Abstract: Olfaction plays an essential role in everyday life. Anosmia, the loss of the sense of smell, affects approximately 20% of adults globally and its prevalence rises with age. This can lead to malnutrition, reduced ability to detect environmental hazards, and high rates of depression. The olfactory epithelium (OE) is a specialized tissue deep within the nasal cavity that primarily contains olfactory sensory neurons (OSNs), which are crucial cells that detect odorants and relay information to the olfactory neural pathway. Current clinical assessments for anosmia cannot discern whether the cause lies within the OSNs (sensorineural) or further down the pathway (central). The ability to image the OE would help assess the health of OSNs, distinguish between sensorineural and central causes of anosmia, and inform treatments. However, the OE is a small region of tissue that is non-uniform and difficult to distinguish from its surrounding respiratory epithelia (RE). This work explores the use of an FDA-approved dye, Indocyanine Green (ICG), to highlight tissue structure differences and help distinguish between OE and RE. We found that machine learning models using texture analysis information were able to classify images of the two tissue types correctly with ~80% accuracy, while a convolutional neural network was able to classify the images directly with ~90% accuracy. This advancement will enable accurate identification and mapping of the spatial distribution of OSNs within the nasal cavity, enhancing studies on olfactory degeneration and regeneration.


Bio: Skylar Suarez has a Bachelor of Science degree in Computer Science and Engineering from the University of South Florida. She spent nearly a decade as a software and hardware engineer, then decided to follow my dream of being involved in medicine and becoming a professor. She is a PhD Candidate at the Department of Biomedical Engineering at the University of Colorado Anschutz Medical Campus, where she also teaches courses in biological data analysis and machine learning. She specializes in neural engineering, particularly in the development and design of neurosurgical tools for assessing neurodegenerative disorders.


Location: New Computer Science, Room 220 OR Join by Zoom

OVERVIEW


This workshop, Expanding Horizons in AI with HPC, aims to explore the dynamic intersection of AI and HPC, focusing on how advanced computing can accelerate AI research and applications. As AI models become more complex and data-intensive, traditional computing systems struggle to meet the demand for scalability, efficiency, and speed. HPC offers a solution by providing the necessary infrastructure for training large-scale models, enhancing AI algorithms, and enabling breakthroughs in fields such as deep learning, natural language processing, and autonomous systems.

Through a combination of expert presentations and panel discussions, participants will gain insights into the latest developments in AI-HPC integration. Attendees will also engage in discussions on the future trends, challenges, and ethical considerations surrounding the use of HPC in AI.

The workshop is designed for AI researchers, data scientists, engineers, and HPC professionals seeking to enhance their understanding of how high-performance computing can drive innovation and expand the potential of AI in solving complex, real-world problems.

The workshop will be held at the Wang Center at Stony Brook University.

https://you.stonybrook.edu/hpcai/

PROGRAM

The program features sessions on HPC Architectures for AI, AI Applications in HPC, LLM's in HPC, and AI in HPC Workflows, and open student presentations. The tentative program and list of confirmed speakers is available at https://you.stonybrook.edu/hpcai/program/.

CALL FOR STUDENT PRESENTATIONS & PARTICIPATION

We are excited to offer students the opportunity to present their work in the area of high-performance scientific computing and artificial intelligence at the workshop. We are calling for students to submit their talk proposals (Name + Title) by April 15 to hpc_ai_workshop@stonybrook.edu. The committee will select the best submission to be presented at the workshop. Accepted speakers will be notified by April 22, 2025.

All students, regardless of whether they are presenting, may reach out to hpc_ai_workshop@stonybrook.edu for financial support to cover travel and lodging costs.

REGISTRATION

Registration is available at https://www.eventbrite.com/e/expanding-horizons-in-ai-with-hpc-tickets-1256469978529?aff=oddtdtcreator until May 2nd. The registration fee covers the workshop participation and the social event in the evening of May 9.

Regular registration: $200
Student registration: $100


IMPORTANT NOTE

The registration fee was meant to cover the room rent, catering, and dinner. Thanks to an RF seed grant, we are able to drop the registration fees for SBU students and staff/faculty. We still ask for an informal registration via email to hpc_ai_workshop@stonybrook.edu until April 27, so we can plan for catering and dinner.
Please get in touch with us if you have already registered as an SBU student/faculty/staff member for the workshop so we can handle any reimbursement.

The program is now online at https://you.stonybrook.edu/hpcai/program/.

2026 MIC Symposium on Multilingualism

Language, Sustainability, and AI


Friday, October 16, 9:00am - 5:00pm | Humanties 1006

Presented by: Center for Multilingual and Intercultural Communication (MIC)

Co-Sponsored by: College of Arts and Sciences (CAS), AI Innovation Institute (AI3), Office of Diversity, Inclusion, and Intercultural Initiatives (DI3), The Sir Run Run Shaw Endowment, and Department of Asian and Asian American Studies (AAAS)


Opening Remarks: David Wrobel (Dean, College of Arts and Sciences) and Lav Varshney (Director, AI Innovation Institute)

Keynote Speakers: Salikoko Mufwene, David Wei Dai, and Susan Brennan

  • 9:30 - 10:15am: Keynote #1 - Salikoko S. Mufwene
  • 10:15 - 10:45am: Faculty Response Panel #1 (chaired by S. N. Sridhar)
  • 11:00 - 11:45am: Keynote #2 - David Wei Dai
  • 11:45 - 12:15pm: Faculty Response Panel #2 (chaired by Yi Wang)
  • 2:00 - 2:45 pm: Keynote #3 - Susan E. Brennan
  • 2:45 - 3:15pm: Faculty Response Panel #3 (chaired by Jiwon Hwang)
  • 3:30 - 4:45pm: Synthesis and Open Discussion with all speakers, panelists, and participants (moderated by Shyam Sharma)

If you need special accomodation, please contact chikako.nakamura@stonybrook.edu

The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.
University Libraries Present: AI as Author? New Considerations When Evaluating Sources.
In this workshop, librarian Christine Fena will review some ways AI is being integrated into published work within the worlds of news and scholarly publication, and discuss how this might impact how to evaluate and understand sources during the research process.
10/2 12:30-1:30 pm on Zoom.
Register via link: https://stonybrook.campuslabs.com/engage/event/10460202

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.

Speakers

Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division

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

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

Meeting ID: 161 528 9117
Passcode: 991382

Abstract: Language offers a uniquely powerful lens for understanding the mind: one that can access latent psychological realities often missed by traditional measurement tools. However, as language models expand their ability to capture semantics through context length, expansion into deeper levels of semantics is less explored, especially with respect to understanding cognitive patterns of authors. This dissertation proposes that we can uncover deeper cognitive and affective patterns that reflect more accurate underlying mental states by analyzing language at higher levels of discourse semantics and by modeling latent states.


First, the dissertation focuses on uncovering cognitive styles or thinking patterns manifesting in language. We demonstrate that modeling language at deeper semantic levels such as discourse relations, can unveil latent psychological states and traits, including cognitive styles that influence both mental health and behavior. Introducing a novel blend of transfer and active learning, we efficiently curated a new set of linguistic data on cognitive styles like dissonance. This approach allows for more precise measurement when dealing with rare-classes and low-resource tasks. As a second contribution, effective validation methods are introduced to language-based assessments of the underlying cognitive styles. Controlled behavioral experiments and online studies show that cognitive styles detected through linguistic signals reliably predict real-world behaviors such as decision-making and engagement with extremist communities, both at the individual and community levels, sometimes months in advance

The research further moves beyond traditional measurement tools like questionnaires and expert judgments, which rely on Classical Test Theory, by establishing that language-based assessments more closely approximate true psychological states. The mechanisms by which these assessments outperform standard tools are explained, highlighting their predictive power for behaviors linked to underlying traits. Finally, a more sophisticated approach is explored by modeling psychological outcomes with Item Response Theory (IRT), an improvement over Classical Test Theory. Adaptive language-based assessments are introduced, showing that targeted, adaptive testing based on latent IRT scores can efficiently and accurately capture multiple psychological dimensions.

Taken together, these contributions argue for a shift towards language-based psychological assessments. By integrating deeper discourse-level semantics with measurement theory, this dissertation charts a path towards truer scores of mental states: ones that are more precise, and reflective of the complexity of human cognition and emotions.

Speaker: Vasudha Varadarajan

https://stonybrook.zoom.us/j/99180374682?pwd=w2zZTkQsfunrBZhHgEweR54NjKabZ2.1&jst=2

Please join University Libraries on March 29 at 1:00 via Zoom as we welcome Dr. Zhang, SUNY Empire Innovation Professor at SBU's Power Lab. This lab is pioneering the research of coordinated networked microgrids (NMs) that can possibly help to restore neighboring distribution grids after a major blackout. That these NMs hold promise to significantly enhance the day-to-day reliability of the power grids, we are proud to host Dr. Zhang as a member of our STEM Speaker Series. Registration required.
https://library.stonybrook.edu/library-events/stem-speaker-series-ai-enabled-provably-resilient-networked-microgrids-with-dr-peng-zhang/