Andrew Farnsworth is a Research Associate in the Information Science Program. Andrew began birding at age 5 and quickly developed his long-standing fascinations with bird migration. His current research efforts advance our use and application of rapidly expanding technologies available for studying bird movements at very small to very large scales including weather surveillance radar, acoustic monitoring, video monitoring and citizen science. Andrew’s current research projects aside from BirdCast also include BirdVox, an effort to apply machine learning techniques to automate the detection and classification of bird sounds and Dark Ecology, an effort to mine the more than 2.5 decade archive of US radar data in collaboration with University of Massachusetts Amherst. Additional research interests include a growing effort to understand artificial light, its effects on migrating birds in urban areas, in particular, and methods for quantifying changes in migratory behaviors to study these effects. Andrew has assembled a talented team of postdoctoral associates and graduate students working on a diverse array of migration-related projects. He completed a M.S. in Zoology with Dr. Sidney Gauthreaux using radar and acoustic technologies, followed by a Ph.D. in Ecology and Evolutionary Biology with Dr. John Fitzpatrick that focused heavily on flight call ecology and evolution and the application of knowledge of these topics to aid in conservation action aimed at migratory species and systems.
Steve Kelling’s primary focus is to coordinate a team of ornithologists, project managers, statisticians, application developers, and data managers to develop programs, tools, and analyses to gather, understand, and disseminate information on birds and the environments they inhabit. His responsibilities revolve around four broad topics: the management of eBird, a citizen-science project that gathers millions of bird observations from around the globe; the use of novel digital library strategies to create global communities of interested users centered around primary scientific references; the organization of the rich data resources of the bird-monitoring community and integrating these resources within existing bioinformatic infrastructures; and using unique statistical and computer science strategies to analyze the distribution and abundance of wild bird populations.
The primary goal of Professor Sheldon’s research is to develop algorithms to understand and make decisions about the environment using large data sets. He seeks to answer foundational questions (what are the general models and principles that underlie big data problems in ecology?) and also to build applications that transform large-scale data resources into scientific knowledge and policy. Some examples of his work include: spatial optimization to conserve endangered species, continent-scale modeling of bird migration, and biological interpretation of weather radar data across the US. Methodologically, Professor Sheldon’s primary interests are machine learning, probabilistic inference, and network modeling. His work has contributed broadly applicable new approaches for reasoning about aggregate data in probabilistic graphical models, and for optimization of diffusion processes in networks.
Adriaan Dokter is an ecologist and physicist, interested in animal movement, foraging ecology and spatial statistics. His research bridges the disciplines of physics, meteorology, computer science and ecology, addressing questions on the effects of global change on the distribution and seasonal migration of birds.
Kyle is a Rose Postdoctoral fellow at the Cornell Lab of Ornithology. His work focuses on understanding avian migration systems at the macroscale through the integration of radar, atmospheric measurements, and species-based observations, like eBird. He completed his M.S. with Jeffrey Buler at the University of Delaware and his Ph.D with Jeffrey Kelly at the University of Oklahoma.
Cecilia Nilsson is a behavioral ecologist, interested in flight behavior and bird migration. Previously she has worked with tracking radars to study flight behaviour of individual migrants, testing hypotheses about the costs and constraints that shape their migration. Currently she is working at the Cornell Lab of Ornithology studying large scale patterns of flight behavior using data from networks of weather radars.
Garrett is a PhD Computer Science student at UMass Amherst, advised by Daniel Sheldon. He is interested in developing algorithms to help scientists in all fields do their jobs more efficiently and effectively; currently he works with ornithologists and conservationists to study continent-wide bird migration patterns. Prior to UMass, he worked at MIT Lincoln Lab. He received an MEng in Computer Science in 2011 and a B.S. In Applied Physics in 2010, both from Cornell University.
Benjamin is a graduate student at the University of Oxford studying the evolution and flexibility of avian migration. He enjoys using a range of tools and approaches—from light-level geolocators, to radar, to genomics—to better understand the determinants of migration. Benjamin studied biology and statistics at Cornell University and has been BirdCasting since 2012.
Kevin studies Computer Science and Computational Sustainability at UMass Amherst as a Ph.D. student under Dr. Dan Sheldon. He is interested in bringing machine learning and graphical models to bear on problems from ornithology, marine biology, and lepidoptera, with previous work on large scale migratory bird studies using data from the national Doppler radar network, Zonneveld models for moth and butterfly populations, and several other sources.
Rich Caruana is Senior Researcher at Microsoft Research. Most of his research is in machine learning and data mining, and in the application of these to challenging problems in medical decision making and ecology. Rich has collaborated with Cornell’s Lab of Ornithology on a number of projects, and now serves as an external advisor on BirdCast. Recently Rich has begun a new collaboration with members of the BirdCast Team to investigate methods for providing ground truth observations of birds in flight to help calibrate radar measurements.
Carley Eschliman is an editorial assistant at the Cornell Lab for the summer of 2018. She is currently pursuing majors in both Atmospheric Science and Communication in the Cornell College of Agriculture and Life Science. Originally hailing from the great state of Kansas, Carley has been interested in weather since she first caught a glimpse of a tornado in her early childhood. She hopes that her studies at Cornell will equip her with skills to better bridge the communication gap between climate scientists and the general public. Through her work at the Cornell Lab, she wishes to encourage a closer partnership between birders and meteorologists, showing each group that there is a lot still left to learn about what is going on in the troposphere.
Tom Dietterich leads the development and application of advanced machine learning algorithms to model bird migration. These algorithms analyze the data provided by eBird volunteers, weather radar imagery, and weather forecasts to discover migration patterns and understand the factors that affect the speed and direction of bird flight during migration. Dietterich is President-Elect of the Association for the Advancement of Artificial Intelligence.
Wesley Hochachka is a senior research associate at Cornell University who has studied many aspects of the ecology and evolutionary biology of birds. Much of his research has revolved around identifying patterns and inferring processes over long time periods or large spatial extents. One of his current focuses is on the use of volunteer-collected (citizen science) data in order to understand the factors that determine where individual species live and why their distributions might change over time. Much of this work is as a member of the team that works on the analyses of data from eBird and the Avian Knowledge Network. The second current focus of his work is in disease ecology, as a member of a long-term collaboration that is studying the relationship between one finch species and a bacterial pathogen that jumped from poultry to these finches and subsequently caused substantial population declines in its new host.
Giles Hooker’s primary focus is on the interface of mathematical models for biological processes and data observed from them. He has particular expertise in using machine learning methods, functional data analysis and nonlinear dynamics. A particular focus of his research is in the interface of data mining with statistics, assessing the statistical evidence for patterns found in data and using machine learning models within more classical statistical modeling frameworks. His work is applied in ecology, epidemiology, evolution and in modeling large-scale bird ecology.
Frank A. La Sorte is a researcher ecologist whose work focuses on exploring the patterns and dynamics of biological diversity across space and time at continental to global scales. Currently he is developing a collaborative project to examine population-level migration trajectories of North American birds using the eBird database. The goal of this work to provide integrative and comprehensive scientific insights that will inform our current understanding of the biological phenomenon of avian migration. This research project provides unique opportunities to test and refine current migration theory and much needed information to support better informed policy and conservation initiatives.
Liping Liu brings expertise in machine learning and probabilistic graphical models to the project. In previous work with Rebecca Hutchinson, he developed an R package for fitting latent variable models with boosted regression trees. He will be extending this work to create the Semi-Parametric Latent Process Models required for BirdCast.
David Nicosia has been a meteorologist with NOAA for over 20 years after receiving his B.S and M.S in Meteorology from Penn State. Dave has worked many severe weather and flood events and has published numerous papers in meteorology. Dave also has been an avid birder for most of his life and is a leader for Cornell’s Lab of Ornithology’s Spring Field Ornithology class and also leads birding field trips for the Broome Naturalist’s Club.
Tao Sun’s research interests include machine learning and probabilistic graphical models. He has contributed to BirdCast by developing a synthetic data generator for weather-dependent bird migration, and by developing and testing different algorithms for approximate inference in Collective Graphical Models. He will continue this line of work to scale inference algorithms up to continent-sized problems, and to explore other applications and extensions of Collective Graphical Models.