From awd at cs.cmu.edu Wed Jan 12 08:56:14 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Wed, 12 Jan 2022 08:56:14 -0500 Subject: Please let me know if you published or submitted any papers acknowledging NIH project R01HL141916 Message-ID: -------------- next part -------------- An HTML attachment was scrubbed... URL: From awd at cs.cmu.edu Wed Jan 12 18:25:11 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Wed, 12 Jan 2022 18:25:11 -0500 Subject: Fwd: [Section on Statistical Computing] : SDSS 2022 - Refereed Abstract Submission Deadline Extended to January 18th In-Reply-To: <0100017e50956215-7bcea83b-edcb-4728-aa68-9be907e35f8a-000000@email.amazonses.com> References: <0100017e50956215-7bcea83b-edcb-4728-aa68-9be907e35f8a-000000@email.amazonses.com> Message-ID: This venue has a relatively low paper size threshold so if you have a compact data science story sitting around, or something that needs to be published but it is hard to find a good venue for it, you may want to consider submitting to SDSS. Additional benefit is that the conference will be held in Pittsburgh this year. Artur ---------- Forwarded message --------- From: Emily Dodwell via American Statistical Association < Mail at connectedcommunity.org> Date: Wed, Jan 12, 2022 at 6:20 PM Subject: [Section on Statistical Computing] : SDSS 2022 - Refereed Abstract Submission Deadline Extended to January 18th To: The Program Committee for the 2022 Symposium on Data Science & Statistics is soliciting submissions for both refereed and lightning presentations.... Please note that using your email client's "Reply" option will send your response to all members of the discussion group. To send a private message to one person, click the blue "Reply to Sender" button at the upper right of their post. ------------------------------ Section on Statistical Computing Post New Message Online SDSS 2022 - Refereed Abstract Submission Deadline Extended to January 18th Reply to Group Online Reply to Sender [image: Emily Dodwell] Jan 12, 2022 6:18 PM Emily Dodwell The Program Committee for the 2022 Symposium on Data Science & Statistics is soliciting submissions for both refereed and lightning presentations. *The deadline for refereed submissions has been extended to Tuesday, January 18th.*All refereed and lightning submissions should focus on one of the following six tracks: - Computational Statistics - Data Visualization - Education - Machine Learning - Practice and Applications - Software & Data Science Technologies Each refereed abstract submission should consist of a 2,000-character abstract overview and two-page extended abstract, which includes any references and figures. The presentation associated with a refereed submission is up to 30 minutes in length, and registration will be required for all program participants. For more information about the conference and submission requirements, please see the "Submit an Abstract" section of the SDSS 2022 site: ww2.amstat.org/meetings/sdss/2022/submitanabstract.cfm SDSS is currently scheduled to be held in Pittsburgh, PA from June 7-10, 2022; we continue to closely monitor the evolving pandemic and will re-evaluate the safety of gathering in person if required. Thank you, Emily Dodwell SDSS 2022 Vice Chair ------------------------------ Principal Inventive Scientist AT&T Chief Data Office - Data Science & AI Research ------------------------------ *Reply to Group Online * *Reply to Sender * *View Thread * *Recommend * *Forward * *Flag as Inappropriate * *Post New Message Online * You are subscribed to "Section on Statistical Computing" as awd at cs.cmu.edu. To change your subscriptions, go to My Subscriptions . To unsubscribe from this community discussion, go to Unsubscribe . -------------- next part -------------- An HTML attachment was scrubbed... URL: From predragp at andrew.cmu.edu Sat Jan 15 10:56:44 2022 From: predragp at andrew.cmu.edu (Predrag Punosevac) Date: Sat, 15 Jan 2022 10:56:44 -0500 Subject: Locked out of compute nodes In-Reply-To: References: Message-ID: CMU network died and broke our OpenVPN pipes. Try now :-) I am CC-ing users at autonlab as everyone is affected. Predrag On Sat, Jan 15, 2022 at 9:38 AM Benedikt Boecking wrote: > Hi Predrag, > > I am locked out of the compute nodes this morning :/ > > I am unable to connect to bash as the connection just times out. > When I try to use lop2 as a gateway, it says that my connection is denied > as I am not enrolled in duo. > When I try to use lion as a gateway, lion asks for my password but my > password gets rejected. > > Any help would be appreciated as I am trying to race towards the ICML > deadline. > > Thanks! > > Best, > Ben > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From boecking at andrew.cmu.edu Tue Jan 18 19:23:49 2022 From: boecking at andrew.cmu.edu (Benedikt Boecking) Date: Tue, 18 Jan 2022 19:23:49 -0500 Subject: Gpu17 scratch space Message-ID: <7AD78490-C35E-4857-8AAE-BB1BB7CEF62F@andrew.cmu.edu> Hi all, If you have any files on the scratch space of gpu17 that you do not require anymore, please remove them as we are running out of disc space. Thanks! Best, Ben -------------- next part -------------- An HTML attachment was scrubbed... URL: From awd at cs.cmu.edu Fri Jan 21 16:12:14 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Fri, 21 Jan 2022 16:12:14 -0500 Subject: Fwd: FW: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26, 2022 - 3PM (EST) ---ZOOM INFO BELOW In-Reply-To: <56d39bd535724fb68b6ecb2b999d75d2@andrew.cmu.edu> References: <56d39bd535724fb68b6ecb2b999d75d2@andrew.cmu.edu> Message-ID: This can be of interest to many of us. Cheers, Artur ---------- Forwarded message --------- From: Christy Melucci Date: Fri, Jan 21, 2022 at 4:01 PM Subject: FW: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26, 2022 - 3PM (EST) ---ZOOM INFO BELOW To: ml-core-faculty at cs.cmu.edu , ml-seminar at cs.cmu.edu Cc: Visweswaran, Shyam , Bartolotta, Genine M < bartgm at pitt.edu>, Batmanghelich, Kayhan , Roni Rosenfeld < roni at cs.cmu.edu> *From:* Bartolotta, Genine M *Sent:* Friday, January 21, 2022 3:46 PM *Cc:* Batmanghelich, Kayhan ; Visweswaran, Shyam < shv3 at pitt.edu> *Subject:* MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - JANUARY 26, 2022 - 3PM (EST) ---ZOOM INFO BELOW *Machine Learning in Medicine (MLxMed)* *A Virtual Seminar Series in Pittsburgh* *Hosted by the Department of Biomedical Informatics* *Wednesday, January 26, 2022* *3:00 PM ? 4:00 PM Eastern Time University of Pittsburgh, UPMC, and CMU* *Artificial Intelligence in Clinical Medicine:* *What makes a good machine learning model for clinical applications?* *Zoom* *https://pitt.zoom.us/j/97941360439* *(**details are listed at the end**)* *Collin M. Stultz, MD, PhD* Nina T. and Robert H. Rubin Professor in Medical Engineering and Science, Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology *Abstract: *Although applications of Machine Learning (ML) are now pervasive in the clinical literature, ML has yet to be embraced by the clinical community. So, what constitutes a good machine learning model for clinical applications? Certainly, a necessary condition for the success of any machine learning model is that it achieves an accuracy that is superior to pre-existing methods. In the healthcare sphere, however, accuracy alone does not, nor should it, ensure that a model will gain clinical acceptance. In view of the fact that no model, in practice, has 100% accuracy, attempts to understand when a given model is likely to fail should form an important part of the evaluation of any machine learning model that will be used clinically. Moreover, the most useful clinical models are explainable in the sense that it is possible to clearly articulate why the model arrives at a particular result for a given set of inputs. In this talk I will expand upon these challenges that make the creation of clinically useful ML models particularly difficult, and discuss ways in which they can be overcome. *About MLxMed Seminar Series* *(**http://ml-in-medicine.org/* *)* Medicine is complex and data-driven while discovery and decision making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will feature prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support. For more information see MLxMed website. *Zoom Information* *When: January 26, 2022 03:00 PM Eastern Time (US and Canada)* *Please click the link below to join the webinar:* *https://pitt.zoom.us/j/97941360439* Or One tap mobile : US*: +12678310333,,97941360439# or 8778535247,,97941360439#* (Toll Free) Or Telephone: Dial(for higher quality, dial a number based on your current location): *US: +1 267 831 0333 or 877 853 5247 (Toll Free)* *Webinar ID: 979 4136 0439* International numbers available*: **https://pitt.zoom.us/u/ahon5yBXB* Or an H.323/SIP room system: H.323: 162.255.37.11 (US West) 162.255.36.11 (US East) 115.114.131.7 (India Mumbai) 115.114.115.7 (India Hyderabad) 213.19.144.110 (Amsterdam Netherlands) 213.244.140.110 (Germany) 103.122.166.55 (Australia Sydney) 103.122.167.55 (Australia Melbourne) 149.137.40.110 (Singapore) 64.211.144.160 (Brazil) 149.137.68.253 (Mexico) 69.174.57.160 (Canada Toronto) 65.39.152.160 (Canada Vancouver) 207.226.132.110 (Japan Tokyo) 149.137.24.110 (Japan Osaka) *Meeting ID: 979 4136 0439* *SIP: **97941360439 at zoomcrc.com* <97941360439 at zoomcrc.com> *Genine M. Bartolotta* *Department of Biomedical Informatics* *University of Pittsburgh, School of Medicine* The Offices at Baum, Fourth Floor 5607 Baum Boulevard Pittsburgh, PA 15206-3701 Phone: (412) 624-5100 Cell Phone: (412) 877-4872 FAX: (412) 648-9118 E-mail: *bartgm at pitt.edu (preferred)* bartolottagm at upmc.edu *This e-mail may contain confidential information of the sending* *organization. Any unauthorized or improper disclosure, copying,* *distribution, or use of the contents of this e-mail and attached* *document(s) is prohibited. The information contained in this* *e-mail and attached document(s)is intended only for the* *personal and confidential use of the recipient(s) named* *in original e-mail and attached document(s).* -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: MACHINE LEARNING in MEDICINE - VIRTUAL SEMINAR - January 26, 2022 - 3 PM (EST).pdf Type: application/pdf Size: 168999 bytes Desc: not available URL: From chiragn at cs.cmu.edu Mon Jan 24 00:13:37 2022 From: chiragn at cs.cmu.edu (Chirag Nagpal) Date: Mon, 24 Jan 2022 00:13:37 -0500 Subject: Lov machine usage Message-ID: Hello All I know it is ICML time but the lov machines are being used at 100% by only 1-2 users for over a month now. Please be generous and reasonable when using the lab infrastructure. At a time, do not use more than 1-2 machines. If you need more compute than that at a time, perhaps consider alternative options like AWS. [image: image.png] PS. cc'ed are especially encouraged to check their usage. Thank you -- *Chirag Nagpal* PhD Candidate, Auton Lab School of Computer Science Carnegie Mellon University cs.cmu.edu/~chiragn -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image.png Type: image/png Size: 51169 bytes Desc: not available URL: From awd at cs.cmu.edu Tue Jan 25 10:13:22 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Tue, 25 Jan 2022 10:13:22 -0500 Subject: Saswati Ray moving on Message-ID: Team, With mixed feelings I need to announce that Saswati will be leaving the Lab soon. Her last day in the office will be Monday February 7th. Saswati, what is hard to believe, is a 15-year veteran of the Auton lab. And (less hard to believe :p ) our most experienced research programmer. She is one of the few current team members who realizes how much good stuff exists in the Lab code repository written since 1990-ies in C. After starting as a system-level programmer, she has been continuously expanding her skill set to eventually become the queen of multiple Auton things and projects, including our highly flexible random forest implementation. Over the past many years she has been managing the development of ML capabilities in the ERNIE project. In the process, everyone at the prime contractor - Lawrence Livermore National Laboratory - and the government sides fell deeply in love with her. Saswati's efforts have helped ERNIE to beat all the conceivable alternatives so systematically that the system is on the path to a large scale deployment at every US border crossing now, and in a recent independent evaluation conducted at about 20 large sea ports it enabled order-of-magnitude reductions of the need for secondary inspections of large cargo containers by the US Customs personnel while significantly improving the probability of detecting real radiation threat vs the pre-existing systems. More recently, Saswati led engineering efforts to develop our automated machine learning tool called Auto^nML. Her common-sense-driven approach to conditioning data for analysis and simple but effective model selection procedures helped make Auto^nML a persistent winner of multiple DARPA competitions over the past few years. I used to equate that accomplishment to winning 7 or more Super Bowls or Stanley Cups in a row. Indeed, it is not a small feat to beat so systematically many highly capable competitors from lead AI universities, even though Saswati and our DARPA D3M/MILEI team have been equally systematically sharing their code with the community. Yet, Auto^nML was still victorious at the next event. Saswati has many more impressive accomplishments under her belt, and she is undeniably one of the nicest colleagues I had a pleasure to work with. So, it is with mixed feelings indeed, sadness prevailing, that I write this note. I am sure we will all wish her all the best with her next endeavor, and let her know that in case she ever gets bored or not challenged enough, she should know her way back. Once an Autonian, always an Autonian. Cheers, Artur PS We will be having a small but memorable farewell party for Saswati; time, place and format TBD. PSPS Some of you will shortly be asked to engage in capturing Saswati's workload so that we will have no disruptions in our deliverables and other project obligations. And, Everyone, if you please can think of anything that we should do to brace for impact before Feb 7, please let me and Saswati know. -------------- next part -------------- An HTML attachment was scrubbed... URL: From awd at cs.cmu.edu Tue Jan 25 11:35:55 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Tue, 25 Jan 2022 11:35:55 -0500 Subject: Fwd: FW: Statistics/StatML and Societal Problems Job Talk Candidate _Anish Agarwal_Monday, 2/1/2022 at noon In-Reply-To: <1d5a107e4b2646f5a09814c4949af502@andrew.cmu.edu> References: <1d5a107e4b2646f5a09814c4949af502@andrew.cmu.edu> Message-ID: HeinzCollege activity is not always populated via the SCS channels, and in this case it may be of interest to some of the Autonians. Cheers Artur ---------- Forwarded message --------- *From:* Heinz-faculty *On Behalf Of *Natalia Pascal *Sent:* Tuesday, January 25, 2022 11:06 AM *To:* heinz-faculty at lists.andrew.cmu.edu; heinz-phd at lists.andrew.cmu.edu *Cc:* Amelia M Haviland ; Irina Novikova < irinasn at andrew.cmu.edu> *Subject:* Statistics/StatML and Societal Problems Job Talk Candidate _Anish Agarwal_Monday, 2/1/2022 at noon Hello All! *Anish Agarwal, *Ph.D., EECS, MIT is scheduled to present* on Monday, February 1st, 2022* *at noon ? 1:20 PM, via zoom, *as a part of the Assistant Professor (Tenure Track) - Statistics/StatML and Societal Problems faculty search. Join Zoom Meeting https://cmu.zoom.us/j/99684715715?pwd=VWlaT2tqemYvdFpVVlBObjk5QTBoQT09 Meeting ID: 996 8471 5715 Passcode: 569553 *Anish Brief Bio:* Anish is currently a postdoctoral fellow at the Simons Institute at UC Berkeley. He did his PhD at MIT in EECS where he was advised by Alberto Abadie, Munther Dahleh, and Devavrat Shah. His research focuses on designing and analyzing methods for causal machine learning, and applying it to critical problems in social and engineering systems. He currently serves as a technical consultant to TauRx Therapeutics and Uber Technologies on questions related to experiment design and causal inference. Prior to the PhD, he was a management consultant at Boston Consulting Group. He received his BSc and MSc at Caltech. *Title: Causal Inference for Socio-Economic and Engineering Systems* *Abstract:* *What will happen to Y if we do A? * A variety of meaningful socio-economic and engineering questions can be formulated this way. To name a few: What will happen to a patient?s health if they are given a new therapy? What will happen to a country?s economy if policy-makers legislate a new tax? What will happen to a company?s revenue if a new discount is introduced? What will happen to a data center?s latency if a new congestion control protocol is used? In this talk, we will explore how to answer such counterfactual questions using observational data---which is increasingly available due to digitization and pervasive sensors---and/or very limited experimental data. The two key challenges in doing so are: (i) counterfactual prediction in the presence of latent confounders; (ii) estimation with modern datasets which are high-dimensional, noisy, and sparse. Towards this goal, the key framework we introduce is connecting causal inference with tensor completion, a very active area of research across a variety of fields. In particular, we show how to represent the various potential outcomes (i.e., counterfactuals) of interest through an order-3 tensor. The key theoretical results presented are: (i) Formal identification results establishing under what missingness patterns, latent confounding, and structure on the tensor is recovery of unobserved potential outcomes possible. (ii) Introducing novel estimators to recover these unobserved potential outcomes and proving they are finite-sample consistent and asymptotically normal. The efficacy of the proposed estimators is shown on high-impact real-world applications. These include working with: (i) TaurRx Therapeutics to propose novel clinical trial designs to reduce the number of patients recruited for a trial and to correct for bias from patient dropouts. (ii) Uber Technologies on evaluating the impact of certain driver engagement policies without having to run an A/B test. (iii) U.S. and Indian policy-makers to evaluate the impact of mobility restrictions on COVID-19 mortality outcomes. (iv) The Poverty Action Lab (J-PAL) at MIT to make personalized policy recommendations to improve childhood immunization rates across different villages in Haryana, India. Finally, we discuss connections between causal inference, tensor completion, and offline reinforcement learning. *Please reach out with any questions to the head of the committee: Professor* Amelia M Haviland, amelia at andrew.cmu.edu We hope to see you there! *Natalia Pascal* Research and Administrative Coordinator Carnegie Mellon University Heinz College of Information Systems and Public Policy 412.268.7856; E-mail: npascal at andrew.cmu.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From awd at cs.cmu.edu Mon Jan 31 20:45:16 2022 From: awd at cs.cmu.edu (Artur Dubrawski) Date: Mon, 31 Jan 2022 20:45:16 -0500 Subject: Happy Lunar New Year! Message-ID: This new year must be better than the previous one, enjoy! Artur -------------- next part -------------- An HTML attachment was scrubbed... URL: From predragp at andrew.cmu.edu Mon Jan 31 21:25:04 2022 From: predragp at andrew.cmu.edu (Predrag Punosevac) Date: Mon, 31 Jan 2022 21:25:04 -0500 Subject: Happy Lunar New Year! In-Reply-To: References: Message-ID: Happy New Year to everyone celebrating it tonight! Predrag On Mon, Jan 31, 2022 at 8:46 PM Artur Dubrawski wrote: > This new year must be better than the previous one, enjoy! > > Artur > -------------- next part -------------- An HTML attachment was scrubbed... URL: