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Ho-Joon Lee

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Dr. Lee’s research in data science concerns biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to multi-dimensional large-scale data. His projects have covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels, which also incorporates large-scale public databases such as GO for functional annotation, PDB for molecular structures, and PubChem and LINCS for drugs or small compounds. He previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (Lee et al., Cell Reports 2017), for which he developed integrative analytical tools using diverse approaches from machine learning and network theory. His ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, and cancer metabolism and (2) mass spectrometry-based omics data analysis.

Figure 1. Proteomics and metabolomics analysis of IL-3 activation and cell cycle (Lee et al., Cell Reports 2017). (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.

 

Samuel K Handelman

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Samuel K Handelman, Ph.D., is Research Assistant Professor in the department of Internal Medicine, Gastroenterology, of Michigan Medicine at the University of Michigan, Ann Arbor. Prof. Handelman is focused on multi-omics approaches to drive precision/personalized-therapy and to predict population-level differences in the effectiveness of interventions. He tends to favor regression-style and hierarchical-clustering approaches, partially because he has a background in both statistics and in cladistics. His scientific monomania is for compensatory mechanisms and trade-offs in evolution, but he has a principled reason to focus on translational medicine: real understanding of these mechanisms goes all the way into the clinic. Anything less that clinical translation indicates that we don’t understand what drove the genetics of human populations.

Jinseok Kim

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Jinseok Kim, Ph.D., is Research Assistant Professor in the Institute for Social Research at the University of Michigan, Ann Arbor.  Prof. Kim works on resolving named entity ambiguity in large-scale scholarly data (publication, patent, and funding records) in digital libraries. Especially, his current research is focused on developing methods for disambiguating author and affiliation names at a digital library scale using various supervised machine learning approaches trained on automatically labeled data . Disambiguated data from multiple sources will be integrated to be analyzed for insights into research production, scientific collaboration, funding evaluation, and research policy at a national level.

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.

Jon Zelner

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Jon Zelner, PhD, is Assistant Professor in the department of Epidemiology in the University of Michigan School of Public Health. Dr. Zelner holds a second appointment in the Center for Social Epidemiology and Population Health.

Dr. Zelner’s research is focused on using spatial analysis, social network analyisis and dynamic modeling to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease. Jon is also interested in understanding how the social and biological causes of illness interact to generate observable patterns of disease in space and in social networks, across outcomes ranging from infection to mental illness.

 

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

A large spatial cluster of multi-drug resistant tuberculosis (MDR-TB) cases in Lima, Peru is highlighted in red. A key challenge in my work is understanding why these cases cluster in space: can social, spatial, and genetic data tell us where transmission is occurring and how to interrupt it?

 

 

Adriene Beltz

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The goal of my research is to leverage network analysis techniques to uncover how the brain mediates sex hormone influences on gendered behavior across the lifespan. Specifically, my data science research concerns the creation and application of person-specific connectivity analyses, such as unified structural equation models, to time series data; these are intensive longitudinal data, including functional neuroimages, daily diaries, and observations. I then use these data science methods to investigate the links between androgens (e.g., testosterone) and estradiol at key developmental periods, such as puberty, and behaviors that typically show sex differences, including aspects of cognition and psychopathology.

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

A network map showing the directed connections among 25 brain regions of interest in the resting state frontoparietal network for an individual; data were acquired via functional magnetic resonance imaging. Black lines depict connections common across individuals in the sample, gray lines depict connections specific to this individual, solid lines depict contemporaneous connections (occurring in the same volume), and dashed lines depict lagged connections (occurring between volumes).

Kai S. Cortina

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Kai S. Cortina, PhD, is Professor of Psychology in the College of Literature, Science, and the Arts at the University of Michigan, Ann Arbor.

Prof. Cortina’s major research revolves around the understanding of children’s and adolescents’ pathways into adulthood and the role of the educational system in this process. The academic and psycho-social development is analyzed from a life-span perspective exclusively analyzing longitudinal data over longer periods of time (e.g., from middle school to young adulthood). The hierarchical structure of the school system (student/classroom/school/district/state/nations) requires the use of statistical tools that can handle these kind of nested data.

 

Mingyan Liu

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Mingyan Liu, PhD, is Professor of Electrical Engineering and Computer Science, College of Engineering, at the University of Michigan, Ann Arbor.

Prof. Liu’s research interest lies in optimal resource allocation, sequential decision theory, online and machine learning, performance modeling, analysis, and design of large-scale, decentralized, stochastic and networked systems, using tools including stochastic control, optimization, game theory and mechanism design. Her most recent research activities involve sequential learning, modeling and mining of large scale Internet measurement data concerning cyber security, and incentive mechanisms for inter-dependent security games. Within this context, her research group is actively working on the following directions.

1. Cyber security incident forecast. The goal is to predict an organization’s likelihood of having a cyber security incident in the near future using a variety of externally collected Internet measurement data, some of which capture active maliciousness (e.g., spam and phishing/malware activities) while others capture more latent factors (e.g., misconfiguration and mismanagement). While machine learning techniques have been extensively used for detection in the cyber security literature, using them for prediction has rarely been done. This is the first study on the prediction of broad categories of security incidents on an organizational level. Our work to date shows that with the right choice of feature set, highly accurate predictions can be achieved with a forecasting window of 6-12 months. Given the increasing amount of high profile security incidents (Target, Home Depot, JP Morgan Chase, and Anthem, just to name a few) and the amount of social and economic cost they inflict, this work will have a major impact on cyber security risk management.

2. Detect propagation in temporal data and its application to identifying phishing activities. Phishing activities propagate from one network to another in a highly regular fashion, a phenomenon known as fast-flux, though how the destination networks are chosen by the malicious campaign remains unknown. An interesting challenge arises as to whether one can use community detection methods to automatically extract those networks involved in a single phishing campaign; the ability to do so would be critical to forensic analysis. While there have been many results on detecting communities defined as subsets of relatively strongly connected entities, the phishing activity exhibits a unique propagating property that is better captured using an epidemic model. By using a combination of epidemic modeling and regression we can identify this type of propagating community with reasonable accuracy; we are working on alternative methods as well.

3. Data-driven modeling of organizational and end-user security posture. We are working to build models that accurately capture the cyber security postures of end-users as well as organizations, using large quantities of Internet measurement data. One domain is on how software vendors disclose security vulnerabilities in their products, how they deploy software upgrades and patches, and in turn, how end users install these patches; all these elements combined lead to a better understanding of the overall state of vulnerability of a given machine and how that relates to user behaviors. Another domain concerns the interconnectedness of today’s Internet which implies that what we see from one network is inevitably related to others. We use this connection to gain better insight into the conditions of not just a single network viewed in isolation, but multiple networks viewed together.

A predictive analytics approach to forecasting cyber security incidents. We start from Internet-scale measurement on the security postures of network entities. We also collect security incident reports to use as labels in a supervised learning framework. The collected data then goes through extensive processing and domain-specific feature extraction. Features are then used to train a classifier that generates predictions when we input new features, on the likelihood of a future incident for the entity associated with the input features. We are also actively seeking to understand the causal relationship among different features and the security interdependence among different network entities. Lastly, risk prediction helps us design better incentive mechanisms which is another facet of our research in this domain.

A predictive analytics approach to forecasting cyber security incidents. We start from Internet-scale measurement on the security postures of network entities. We also collect security incident reports to use as labels in a supervised learning framework. The collected data then goes through extensive processing and domain-specific feature extraction. Features are then used to train a classifier that generates predictions when we input new features, on the likelihood of a future incident for the entity associated with the input features. We are also actively seeking to understand the causal relationship among different features and the security interdependence among different network entities. Lastly, risk prediction helps us design better incentive mechanisms which is another facet of our research in this domain.