Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.
CSE 600 Talk: Squeezing Software Performance via Eliminating Wasteful Operations presented by Xu Liu

ABSTRACT: Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations, such as redundant or useless memory loads and stores. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. In this talk, I will describe various wasteful memory operations, which pervasively exist in modern
software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentation-based profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning
instrumentation, which reduces the runtime overhead from 10x to 3% on average. I will show how our approach works for native binaries and various managed languages such as Java, yielding new performance insights for optimization.

BIO: Xu Liu is an assistant professor in the Department of Computer Science at College of William & Mary. He obtained his PhD from Rice University in 2014 and joined the College of William & Mary in the same year. Prof. Liu works on building performance tools to pinpoint and optimize inefficiencies in HPC code bases. He has developed several open-source profiling tools, which are used worldwide at universities, DOE national laboratories and industrial companies. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19 and ASPLOS'17 Highlights, as well as Distinguished Paper Award at ICSE'19. His recent ASPLOS'18 paper has been selected as ACM SIGPLAN Research Highlights in 2019 and nominated for CACM Research Highlights. Prof. Liu is the receipt of 2019 IEEE TCHPC Early Career Researchers Award for Excellence in High Performance Computing. Prof. Liu served on the program committee of conferences such as SC, PPoPP, IPDPS, CGO, HPCA and ASPLOS.
The Renaissance School Of Medicine Department of Scientific Affairs and its Single Cell Genomics facility are excited to host a special seminar and discussion on AI and single cell genomics analysis:

With the decreasing cost of sequencing, many biobanks and large research cohorts have moved to whole genome sequencing (WGS) and single-cell RNA-seq. However, making use of this deluge of data remains a challenge. I will discuss statistical and deep learning approaches that we are exploring to address the challenge of noncoding variant interpretation, including our work as part of the Alzheimer's disease sequencing project.

Speaker: David A. Knowles, PhD. Asst. Professor of Computer Science, Interdisciplinary Appointee in Systems Biology, Columbia University Core Faculty Member, New York Genome Center

Join us in person: Health Science Tower Level 3, Lecture Hall 5
Join your friends at DoIT for a workshop on Zoom's AI Companion

AI Companion is a meeting summary tool that can capture and summarize what is said in a Zoom meeting transcript, eliminating the need for a notetaker. In addition, meeting participants can use AI Companion to ask questions to a chat bot to get clarity on information within a meeting, and they can also use it to create stylistic virtual backgrounds.

Register here for the online session

Abstract: Millions of individuals living in disadvantaged communities are burdened by poverty, illegal drug activities, health concerns, and the lack of reliable and affordable access to facilities (e.g., schools, hospitals, and transit stations). To address these societal problems efficiently with broad support, initiatives have called to engage agents (e.g., residents, community leaders, or stakeholders) and consider their preferences on community improvement decisions to make collective community decisions. In this talk, we will focus on our ongoing AI-empowered collective decision-making approaches to improve the accessibility of individuals to facilities by (a) locating facilities to provide essential services and (b) strengthening existing infrastructures via structural modifications (e.g., constructing new roads, bridges, multi-use paths, or shuttle services) subject to individuals' preferences on the locations of the facilities and which communities to improve access, respectively. In particular, we will discuss our (theoretical and algorithmic) studies on modeling these approaches under several settings (e.g., accounting for fairness and agent preferences) and designing fair, transparent, strategy proof, and (approximately) optimal mechanisms to elicit (true) individual preferences and determine collective community decisions in order to improve facility accessibility. Finally, we will discuss other ongoing and future collective decision-making efforts in urban planning and public health (i.e., our recent studies on substance use research) to improve communities.

Bio: Hau Chan is an assistant professor in the School of Computing at the University of Nebraska-Lincoln. He received his Ph.D. in Computer Science from Stony Brook University in 2015 and completed three years of Postdoctoral Fellowships, including at the Laboratory for Innovation Science at Harvard University in 2018. His main research lies in multi-agent aspects of AI for Society and Social Good, focusing on developing modeling and algorithmic foundations for tackling societal problems involving agents and predicting agent behavior in societal contexts, leveraging AI, game theory, mechanism design, and machine learning to better inform policymaking and (collective) decision-making. His team has been addressing societal challenges and fairness issues in various domains, including security (e.g., reducing vulnerability), public health (e.g., reducing substance use and homelessness), and urban planning (e.g., improving accessibility to public facilities), collaborating with domain experts. His research has been supported by NSF, NIH, and USCYBERCOM. He has received several Best Paper Awards at SDM and AAMAS and distinguished/outstanding SPC/PC member recognitions at IJCAI and WSDM. He has given tutorials and talks on computational game theory and mechanism design at venues such as AAMAS and IJCAI, including an Early Career Spotlight at IJCAI 2022. He has served as co-chairs for the AI and Social Good Track, Demonstration Track, Student Activities, Doctoral Consortium, Job Fair, Scholarships, Finance, and Diversity & Inclusion Activities at AAAI, AAMAS, and IJCAI.

Location: Old Computer Science, room 1310

Abstract: Astronomers slowly made sense of the cosmos by following the stars night after night. I suggest we examine human identity in a similar way. Let's observe the words individuals use to describe themselves day after day. In this presentation, I will introduce ipseology - a new approach to studying human selves. Ipseology is the systematic, empirical study of ipseity: selfhood, individuality and the elements of identity. The primary idea is that we can learn a lot about people from their self-authored self-descriptions - especially if we follow their revisions over time. I will discuss results from sampling millions of social media bios over more than a decade and present new approaches for observation in the Post-API age.

Bio: Dr. Jason Jeffrey Jones is a computational social scientist whose expertise includes online experiments, social networks, high-throughput text analysis and machine learning. He is interested in humans' perceptions of themselves and the developing role of artificial intelligence in society.

Dr. Jones is the director of CSSERG (pronounced sea surge): the Computational Social Science of Emerging Realities Group. CSSERG is a team of scholars committed to cross-disciplinary collaboration, united by common computational methodologies and always with eyes on the near future. CSSERG has studied the effectiveness of virtual reality in evoking empathy, the dynamics of gender stereotypes in language over decades and temporal trends in personally expressed identity.

This seminar will take place in person and online (zoom link below):

Join Zoom Meeting
https://stonybrook.zoom.us/j/93686609778?pwd=KdHVyIbU3ymML6hTchXsm6JLYKLSru.1

Meeting ID: 936 8660 9778
Passcode: 638699

The event will take place on Zoom and will feature two distinguished guest speakers: SBU alumnus, Velchamy Sankarlingam, president of Product and Engineering at Zoom, and Simeon Ananou, vice president for Information Technology and CIO at Stony Brook University. The discussion will be moderated by Haresh Gurnani, dean of the College of Business at Stony Brook University.

Exploring AI's Impact on Communication and Connection

Artificial Intelligence (AI) has rapidly evolved, becoming an integral part of various industries, including education and business. This event aims to delve into how AI is reshaping the way we learn and work, particularly in enhancing communication and fostering human connections. Velchamy Sankarlingam, an SBU alumnus and a key figure at Zoom, will share his insights on how AI-driven tools are revolutionizing virtual communication platforms, making interactions more seamless and effective.

Simeon Ananou, with his extensive experience in information technology, will provide a perspective on how AI is being integrated into educational institutions to improve learning outcomes and administrative efficiency. His role at Stony Brook University places him at the forefront of implementing innovative technologies that benefit both students and staff.

A Conversation Led by Expertise

Dean Haresh Gurnani, known for his leadership and expertise in business education, will guide the conversation, ensuring that the discussion remains focused on the practical implications of AI. He will explore how AI is not only boosting productivity but also enriching overall experiences in the workplace and educational settings. The event will include an interactive Q&A session, allowing attendees to engage directly with the speakers and gain deeper insights into the topics discussed.

As AI continues to develop, events like this are crucial for understanding its impact and potential. Stony Brook University's College of Business is committed to providing platforms for such important discussions, fostering an environment where innovation and education intersect.

This event is open to all. Please visit https://www.givecampus.com/schools/StonyBrookUniversity/events/artificial-intelligence-reshaping-learning-and-work to register.

Understand Prompting the crucial part to interface with models

Discover how to prompt effectively by exploring the details behind your AI interactions. This isn't just about basic prompting; it's about understanding how to articulate your ideas clearly. We'll showcase a few prompts and how they work. Discover how giving AI the right details can truly boost your productivity and help you reclaim valuable time in your day.

In this session, you will

  1. Utilize AI models effectively
  2. Understanding different prompts
  3. Find out tips that we use with AI

Register: https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_dht1o3rNzlZhHka?source=event+manager&session=0805251000ai