Data Science Postdoctoral Fellows

Chang-Yu Chang

Chang-Yu Chang

Department of Biology

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

Sunghye Cho

Linguistic Data Consortium

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

Sam Dillavou

Department of Physics and Astronomy

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 Lee

Sarah Lee

Department of Linguistics

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.

Sergey Molodtsov

Sergey Molodtsov

Department of Earth and Environmental Science

Sergey Molodtsov is a physical oceanographer who is interested in studying large scale ocean circulation and its role in the climate system. Such research always involves dealing with large observational and modelling oceanographic and climate datasets, e.g. remote sensing imagery, general circulation models output and in-situ observational oceanographic datasets. He is applying machine learning to identify different relationships between various components of the ocean and climate system. Sergey holds a Ph.D. in physical oceanography from Texas A&M University.

Kieran Murphy

Kieran Murphy

Department of Bioengineering; Physics and Astronomy

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.

Roland Neil

Roland Neil

Department of Criminology

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.

Erçağ Pinçe

Erçağ Pinçe

Department of Physics and Astronomy

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.

Ivan Simpson-Kent

Ivan Simpson-Kent

Department of Psychology

Ivan Simpson-Kent received his Ph.D. in Medical Science from Cambridge University in 2021. Prior to his Ph.D., Dr. Simpson-Kent was a Fulbright Fellow in Zoology at Universität Regensburg in Germany, and received a B.S. in neuroscience with secondary major in philosophy and minor in mathematics from the University of Scranton. During his postdoctoral fellowship, Dr. Simpson-Kent is investigating the associations between environmental factors, cognition, and brain development in children. To do so, he uses methods from network science and structural equation modeling to help tease apart the myriad of complex interactions among these variables and levels of organization.

Dimitrios Tanoglidis

Dimitrios Tanoglidis

Department of Physics and Astronomy

Dimitrios Tanoglidis is an astrophysicist who is excited about developing machine learning solutions for fast and efficient analysis of large astronomical datasets coming from modern galaxy surveys. In past work, he used data from the Dark Energy Survey and machine learning to discover extremely faint galaxies, adapted an object detection model for the processing of astronomical images, and developed a neural network for automated galaxy parameter inference with uncertainty quantification. He is also interested in the broader societal impact of machine learning/AI. Dimitrios holds a Ph.D. in astrophysics from the University of Chicago, and is originally from Crete, Greece.

Colin Twomey

Colin Twomey

Department of Biology

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).

DDD will host a program of Penn Arts and Sciences Data Science Postdoctoral Fellows. There are two paths to becoming a fellow

  1. Via DDD support (up to 50 percent salary support) for up to three years. These postdocs could either already be at Penn or they could be new hires. SAS faculty may apply for postdoc funding here
  2. This is available to all current or incoming SAS postdocs: if data science is a significant part of your research and you are interested in cross-disciplinary interactions, you are invited to apply to become a Fellow of our program. You will join an exciting group of postdocs spanning the social and natural sciences, will have weekly interactions with faculty and visitors, and will have a research fund (up to $5K total) to support your travel and visitors. Postdocs may apply here

For more questions send us an email.