February 2025 - Sanghani-students

Innovation Campus: CS Seminar Announcement (Friday, February 21 11:15am-12:15pm, ICAB 4110)
by Lu, Chang-Tien 01 May '25

01 May '25

Learning, introspection, and anticipation for effective and reliable task planning under uncertainty Speaker: Prof. Gregory J. Stein George Mason University Friday, February 21, 2025 11:15AM- 12:15PM, ICAB 4110 Abstract The next generation of service and assistive robots will need to operate under uncertainty, expected to complete tasks and perform well despite missing information about the state of the world or the future needs of itself and other agents. In this talk, I will present a number of recent and ongoing projects that improve long-horizon navigation and task planning in uncertain home-like environments. First, I will discuss our recent developments that improve performance and reliability in unfamiliar environments-environments potentially dissimilar from any seen during training-with a technique that enables fast and reliable deployment-time policy selection despite uncertainty. Second, I will discuss anticipatory planning, by which our robot anticipates and avoids side effects of its actions on undetermined future tasks it may later be assigned; our approach guides the robot towards behaviors that encourage preparation and organization, improving its performance over lengthy deployments. Biography [cid:image001.jpg@01DB83BB.F2C5BD90] Prof. Gregory J. Stein is an Assistant Professor of CS at George Mason University, where he runs the Robotic Anticipatory Intelligence & Learning Group and is the director of the GMU Autonomous Robotics Lab. His research at the intersection of robotics, planning, and learning, develops approaches for planning and learning that allow robots to better understand the impact of their actions, so that they may plan quickly, intelligently, and reliably in a dynamic and uncertain world. Before joining Mason, he received his PhD in 2020 from MIT's EECS Department and previously graduated summa cum laude from Cornell University with a B.S. in Engineering Physics. His work was a finalist for Best Paper at the 2018 Conference on Robot Learning, at which he was additionally awarded Best Oral Presentation.

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Amazon - VT Initiative: Call for Research Proposals and Ph.D. Fellowship Nominations!
by Shalaby, Wanawsha 17 Apr '25

17 Apr '25

Hello, In 2022, 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://amazon.cs.vt.edu/> includes machine learning-focused research projects, doctoral student fellowships, community outreach, and an establishment of a shared advisory board. The third set of awards for fellowships and research projects were announced in Fall 2024, you can learn more about the awardees (link to this page: https://news.vt.edu/articles/2024/10/amazon-virginia-tech-initiative-announ…) The Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning (ML) is soliciting proposals<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…> from Virginia Tech faculty in the College of Engineering for research projects with anticipated support beginning Fall Semester 2025 (FY 2026). The objective of this initiative is to advance research and innovation in Artificial Intelligence (AI) and ML, specifically in the areas of efficient and robust machine learning. Virginia Tech faculty members are invited to submit proposals for research projects that work to revolutionize the way ML is developed, evaluated, and embedded in everyday contexts, devices, and applications. Please note, as instructed in the Call for Research Projects<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…>, abstracts are due February 20, 2025. If you have any questions regarding the submission process, please review our FAQ<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…>. Additionally, the program invites nominations<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…> for doctoral student fellows with anticipated support beginning Fall Semester 2025. Fellowships will be awarded to Virginia Tech doctoral students that includes one academic year of funding, tuition, travel support to conferences, and an invitation to interview for an Amazon internship intended to provide students a greater understanding of industry and use-inspired research. Students selected for fellowships will be known as Amazon Fellows. The fellowship enables students during the academic year to pursue independent research projects in the areas of machine learning and artificial intelligence and the optional paid summer internship at Amazon will enable them to gain valuable industry insight and experience through direct engagement with Amazon researchers. Please note, as instructed in the Call for Fellowship Nominations<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…>, nomination packets are due April 18, 2025. If you have any questions regarding the submission process, please review our FAQ<https://amazon.cs.vt.edu/content/dam/amazon_cs_vt_edu/year-4/Amazon%20-%20V…>. 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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[PARTICIPANTS NEEDED] Branching Code Paths Study
by Harden, Jesse 28 Feb '25

28 Feb '25

Greetings, We are looking for participants in a user study about branching code paths in data science endeavors. As part of this study, you will complete a pre-study survey, 2 data analysis tasks, a post-task survey, and potentially participate in an interview session. During the pre-study survey, you will answer questions about your prior experience with Python and computational notebooks. You will then be asked to submit your availability to complete 2 separate sessions for data analysis. You will then be assigned a time to complete the 2 sessions task based on your stated availability. After you complete the task, you will fill out a survey regarding your experiences with the task, as well as the software and space used for the task The task and post-task survey combined is expected to take no more than 3 hours. To participate, you must have prior experience with Jupyter Notebooks and data analysis in Python. Participation in this study is voluntary, and any data you provide will be kept confidential. If you choose to participate in this study and complete the task and post-task survey, you will be compensated $30 in the form of an Amazon gift card. You must complete both the task and survey to receive the compensation. If you are interested, please complete this pre-study survey<https://virginiatech.questionpro.com/t/AWlQ0Z5TyJ> to determine if you are eligible. Additionally, if you have any questions, please contact Jesse Harden at jessemh(a)vt.edu<mailto:jessemh@vt.edu>. Thank you for your time and consideration, Jesse Harden

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PARTICIPANTS NEEDED: Branching Code Paths in Data Science Study
by Harden, Jesse 13 Feb '25

13 Feb '25

Greetings, We are looking for participants in a user study about branching code paths in data science endeavors. As part of this study, you will complete a pre-study survey, 2 data analysis tasks, a post-task survey, and potentially participate in an interview session. During the pre-study survey, you will answer questions about your prior experience with Python and computational notebooks. You will then be asked to submit your availability to complete 2 separate sessions for data analysis. You will then be assigned a time to complete the 2 sessions task based on your stated availability. After you complete the task, you will fill out a survey regarding your experiences with the task, as well as the software and space used for the task. The task and post-task survey combined is expected to take no more than 3 hours. To participate, you must have prior experience with Jupyter Notebooks and data analysis in Python. Participation in this study is voluntary, and any data you provide will be kept confidential. If you choose to participate in this study and complete the task and post-task survey, you will be compensated $30 in the form of an Amazon gift card. You must complete both the task and survey to receive the compensation. If you are interested, please complete this pre-study survey<https://virginiatech.questionpro.com/t/AWlQ0Z5TyJ> to determine if you are eligible. Additionally, if you have any questions, please contact Jesse Harden at jessemh(a)vt.edu<mailto:jessemh@vt.edu>. Thank you for your time and consideration, Jesse Harden

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Sanghani Center joins LinkedIn! Connect with us!
by Micale, Barbara 10 Feb '25

10 Feb '25

Sanghani Center joins LinkedIn! We are excited about using this platform as another way of spreading the word about our expert faculty and talented students and the innovative research being done at the Center. We welcome you to connect – and engage – with us on LinkedIn at Sanghani Center for AI & DA (https://www.linkedin.com/company/sanghani-center-for-ai-da/) Best, blm Barbara L. Micale Public Relations Specialist Sanghani Center for Artificial Intelligence and Data Analytics<https://sanghani.cs.vt.edu/> Virginia Tech blmicale(a)vt.edu<mailto:blmicale@vt.edu> X (formerlyTwitter): @SanghaniCtrVT

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Innovation Campus: CS Seminar Announcement (Friday, February 7 11:15am-12:15pm, ICAB 4110)
by Lu, Chang-Tien 05 Feb '25

05 Feb '25

Inner Dialogue: Reflection by Self-Question-Answering and Human-AI Collaboration Speaker: Prof. Tim Wen-Hsiang Lu National Cheng Kung University, Taiwan Friday, February 7, 2025 11:15AM- 12:15PM, ICAB 4110 Abstract Our recent initial work supported by Google proposes a novel framework "Inner Dialogue" that approaches problem-solving through a self-questioning and answering mechanism, offering a multifaceted, transparent, and interpretable process. We also introduce an interactive mode where humans can assume various roles, such as supervisor, asker, or answerer, allowing them to infuse their perspectives into the system's reasoning. Each aspect of the problem is explored in depth, enriching the information available at each stage. A supervisory role oversees the questioning and answering process to enhance control, ensuring the system remains centralized and manageable rather than allowing an LLM to operate autonomously. We also introduce automatic LLM evaluation prompts, human evaluation metrics, and scoring criteria: diversity, relevance, factuality. Furthermore, we examine the consistency between human and LLM evaluators, identifying areas where discrepancies may arise, and which model is more suitable for evaluating specific aspects. Biography [cid:image002.png@01DB77BC.0F5C3620] Tim Wen-Hsiang Lu is a professor in the department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan. He received his M.S. and Ph.D. degrees from the department of Computer Science and Information Engineering, National Chiao Tung University, in 1990 and 2003. In 2004, he received Dragon Golden Paper Award sponsored by the Acer Foundation. His research interests include natural language processing, speech recognition/synthesis, machine translation, information retrieval, web/text mining, medical informatics. His research results have been published in top-tier journals and conferences, such as ACM TOIS, ACM TALIP, ACM SIGIR, ACL, etc.

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FW: Register Today for March 4, 2025 3:30pm Montgomery Lecture by Dr. Barry L. Nelson
by Brian Mayer 04 Feb '25

04 Feb '25

FYI From: Van Mullekom, Jennifer <vanmuljh(a)vt.edu> Sent: Tuesday, February 4, 2025 7:55 AM To: stat-fs-g(a)vt.edu; 'stat-gs-g(a)vt.edu' <stat-gs-g(a)vt.edu> Cc: Schabenberger, Oliver <schabenb(a)vt.edu>; Mutchler, Scott <smutchle(a)vt.edu>; Lattimer, Alan <alattime(a)vt.edu>; Brian Mayer <bmayer(a)cs.vt.edu> Subject: Register Today for March 4, 2025 3:30pm Montgomery Lecture by Dr. Barry L. Nelson All, Please see the flyer below for details on this year's Douglas C. Montgomery Distinguished Lecture given by Dr. Barry L. Nelson on March 4, 2025 at 3:30pm in the Holtzman Auditorium. This series is jointly sponsored by the Grado Department of Industrial & Systems Engineering and the Department of Statistics. A reception and student poster session will follow the talk in the Grand Hall adjacent to the Auditorium. Dr. Nelson's lecture will be informative and interesting as he explores the use of simulation for data driven decisions, a topic relevant to both ISE and Statistics. As Dr. Nelson emphasizes in his abstract, his talk will be accessible to audiences of all levels in adjacent fields. Feel free to circulate this flyer to your network since there is a virtual attendance option. If you are teaching, please consider posting on your Canvas site to encourage both undergraduate and graduate students to attend. Please click on this link https://aimsbbis.vt.edu/MontgomeryLecture2025 <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Faimsbbis. vt.edu%2FMontgomeryLecture2025&data=05%7C02%7Cmayer2%40vt.edu%7C99c7abaeb5fd 4de4b6c808dd451b211a%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C6387427050 38104151%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCI sIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=t2ZTU7n 8Lki6JCg7O%2B%2FB0ReIpyMFXgOSRe2Ej4fowtU%3D&reserved=0> to be taken to the registration page for the seminar and reception immediately following. We look forward to your participation. The registration form is managed through Advancement and will require you to "check out". There is no fee for this event. Make sure you designate in person or virtual attendance. In person attendees will be included in the reception headcount. Virtual attendees will receive the webinar link. You will be asked if you would like to give to the Department of Statistics or the Grado Department of Industrial & Systems Engineering at checkout but there is no obligation to do so. You can simply click on Not Today and continue the registration process if you choose not to give. For those of you who may not know, Dr. Montgomery is a Virginia Tech alum in ISE (a triple Hokie!). He is a prolific researcher whose work is largely on the statistical side of ISE in the areas of experimental design, regression, and quality control. He is a longtime collaborator with many VT Statistics Department faculty, friends, and alums. We are fortunate to have him generously sponsor this series that brings our two departments together. Thanks so much for supporting this event with your attendance! Best, Jen <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Faimsbbis. vt.edu%2FMontgomeryLecture2025&data=05%7C02%7Cmayer2%40vt.edu%7C99c7abaeb5fd 4de4b6c808dd451b211a%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C6387427050 38124097%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCI sIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=VAewBp9 RvkapB%2BKwPLC8JK3e%2FLIHK8p4wiIvlPj%2Fxog%3D&reserved=0> Jennifer H Van Mullekom, PhD Director of SAIG Statistical Applications and Innovations Group www.saig.stat.vt.edu <https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.saig.s tat.vt.edu%2F&data=05%7C02%7Cmayer2%40vt.edu%7C99c7abaeb5fd4de4b6c808dd451b2 11a%7C6095688410ad40fa863d4f32c1e3a37a%7C0%7C0%7C638742705038140140%7CUnknow n%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIs IkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=RpGCDkZc99JkKShGKG0zODv6 nwtQCk39LruuDxH7bVE%3D&reserved=0> Professor of Statistical Practice Virginia Tech Department of Statistics 416B Hutcheson Hall, Blacksburg, VA 24061 <mailto:vanmuljh@vt.edu> vanmuljh(a)vt.edu Click here to request a SAIG collaboration <https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsaig.stat .vt.edu%2Fsaig-services%2Frequest-saig-consultation.html&data=05%7C02%7Cmaye r2%40vt.edu%7C99c7abaeb5fd4de4b6c808dd451b211a%7C6095688410ad40fa863d4f32c1e 3a37a%7C0%7C0%7C638742705038156684%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiO nRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D% 7C0%7C%7C%7C&sdata=FW5IpvEhhWy%2FdeaV4tiAk55hK4Oc0UxTvUdSJP6NWd4%3D&reserved =0>

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Invitation to respond to the form "Participation at the American Association for the Advancement of Science Annual Meeting (AAAS)"
by Barbara Micale (via Google Forms) 03 Feb '25

03 Feb '25

I've shared an item with you: Participation at the American Association for the Advancement of Science Annual Meeting (AAAS) https://docs.google.com/forms/d/e/1FAIpQLSdjD_EAp88Om4Nn2YBrbzO_ttK9X_ppk2e… It's not an attachment -- it's stored online. To open this item, just click the link above.

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