The Della Pietra Lecture Series is pleased to present this lecture by Scott Aaronson:

Abstract: I'll survey some areas where I think theoretical computer science, math, and statistics can potentially contribute to the urgent quest to align powerful AI with humane values. These areas include: the watermarking of AI outputs, mechanistic interpretability (including Paul Christiano's No-Coincidence Principle, and succinct digests of the training process to aid interpretability), and theoretical guarantees for out-of-distribution generalization.

Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.

Location: Della Pietra Family Auditorium (SCGP 103)
How to Succeed in Language Design Without Really Trying presented by Professor Brian Kernighan

ABSTRACT: Why do some languages succeed while others fall by the wayside? I've helped create nearly a dozen languages (mostly small) over the years; a handful are still in widespread use, while others have languished or simply disappeared. I've also been present at the creation of several other languages, including some really major ones. In this talk I'll give my humble, but correct, opinion on factors that affect success and failure, and try to offer some insight into what to do if you're trying to design a new language yourself, and why that might be a good thing.

BIO: Brian Kernighan received a PhD in electrical engineering from Princeton in 1969. He joined the Computer Science department at Princeton in 2000, after many years at Bell Labs. He is a co-creator of several programming languages, including AWK and AMPL, and of a number of tools for document preparation. He is the co-author of a dozen books and some technical papers, and holds 5 patents.
He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. His research areas include programming languages, tools and interfaces that make computers easier to use, often for non-specialist users. He has also written two books on technology for
non-technical audiences: Understanding the Digital World in 2017 and Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers, published in 2018. His most recent book, Unix: A History and a Memoir, was published in October 2019.
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

An interactive session to discover how to create ALT text tags from images and create high-impact visuals, from identification to communicating ideas with images.

Discover how to use AI to create ALT text from images as well as identify objects in your environment, and build relatable visuals for high-impact presentations. Images communicate ideas as a way to understand concepts. AI-generated images have helped allow anyone to create these.

In this session, you will

  1. Creating image ALT Tags
  2. Transform ideas into images that are visually appealing
  3. Identify objects from visuals

Register here.
Learn how these two AI tools will help you this year. AI has been all over, but figuring out the tools that we may use is critical. Background remover of images and a replacement for Google Search may disrupt the industry this year. Learn and refresh your knowledge about these tools.


Abstract:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.

Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
18th Annual Engineering Ball Flowerfield, St. James, NY Thursday April, 2nd, 7:00 to 10:00 pm Pick up your tickets in 231 Engineering (Monday - Friday, 10:00 am to 4 pm) Presenting Partner: L3Harris

International Love Data Week is a global event dedicated to celebrating data in all its forms. This year, Stony Brook University is excited to celebrate Love Data Week with a series of 30-minute webinars aimed to promote proficiency with data, showcase innovative data projects, and foster a community of data enthusiasts across campus. Hosted by the Division of Educational & Institutional Effectiveness and facilitated by the Office of Educational Effectiveness, we invite all SBU faculty, staff and students to join in the festivities, learn from colleagues in our campus community, and fall in love with the power of data!

Learn more here.

























A new virtual seminar series on Games, Decisions, and Networks will start this Friday. The series aims at bringing together researchers working on foundations and applications of games theory, decision theory, and networks from computer science, control, economics and operation research. 





The advisory board for the series comprises Asu Ozdaglar (MIT), Christos Papadimitriou (Columbia), Drew Fudenberg (MIT), Eva Tardos (Cornell), Matthew O. Jackson (Stanford), Ramesh Johari (Stanford), and Tamer Başar (UIUC). 
The first talk will be given by Costantinos Daskalakis (MIT) on January 22nd at noon ET, titled Equilibrium Computation and the Foundations of Deep Learning. Upcoming speakers include


- Rakesh Vohra (Upenn)
- Sanjeev Goyal (Cambridge)
- Aaron Roth (Upenn)
- Aislinn Bohren (Upenn)
- Jason Marden (UCSB)

and more to be added!
Abstract: The capacity to adapt machine learning models to various contexts, information, and objectives is particularly valuable. In this thesis, I focus on developing Class Conditional Guided Models. These are models that can be adaptively biased towards a class of interest via a conditional input. My primary focus lies in the efficiency of these models. They are constructed to require training only once, with the ability to quickly and conveniently adapt during testing time without necessitating fine-tuning or retraining.
Firstly, I propose RelationVAE, a novel generative model designed for few-shot scenarios, utilizing the prior knowledge of class similarity relationships. RelationVAE is designed to condition on the embeddings of the neighbor classes (i.e. classes with similarity relationships), to generate more reliable samples by making them more similar to the neighbor class. This enables adaptation of the generative model to the provided prior knowledge about class relationships.
As a second focus, I introduce scGAN, a shadow segmentation technique that enables adaptation to varying shadow distributions in different testing environments. scGAN is designed to condition on a sensitivity parameter, a scalar, to control the amount of the shadow detected. In the testing phase, the parameter is set to appropriate values, allowing the model to quickly adapt to specific test environments.
In my third contribution, I propose S-SEG, a methodology for fine-grained counting allowing adaptation to different granularities of fine-grained classes. In fine-grained problems, the distinction between classes is subtle and inconsistent across images, leading to variations in the granularity of the target class from one image to another. S-SEG is designed to be conditioned on an additional input, the sensitivity parameter, to control the granularities of the target class during inference.
My fourth contribution is a text-to-image synthesis method which allows controlling the number of the generated objects of a target class. I propose to generate an intermediate condition, the density map, which reflects the number of objects, together with their layout. This intermediate condition is used to effectively guide the generative model to generate objects with accurate counts.

Speaker: Vu Nguyen

Zoom: https://stonybrook.zoom.us/j/97114455337?pwd=Z4rB9dWcstlahUIs8PRrvQ9b2ZK2Df.1
Meeting ID: 971 1445 5337
Passcode: 272300