Lisa Levinson

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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.

Wenhao Sun

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We are interested in resolving outstanding fundamental scientific problems that impede the computational materials design process. Our group uses high-throughput density functional theory, applied thermodynamics, and materials informatics to deepen our fundamental understanding of synthesis-structure-property relationships, while exploring new chemical spaces for functional technological materials. These research interests are driven by the practical goal of the U.S. Materials Genome Initiative to accelerate materials discovery, but whose resolution requires basic fundamental research in synthesis science, inorganic chemistry, and materials thermodynamics.

Amiyatosh Purnanandam

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My primary research is focused on measurement and monitoring of risks in banks, both at the individual bank level and at the level of financial system as a whole. In a recent paper, we have developed a high-dimension statistical approach to measure connectivity across different players in the financial sector. We implement our model using stock return data for US banks, insurance companies and hedge funds. Some of my early research has developed analytical tools to measure banks’ default risk using option pricing models and other tools of financial economics. These projects have often a significant empirical component that uses large financial datasets and econometric tools. Of late, I have been working on several projects related to the issue of equity and inclusion in financial markets. These papers use large datasets from financial markets to understand differences in the quantity and quality of financial services received by minority borrowers. A common theme across these projects is the issue of causal inference using state-of-the art tools from econometrics. Finally, some of ongoing research projects are related to FinTech with a focus on credit scoring and online lending.

Salar Fattahi

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Today’s real-world problems are complex and large, often with overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in energy systems, the system operators should solve optimal power flow, unit commitment, and transmission switching problems with tens of thousands of continuous and discrete variables in real time. In control systems, a long standing question is how to efficiently design structured and distributed controllers for large-scale and unknown dynamical systems. Finally, in machine learning, it is important to obtain simple, interpretable, and parsimonious models for high-dimensional and noisy datasets. Our research is motivated by two main goals: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for these optimization problems that come equipped with certifiable optimality guarantees. We aim to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.

9.9.2020 MIDAS Faculty Research Pitch Video.

My research lies at the intersection of optimization, data analytics, and control.

Yulia Sevryugina

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Study of Pandemic Publishing: How Scholarly Literature is Affected by COVID-19 Pandemic
This project addresses the quality of recently published COVID-19 publications. With the COVID-19 pandemic, researchers publish a lot their research as preprints. And while preprints are an important development in scholarly publishing, they are works in progress that need further refinement to become a more rigorous final product. Scholarly publishers are also taking initiatives to accelerate publication process, for example, by asking reviewers to curtail requests for additional experiments upon revisions. Sacrificing rigor for haste inevitably increases the likelihood of article correction and retraction, leading to spread of false information within supposedly trustworthy sources that have a peer-reviewing process in place to ensure proper verification. I study the quality of COVID-19 related scholarly works by using CADRE’s datasets to identify signs of incoherency, irreproducibility, and haste.

9.9.2020 MIDAS Faculty Research Pitch Video.

MIDAS Faculty Research Pitch, Fall 2021

Alex Gorodetsky

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Alex Gorodetsky’s research is at the intersection of applied mathematics, data science, and computational science, and is focused on enabling autonomous decision making under uncertainty. He is especially interested in controlling, designing, and analyzing autonomous systems that must act in complex environments where observational data and expensive computational simulations must work together to ensure objectives are achieved. Toward this goal, he pursues research in wide-ranging areas including uncertainty quantification, statistical inference, machine learning, control, and numerical analysis. His methodology is to increase scalability of probabilistic modeling and analysis techniques such as Bayesian inference and uncertainty quantification. His current strategies to achieving scalability revolve around leveraging computational optimal transport, developing tensor network learning algorithms, and creating new multi-fidelity information fusion approaches.

Sample workflow for enabling autonomous decision making under uncertainty for a drone operating in a complex environment. We develop algorithms to compress simulation data by exploiting problem structure. We then embed the compressed representations onto onboard computational resources. Finally, we develop approaches to enable the drone to adapt, learn, and refine knowledge by interacting with, and collecting data from, the environment.

Lucia Cevidanes

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We have developed and tested machine learning approaches to integrate quantitative markers for diagnosis and assessment of progression of TMJ OA, as well as extended the capabilities of 3D Slicer4 into web-based tools and disseminated open source image analysis tools. Our aims use data processing and in-depth analytics combined with learning using privileged information, integrated feature selection, and testing the performance of longitudinal risk predictors. Our long term goals are to improve diagnosis and risk prediction of TemporoMandibular Osteoarthritis in future multicenter studies.

The Spectrum of Data Science for Diagnosis of Osteoarthritis of the Temporomandibular Joint

Joshua Stein

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As a board-certified ophthalmologist and glaucoma specialist, I have more than 15 years of clinical experience caring for patients with different types and complexities of glaucoma. In addition to my clinical experience, as a health services researcher, I have developed experience and expertise in several disciplines including performing analyses using large health care claims databases to study utilization and outcomes of patients with ocular diseases, racial and other disparities in eye care, associations between systemic conditions or medication use and ocular diseases. I have learned the nuances of various data sources and ways to maximize our use of these data sources to answer important and timely questions. Leveraging my background in HSR with new skills in bioinformatics and precision medicine, over the past 2-3 years I have been developing and growing the Sight Outcomes Research Collaborative (SOURCE) repository, a powerful tool that researchers can tap into to study patients with ocular diseases. My team and I have spent countless hours devising ways of extracting electronic health record data from Clarity, cleaning and de-identifying the data, and making it linkable to ocular diagnostic test data (OCT, HVF, biometry) and non-clinical data. Now that we have successfully developed such a resource here at Kellogg, I am now collaborating with colleagues at > 2 dozen academic ophthalmology departments across the country to assist them with extracting their data in the same format and sending it to Kellogg so that we can pool the data and make it accessible to researchers at all of the participating centers for research and quality improvement studies. I am also actively exploring ways to integrate data from SOURCE into deep learning and artificial intelligence algorithms, making use of SOURCE data for genotype-phenotype association studies and development of polygenic risk scores for common ocular diseases, capturing patient-reported outcome data for the majority of eye care recipients, enhancing visualization of the data on easy-to-access dashboards to aid in quality improvement initiatives, and making use of the data to enhance quality of care, safety, efficiency of care delivery, and to improve clinical operations. .

P.T. Sathe

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P.T. Sathe is a lecturer in Industrial and Operations Engineering at the University of Michigan.

John Silberholz

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Most of my research related to data science involves decision making around clinical trials. In particular, I am interested in how databases of past clinical trial results can inform future trial design and other decisions. Some of my work has involved using machine learning and mathematical optimization to design new combination therapies for cancer based on the results of past trials. Other work has used network meta-analysis to combine the results of randomized controlled trials (RCTs) to better summarize what is currently known about a disease, to design further trials that would be maximally informative, and to study the quality of the control arms used in Phase III trials (which are used for drug approvals). Other work combines toxicity data from clinical trials with toxicity data from other data sources (claims data and adverse event reporting databases) to accelerate detection of adverse drug reactions to newly approved drugs. Lastly, some of my work uses Bayesian inference to accelerate clinical trials with multiple endpoints, learning the link between different endpoints using past clinical trial results.