Jeff Phillips

Associate Prof, SoC, @UUtah. Director, @UUDataScience. Author "Mathematical Foundations for Data Analysis" (

[Scientific Computing and Imaging Institute @uusci]

As researchers across @UUtah build, use, & study AI, they’re uncovering high-stakes ethical questions that can’t be solved by technologists or humanists alone. They recently worked through such Qs together at a SCI-hosted workshop: https://t.co/6qotJmFVBF

[Scientific Computing and Imaging Institute @uusci]

Calling @UUtah researchers across tech, humanities, & social sciences: we’ve expanded capacity for our April 3 AI + Ethics Workshop. Discover AI work beyond your field & shape new approaches to some of the most important ethical challenges of our time: https://t.co/OZFBK3dwDt

[UU Data Science @UUDataScience]

Please join us for today's at 11 AM MST Data Science and AI lecture series to hear Varun Shankar in his talk titled "Kernel Methods for Operator Learning." We look forward to seeing you either in person or online! https://t.co/R9HbBpuM26 #datascience #ai #ucdsai #ml

[Jeff Phillips @probablyjeff]

@sarielhp @RishiSonthalia I would call this the 2-Zipfian distribution. I guess that is if the sequence is finite.

[SpringerMath @SpringerMath]

Jeff M. Phillips @KahlertSoC:
Mathematical Foundations for Data Analysis (2021)
#SpringerSeriesintheDataScience #DataScience #CGWeek23

Print book (hard cover, 306 pages): USD34.99 🎉 until Jun 30 #Springer #YellowSale

Textbook 🌿
https://t.co/04wbWouaNv

[Jeff Phillips @probablyjeff]

The Gradient Descent chapter of my book is available online.

Data analysis textbook from a computational geometer’s perspective.

[SpringerMath @SpringerMath]

Jeff M. Phillips @KahlertSoC:
Mathematical Foundations for Data Analysis (2021)
#SpringerSeriesintheDataScience #DataScience
#CGWeek23

🔓Free view 🔗Chapter 6. Gradient Descent https://t.co/ZX4OW099zx

Textbook, suitable for an early undergraduate up to a graduate course 🌿

[Devin Lange @Dev_Lange]

If you are in Leipzig, Germany, next week, please check out my talk (https://t.co/Abvco1cbmB), on Ferret: Reviewing Tabular Datasets for Manipulation! H/T Shaurya Sahai, @probablyjeff, and @alexander_lex. 🧵 (1/8)

[Scientific Computing and Imaging Institute @uusci]

Congratulations to Bei Wang on her NSF career award.
A Measure Theoretic Framework for Topology-Based Visualization. https://t.co/fpwTOl68Vt

[Jeff Phillips @probablyjeff]

A chapter on Sampling and Convergence of my book (Mathematical Foundations for Data Analysis, https://t.co/xYqlzI9EkR) is free through Jan 15.

[SpringerMath @SpringerMath]

Data science is amongst one of the most interdisciplinary areas of the research sciences. Explore a selection of featured data science books and journals on the topics of business, economics & finance for free through January 15th! https://t.co/6Chyu3wOFR

[Tech At Bloomberg @TechAtBloomberg]

Congratulations to @UUtah's @keviv9 (from @UtahSoC & @UUDataScience) on being named one of the 2021-2022 @Bloomberg #DataScience Ph.D. Fellows!

Learn more about his research focus and the other Fellows in this year's cohort: https://t.co/xOzI5aAwaK
#AI #ML #NLProc

[TCS blog aggregator @cstheory]

Multiple open-rank tenure track positions at University of Utah (apply by January 15, 2022) https://t.co/UbRuZX0sJ2

[Lior Pachter @lpachter]

It's time to stop making t-SNE & UMAP plots. In a new preprint w/ Tara Chari we show that while they display some correlation with the underlying high-dimension data, they don't preserve local or global structure & are misleading. They're also arbitrary.🧵https://t.co/XkAOTKlOcs

[UU Data Science @UUDataScience]

Introduction to Data Science Club :

https://t.co/4EbwpHYa8n

@UUDataScience @UtahSoC @UUtah

[Blair D. Sullivan @BlairDSullivan]

The #wasatch don't disappoint, even on a Thursday.

[Sunipa Dev @sunipa_dev]

Excited to be featured on the @compcomcon blog and webpage for my work as a CI Fellow!

https://t.co/qJTiNRbC8e

[Sunipa Dev @sunipa_dev]

Here’s our tutorial webpage from #AAAI2021 https://t.co/QQH88CWqf6 for ‘ A Visual Tour of Bias Mitigation Techniques in Word Representations’
@probablyjeff @RathoreArchit @BeiWangPhillips @viveksrikumar
#aaai

[Jeff Phillips @probablyjeff]

Congrats to my student @sunipa17 and all of the other 2020 CI Fellows!

[TCS blog aggregator @cstheory]

Orientation-Preserving Vectorized Distance Between Curves https://t.co/mDJBAq3aDR

[Tao Li @tao__li]

3/3 It’s a balanced approach of bias mitigation and information retention. Our intrinsic and extrinsic evaluations on gender-occupation bias show it performs on par with different projective methods while retaining more coherent information.

[Tao Li @tao__li]

2/3 Projective debiasing can be too aggressive as it removes extra information that should actually be preserved. To address this, we propose a graded rotation operation to correct and rectify biases in embedding space (GloVe/RoBERTa).