[Sds-seminars] S&DS In-Person Seminar, Raaz Dwivedi, 2/06, 4pm-5pm, "From HeartSteps to HeartBeats: Personalized Decision-making"

[elizavette.torres at yale.edu]

 <https://statistics.yale.edu/>     <https://statistics.yale.edu/>
Department of Statistics and Data Science  

In-person seminars are held at Mason Lab, room 211, 9 Hillhouse Avenue with
the option of virtual participation,

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 <x-apple-data-detectors://10/> 3:30pm -   Pre-talk meet and greet teatime -
Dana House, 24 Hillhouse Ave. 


Raaz Dwivedi, Harvard & MIT




Date: Monday, February 06, 2023

Time: 4:00PM to 5:00PM

Location: Mason Lab 211
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us
> see map 

9 Hillhouse Ave

New Haven, CT 06511

 <https://raazdwivedi.github.io/> Website

 

 

 

 

 

Title: From HeartSteps to HeartBeats: Personalized Decision-making

 

Information and Abstract: 

Ever-increasing access to data and computational power allows us to make
decisions that are personalized to users by taking their behaviors and
contexts into account. These developments are especially useful in domains
like mobile health and medicine. For effective personalized decision-making,
we need to revisit two fundamental tasks: (1) estimation and inference from
data when there is no model for a decision's effect on a user and (2)
simulations when there is a known model for a decision's effect on a user.
Here we must overcome the difficulties facing classical approaches, namely
statistical biases due to adaptively collected data and computational
bottlenecks caused by high-dimensional models.

This talk addresses both tasks. First, I provide a nearest-neighbor approach
for unit-level statistical inference in sequential experiments. I also
introduce a doubly robust variant of nearest neighbors that provides sharp
error guarantees and helps measure a mobile app's effectiveness in promoting
healthier lifestyle with limited data. For the second task, I introduce
kernel thinning, a practical strategy that provides near-optimal
distribution compression in near-linear time. This method yields significant
computational savings when simulating models of cardiac functioning.

Bio: Raaz Dwivedi is a FODSI postdoc fellow advised by Prof. Susan Murphy
and Prof. Devavrat Shah in CS and Statistics, Harvard and EECS, MIT
respectively. He earned his Ph. D. at EECS, UC Berkeley, advised by Prof.
Martin Wainwright and Prof. Bin Yu; and his bachelor's degree at EE, IIT
Bombay, advised by Prof. Vivek Borkar. His research builds statistically and
computationally efficient strategies for personalized decision-making with
theory and methods spanning the areas of causal inference, reinforcement
learning, random sampling, and high-dimensional statistics. He won the
President of India Gold Medal at IIT Bombay, the Berkeley Fellowship,
teaching awards at UC Berkeley and Harvard, and a best student paper award
for his work on optimal compression.

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (
<https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f
8b73c34-a27b-42a7-a073-af2d00f90ffa>
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8
b73c34-a27b-42a7-a073-af2d00f90ffa)

For more details and upcoming events visit our website at
<http://statistics.yale.edu/> http://statistics.yale.edu/

 


Department of Statistics and Data Science


Yale University
24 Hillhouse Avenue
New Haven, CT 06511

t 203.432.0666
f 203.432.0633

 

 

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