February 2023 - Sanghani-faculty

CANCELED: March Sanghani Center Faculty Meeting
by Shalaby, Wanawsha 22 Feb '23

22 Feb '23

Hi All, With spring break approaching, we will not be having our monthly Sanghani Center Faculty meeting next week for the month of March. We will reconvene our meetings on Friday, April 7 at 12 pm. On a different note, we're currently in the process of planning a meet and greet reception with Mehul and Hema Sanghani at the Innovation Campus this semester. We have yet to settle on a date but will be sending out information regarding that soon. We welcome all Sanghani Center faculty to attend and are happy to help make travel arrangements for everyone. Please keep a lookout for updates. Thanks, Wanawsha

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Sanghani Center for AI and Analytics Seminar Series ft. Prof. James Bailey
by Juanita Victoria 16 Feb '23

16 Feb '23

Zoom: https://virginiatech.zoom.us/j/86492209782? [cid:image001.png@01D93A12.0997DCB0]

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CS faculty candidate Kaixiong Zhou Seminar tomorrow @ 11am EST
by Ismini Lourentzou 14 Feb '23

14 Feb '23

Dear CS and Sanghani colleagues Please join us for Kaixiong Zhou's seminar <https://cs.vt.edu/News/Seminars/Kaixiong-Zhou.html> tomorrow, Wednesday, Feb. 15, 11:00 am - 12:00 pm via Zoom <https://virginiatech.zoom.us/s/82153220020?pwd=V09Ea25McW9WcGFldXlDZzAzazRU…>. *Abstract*: Large-scale graph data is ubiquitous in science and industry, including social networks, knowledge graphs, and biochemical molecules. Graph neural networks (GNNs) have emerged as de facto standard models to analyze node features and graph topology. Despite the prominent effectiveness obtained in recent progress, GNNs are notoriously challenged throughout the machine learning cycle, including the low model depth scalability, limited data processing scalability, and inefficient learning paradigms. In this talk, we will discuss some recent results in improving the scalability and efficiency of GNN models. These results are (i) answering how to develop deep graph neural networks from theories, tools, and benchmark platforms; (ii) designing scalable graph machine learning algorithms to save memory usage and computation time in the large-scale graph data; and (iii) proposing graph prompt learning and quantum graph neural networks to enhance modeling efficiency in massive data. *Biography*: Kaixiong Zhou is a fifth-year Ph.D. student in the Department of Computer Science at Rice University, advised by Professor Xia "Ben" Hu. His research focuses on large-scale graph machine learning, particularly in deep graph analysis, efficient graph representation learning, graph quantum computing, and the science applications in biochemical informatics. With his collaborators, his contributions include several benchmark platforms and automated models for scalable graph analytics and initial work on quantum graph neural networks. Prior to joining Rice University, he obtained B.S. and M.S. degrees in electrical engineering from Sun Yat-Sen University and the University of Science and Technology of China, respectively. Best, Ismini -- *Ismini Lourentzou* <https://isminoula.github.io/> Assistant Professor Virginia Tech | Computer Science Email: ilourentzou(a)vt.edu

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Fwd: Inquiry about Interest in the University Open Source Program Office (OSPO) Initiative
by Shalaby, Wanawsha 07 Feb '23

07 Feb '23

FYI. ________________________________ From: Brian Mayer <bmayer(a)cs.vt.edu> Sent: Tuesday, February 7, 2023 4:04 PM To: Shalaby, Wanawsha <wanah92(a)vt.edu>; Lu, Chang-Tien <clu(a)vt.edu>; naren <naren(a)cs.vt.edu> Subject: Fwd: Inquiry about Interest in the University Open Source Program Office (OSPO) Initiative Wanawsha do you want to forward this to the faculty listserv. ---------- Forwarded message --------- From: Yinlin Chen <ylchen(a)vt.edu<mailto:ylchen@vt.edu>> Date: Tue, Feb 7, 2023 at 2:57 PM Subject: Inquiry about Interest in the University Open Source Program Office (OSPO) Initiative To: Narendran Ramakrishnan <naren(a)vt.edu<mailto:naren@vt.edu>>, Chang-Tien Lu <ctlu(a)vt.edu<mailto:ctlu@vt.edu>> Cc: <bmayer(a)cs.vt.edu<mailto:bmayer@cs.vt.edu>>, Yinlin Chen <ylchen(a)vt.edu<mailto:ylchen@vt.edu>> Dear Dr. Ramakrishnan and Dr. Lu, I hope this email finds you well. I am writing on behalf of the University Libraries regarding our planned submission of a letter of inquiry to the Alfred P. Sloan Foundation for a two-year grant proposal to establish a local University Open Source Program Office (OSPO). I brought this up with Dr. Fox, and he suggested that I reach out to Sanghani Center too. The OSPO will serve as a centralized unit to promote and support open-source software in research at Virginia Tech. Its primary objectives include increasing adoption and contributions to open-source projects; providing training, mentoring, and educational resources to faculty, staff, and students; and facilitating collaboration with other open-source communities. The University Libraries has a long history of supporting open access and open research, and the establishment of the OSPO aligns with our vision and missions. Moreover, the White House OSTP mandates free public access to federally funded research, to be fully implemented by 2025. We think this call is an ideal and timely opportunity. The establishment of the OSPO will help integrate open-source practices into research projects, and could potentially increase the chances for future grants. I am reaching out to seek your support and kindly request that you forward this message to your department's mailing list. If there are any potential faculty members who may be interested in collaborating, please ask them to reach out to me at ylchen(a)vt.edu<mailto:ylchen@vt.edu>. We are currently compiling a list of potential collaborators and early adopters on campus. Thank you for considering our inquiry. We would greatly appreciate your response regarding this proposal idea. Regards, Yinlin == Yinlin Chen, Ph.D. Assistant Professor Assistant Director, Center for Digital Research & Scholarship Virginia Tech University Libraries 560 Drillfield Drive (0434) Blacksburg, VA 24061 540-231-0271 ylchen(a)vt.edu<mailto:ylchen@vt.edu> https://www.yinlinchen.com/ -- Brian Mayer bmayer(a)cs.vt.edu<mailto:bmayer@cs.vt.edu> 540-231-5907 Sanghani Center for Artificial Intelligence and Data Analytics<https://sanghani.cs.vt.edu/> - Virginia Tech<https://vt.edu/>

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Sanghani
by Shalaby, Wanawsha 03 Feb '23

03 Feb '23

Hi All, Welcome back an! Hope everyone's first week of classes went well. Our first Sanghani Center Faculty meeting this semester is scheduled for Friday, Feb. 3 at 12:00 - 1:30 pm. As usual, if you have any recent news or updates since our last faculty meeting, please send them my way so that I may add them to our meeting slides. Updates include awards, proposals, grants, conference presentations, events, etc. Please send all updates by Wednesday, Feb. 1. I've booked Torg 1100C for those of you in Blacksburg who would like to join in person. Those of us in the DC area will still be joining via zoom. I'll be sending out a reoccurring calendar invite for our scheduled meetings this semester. Below is the Zoom information for our meeting, please note that it's the same link for all our Sanghani Faculty meetings this semester. Join Zoom Meeting Link: https://virginiatech.zoom.us/j/82892613942 Meeting ID: 828 9261 3942 One tap mobile +13017158592,,82892613942# US (Washington DC) +13052241968,,82892613942# US Dial by your location +1 301 715 8592 US (Washington DC) Meeting ID: 828 9261 3942 Thanks, Wanawsha Wanawsha Shalaby Manager of Operations UrbComp Program Coordinator Sanghani Center for AI and Data Analytics - Virginia Tech 900 N. Glebe Road, Arlington, VA 22203 Phone: (571) 858-3361 E-Mail: wanah92(a)vt.edu<mailto:wanah92@vt.edu>

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FEB 6 - INFORMATION SESSION: Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning (ML)
by Shalaby, Wanawsha 01 Feb '23

01 Feb '23

EVENT: Information Session on The Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning (ML) - VIA ZOOM DATE: Monday, February 2nd TIME: 1 - 2 PM EST REGISTRATION: Please register via Eventbrite<https://www.eventbrite.com/e/information-session-amazon-virginia-tech-initi…> to receive the Zoom link Last year, Virginia Tech and Amazon announced their partnership to advance research and innovation in artificial intelligence and machine learning. The Amazon - Virginia Tech Initiative for Efficient and Robust Machine Learning<https://sanghani.cs.vt.edu/amazon-virginia-tech-initiative/> includes machine learning-focused research projects, doctoral student fellowships, community outreach, and an establishment of a shared advisory board. The first set of awards for fellowships and research projects were made in Fall 2022 and you can learn more about the awardees (link to this page: https://vtx.vt.edu/articles/2022/10/amazon-virginia-tech-initiative-announc…). We will be hosting a virtual information session about this year's opportunities: Call for PhD student fellowships<https://sanghani.cs.vt.edu/wp-content/uploads/2023/01/2023-Amazon-VT-CPF_Fe…>, Call for Research Projects<https://sanghani.cs.vt.edu/wp-content/uploads/2023/01/2023-Amazon_VT-CFP_Pr…>. Funding for the research projects and fellowships would begin in Fall Semester 2023 (FY 2024). The information session will include an overview and objective of the initiative, funding experience from current VT faculty award recipient, and an overview of topic areas of interest from Amazon Science Teams. Faculty and PhD students interested in machine learning, data science, AI, and NLP are encouraged to attend and participate. Please register using the link below to receive the Zoom information. Eventbrite Registration: https://www.eventbrite.com/e/information-session-amazon-virginia-tech-initi… If you have any questions leading up to the event, contact Wanawsha Shalaby at wanah92(a)vt.edu. Thanks, Wanawsha Wanawsha Shalaby Manager of Operations UrbComp Program Coordinator Sanghani Center for AI and Data Analytics - Virginia Tech 900 N. Glebe Road, Arlington, VA 22203 Phone: (571) 858-3361 E-Mail: wanah92(a)vt.edu<mailto:wanah92@vt.edu>

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