<< January 2019 >>

Tuesday, January 22, 2019

Seminar 217, Risk Management: Instrumental variables as bias amplifiers with general outcome and confounding

Seminar: Risk Seminar | January 22 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

 Speakers: Peng Ding, UC Berkeley

 Consortium for Data Analytics in Risk

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this unconfoundedness assumption is. This belief has had a huge...   More >

Monday, January 28, 2019

Support points – a new way to reduce big and high-dimensional data

Seminar: Neyman Seminar | January 28 | 4-5 p.m. | 1011 Evans Hall

 Simon Mak, Georgia Institute of Technology

 Department of Statistics

This talk presents a new method for reducing big and high-dimensional data into a smaller dataset, called support points (SPs). In an era where data is plentiful but downstream analysis is oftentimes expensive, SPs can be used to tackle many big data challenges in statistics, engineering and machine learning. SPs have two key advantages over existing methods. First, SPs provide optimal and...   More >

Tuesday, January 29, 2019

Seminar 217, Risk Management: The coordination of centralised and distributed generation

Seminar: Risk Seminar | January 29 | 11 a.m.-12:30 p.m. | 1011 Evans Hall

 Speakers: Matteo Basei, UC Berkeley

 Consortium for Data Analytics in Risk

We analyse the interaction between centralised carbon-emissive technologies and distributed non-emissive technologies. A representative consumer can satisfy her electricity demand by investing in solar panels and by buying power from a centralised firm. We consider the point of view of the consumer, the firm and a social planner, formulating suitable McKean-Vlasov control problems with stochastic...   More >

Wednesday, January 30, 2019

The Stratified Micro-randomized Trial Design: Sample Size Considerations for Testing Nested Causal Effects of Time-varying Treatment

Seminar: Neyman Seminar | January 30 | 4-5 p.m. | 1011 Evans Hall

 Walter Dempsey, Harvard University

 Department of Statistics

Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Delivery of these treatments is increasingly triggered by detections/predictions of vulnerability and receptivity, which may have been impacted by prior treatments. Furthermore the...   More >

Thursday, January 31, 2019

Dissecting Gene Regulation with Machine Learning: Discoveries and Challenges

Seminar: Statistics and Genomics Seminar | January 31 | 4-5 p.m. | 1011 Evans Hall

 Professor Katie Pollard, Department of Epidemiology and Biostatistics, UC San Francisco, Gladstone Institute, and Chan-Zuckerberg Biohub

 Department of Statistics

Machine learning is a popular statistical approach in many fields, including genomics. We and others have used a variety of supervised machine-learning techniques to predict genes, regulatory elements, 3D interactions between regulatory elements and their target genes, and the effects of mutations on regulatory element function. I will highlight a few of these studies, emphasizing the strengths...   More >