Joyce Chai

Joyce Chai

By |

My research interests are in the area of natural language processing, situated dialogue agents, and artificial intelligence. I’m particularly interested in language processing that is sensorimotor-grounded, pragmatically-rich, and cognitively-motivated. My current work explores the intersection of language, vision, and robotics to facilitate situated communication with embodied agents and applies different types of data (e.g., capturing human behaviors in communication, perception, and, action) to advance core intelligence of AI.

Tanya Rosenblat

Tanya Rosenblat

By |

My main research interest lies in experimental economics, social networks and social learning. I am particularly interested in how people aggregate information from social networks and news sources and form posterior beliefs. I use regression techniques to uncover causal relationships as well as classification to reduce the dimensionality of data.

Some of my recent research looks at how people update beliefs when they derive direct utility from beliefs. This occurs, for example, when people receive feedback on their ability. They often seem to weigh positive information more strongly than negative information. I am also interested in understanding differences between statistical and anecdotal reasoning. Under statistical reasoning, people have known objectives and they update beliefs through Bayes’ rule. Under anecdotal reasoning, people recall anecdotes that are relevant for forming a belief about a new objective that has not been encountered before. In these situations, memory recall and recognition are important to understand the formation of beliefs.

Mean absolute belief revisions by prior belief in response to positive/negative information. Prior deciles are ordered in increasing (decreasing) order for positive (negative) information. Bayesian should have equal responses.

Sabine Loos

Sabine Loos

By |

My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.

My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.

I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.

Geospatial model for predicting inequities in recovery from the 2015 Nepal earthquake

rachel sutton

Rachel Sutton

By |

She/Her

Rachel Sutton is a Project Manager with MIDAS. She received her Bachelor’s in Latin Language and Literature from University of Michigan and is currently pursuing a Master’s in Higher Education as a part-time student with the School of Education at University of Michigan, focusing on Philanthropy, Advancement, and Development. Her main interests in data science include its intersection with diversity, equity, and inclusion initiatives, and its utility in realizing social justice ideals.

Outside of work and school, Rachel spends most of her time friends, family, and her basset hound Olive.

Picture of Besa Xhabija

Besa Xhabija

By |

Dr. Xhabija joined the Department of Natural Sciences in September 2022 as an Assistant Professor of Biochemistry. Her laboratory aims to understand the effects of toxins on early embryonic development utilizing embryonic stem cells because they provide a new tool and opportunity to investigate the impact of environmental exposures and their interactions with genetic factors on human development and health. To fully realize these potentials, she believes that it is important to understand the molecular basis of the defining characteristic of the stem cells. More specifically, she is interested in investigating how stem cells play a role in shaping the expression program during development and how mechanisms of self-renewal and differentiation during mammalian development regulate cellular fate decisions.

Picture of David Brang

David Brang

By |

My lab studies how information from one sensory system influences processing in other sensory systems, as well as how this information is integrated in the brain. Specifically, we investigate the mechanisms underlying basic auditory, visual, and tactile interactions, synesthesia, multisensory body image perception, and visual facilitation of speech perception. Our current research examines multisensory processes using a variety of techniques including psychophysical testing and illusions, fMRI and DTI, electrophysiological measures of neural activity (both EEG and iEEG), and lesion mapping in patients with brain tumors. Our intracranial electroencephalography (iEEG/ECoG/sEEG) recordings are a unique resource that allow us to record neural activity directly from the human brain from clinically implanted electrodes in patients. These recordings are collected while patients perform the same auditory, visual, and tactile tasks that we use in our other behavioral and neuroimaging studies, but iEEG measures have millisecond temporal resolution as well as millimeter spatial precision, providing unparalleled information about the flow of neural activity in the brain. We use signal processing techniques and machine learning methods to identify how information is encoded in the brain and how it is disrupted in clinical contexts (e.g., in patients with a brain tumor).

Davon Norris

By |

I try to understand how our tools for determining what is valuable, worthwhile, or good are implicated in patterns of inequality with an acute concern for racial inequality. Often, this means my work investigates the functioning and consequences of a range of scores or ratings, from the less complex government credit ratings to the extremely complex algorithmic scores like consumer credit scores.

In related work, as a part of a multi-university team of researchers, I am using administrative credit report data from one of the largest credit reporting agencies to study credit and debt outcomes for millions of consumers in the United States.

Michael Craig

By |

Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address new challenges driven by decarbonization, climate adaptation, and equity objectives. He then applies these models to real-world systems to generate decision-relevant insights that account for engineering, economic, climatic, and policy features. His energy system models leverage optimization and simulation methods, depending on the problem at hand. Applying these models to climate mitigation or adaptation in real-world systems often runs into computational limits, which he overcomes through clustering, sampling, and other data reduction algorithms. His current interdisciplinary collaborations include climate scientists, hydrologists, economists, urban planners, epidemiologists, and diverse engineers.

Susan Hautaniemi Leonard

By |

I am faculty at ICPSR, the largest social science data archive in the world. I manage an education research pre-registration site (sreereg.org) that is focused on transparency and replicability. I also manage a site for sharing work around record linkage, including code (linkagelibrary.org). I am involved in the LIFE-M project (life-m.org), recently classifying the mortality data. That project uses cutting-edge techniques for machine-reading handwritten forms.

Mortality rates for selected causes in the total population per 1,000, 1850–1912, Holyoke and Northampton, Massachusetts

Stefanus Jasin

By |

My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.