The Division of Educational & Institutional Effectiveness is excited to host International Love Data Week at SBU, February 9-13, 2026!
Join us for a mix of 30-minute virtual sessions, an in-person kickoff on Monday, and a student-focused event on Wednesday celebrating data and data-informed decision-making.
Wrap up the week at the Love Data Week Open House on Friday, 2/13 with light refreshments, data-themed swag, photos with Wolfie, and time to connect with presenters.
Learn more and register on https://www.stonybrook.edu/commcms/oee/recognition/Love%20Data%20Week%202026%20Save%20the%20Date%20Placeholder.php



Abstract: The current approach to materials design, driven by strategic experimentation and supported by physics-based simulation across relevant scales, has been the standard for decades. While the theoretical component in this workflow provides valuable understanding of material behavior, it often fails to deliver actionable guidance for implementation. Advances in artificial intelligence and machine learning (AI/ML), together with high-performance computing (HPC), now offer a viable pathway to close this gap and accelerate both discovery and process optimization. This presentation will outline practical approaches for integrating AI/ML with HPC-enabled, high-throughput computation to explore high-dimensional search spaces. Examples will include the development of engineering alloys for extreme environments, the use of neural networks to rapidly improve computational thermodynamic models, and vapor processing optimization for the manufacturing of ultra-high-temperature ceramics. I will highlight how scientific insight and domain expertise remain essential for translating surrogate model predictions into impactful outcomes. Finally, I will conclude with current challenges and future opportunities for AI/HPC-driven materials research.

Speaker: Dongwon Shin
This seminar will be held in person and online

Join Zoom Meeting: https://stonybrook.zoom.us/j/93730374357?pwd=YDLJ7ELqOQnTZEQhlN8Pa4TuhaiFK8.1

Abstract: Traditional questionnaires remain the primary method for assessing psychological outcomes and beliefs, capturing individuals' and populations' inner states. This dissertation presents an alternative computational method that overcomes key limitations in current mental health monitoring, particularly in spatiotemporal resolution, responses to major events, and automatic belief identification. By analyzing ∼1 billion Tweets from 2 million geo-located users, we created a big data pipeline for estimating depression and anxiety at the county-week level. These Language-Based Mental Health Assessments (LBMHA) demonstrated higher reliability and validity than traditional survey measures. Our approach effectively captured mental health trends and highlighted significant increases in mental illness following major events. Using the LBMHA pipeline, we conducted quasi-experiments, research designs that simulate randomized control trials, to generate explanations for mental health changes due to COVID-19 incidence/death. Utilizing these time-series analyses, we conducted discontinuity forecasting for community-specific anxiety shifts using statistical learning via ensemble and contextual models. To likewise investigate individual internal states, we created a novel task and annotated dataset for self belief language identification. Our fine-tuned language model for self-belief classification, despite its relatively small scale, outperformed GPT-4o. The self belief topics identified by our model successfully predicted depression, anxiety, and stress, offering insights into the relationship between self-conceptualization and mental health. The adoption of scalable language-based assessments with modern distributed computation presents a promising avenue for advancing community and individual mental health research.

Speaker: Siddharth Mangalik

https://stonybrook.zoom.us/j/91251321639?pwd=faggV5jZ7ByFDCFmnLXD3HiYxjQ1Eb.1&jst=2


Date of Event

Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University

Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine

Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms

Time: Wednesday, Feb 17, 2021 3 pm - 4 pm

Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293

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

Jianda Chen, EBNN - Improving the stability and accuracy of PDE-ML hybrid AGCMs

Boyang Li, CDS - Accelerating Materials Discovery using Machine Learning

Jaehye on Do, NPP Isotopes - Using LLMs for Isotopes Research and Production

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

Meeting ID: 161 528 9117
Passcode: 991382

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.

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.

AI for Neutrino Oscillation Fits

Abstract: Neutrino oscillation experiments face the problem of performing likelihood fits in a very highdimensional space to extract the oscillation parameters from measured spectra. The current strategy for this is to fix all but a few parameters, reducing the dimensionality of the fit to a manageable number, but this risks missing correlations between the parameters, which can impact the systematics of the measurement. This is an area where artificial intelligence and machine learning could make great improvements. I will discuss the problem, explain how it is currently dealt with, and sketch one possible way of implementing AI to solve it, using a sampling method combining Smolyak's algorithm, for efficient sampling using sparse grids, with an adaptive grid refinement to increase sampling in regions that are more likely to contain the global minimum.

Speaker: Steven Linden is a physicist in the Instrumentation Department at BNL working on neutrino and dark matter experiments. He got his PhD from Yale in 2010 doing analysis on the MiniBooNE experiment and then worked on various dark matter detectors (MiniCLEAN, Pico, SENSEI) at SNOLAB in Canada for nearly ten years before moving to BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1614473319?pwd=e4QSSgFHqDzHx870ixJpwuG3yqBere.1

Meeting ID: 161 447 3319
Passcode: 733283

Submit an abstract celebrating research, new discoveries and achievements in medicine and science!

We encourage faculty, nurse practitioners, post-doctoral fellows, fellows, residents, medical students, graduate students and undergraduate students to submit an abstract. Original research, case reports and case series are welcome.

Abstract submission deadline: FEBRUARY 7, 2025

For more details, visit here.


Abstract: The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on AI Exposure cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this work, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate - and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today's costs U.S. businesses would choose not to automate most vision tasks that have AI Exposure, and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs fall rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual - and therefore there is room for policy and retraining to mitigate unemployment impacts.

Details of this work can be found here.

Speaker Bio: Neil Thompson is the Director of the FutureTech research project at MIT's Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT's Initiative on the Digital Economy.

Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore's Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness' National Commission on Innovation & Competitiveness Frontiers.

He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.

Location: IACS Seminar Room


Place:  https://stonybrook.zoom.us/j/99167126152?pwd=TFpEYzM0aFhiOFJxSFJEb1JSS3YyQT09  

Time: 3 PM EST - Dec, 16th, 2020 

Abstract: 

Shadows provide useful cues to analyze visual scenes but also hamper many computer vision algorithms such as image segmentation, object detection, or tracking. For those reasons, shadow detection and shadow removal have been well-studied in computer vision.

Early work on shadow detection and removal focused on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and are slow during inference due to their reliance on hand-designed image features. Recently, deep-learning approaches have achieved breakthroughs in performance for both shadow detection and removal. They learn to extract useful features through training while being extremely efficient during inference. However, these models are data-dependent, opaque, and ignore the physical aspects of shadows. Thus they often lack generalization and produce inconsistent results.

We propose incorporating physical illumination constraints of shadows into deep-learning models. These constraints force the networks to more closely follow the physics of shadows, enabling them to systematically and realistically modify shadows in images. For shadow detection, we present a novel Generative Adversarial Network (GAN) based model where the generator learns to generate images with realistic attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters of a shadow image formation model that removes shadows. The system outputs high-quality shadow-free images with little or no image artifacts and achieves state-of-the-art performance in shadow removal when trained on a fully-supervised setting. Moreover, the system is easy to train and constrain since the shadow removal mapping is strictly defined by the simplified illumination model with interpretable parameters. Thus, it can be trained even with a much weaker form of supervision signal. In particular, we show that we can use two sets of patches, shadow and shadow-free, to train our shadow decomposition framework via an adversarial system. These patches are cropped from the shadow images themselves.
Therefore, this is the first deep-learning method for shadow removal that can be trained without any shadow-free images, providing an alternative solution to the paired data dependency issue. The advantage of this training scheme is even more pronounced when tested on a novel domain such as video shadow removal where the method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and further improves shadow removal results.
Hidden Biases. Ethical Issues in NLP, and What to Do about Them presented by Dirk Hovy of Bocconi University

ABSTRACT: Through language, we fundamentally express who we are as humans. This property makes text a fantastic resource for research into the complexity of the human mind, from social sciences to humanities. However, it is exactly that property that also creates some ethical problems. Texts reflect the authors' biases, which get magnified by statistical models. This has unintended consequences for our analysis: If our data is not reflective of the population as a whole, if we do not pay attention to the biases contained, we can easily draw the wrong conclusions, and create disadvantages for our users.

In this talk, I will discuss several types of biases that affect NLP models, their sources, and potential counter measures: (1) Bias stemming from data, i.e., selection bias (if our texts do not adequately reflect the population we want to study), label bias (if the labels we use are skewed) and semantic bias (the latent stereotypes encoded in embeddings); (2) Biases deriving from the models themselves, i.e., their tendency to amplify any imbalances that are present in the data; (3) Design bias, i.e., the biases arising from our (the researchers) decisions which topics to analyze, which data sets to use, and what to do with them. For each bias, I will provide examples and discuss the possible ramifications for a wide range of applications, and various ways to address and counteract these biases, ranging from simple labeling considerations to new types of models.

BIO: Dirk Hovey is an associate professor of Computer Science in the department of marketing at Bocconi University. He received his PhD from the University of Southern California in Los Angeles, where he worked as a research assistant at the Information Sciences Institute. 

He works in Natural Language Processing (NLP), a subfield of artificial intelligence. His research focuses on computational social science. His interests include integrating sociolinguistic knowledge into NLP models, using large-scale statistics to model the interaction between people's socio-demographic profile and their language use, and ethics for data science and algorithmic fairness.