Subject: RADIOLOGY GRAND ROUNDS CT Colonography: An Effective Test for Colorectal Cancer Screening- Judy Yee, M.D.
When: Wednesday, May 12, 2021 12:00 PM-1:00 PM (UTC-05:00) Eastern Time (US & Canada).
Where: JOIN ZOOM MEETING

 

Judy Yee, MD

Chair, Department of Radiology

Professor, Department of Radiology

Abdominal Imaging

 

Join Zoom Meeting

https://einsteinmed.zoom.us/j/97782190723?pwd=clMzMys2SlZjZzJId1hUNzMyVUQ2UT09

 

Meeting ID: 977 8219 0723

Passcode: 101083

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room


Speakers

Sanket Jantre
Tao Zhang
Xi Yu


Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382

Abstract:

Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.

In this thesis, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend our network to capture the full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. In the future, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.

Speaker: Aggelina Chatziagapi

Where: NCS, Room 220

Zoom link: https://stonybrook.zoom.us/j/98775312249?pwd=uORNAnSdcssrPZdqOsqaMAF5aLcRD9.1
ID: 98775312249
Passcode: 505777

Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)

 

Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST

 

Title: Machine Learning in Finance

Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key  participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.

Bio: (https://www.techatbloomberg.com/people/gary-kazantsev/) Gary is the Head of Quant Technology Strategy in the Office of the CTO at Bloomberg. Prior to taking on this role, he created and headed the company's Machine Learning Engineering group, leading projects at the intersection of computational linguistics, machine learning and finance, such as sentiment analysis of financial news, market impact indicators, statistical text classification, social media analytics, question answering, and predictive modeling of financial markets.

Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.

He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.


Join Zoom Meetinghttps://stonybrook.zoom.us/j/93374426887?pwd=cE9zeW51VXFEN2R0YnNPbHF1WFp0Zz09Meeting ID: 933 7442 6887Passcode: 330347One tap mobile+16468769923,,93374426887# US (New York)+13126266799,,93374426887# US (Chicago)Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US (Washington DC) +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma)Meeting ID: 933 7442 6887

Predicting Subjective Attributes in Visual Data - Zijun Wei

ABSTRACT: Recent progress in deep neural networks has revolutionized many computer vision tasks such as image classification, detection and segmentation. However, in addition to excelling in tasks that predict well-defined objective information, human-centered artificial intelligence systems should also be able to model subjective attributes, as defined by human perceptual behavior, that goes beyond the pure physical content of visual data. Example subjective tasks are the prediction of spatial or temporal regions that are interesting to humans (e.g., attract attention or are visually pleasing) and the recognition of subjective attributes (e.g., visually elicited sentiments). Better models for these tasks will improve the human-computer interaction experience in various applications. This thesis investigates several approaches to address the challenges in predicting those subjective attributes in visual data over a diverse set of tasks. I first present a novel framework for real-time automatic photo composition. The framework consists of a cost-effective data collection workflow, an efficient model training pipeline and a lightweight module to account for personalized preferences. Then I develop a novel and general algorithm to detect interesting segments in sequential data, which can be naturally applied to video summarization tasks. Furthermore, I propose methods that learn to represent sentiments elicited by images, in an unsupervised manner, using linguistic features extracted from large scale Web data. To conclude this thesis, I introduce a human-vision-inspired image classification algorithm that also predicts spatial visual attention even though no attention data was used for training it.  

The University at Albany will host a national gathering of professionals and academics that will focus on the transformative potential of AI while addressing the ethical, technical and institutional challenges posed by AI in education.

The Symposium will feature dynamic keynotes, hands-on workshops and engaging conversations with other participants and subject matter experts.

Topics

  • Teaching, Learning and Workforce Development
  • Research, Creative Arts, and Practice
  • Ethics, Governance and Academic Administration

For event information and registration, visit Events@Albany.

Are you tired of drowning in a sea of resumes and losing top talent in the hiring whirlwind? Transform your hiring process through a different lens and learn about AI in the Workplace and the Applicant Tracking System (ATS). Whether you're a recent graduate seeking your first job or an undergraduate student looking to delve into more career-oriented opportunities, this workshop by SBU Career Center is designed to equip you with the knowledge and strategies needed to succeed.

Register here: https://stonybrook.joinhandshake.com/stu/events/1568133?

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Abstract: Two-dimensional (2D) materials such as graphene, hBN, and TMDs offer atomically sharp interfaces and unprecedented tunability when vertically assembled into van der Waals heterostructures. These stacks have enabled discoveries ranging from moiré superconductivity and correlated insulators to quantum emitters and next-generation nanoelectronic devices. Yet constructing high-quality heterostructures remains largely artisanal: researchers manually identify exfoliated flakes, align a polymer stamp by eye, and finely adjust temperature and contact geometry through tacit skill. This manual workflow is difficult to reproduce, scales poorly, and prevents systematic exploration of the enormous combinatorial space of materials, twist angles, and interfacial conditions. AutoLab is an autonomous platform that translates this tacit human expertise into programmable, feedback-driven control. Instead of pressing flakes with predefined trajectories, AutoLab uses machine vision to detect polymer-wafer contact, dynamically regulates contact evolution through closed-loop actuation and temperature control, and captures high-quality flakes with the cleanliness and precision of expert manual fabrication. The system integrates perception, decision making, and motion planning into a single robotic framework, enabling reproducible stacking, wafer-level coverage, and accelerated discovery. Beyond 2D materials, AutoLab illustrates a broader paradigm for AI-native scientific automation: codifying human experimental reasoning into algorithms that interrogate data in real time, adaptively adjust instrumentation, and generate scalable, high-fidelity datasets. Such platforms could generalize to diverse research domains--quantum device fabrication, optical alignment, surface science, autonomous microscopy, and other workflows where expert intuition currently limits throughput and reproducibility. By bridging artisanal manipulation and robotic autonomy, AutoLab points toward a future where scientific discovery is accelerated by machines that not only execute instructions, but learn, respond, and collaborate with human scientists.

Biography: Dr. Yutao Li is a research associate from Department of Condensed Matter Physics and Material Science, Brookhaven National Laboratory. He has 8 years of experience in 2D material sample fabrication, and investigation in their electronic transport, optical and mechanical properties.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

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.
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.