[Lilab] Fwd: ANNOUNCEMENT: WNAR WEBINAR BY Cynthia Rudin, Duke University, 9:00am-10:am PDT, May 12, 2023

Mon Apr 24 16:16:52 PDT 2023

Hi All,

You are highly recommended to sign up for and attend this seminar.

---------- Forwarded message ---------
From: WNAR of IBS <wnar at wnar.org>
Date: Mon, Apr 24, 2023 at 4:13 PM
Subject: ANNOUNCEMENT: WNAR WEBINAR BY Cynthia Rudin, Duke University,
9:00am-10:am PDT, May 12, 2023
To: Wei Vivian Li <vivianlistat at gmail.com>

*WNAR Webinar by Dr. Cynthia Rudin*

*Understanding How Dimension Reduction Tools Work*

It is a great pleasure to announce the upcoming WNAR Webinar by our
distinguished colleague Dr. Cynthia Rudin from Duke University.

*Time: 9:00am - 10:00am PDT (12:00 PM - 1:00 PM EDT), Friday May 12, 2023*

*This is a free event and pre-**registration is required at: *

*https://www.eventbrite.com/e/623976609837
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*Zoom Dial-in information and webinar link will be sent 24 hours before the
event.*

(Check out WNAR YouTube channel
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for webinar recordings)

*Abstract:*

Dimension reduction (DR) techniques such as t-SNE, UMAP, and TriMap have
demonstrated impressive visualization performance on many real-world
datasets. They are useful for understanding data and trustworthy
decision-making, particularly for biological data. One tension that has
always faced these methods is the trade-off between preservation of global
structure and preservation of local structure: past methods can either
handle one or the other, but not both. In this work, our main goal is to
understand what aspects of DR methods are important for preserving both
local and global structure: it is difficult to design a better method
without a true understanding of the choices we make in our algorithms and
their empirical impact on the lower-dimensional embeddings they produce.
Towards the goal of local structure preservation, we provide several useful
design principles for DR loss functions based on our new understanding of
the mechanisms behind successful DR methods. Towards the goal of global
structure preservation, our analysis illuminates that the choice of which
components to preserve is important. We leverage these insights to design a
new algorithm for DR, called Pairwise Controlled Manifold Approximation
Projection (PaCMAP), which preserves both local and global structure. Our
work provides several unexpected insights into what design choices both to
make and avoid when constructing DR algorithms.

I will be discussing work from the following papers:

Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik

Understanding How Dimension Reduction Tools Work: An Empirical Approach to
Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

Journal of Machine Learning Research (JMLR), 2021

https://jmlr.org/papers/v22/20-1061.html
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Haiyang Huang, Yingfan Wang, Cynthia Rudin, and Edward P. Browne

Towards a Comprehensive Evaluation of Dimension Reduction Methods for
Transcriptomic Data Visualization

Communications Biology (Nature), 2022.

https://www.nature.com/articles/s42003-022-03628-x
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*Speaker Bio:*

Cynthia Rudin is a professor of computer science, electrical and computer
engineering, statistical science, mathematics, and biostatistics &
bioinformatics at Duke University, and directs the Interpretable Machine
Learning Lab. Previously, Prof. Rudin held positions at MIT, Columbia, and
NYU. She holds an undergraduate degree from the University at Buffalo, and
a PhD from Princeton University. She is the recipient of the 2022 Squirrel
AI Award for Artificial Intelligence for the Benefit of Humanity from the
Association for the Advancement of Artificial Intelligence (AAAI). This
award is the most prestigious award in the field of artificial
intelligence. Similar only to world-renowned recognitions, such as the
Nobel Prize and the Turing Award, it carries a monetary reward at the
million-dollar level. Prof. Rudin is also a three-time winner of the
INFORMS Innovative Applications in Analytics Award, was named as one of the
"Top 40 Under 40" by Poets and Quants in 2015, and was named by
Businessinsider.com as one of the 12 most impressive professors at MIT in
2015, and is a 2022 Guggenheim Fellow. She is a fellow of the American
Statistical Association, the Institute of Mathematical Statistics, and AAAI.

Prof. Rudin is the past chair of both the INFORMS Data Mining Section and
the Statistical Learning and Data Science Section of the American
Statistical Association. She has also served on committees for DARPA, the
National Institute of Justice, AAAI, and ACM SIGKDD. She has served on
several committees for the National Academies of Sciences, Engineering and
Medicine, including the Committee on Applied and Theoretical Statistics,
the Committee on Law and Justice, the Committee on Analytic Research
Foundations for the Next-Generation Electric Grid, and the Committee on
Facial Recognition Technology.  She has given keynote/plenary talks at
several conferences including INFORMS, KDD (twice), AISTATS, SDM, ICDM,
Machine Learning in Healthcare (MLHC), Fairness, Accountability and
Transparency in Machine Learning (FAT-ML), ECML-PKDD, and the Nobel
Conference. Her work has been featured in news outlets including the NY
Times, Washington Post, Wall Street Journal, the Boston Globe,
Businessweek, and NPR.

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