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Speakers at Northwestern Computational Research Day

The Northwestern Computational Research Day provides opportunities for University faculty, researchers, graduate students, and postdocs to discuss successful practices and challenges in research computing.

Keynote Presentations

Title TBD—Louis Room 10:15 a.m. - 11:15 a.m.
Victoria Stodden, Associate Professor of Information Sciences, University of Illinois at Urbana

Abstract: Please check back for updates.

 

Title TBD—Louis Room 1:30 p.m. - 2:30 p.m.

Shane Larson, Associate Director of CIERA (Center for Interdisciplinary Exploration and Research in Astrophysics) and WCAS Research Associate Professor of Physics and Astronomy

Abstract:   Please check back for updates. 

 

Morning Speaker Session

All sessions held at 11:30 a.m. - 12:15 p.m.

Learning Probabilistic Models for Graph Partitioning and Community Detection—Northwestern Room A
Aravindan Vijayaraghavan, Assistant Professor, Electrical Engineering and Computer Science; Robert R. McCormick School of Engineering and Applied Science
Abstract: The Stochastic Block Model or the Planted Partition Model is the most widely used probabilistic model for community detection and clustering graphs in various fields, including machine learning,statistics, and social sciences. Many existing algorithms (e.g. spectral algorithms) successfully learn the communities or clusters when the data is drawn exactly according to the model. However, many of these guarantees do not hold in the presence of modeling errors, or when there is overlap between the different communities. In this talk, I will address the following question: can we design robust efficient algorithms for learning probabilistic models for community detection that work in the presence of adversarial modelling errors?  I will describe different computationally efficient algorithms that probably recover communities or clusters (up to small recovery error).These algorithmic results will work for probabilistic models that are more general than the stochastic block model, or when there are different kinds of modeling errors or noise.

Mechanistic Modeling of the (Bio)Conversion of (Bio)Macromolecules—Arch Room
Linda Broadbelt, Professor, Chemical and Biological Engineering; Robert R. McCormick School of Engineering and Applied Science

Abstract: Fast pyrolysis, a potential strategy for the production of transportation fuels from biomass, involves a complex network of competing reactions, which result in the formation of bio-oil, non-condensable gaseous species, and solid char. Bio-oil is a mixture of anhydro sugars, furan derivatives, and oxygenated aromatic and low molecular weight (LMW) compounds. Previously, the successful modeling of fast pyrolysis reactors for biomass conversion was hampered by lumped kinetic models, which fail to predict the bio-oil composition. Hence, a fundamental understanding of the chemistry and kinetics of biomass pyrolysis is important to evaluate the effects of process parameters like temperature, residence time and pressure on the composition of bio-oil.  In this talk, a mechanistic model that was recently developed to characterize the primary products of fast pyrolysis of cellulose is described. The kinetic model of pyrolysis of pure cellulose was then extended to describe cellulose decomposition in the presence of sodium salts.  To quantify the effect of sodium, a density functional theory study of glucose dehydration, an important class of decomposition reactions of a cellulose-derived intermediate, was carried out. The theoretical results reveal alterations in the reaction rate coefficients when sodium is present and a change in the relative rates of different reactions.  These kinetic parameters were used in the kinetic model to describe Na-mediated pathways, capturing trends in the experimental product distributions as the salt loading was increased based on classic catalytic cycles.  In contrast to pyrolysis, conversion of macromolecules such as cellulose in Nature takes place at ambient temperature, aided by enzymes.  Mechanistic details of the action of these enzymes will also be discussed and contrasted to high-temperature pyrolysis pathways. 

We have also developed a computational discovery platform for identifying and analyzing novel biochemical pathways to target chemicals.  Automated network generation that defines and implements the chemistry of what we have coined “generalized enzyme functions” based on knowledge compiled in existing biochemical databases is employed.  The output is a set of compounds and the pathways connecting them, both known and novel.  To identify the most promising of the thousands of different pathways generated, we link the automated network generation algorithms with pathway evaluation tools. The simplest screening metrics to rank pathways are pathway length and number of known reactions. More sophisticated screening tools include thermodynamic feasibility and potential of known enzymes for carrying out novel reactions. Our method for automated generation of pathways creates novel compounds and pathways that have not been reported in biochemical or chemical databases.  Thus, our method goes beyond a survey of existing compounds and reactions and provides an alternative to the conventional approaches practiced to develop novel biochemical processes that harness the power of enzymes as catalysts.

Title TBD—Lake Room
Denise Scholtens, Professor of Preventive Medicine (Biostatistics) and Neurological Surgery; Chief, Division of Biostatistics

Abstract: Integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modeling familial omics data, for example, to study fetal programming of related maternal-offspring phenotypes. Efforts to find ‘genetically determined metabotypes’ using classic genome-wide association study (GWAS) approaches have proven useful for characterizing complex disease, but conclusions are often limited to a disjointed series variant-metabolite associations. Our research group is adapting Bayesian network models to integrate metabotypes with maternal-fetal genetic dependencies and metabolic profile correlations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study, we demonstrate that strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, spinglass clustering of metabolites and conditional linear Gaussian methods clarify fetal programming of newborn adiposity related to maternal glycemia. Exploration of network growth over a range of penalty parameters, coupled with interactive plotting and external validation using publically available results, facilitate interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals.

 

Afternoon I Speaker Sessions

Sessions held at 2:30 p.m. - 3:15 p.m.

Title TBD—Northwestern Room A
Giles NovakProfessor, Physics and Astronomy; Judd A. and Marjorie Weinberg College of Arts and Sciences
Abstract: Please check back for updates.

Leveraging computational processes and neuroimaging data to understand the developing human brain—Northwestern Room B
Elizabeth NortonAssistant Professor, Communication Sciences and Disorders; School of Communication
Abstract: The human brain is impressively complex, and only recently have we started to understand the ways in which differences in brain structure and function can lead to meaningful behavior differences in individuals. An even greater challenge for our field to tackle is to understand how the brain changes over development. Neuroimaging studies using functional and structural MRI, EEG, and other technologies now let us peer in to the minds of adults and children with disorders such as dyslexia or autism, and even into preschoolers and infants who might develop these disorders. This presentation will discuss the computational tools that we use in our lab as cognitive neuroscientists to better understand these brain processes, such as software that allows us to track fibers and segment meaningful brain areas from MRI data, and to analyze and integrate EEG data from two people during interaction. We will discuss how brain measures and computational approaches might be used in a practical way to understand and predict common developmental disorders.


Title TBD—Lake Room
Michelle BirkettAssistant Professor, Medical Social Sciences; Feinberg School of Medicine
Abstract: Please check back for updates.

 

Afternoon II Speaker Sessions

Sessions held at 3:30 p.m. - 4:15 p.m.

Title TBD—Lake Room
Justin Starren, Professor, Preventive Medicine, Health and Biomedical Informatics Division; Feinberg School of Medicine
Abstract: 
Please check back for updates.

Vision Science in Visualization—Northwestern Room B
Christie Nothelfer, PhD candidate in the Brain, Behavior & Cognition program in the Department of Psychology 
Abstract: Data are ubiquitous. Data visualizations are powerful ways to explore data, and a persuasive way to communicate the patterns within. How do we design effective visualizations? One route is to incorporate research on human vision, to design visualizations that leverage the strengths and weaknesses of the human perceptual system in visualizations. While the visualization and vision science research communities have historically maintained minimal contact, they have much to teach each other, and collaborative research questions can advance both fields. For example, should we use multiple visual features, like colors and shapes, to distinguish groups of data in a scatterplot, even if it adds more visual complexity? How many sections of a line chart can we perceive and remember? How quickly can we extract relationships between data points, such as whether a bar graph contains more increases or decreases in value? I will present research at this exciting intersection of visualization and vision science, and demonstrate how both research communities can benefit from cross-disciplinary work.

Last Updated: 22 February 2018

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