches to conservation and sustainability challenges. This led her to pursue a PhD at Caltech, where her research focuses on computer vision for global-scale biodiversity monitoring. Her work is funded by an NSF Graduate Research Fellowship, a PIMCO Data Science Fellowship, and an Amazon AI4Science Fellowship. She works closely with Microsoft AI for Earth and Wildlife Insights (via Google Research) to translate her work into usable tools. Sara’s experiences as a professional ballerina, a queer woman, and a nontraditional student have taught her the value of unique and diverse perspectives in the research community. She’s passionate about increasing diversity and inclusion in STEM through mentorship, teaching, and outreach.
Pietro Perona
Professor Perona's research focuses on vision: how do we see and how can we build machines that see. He is currently interested visual recognition, more specifically visual categorization. He is studying how machines can learn to recognize frogs, cars, faces and trees with minimal human supervision, and how machines can learn from human experts. His project 'Visipedia' has produced two smart device apps (iNaturalist and Merlin Bird ID) that anyone can use to recognize the species of plants and animals from a photograph.
In collaboration with Professors Anderson and Dickinson, Professor Perona is building vision systems and statistical techniques for measuring actions and activities in fruit flies and mice. This enables geneticists and neuroethologists to investigate the relationship between genes, brains and behavior.
Professor Perona is also interested in studying how humans perform visual tasks, such as searching and recognizing image content.
Mentors
Elijah Cole
Elijah is a Computing and Mathematical Sciences Ph.D. student in the Computational Vision Group at Caltech, advised by Pietro Perona. He's also involved in the Visipedia project. He's interested in computer vision, machine learning, and using these techniques to enable scientific progress in ecology and medicine. His work is supported by an NSF Graduate Research Fellowship.
Jason Parham
Jason Parham is a senior research engineer at Wild Me and works to apply the latest machine learning and computer vision algorithms in wildlife applications. Jason holds a B.S. in Computer Science / Mathematics from Pepperdine University (2008), M.S. in Computer Science from RPI (2015), and a Ph.D. in Computer Science from RPI (2021; advisor Dr. Charles Stewart). Jason's doctoral research on "Animal Detection for Photographic Censusing" complements his applied work at Wild Me and offers a robust, end-to-end system for building large animal ID databases for conservation. Jason is also the co-developer and current maintainer of Wildbook's Image Analysis (WBIA) Python toolkit. The machine learning algorithms available in WBIA are used to detect, classify, ID, and catalog animal populations worldwide and are available open-source on GitHub, PyPI, and as a pre-configured Docker container.
Benjamin Kellenberger
Benjamin is a researcher with Devis Tuia at the ECEO lab of EPFL, Sion, Switzerland. He works in the fields of Remote Sensing, Computer Vision, and Machine Learning. His focus is primarily on animal conservation from above—using aerial imagery from airplanes, drones, etc. and machine-based tools to efficiently identify, count, and with that protect endangered animal species.
Instructors
Bjorn Lutjens
Bjorn is a PhD Candidate at the Human Systems Laboratory in the MIT Department of Aeronautics and Astronautics. His research is tackling climate change with machine learning, together with Prof. Dava Newman, Cait Crawford, and Chris Hill. Bjorn's work has won grants by NSF, Climatechange.ai, ESA, Portugal Space, NASA, IBM, Microsoft, NVIDIA, MIT Pkg, and MIT Legatum. Bjorn advised two teams of senior researchers at the NASA/SETI Frontier Development Lab, co-founded the ForestBench Consortium, interned with IBM Future of Climate and BRT (John Deere), earned an M.Sc. from MIT in safe and robust deep reinforcement learning, and a B.Sc. from TUM in Engineering Science.
Justin Kay
Justin is the CTO and co-founder of Ai.Fish, a computer vision company focused on applications in fisheries and marine conservation. In that role he has led a range of computer vision projects in ecology in partnership with government organizations such as NOAA and DFO (Canada) and NGOs such as The Nature Conservancy, the Environmental Defense Fund, and the National Fish and Wildlife Foundation. He is also a Postbac Researcher in the Computational Vision Lab at Caltech where he works on automated computer vision methods for monitoring salmon migration. His research interests include deployable machine learning with applications in biodiversity monitoring and climate change mitigation. Justin will be an incoming PhD student and NSF CSGrad4US Fellow in the MIT EECS department in Fall 2023, advised by Sara Beery.
Suzanne Stathatos
Suzanne’s drive to protect the natural world led her to graduate school at Caltech. She is a Computing and Mathematical Sciences PhD student, advised by Pietro Perona. Her interests include leveraging machine learning and computer vision techniques to solve ecological problems. She has worked with Rainforest Connection to enhance audio detection of forest predation, and more recently, with Trout Unlimited to improve identification and tracking of salmon populations in sonar videos. Suzanne holds an B.A. in History and an M.S. in Computer Science from Stanford University. Prior to Caltech, Suzanne worked as a software engineer at Amazon and NASA’s JPL. These experiences have sharpened her appreciation for interdisciplinary perspective and the real world impact of precise computational techniques.
Sam Lapp
Sam is a machine learning and sound geek with a passion for biodiversity conservation. He asks, How can we prevent biodiversity loss? Where do we develop reciprocity between humans, technology, and ecosystems? His current research focuses on developing methods for bioacoustics, the strategy of studying ecology through the sounds produced by living things. His projects include developing the open-source Python package OpenSoundscape and applying machine learning methods to the conservation of birds and frogs. Sam is currently working on his Ph.D. in the Kitzes Lab at the University of Pittsburgh.
Teaching Assistants
Tarun Sharma
Tarun is a PhD student in the Computation and Neural Systems (CNS) program at Caltech. He works in the lab of Professor Michael Dickinson on the flight and gaze stabilization systems of the fruit fly. His research interests involve computer vision applications for neuroethology and animal monitoring for ecological purposes. Before Caltech, he completed his undergraduate in Computer Science and Engineering at PES Institute of Technology, Bangalore. He also worked at Brown University as a research assistant for a year with Professor Thomas Serre.
Shir Bar
Shir is a Ph.D. candidate at Tel Aviv University, co-advised by Roi Holzman (School of Zoology) and Shai Avidan (School of Electrical Engineering). Her research interests are in the application of computer vision for ecology, specifically for identifying animal behaviors. Coming from a background in marine ecology, she is particularly interested in the challenges of applying existing computer vision methods to underwater imagery. Shir holds an M.Sc in Ecology, Evolution, and Behavior from the Hebrew University of Jerusalem, which she completed at the Interuniversity Institute for Marine Sciences in Eilat; where she is now based. She has also worked in various international NGOs and has a rich background in fieldwork. Her experiences in and out of academia have made her feel there is a strong need for interdisciplinary research to integrate computer vision approaches into the ecological research pipeline.
Surya Narayanan Hari
Surya Narayanan is a PhD student studying Machine Learning. He is interested in biological applications and new model architectures. In his free time, he enjoys exploring LA, sports and gardening.
Manuel Knott
Manuel is a PhD student in Applied Machine Learning at ETH Zurich, conducting research at the Swiss Data Science Center and the Swiss Federal Laboratories for Materials Science and Technology. His work revolves around Computer Vision applications for food safety. Before his PhD, Manuel spent three years working in Industry as a Machine Learning Engineer. He is currently a visiting student researcher at the Caltech Vision Lab.