Thursday, 23 January 2020, 4-5 pm
A13 Crawford Hall

Speaker: Roger French, Kyocera Professor, Materials Science & Engineering; Director, SDLE Research Center; Faculty Director, Applied Data Science Program
Title: Data Science and Machine Learning Applied to Silicon Photovoltaic Solar Panels: Doing Energy Science at Scale with Time-series and Image Datasets

This talk will begin with a review of the Undergraduate Minor in Applied Data Science, which is directly available inside the College of Arts & Sciences, and a presentation of plans for the creation of a Graduate certificate.

Abstract: Advances in computing, communication, and data collection have facilitated collection of petabyte-scale datasets from which data-driven models can be built. This digital transformation affects society, industry, and academia, since data-driven models can challenge how things are done and offer new opportunities for developing how things work.

At CWRU we have offered the university-wide Applied Data Science (ADS) program since 2015. The ADS program teaches non-computer science students, producing “T-shaped” graduates with deep knowledge in their domain plus strong data science skills. The ADS program provides both an undergraduate minor and graduate level courses for which a University Certificate is being developed. ADS students learn the foundations: coding, inferential statistics, exploratory data analysis, modeling and prediction, and they complete a semester long data science project for their ADS portfolio. The courses are taught using a practicum approach, with an open data science toolchain consisting of R, Python, Git, Markdown, Machine Learning, and TensorFlow on GPUs.

We utilize data science and big-data analytics to address critical problems in energy science. As solar power grows, we need to fully understand and predict the power output of photovoltaic (PV) modules over their entire > 30 year lifetimes. Degradation science [reference 1] combines data-driven statistical and machine learning with physical and chemical science to examine degradation mechanisms in order to improve PV materials and reduce system failures. We use distributed and high performance computing, based on Hadoop2 and the NoSQL Hbase, to ingest, analyze, and model large volumes of time-series datasets from 3.4 GW of PV power plants [reference 2]. We have developed an automated image processing and deep learning pipeline applied to electroluminescent (EL) images of PV modules to identify degradation mechanisms and predict their associated power losses [reference 3]. Unbiased, data-driven analytics, now possible using data science methodologies, represents a new front in our research studies of critically important and complex systems.

References
1. R.H. French, et al., Degradation science: Mesoscopic evolution and temporal analytics of photovoltaic energy materials, Curr. Op.Sol. State & Matls. Sci. 19 (2015) 212–226.

2. Y. Hu, et al., A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites, IEEE Journal of Photovoltaics. 7 (2017) 230–236.

3. A. M. Karimi, et al., Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification, IEEE Journal of Photovoltaics. (2019) 1–12.

Thursday, 30 January 2020, 4-5 pm
A13 Crawford Hall

Speaker: Tim Beal, Florence Harkness Professor of Religion, with Justin Barber and Michael Hemenway, AI Institute at Iliff School of Theology
Title: Face of the Deep: Perverse Engagements with Neural Machine Translation

Abstract: The broad aim of this project is to explore new possibilities for translation within a post-print digital media environment. Working with emerging technologies of neural machine translation (NMT) and natural language processing (NLP) in the programming language of Python, we are exploring new models and methods for translating Hebrew biblical and other ancient texts. Whereas print translation pushes the translator toward closure, deciding on a single translation and relegating alternatives to footnotes or parentheses, how might new media technologies make it possible to provide readers/users access to the processes of translation, hosting an encounter that attends to the rich ambiguities and polyvocalities of the other text in translation? How might we deploy new media technologies in ways that radically alter translation, not only transforming the processes of translation but also involving users in those processes?

Our interests as humanities scholars in ambiguity, polyvocality, and the irreducible otherness of the text in translation fly in the face of the burgeoning industry of NMT (e.g., Google Translate). Whereas the consumer-oriented goal of NMT is to erase ambiguity and make the processes of translation invisible and immediate (so users barely realize translation is taking place), we aim to build models using NMT and other NLP tools perversely, to slow down and make visible the complex processes of translation, in order to invite users to participate in those processes.

Thursday, 6 February 2020, 4-5 pm
A13 Crawford Hall

Speaker: Daniela Calvetti, The James Wood Williamson Professor of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University
Title: Meditating Brain: Analysis and Interpretation of MEG data during meditation

Abstract:
In this talk, we describe the ongoing research project to understand the fine scale spatio-temporal changes in the brain activity during meditation. The raw data consist of hours of Magnetoencephalography (MEG) recordings of professional meditators from the Theravada Buddhist tradition. The data was registered with one millisecond time resolution, the meditators alternating between focussed attention (Samatha), open monitoring, or mindfulness (Vipassana), and eyes closed resting state. The data is first processed to obtain a time series activity map of the brain, and subsequently interpreted by using data driven model reduction techniques. The work is part of a collaboration with researchers at CWRU (Daniela Calvetti, Erkki Somersalo, Brian Johnson (currently at Yelp) and University of Rome “La Sapienza”, Italy (Annalisa Pascarella, Francesca Pitolli, Barbara Vantaggi).


Thursday, 13 February 2020, 3:30-5 pm
Freedman Center, Kelvin Smith Library
Meet and Greet: Love Data Week
Meet for casual discussions, with refreshments. Members of the Library team for Data Science and the Freedman Center for Digital Scholarship will attend.
Registration required. Click to register!

Thursday, 20 February 2020, 4-5 pm
A13 Crawford Hall

Speaker:
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Thursday, 27 February 2020, 4-5 pm
A13 Crawford Hall

Speaker: Jennifer Hinnell. PhD Candidate, Department of Linguistics, University of Alberta
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Thursday, 5 March 2020, 4-5 pm
A13 Crawford Hall

Speaker:
Moderator: Tim Beal

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Thursday, 19 March 2020, 4-5 pm
A13 Crawford Hall

Speaker: Peter Whitehouse Peter J. Whitehouse MD-PhD has a primary appointment as Professor of Neurology, with secondary positions as Professor of Psychiatry, Cognitive Science, Neuroscience, and Organizational Behavior, and former appointments (but current interests) in Psychology, Bioethics, History, and Nursing at Case Western Reserve University. He is also currently Professor of Medicine at the University of Toronto, Honorary Research Fellow (zoology and aging) University of Oxford, and Founding President of Intergenerational Schools International. He is a card-carrying transdisciplinarian of the French variety. Major focuses of his current work have been age-associated cognitive challenges (formerly Alzheimer’s disease) and the nature of evidence and evidence of nature.
Title: If Big Data is the answer to Alzheimer’s, what is the question?

Abstract: “Alzheimer’s” is for many a dominant individual and social concern. How do we gather and analyze evidence wisely to understand the phenomenology of aging associated cognitive challenges and help people and communities suffering from the condition? Examined transdisciplinarily what is the nature of evidence? Almost hypothesis-less Big Data is said to be the answer to “curing” Alzheimer’s. But as always, framing the questions and examining the words in them are the best places to start finding helpful answers. How does Alzheimer’s-type dementia relate to aging? Is it one condition? Is preserving brain health more about molecules and genes or communities and politics? What is the evidence for evidence and whose version anyway? What is the story of Big Data in the biopolitics of dementia? How do we create not AI but new symbionic/symbiotic intelligences? What is the bigger story, even grander narrative, we need to tell about the brain and aging in the emergent Anthropocene? Deconstructing the narrative of Alzheimer’s can be part of understanding our collective great derangement in failing to address the collapse of ecosystems and hence also potentially modern civilization? More importantly, how to do we use data, narrative, and metaphor together to create not only knowledge but wisdom to reinvent ourselves and our societies to be more resilient and sustainable?

Thursday, 26 March 2020, 4-5 pm
301 Rockefeller

Tentative

Speaker: Art & Physics
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Thursday, 2 April 2020, 4-5 pm
A13 Crawford Hall

Speakers: Members of the AI Institute at Iliff School of Theology
Title: ai.iliff – conversational ai for online learning

Abstract: ai.iliff is a Henry Luce Foundation funded AI institute housed within the Iliff School of Theology in Denver, CO. ai.iliff developed out of several years of experience learning with machines as partners in the process of scholarship and research in the humanities. Building upon the recent advances in pre-trained language models for NLP tasks, we are using our TRUST model for ai design to build conversational  ai applications to enhance student learning in online education.

Thursday, 9 April 2020, 4-5 pm
A13 Crawford Hall

Speaker: Kelly McMann.
Title:

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Thursday, 16 April 2020, 4-5 pm
A13 Crawford Hall

Speaker: Shannon French
Moderator: Tim Beal
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Thursday, 23 April 2020, 4-5 pm
A13 Crawford Hall

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Previous colloquia

Thursday, 16 January 2020, 4-5 pm
A13 Crawford Hall

Speaker: Mark Turner and The Red Hen Team
Title: Big Data Science for Multimodal Communication—An Overview of the International Distributed Little Red Hen Lab.

View Recording

Abstract: The International Distributed Little Red Hen Lab™ is a global big data science laboratory and cooperative for research into multimodal communication. Red Hen’s main goal is theory of multimodal communication. See Overview of the Red Hen Vision and Program. Red Hen’s secondary goal is the development of computational, statistical, and technical tools for big data science on multimodal communication. See e.g. Red Hen Lab’s Google Summer of Code 2019 Ideas page and Projects page. Red Hen’s tertiary goal is pedagogy: see her Τέχνη Public Site—Red Hen Lab’s Learning Environment