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Somangshu Mukherji

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Somangshu (Sam) Mukherji, PhD, is Assistant Professor of Music Theory in the School of Music, Theatre & Dance at the University of Michigan, Ann Arbor.

Sam Mukherji‘s work lies at the interface of traditional Western tonal theory, the theory and practice of popular and non-Western idioms, and the cognitive science of music. Within this framework, the main focus of his research has been on the prolongational, grammatical aspects of Western tonality, and their connection to the tonal structures of Indian music, and the blues-based traditions within rock and metal. This emphasis makes his work similar to that of a linguist who explores relationships between the world’s languages-and, therefore, Mukherji’s research has been influenced in particular by ideas from linguistic theory as well, especially the Minimalist Program in contemporary generative linguistics. For this reason, he has investigated connections not only between different musical idioms but also between music and language-and musical and linguistic theory-more generally. Much of his work explores overlaps between Minimalist linguistics, and related, generative approaches within music theory (such as those found in the writings of Heinrich Schenker), and he has also written extensively about what such ‘musicolinguistic’ connections imply for the wider study of human musical behavior, cognition, and evolution.

Rocio Titiunik

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Prof. Titiunik’s research interests lie primarily in quantitative methodology for the social sciences, with emphasis on quasi-experimental methods for causal inference and political methodology. She is particularly interested in the application and development of non-experimental methods for the study of political institutions, a methodological agenda that is motivated by her substantive interests on democratic accountability and the role of party systems in developing democracies. Some of her current projects include the application of web scraping and text analysis tools to measure political phenomena.

Derek Harmon

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My research focuses on the intended and unintended consequences of language in financial markets. I examine this relationship across a number of contexts, such as the Federal Reserve, initial public offerings, and mergers and acquisitions. More broadly, my work aims to develop new theoretical and methodological approaches to understand the role of language in society.

Peter Adriaens

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My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies.  Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.

V. G. Vinod Vydiswaran

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V.G.Vinod Vydiswaran, PhD, is Assistant Professor in the Department of Learning Health Sciences with a secondary appointment in the School of Information at the University of Michigan, Ann Arbor.

Dr. Vydiswaran’s research focuses on developing and applying text mining, natural language processing, and machine learning methodologies for extracting relevant information from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets). His previous work includes developing novel information retrieval models to assist clinical decision making, modeling information trustworthiness, and addressing the vocabulary gap between health professionals and  laypersons.

Steven J. Katz

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Dr. Katz’s research addresses cancer treatment communication, decision-making, and quality of care. His work aims to examine the dynamics of how precision medicine presents itself in the exam room via provider and patient communication and shared decision-making. Dr. Katz leads the Cancer Surveillance and Outcomes Research Team (CanSORT), an interdisciplinary research program centered at the University of Michigan and focused on population and intervention studies of the quality of care and outcomes of cancer detection and treatment in diverse populations.  Dr. Katz and CanSORT have been collaborating with Surveillance, Epidemiology, and End Results (SEER) cancer registries since 2002 to study breast cancer treatment decision making at the population level. We obtain patient clinical and demographic information from SEER and combine this with surveys of patients and physicians to create comprehensive data sets that enable us to study testing and treatment trends and the challenges of individualizing treatments for breast cancer patients. In 2015 we added a new dimension to our research by partnering with evaluative testing firms to obtain tumor genomic and germline genetic test results for over 30,000 breast and ovarian cancer patients in the states of California and Georgia. We are also pursuing insurance claims data to assist with our analysis of physician network effects.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Steven Katz, MD discusses BRCA and multigene sequence testing at the labs of Ambry Genetics.

Andrew Grogan-Kaylor

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My core intellectual interest is the way in which parenting behaviors, like the use of physical punishment, or parental expressions of emotional warmth, have an effect on child outcomes like aggression, antisocial behavior, anxiety and depression, and how these dynamics play out across contexts, neighborhoods, and cultures.  A lot of my work is done with international samples. In my work I use statistical models, like multilevel models and some econometric models, and software like Stata, R, HLM and ArcGIS, to examine things like growth and change over time, or community, school or parent effects on children and families.  I have emerging interests in text-mining and natural language processing.

Visualization of multilevel modeling using High School and Beyond data set.

Visualization of multilevel modeling using High School and Beyond data set.

Jason Mars

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Jason Mars is a professor of computer science at the University of Michigan where he directs Clarity Lab, one of the best places in the world to be trained in A.I. and system design. Jason is also co-founder and CEO of Clinc, the cutting-edge A.I. startup that developed the world’s most advanced conversational AI.

Jason has devoted his career to solving difficult real-world problems, building some of the worlds most sophisticated salable systems for A.I., computer vision, and natural language processing. Prior to University of Michigan, Jason was a professor at UCSD. He also worked at Google and Intel.

Jason’s work constructing large-scale A.I. and deep learning-based systems and technology has been recognized globally and continues to have a significant impact on industry and academia. Jason holds a PhD in Computer Science from UVA.

Steven Abney

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Dr. Abney has pursued research in natural language understanding and natural language learning, including information extraction, biomedical text processing, integrating text analysis into web search, robust and rapid partial parsing, stochastic grammars, spoken-language information systems, extraction of linguistic information from scanned page images, dependency-grammar induction for low-resource languages, and semisupervised learning.

Walter S. Lasecki

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My lab creates systems that use a combination of both human and machine computation to solve problems quickly and reliably. We have introduced the idea of continuous real-time crowdsourcing, as well as the ‘crowd agent’ model, which uses computer-mediated groups of people submitting input simultaneously to create a collective intelligence capable of completing tasks better than any constituent member.