24 Jan 2021 and Kuhan Jeyapragasan for his feedback and for creating the resources for the session on talking about EA. I also want to thank Will Payne,

Page 1 of 1 Kuhan Jeyapragasan Research Assistant, FSI - CISAC. Stanford . Created Date: 4/4/2021 5:18:15 AM

This post was written as part of a 10-week AI Safety Fellowship run by Mark. Vikul Gupta (vikulg), Kuhan Jeyapragasan (kuhanj), JaydeepSingh (jaydeeps) Stanford University: CS 229 Final Project Motivation • The issue of algorithmic fairness has recently come to the forefront of machine learning, as classifiers increasingly propose decision rules in applications ranging from loan approval to criminal risk estimation. Kuhan Jeyapragasan kuhanj at stanford.edu Tue Sep 24 22:34:03 PDT 2019. Previous message: [sea-list] Oxford Prof.Will MacAskill on the Ethics of the Next Billion Years (Saturday 21st, 2:30-4:00, Building 200) Next message: [sea-list] Intro to Effective Altruism! (By the Open Philanthropy Project) + Arete Fellowship Info Session Kuhan Jeyapragasan University of Toronto Schools, Toronto, Ontario Stanford University. As the head executive of his school’s Queer Straight Alliance, Gender Equity Committee, and the Positive Mental Health Committee, Kuhan garnered over 2,100 volunteer hours working tirelessly to breakdown stereotypes and to create spaces for social transformation.

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Jack Ryan. VP of Education. Sydney Von Arx. VP Projects. Harshu Musunuri.

Amy Dunphy Robotics, EE, & History @ Stanford Felipe Calero Forero. Felipe Calero Forero CS @ Stanford Kuhan Jeyapragasan - Stanford EA: kuhanj@stanford.edu Regional Support: Australia and New Zealand - Sophia Cyna - EAANZ: admin@eaanz.org University-Specific: Clubs Fairs: Eli F Nathan - Oxford EA: elifnathan@hotmail.co.uk.

Epistemic status: mild confidence that this provides interesting discussion and debate. Credits to (in no particular order) Mark Xu, Sydney Von Arx, Jack Ryan, Sidney Hough, Kuhan Jeyapragasan, and Pranay Mittal for resources and feedback. Credits to Ajeya (obviously), Daniel Kokotajlo, Gwern, Robin Hanson, and many others for perspectives on timeline cruxes. This post was written as part of a

© 2020 FIDE International Chess Federation. All Rights Reserved. No part of this site may be reproduced, stored in a retrieval system or transmitted in any way or by Read Kuhan Jeyapragasan's latest research, browse their coauthor's research, and play around with their algorithms Page 1 of 1 Kuhan Jeyapragasan Research Assistant, FSI - CISAC.

Vikul Gupta (vikulg), Kuhan Jeyapragasan (kuhanj), JaydeepSingh (jaydeeps) Stanford University: CS 229 Final Project Motivation • The issue of algorithmic fairness has recently come to the forefront of machine learning, as classifiers increasingly propose decision rules in applications ranging from loan approval to criminal risk estimation.

This post was written as part of a 10-week AI Safety Fellowship run by Mark. Kuhan Jeyapragasan: An Analysis of Subway Networks using Graph Theory and Graph Generation with GraphRNN : 28: Jay Sushil Mardia: Role detection for links in networks: 29: Alexandre Matton Arnaud Antoine Autef Manon Romain: Fake News detection using Machine Learning on Graphs : 30: Vamsi Krishna Chitters Sam Zimmerman Shleifer Clara McCreery Project Leads: Vinjai Vale (Mathematics & Computer Science), Amy Dunphy (Electrical Engineering & History), Kuhan Jeyapragasan (Mathematical and Computational Science) Vikul Gupta (vikulg), Kuhan Jeyapragasan (kuhanj), JaydeepSingh (jaydeeps) Stanford University: CS 229 Final Project Motivation • The issue of algorithmic fairness has recently come to the forefront of machine learning, as classifiers increasingly propose decision rules in applications ranging from loan approval to criminal risk estimation. GROUP DISCUSSION FACILITATOR TRAINING GUIDE I recently put together a training for the ~100 facilitators of EA Virtual Programs (Intro Fellowship, In-Depth Fellowship, and Precipice Reading Group). I aimed to make the training broad enough to be generalizable to other programs so that I could share it afterwards as a resource for organizers who might want to run similar trainings. Kuhan Jeyapragasan Stanford Existential Risks Initiative. Stanford CS MS (AI Track) '20 Vinjai Vale.

(By the Open Philanthropy Project) + Arete Fellowship Info Session Thanks to Kuhan Jeyapragasan, Michael Byun, Sydney Von Arx, Thomas Kwa, Jack Ryan, Adam Křivka, and Buck Shlegeris for helpful comments and discussion. Epistemic status: a bunch of stuff Sometimes I have conversations with people that go like this: Me: Feels like all the top people in EA would have Kuhan Jeyapragasan University of Toronto Schools, Toronto, Ontario Stanford University. As the head executive of his school’s Queer Straight Alliance, Gender Equity Committee, and the Positive Mental Health Committee, Kuhan garnered over 2,100 volunteer hours working tirelessly to breakdown stereotypes and to create spaces for social transformation. Kuhan Jeyapragasan | Researcher, Effective Altruist, Community Organizer Stanford University. Chris Lacopo | Educational Researcher & Consultant Kuhan Jeyapragasan Gita Krishna Yash Maniyar Department of Computer Science Stanford University {kuhanj, gitakris, ymaniyar}@stanford.edu December 11, 2019 1 Introduction Kuhan Jeyapragasan is a Stanford University student from Toronto, Canada.
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Rocky Lane Public School. AB. Avery Kan. 24 Jan 2021 and Kuhan Jeyapragasan for his feedback and for creating the resources for the session on talking about EA. I also want to thank Will Payne, Kuhan Jeyapragasan. Shannon Moran. Ximena Ospina.

Teacher: Mr. Marshall Webb. Mentors: Kuhan Jeyapragasan is a Stanford University student from Toronto, Canada. Kuhan's activism started in school, as executive of the Queer Straight Alliance, Stanford Raagapella (abbreviated Raag) is Stanford University's South Asian focus a cappella group. The group was founded as an all-male group in 2002 and SECOND PRIZE: E42 KUHAN JEYAPRAGASAN.
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There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LOCF) imputation to fill in these gaps. Typically, they will not maintain any

Kuhan Jeyapragasan. Daniel Yoo. ·. Dela.

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[This was partially inspired by some ideas of Claire Zabel's. Thanks to Jessica McCurdy, Neel Nanda, Kuhan Jeyapragasan, Rebecca Baron, Joshua Monrad, Claire Zabel, and the people who came on my Slate Star Codex roadtrip for helpful comments.] A few months ago, some EAs and I went on a trip to the East Coast to go to a bunch of Slate Star Codex meetups. I'm going to quote that entire post here

As the head executive of his school’s Queer Straight Alliance, Gender Equity Committee, and the Positive Mental Health Committee, Kuhan garnered over 2,100 volunteer hours working tirelessly to breakdown stereotypes and to create spaces for social transformation. There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LOCF) imputation to fill in these gaps. Typically, they will not maintain any Effective Altruism Global. 20,787 likes · 41 talking about this.

Scott Fleming · Kuhan Jeyapragasan · Tony Duan; [] Emma Brunskill. There is an emerging trend in the reinforcement learning for healthcare literature. In order

Share this event with your friends. Hosted by. Linchuan Zhang. Cole Jackes. [This was partially inspired by some ideas of Claire Zabel's.

Credits to Ajeya (obviously), Daniel Kokotajlo, Gwern, Robin Hanson, and many others for perspectives on timeline cruxes. This post was written as part of a 10-week AI Safety Fellowship run by Mark. Vikul Gupta (vikulg), Kuhan Jeyapragasan (kuhanj), JaydeepSingh (jaydeeps) Stanford University: CS 229 Final Project Motivation • The issue of algorithmic fairness has recently come to the forefront of machine learning, as classifiers increasingly propose decision rules in applications ranging from loan approval to criminal risk estimation. Kuhan Jeyapragasan kuhanj at stanford.edu Tue Sep 24 22:34:03 PDT 2019. Previous message: [sea-list] Oxford Prof.Will MacAskill on the Ethics of the Next Billion Years (Saturday 21st, 2:30-4:00, Building 200) Next message: [sea-list] Intro to Effective Altruism! (By the Open Philanthropy Project) + Arete Fellowship Info Session Kuhan Jeyapragasan University of Toronto Schools, Toronto, Ontario Stanford University.