


Tag Archives: seminars
Message to Yale Community about 2018 Day of Data – small correction
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 dataintensive 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 eScience 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
SchulmanGreen, Karen Seto, Alan Gerber, Jessi Cisewski, Casey King, Martin Wainstein & Sophie Janaskie
Registration is now open for the 2018 Yale Day of
Data:https://elischolar.library.yale.edu/dayofdata/2018/.
Bobak Mortazavi, Center for Biomedical Data Science speaker, 11718 in Hope 110 at 4 pm
Attn: team wearbles
Mbbfac REVISED for CLARITY: SAVE THE DATES: 20182019 Search candidate visit dates confirmed
Lilian Kabeche, 10/2210/24 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)
Morgan DeSantis, 11/511/6 (Monday seminar, Tuesday chalk talk) Seychelle Vos, 11/711/8 (Wednesday seminar, Thursday chalk talk) Anders Hansen, 11/2611/28 (Monday seminar, Wednesday chalk talk; shared visit w/YCBI)
Franziska Bleichert, 12/312/4 (Monday seminar, Tuesday chalk talk)
Biostatistics Seminar: 10/23/18 – 12:00 Noon – 47 College St, 106B
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”
Thank you,
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”
Abstract:
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
ST Retreat – “Cancer In Translation” October 16, 2018
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:
REGISTRATION
Feel free to post in your area and forward to anyone who may be interested. Please contact Susan Silva (susan.silva@yale.edu) with any questions.
ST Retreat Agenda – Final.pdf
Cancer In Translation EFlyer FINAL.pdf
Biostatistics Seminar: Transcription factor target identiﬁcation via knockdown experiments
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.
Thank you.
Speaker: Leying Guan
Institution: Stanford University
Time & Place: 12:00 Noon in Room 216, 60 College Street
Title: “Transcription factor target identiﬁcation via knockdown experiments”
Abstract:
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 twogroup model using data from a single knockdown experiment. The null group corresponds to genes that are not direct targets, but can have small nonzero eﬀects. The second method constructs a depth constrained network using data from multiple knockdown experiments. I will give details of both methods and present some simulated as well as real data examples.
Mathapplied Next week: Applied Math Program: Seminar & Refreshments Wednesday, Oct 10, 2018
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 imagelike. 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.
YINS 9/17 Applied DS Seminar, Andrew Barron: Deep Learning
“Accuracy of HighDimensional Deep Learning Networks”
Speaker: Andrew Barron
Professor of Statistics and Data Science at Yale University
Monday, September 17, 4:155:30pm
Location: Yale Institute for Network Science, 17 Hillhouse Ave, Room 328
Talk summary: It has been experimentally observed in recent years that multilayer artificial neural networks have a surprising ability to generalize, even when trained with far more parameters than observations. Is there a theoretical basis for this? The best available bounds on their metric entropy and associated complexity measures are essentially linear in the number of parameters, which is inadequate to explain this phenomenon. Here we examine the statistical risk (mean squared predictive error) of multilayer networks with L1 controls on their parameters and with ramp activation functions (also called lowerrectified linear units). In this setting, the risk is shown to be upperbounded by [(L^3 log d)/n]^{1/2}, where d is the input dimension to each layer, L is the number of layers, and n is the sample size. In this way, the input dimension can be much larger than the sample size and the estimator can still be accurate, provided the target function has such L1 controls and that the sample size is at least moderately large compared to L^3 log d. The heart of the analysis is the development of a sampling strategy that demonstrates the accuracy of a sparse covering of deep ramp networks. Lower bounds show that the identified risk is minimax optimal, this being so already in the subclass of functions with L = 2. This is joint work with Jason Klusowski.
Statseminars Fwd: YINS 9/12 YINS Seminar, Constantinos Daskalakis: Adversarial Networks
“Improving Generative Adversarial Networks using Game Theory and Statistics”
Speaker: Constantinos Daskalakis
Professor of Computer Science and Electrical Engineering at MIT
Wednesday, September 12, 12:001:00pm
Location: Yale Institute for Network Science, 17 Hillhouse Ave, Room 328
Talk summary: Generative Adversarial Networks (aka GANs) are a recently proposed approach for learning samplers of highdimensional distributions with intricate structure, such as distributions over natural images, given samples from these distributions. They are obtained by setting up a twoplayer zerosum game between two neural networks, which learn statistics of a target distribution by adapting their strategies in the game using gradient descent. Despite their intriguing performance in practice, GANs pose great challenges to both optimization and statistics. Their training suffers from oscillations, and they are difficult to scale to highdimensional settings. We study how gametheoretic and statistical techniques can be brought to bear on these important challenges. We use Game Theory towards improving GAN training, and Statistics towards scaling up the dimensionality of the generated distributions.