Over the past decade, researchers in neuroscience, psychology and artificial intelligence have come together to build advanced computer models that mimic how our brain processes what we see. These models are designed to closely copy the brain's visual system, all the way to a key area called the inferior temporal cortex, which plays an important role in recognizing objects.

Because these computer models can be fully observed, scientists can use them to make detailed predictions about how the brain works -- something older, more theoretical models could not do.

Dr. James DiCarlo's work explores whether these computer digital twin models of the brain could help guide safe, non- invasive ways to infl uence brain activity. In his talk, he explains how such a model could be used to design specific patterns of light. When this carefully designed light is added to what the eye naturally sees, it can precisely influence activity in groups of neurons in the inferior temporal cortex.

Since neural activity in this visual brain area may be connected to emotional states like anxiety, this research could eventually open the door to non-invasive approaches that may benefit mental well-being in the future.

Speaker: James J. DiCarlo, MD, PhD, Peter de Florez Professor, MIT Brain and Cognitive Sciences, and Director, MIT Siegel Family Quest for Intelligence

Location: Staller Center Main Stage

The event will be livestreamed at stonybrook.edu/live

Abstract: Sub-grid turbulence is challenging to resolve in climate models; therefore, it is parameterized. Traditionally, turbulent parameterizations have relied on physics-based and equation-based approaches. However, ad hoc and uncertain components in these parameterizations introduce uncertainty in future climate predictions. Recently, data-driven techniques have emerged as an alternative for modeling sub-grid fluxes. I will demonstrate the use of machine learning to model vertical turbulent fluxes in the ocean surface boundary layer and its impact on reducing biases in NOAA's Geophysical Fluid Dynamics Laboratory ocean climate model.

I will show how neural networks, trained to predict the eddy diffusivity profile from high-fidelity yet computationally expensive turbulence schemes, enhance the vertical mixing scheme in the climate model. These networks replace ad hoc components while maintaining the conservation principles of the standard ocean model equations. The enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and modestly improving tropical upper-ocean stratification in ocean-only global simulations. Furthermore, simplified equations that can replace the neural networks show similar improvements but with lower computational cost and better interpretability. They point to structural deficiencies in the baseline parameterization. This work is one of the first successful applications of machine learning to improve a sub-grid parameterization of turbulent mixing in ocean climate models.

IACS Seminar Speaker: Aakash Sane, Princeton University

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97764942108?pwd=MzCWupCe3L9mKdrgfO2bJg3GBbvXuf.1
Meeting ID: 977 6494 2108
Passcode: 519324
The New York Academy of Sciences Presents AI for Materials: From Discovery to Production - A Virtual Symposium

Event Description: This interdisciplinary symposium covers the application of artificial intelligence (AI) throughout the entire life cycle of new materials -- from materials simulations and synthesis to translating research into high-volume industrial production.

Event Link & Registration: nyas.org/AI4Materials2020
The Empirical Methods in Natural Language Processing (EMNLP) conference is a premier international academic conference in the field of artificial intelligence and natural language processing (NLP). Organized annually by the Association for Computational Linguistics (ACL) special interest group on linguistic data (SIGDAT), it focuses on research that uses empirical methods to solve language processing problems.

For more information, and registration, visit the official website.
Abstract:

Photorealistic editing of human facial expressions and head articulations remains a long-standing topic in the computer graphics and computer vision community. Methods enabling such control have great potential in AR/VR applications where a 3D immersive experience is valuable, especially when this control extends to novel views of the scene in which the human subject appears. Traditionally, 3D Morphable Face Models (3DMMs) have been used to control the facial expressions and head pose of a human head. However, the PCA-based shape and expression spaces of 3DMMs lack the expressivity. They cannot model essential elements of the human head such as hair, skin details, and accessories such as glasses that are paramount for realistic reanimation. In this thesis, we present a set of methods that enables facial reanimation, starting from editing expressions in still face images to creating fully controllable neural 3D portraits with control over facial expressions, head pose, and viewing direction of the scene using only casually captured monocular videos from a smartphone to finally achieving studio-like quality from the said monocular captures.
First, we propose a method for editing facial expressions in near-frontal facial images through the unsupervised disentangling of expression-induced deformations and texture changes. Next, we extend facial expression editing to human subjects in 3D scenes. We represent the scene and the subject in it using a semantically guided neural field. This enables control over the subject's facial expressions and the viewing direction of the scene they're in. We then present a method that learns, in an unsupervised manner, to deform static 3D neural fields using facial expression and head-pose dependent deformations, enabling control over facial expressions and head pose of the subject along with the viewing direction of the 3D scene they're in. Next, we propose a method that makes the learning of the aforementioned deformation field robust to strong illumination effects, which adversely impact the registration of the deformation. We then propose an extension of this unsupervised deformation model to 3D Gaussian splatting by constraining it using a 3D morphable model, resulting in a rendering speed of 18 FPS--a 100x speed improvement over prior work. Finally, we propose a method that bridges the quality gap between 3D portraits created using in-the-wild monocular data and multi-view studio capture data. We accomplish this using a two-stage method. First, we train a StyleGAN to relight and inpaint in-the-wild face texture maps (with strong illumination effects and incompletely captured regions). Next, we both reconstruct and generate identity-specific facial details that may be poorly captured in the in-the-wild captures. Once trained, we can generate studio-like complete avatars from monocular phone captures.

Speaker: Shahrukh Athar

Zoom Link:
https://stonybrook.zoom.us/j/94228500743?pwd=RqOBgG6tbJkKaFBlWFwBkYFX0VRovV.1

Meeting ID: 94228500743
Passcode: 661599

The AI Community at Stony Brook University is proud to announce Datathon 2026.

Dive into data analysis and AI/ML, and get ready to build something big. In this year's underwater-themed event, enjoy a weekend of data analysis, hacking, networking, fun activities, and minigames.

Whether you're a seasoned developer, data scientist, designer, or completely new to hacking, this event is your chance to collaborate, learn data science, and create something impactful with data and AI/ML.

What is Datathon?

AI Community's Datathon is the premier data science competition at Stony Brook University, bringing together students of all skill levels for a weekend of data exploration, analysis, and innovation. Just like a typical hackathon, you will be using your skills to build your dream project.

Unlike a regular hackathon, Datathon is focused on data science. You will be given a set of data to work with, analyze, and apply to your project. You can also find your own data to use. Your project will be presented to a panel of judges consisting of professors and industry professionals!

Who Can Participate

  • Students of all skill levels and majors are welcome.
  • Come with a team or find one at the event or on Discord.
  • This event is open to SBU and non-SBU students.

* Non-SBU Undergraduate Students are ineligible to receive prizes
* You must be 18+ or older (Excludes minors who are active SBU students)

Location: SAC Ballroom B

Register here.

The Provost's Lecture Series features talks by SUNY Distinguished Academy faculty members at Stony Brook University, showcasing the outstanding research and scholarship that is taking place at our institution.

Joe Mitchell

SUNY Distinguished Professor, Applied Mathematics and Statistics
Chair, Department of Applied Mathematics and Statistics, College of Engineering and Applied Sciences

A Case for Algorithms: A Computational Geometer's Perspective

Algorithms are all around us in every smart device and technology that has consumed our daily lives. As a computational geometer, I study algorithms to solve problems that involve a geometric perspective on data. I have observed that practically every technology and field of study has a need for effective algorithms involving geometric data. I reflect on some favorite algorithmic problems that are easy to visualize, but challenging to solve, and argue that the formal study of algorithms remains essential in the age of AI.

Reception to follow immediately after the talks.

Register here.