My research focuses on developing and using methods in machine learning and natural language processing to learn about society from text, promoting better and more reproducible data science, and studying the societal impacts of these technologies. I collaborate with colleagues in statistics, linguistics, political science, and other areas of computational social science to investigate how people communicate, the effects of this communication, and to better understand the potential consequences and limitations of data science and artificial intelligence.
Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.
Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.
Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.
I build data science tools to address challenges in medicine and clinical care. Specifically, I apply signal processing, image processing and machine learning techniques, including deep convolutional and recurrent neural networks and natural language processing, to aid diagnosis, prognosis and treatment of patients with acute and chronic conditions. In addition, I conduct research on novel approaches to represent clinical data and combine supervised and unsupervised methods to improve model performance and reduce the labeling burden. Another active area of my research is design, implementation and utilization of novel wearable devices for non-invasive patient monitoring in hospital and at home. This includes integration of the information that is measured by wearables with the data available in the electronic health records, including medical codes, waveforms and images, among others. Another area of my research involves linear, non-linear and discrete optimization and queuing theory to build new solutions for healthcare logistic planning, including stochastic approximation methods to model complex systems such as dispatch policies for emergency systems with multi-server dispatches, variable server load, multiple priority levels, etc.
My research interests are in natural language semantics and psycholinguistics, focusing on verbs. I conduct behavioral psycholinguistic experiments with methodologies such as self-paced reading and maze tasks, as well as surveys of linguistic and semantic judgments. I also study semantic variation using corpora and datasets such as the Twitter Decahose, to better understand how words have developed diverging meanings in different communities, age groups, or regions. I use primarily R and Python to collect, manage, and analyze data. I direct the UM WordLab in the linguistics department, working with students (especially undergraduates) on experimental and computational research focusing on lexical representations.
Xingyu Zhang is a Research Assistant Professor at the School of Nursing’s Applied Biostatistics Laboratory. He received his Ph.D. in Biomedical Science concentrated on biostatistics from the University of Auckland in 2016. Prior to joining the ABL, he was a postdoctoral research fellow in epidemiology and biostatistics at Emory University, and also a visiting research scholar in medical informatics at Georgia Institute of Technology. Dr. Zhang’s research focuses on healthcare outcomes with an emphasis on study design and statistical analysis. The methods he applied include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, meta-analysis, etc.
My research interests span topics in Statistical Machine Learning, Computational Social Science, Natural Language Processing, and Field/Digital Experiments. Substantively, I am interested in understanding the impact of internet technologies on users by empirically studying their interactions with such systems. The research questions that I study are of both predictive as well as causal nature and I examine them by using data from text/natural language and social network domains.
The Language and Information Technologies (LIT) lab, directed by Rada Mihalcea, conducts research in natural language processing, information retrieval, and applied machine learning. The group specifically focuses on projects concerned with text semantics (word/text similarity, large semantic networks), behavior analysis (multilingual opinion analysis, multimodal models for deception detection, emotion recognition, alertness detection, stress/anxiety detection, analysis of counseling speech), big data for cross-cultural analysis (geotagging, understanding cross-cultural differences and worldview), educational applications (pedagogical search engines, automatic short answer grading, conversational technologies for student advising).
Several of the projects in the LIT lab are interdisciplinary, acknowledging the fact that language can be used to deepen our understanding in many different fields, such as psychology, sociology, history, and others. Some of the ongoing projects in the lab are collaborations with psychologists and sociologists, and target a rich modeling of human behavior through language analysis, seeking answers to questions such as “what are the core values of a culture?” and “are there differences in how different groups of people perceive the surrounding world?” The lab is also actively working on multimodal projects to track and understand human behavior, where language analysis is complemented with other channels such as facial expressions, gestures, and physiological signals.
Of interest, Prof. Mihalcea was quoted in a story about sexism and today’s virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Microsoft’s Cortana; Refinery29.