You are invited to attend the 6th annual Yale Day of Data on November 30 in Sterling Memorial Library.
The Yale Day of Data brings together researchers and data experts from across the disciplines to share experiences, challenges, and best practices related to data-intensive research. If you collect, manage, analyze, interpret, or otherwise work with data, this event is for you.
This year’s theme, “Data on Earth,” is intentionally broad, to encompass data about the Earth and the environment, data that help us understand the health and lives of Earth’s inhabitants, and data with global impact.
Keynote: William Michener (Principal Investigator of DataONE, Professor and Director of e-Science Initiatives, University Libraries, University of New Mexico), “Managing Data Throughout the Research Life Cycle to Enable New Science and Support Decision Making”
Talks by Yale faculty and researchers: Tracey Meares, Dena
Schulman-Green, Karen Seto, Alan Gerber, Jessi Cisewski, Casey King, Martin Wainstein & Sophie Janaskie
Registration is now open for the 2018 Yale Day of
Lilian Kabeche, 10/22-10/24 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)
Morgan DeSantis, 11/5-11/6 (Monday seminar, Tuesday chalk talk) Seychelle Vos, 11/7-11/8 (Wednesday seminar, Thursday chalk talk) Anders Hansen, 11/26-11/28 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)
Franziska Bleichert, 12/3-12/4 (Monday seminar, Tuesday chalk talk)
Please join the Biostatistics Seminar scheduled for Tuesday, October 23, 2018 at 12:00 Noon at 47 College St., Room 106B.
Yale calendar Link: “YSPH Biostatistics Seminar: “Testing for Balance in Social Networks”
Speaker: Derek Feng, PhD
Institution: Yale University
Time & Place: 12:00 Noon in Room 106B, 47 College St.
11:45 AM Lunch outside Rm. 106B
Title: “Testing for Balance in Social Networks”
Friendship and antipathy exist in concert with one another in real social networks. Despite the role they play in social interactions, antagonistic ties are poorly understood and infrequently measured. One important theory of negative ties that has received relatively little empirical evaluation is balance theory, the codification of the adage `the enemy of my enemy is my friend’ and similar sayings. Unbalanced triangles are those with an odd number of negative ties, and the theory posits that such triangles are rare. To test for balance, previous works have utilized a permutation test on the edge signs. The flaw in this method, however, is that it assumes that negative and positive edges are interchangeable. In reality, they could not be more different. Here, we propose a novel test of balance that accounts for this discrepancy and show that our test is more accurate at detecting balance. Along the way, we prove asymptotic normality of the test statistic under our null model, which is of independent interest. Our case study is a novel dataset of signed networks we collected from 32 isolated, rural villages in Honduras. Contrary to previous results, we find that there is only marginal evidence for balance in social tie formation in this setting.
BIS Seminar Notice Oct 23_2018.pdf
Dear YCC Members and Community,
Attached are the flyer and the agenda for the “Cancer In Translation” A Signal Transduction Retreat scheduled for Tuesday, October 16, 2018 in the Conference Center at West Campus. The keynote speaker will be David Solit, MD from the Memorial Sloan Kettering Cancer Center.
Please register by end of day Friday, October 12, 2018. For those who have not yet registered, please click the link below:
Feel free to post in your area and forward to anyone who may be interested. Please contact Susan Silva (email@example.com) with any questions.
ST Retreat Agenda – Final.pdf
Cancer In Translation E-Flyer FINAL.pdf
Please join the Biostatistics Seminar October 22, 2018 at 12:00 Noon at 60 College Street, Rm. 216. Please also refer to the flyer attached.
Speaker: Leying Guan
Institution: Stanford University
Time & Place: 12:00 Noon in Room 216, 60 College Street
Title: “Transcription factor target identiﬁcation via knock-down experiments”
The perturbation of a transcription factor should aﬀect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance of detecting direct targets, we propose two new methods. The ﬁrst one is a modiﬁed two-group model using data from a single knock-down experiment. The null group corresponds to genes that are not direct targets, but can have small non-zero eﬀects. The second method constructs a depth constrained network using data from multiple knock-down experiments. I will give details of both methods and present some simulated as well as real data examples.
Note UNUSUAL DAY and LOCATION
APPLIED MATH SEMINAR
Speaker: David van Dijk, Yale
Date: Wednesday, October 10, 2018
Time: 3:45p.m. Refreshments (AKW, 1st Floor Break Area)
4:00p.m. Seminar (LOM 214)
Title: Understanding neural networks inside and out: designing constraints to enhance interpretability
Deep neural networks can learn meaningful representations of data. However, these representations are hard to interpret. In this talk I will present three ongoing projects in which I use specially designed constraints on latent representations of neural nets in order to make them more interpretable. First, I will present SAUCIE (Sparse Autoencoder for Clustering Imputation and Embedding) which is a framework for performing several data analysis tasks on a unified data representation. In SAUCIE we constrain the latent dimensions to be amenable to clustering, batch correction, imputation, and
visualization. Next, I will present a novel class of regularizations termed Graph Spectral Regularizations that impose graph structure on the otherwise unstructured activations of latent layers. By
considering the activations as signals on this graph we can use graph signal processing, and specifically graph filtering, to constrain the activations. I will show that, among other uses, this allows us to extract topological structure, such as clusters and progressions from data. Further, I will show that when the imposed graph is a 2D grid, with a smoothing penalty, the latent encodings become image-like. Such imposed grid structure also allows for addition of convolutional layers, even when the input data is naturally unstructured. Finally, in the third project, I propose a neural network framework, termed DyMoN (Dynamics Modeling network), that is capable of learning any stochastic dynamic process. I show that a DyMoN can learn harmonic and chaotic behavior, of single and double pendula respectively, and can give insight into the dynamics of biological systems.