Chang-Yu Chang is a systems biologist who combines computational and experimental approaches to study the ecology and evolution of microbial communities. His past works focused on engineering the emergent properties of microbial consortia using directed evolution. With Dr. Corlett Wood, he is starting to work on host-associated microbial ecosystems and asks how the interplay between ecological interactions and genomic structures shapes the host phenotypes. Chang-Yu is originally from Taiwan.

Sunghye Cho is a linguist who has extensive research experience in acoustic phonetics and fully automated analyses of natural speech data with large-scale speech corpora. She has examined various characteristics of natural speech produced by patients with language and speech impairments not only at the phonetic, prosodic levels but also at the lexical, semantic, and syntactic levels using cutting-edge technologies in natural language processing and automated speech detection systems. Her recent projects focus on identifying novel language and speech biomarkers of neurodegeneration and autism spectrum disorder and building automatic classification systems using those biomarkers.

Sam Dillavou is a physicist interested in the overlap between physics and machine learning, and how the two fields can inform and support each other. His projects include building physical systems that can perform machine learning tasks (learn) without a processor, studying complex systems like granular flows that have resisted understanding using standard statistical methods, and using machine learning to make experimental science easier and more accessible.

Sarah Hye-yeon Lee is a linguist who is interested in the interface between language and cognition. Her work takes an experimental, data-driven approach to understanding the relationship between language and non-linguistic conceptual structure, as well as to understanding the cognitive mechanisms that underlie real-time language processing. Her research uses a range of behavioral data (e.g. reaction time data, eye-tracking data) and corpus data. Sarah holds a Ph.D. in Linguistics from the University of Southern California.

Kieran is a physicist entranced by information theory, leveraging machine learning to track down information in general relationships through data.  Recent projects have focused on developing methods to localize information: deconstructing complex systems into intelligible approximations — including the Mona Lisa and a simulated glass under shear — and switching around a standard representation learning method to isolate information out of mere groupings of data.

Erçağ Pinçe is an experimental biophysicist investigating the role of bacterial motility in establishing multispecies microbial communities. He addresses the  questions of whether motile cells can alter swimming behavior in response to local hydrodynamical stresses and solve an optimal navigation problem within a complex and disordered environment. Can they adopt optimal navigation policies to become early colonizers and outcompete other microbial species? Erçağ tackles these questions by combining microbiology, high-throughput 3D cell tracking microscopy, and microswimmer modeling based on a reinforcement learning framework. Through statistical analysis of large tracking datasets, he characterizes the motility pattern of naturally isolated bacteria and sets interaction studies amongst different motile phenotypes to uncover multifaceted phenomena of bacterial navigation.

Carlos is a political scientist that leverages applied data science methods and geospatial analysis to study the political economy of development in Latin America and in sub-Saharan Africa. Specifically, his research focuses on crime, health, human capital, migration, and policing. Carlos earned his doctorate in Political Science at the University of California, Berkeley, in August 2021.

Brynn Sherman is a cognitive neuroscientist interested in human memory. In particular, she combines insights from human behavior, data-driven analysis of neuroimaging data, and computational models to understand how we use our memories to adaptively guide future behavior. In her current projects, she is taking empirical and computational approaches to understanding how sleep facilitates the integration of new memories with existing knowledge.

Jingye Yang obtained his Bachelor’s degree from Xi’an Jiaotong University, and later earned a Ph.D. in Mathematics from the University of Pennsylvania. He also held dual Master’s degrees in Applied Mathematics and Computational Science (AMCS) and Statistics from the University of Pennsylvania. As a postdoctoral researcher at the University of Pennsylvania and the Children’s Hospital of Philadelphia, Jingye Yang specializes in Mathematics and Machine Learning within the field of Bioinformatics. He employs a variety of machine learning methods to analyze large-scale biomedical datasets, using mathematical and statistical approaches to tackle pressing bioinformatics challenges.

Roland Neil is a sociologist who uses machine learning algorithms, geospatial analysis, and semi-parametric models to study crime and the criminal justice system. His work focuses on using large administrative datasets and advanced methods of data science to address inferential challenges faced when studying racial disparities and discrimination in policing and the life course origins of offending and criminal justice contact. His recent papers appear in the Proceedings of the National Academy of Sciences, the American Journal of Sociology, and the Annual Review of Criminology. He holds a PhD in sociology from Harvard University.

Roland is now a policy expert at the RAND Corporation.

From “fright waves” in schooling fish to the emergence of shared vocabularies for colors in human language, Colin studies the natural algorithms underlying collective behavior in living systems. His work takes a quantitative, computational approach to understanding the flow of information between group-members, as well as to the development of new methods for inferring the determinants of group-level behavioral patterns (e.g. to identify the latent “communicative needs” for colors in different languages around the world).

Colin is now Interim Executive Director of the Data Driven Discovery Initiative.