SCHEDULE
Vision-Language Modeling for Neuropathological Evaluation
by Lingyi Xu
Recent development in vision-language models has enabled flexible multimodal understanding and instruction-following. In this work, we introduce a vision-language framework for neuropathology that emphasizes diagnostic accuracy through visual QA. Without dense spatial supervision, this framework achieves accurate and reliable diagnostic decision making for a wide array of comorbid neuropathologies, offering a disease-agnostic approach for neuropathological evaluation.
Public Goods Games with Nonlinearities
by Gavin Rees
Public goods games are a model of many-player social dilemmas; we study these games from the perspective of evolutionary game theory, and particularly the evolution of cooperation and altruism. We introduce non-linearities to the benefit of the public good, finding that non-linearities have impacts on the relationship between resource inequality and evolutionary dynamics.
Western Pacific tropical cyclones over the past 500 years: when a deep-learning climate emulator meets a Chinese handwritten historical record
by Mu-Ting Chien
Digitized handwritten Chinese historical records REACHES show that tropical cyclone (TC) landfall frequency peaked in 1650-1680 AD over the past 500 years. However, the environmental conditions that lead to this peak remain unknown. This study uses a novel deep-learning climate emulator, ACE2, and a dynamical model, HiRAM, both forced with the last-millennium reconstructed sea surface temperatures and sea ice to uncover the large-scale climate states that drive the long-term variability in Western Pacific TC frequency and track. We find that simulated TC landfall frequency in East Asia also peaks in ACE2 during the 1650-1680 AD period, consistent with REACHES data. Furthermore, the seasonal cycle of Western Pacific TC activity has two peaks during this period, different from a single peak in the current climate, possibly associated with the shift from the East Asian monsoon to the South Asian monsoon. We investigate the large-scale circulation and environmental conditions that drive changes in TC genesis, track, and seasonal cycle over the past 500 years. Our lessons learned have implications for future changes in TC activities in the Western Pacific. Meanwhile, our work proposes a framework to investigate paleoclimate TCs by combining an AI global climate emulator with proxy data.
Union Busting and Workplace Resistance & What is Alt-Tech?
by Freddy Reiber & Tyler Calabrese
Freddy: Freddy will be talking about the role of technologies in union busting and future or workplace resistance.
Tyler: Tyler will be presenting on a literature review on the alt-right/alt-tech media ecosystem.
Evaluating Language Model Responses to Mental Health Symptom Disclosures & Survey of Predictive Recursive Algorithms for Inference
by Micah Benson & Clark Ikezu
Micah: We use depression and anxiety questionairres to build an evaluation dataset that simulates mental health symptoms disclosures by language model users. We analyze patterns in language model responses and explore how common jailbreaks change these behaviors.
Clark: There has been growing interest in Bayesian predictive inference. This talk will survey predictive recursive algorithms and other related stochastic approximation algorithms for inferring quantities of interest given noisy, (possibly partially) exchangeable observations from some unknown, underlying system.
The Usefulness of Interpretability
by Kevin Quinn
Kevin will be giving a talk on utilizing methods from mechanistic interpretability for safe data collection from scientific literature with LLMs
Stop the Nonconsensual Use of Nude Images in Research (Published at NeurIPS 2025 - Oral)
by Princessa Cintaqia
In order to train, test, and evaluate nudity detection models, machine learning researchers typically rely on nude images scraped from the Internet. Our research finds that this content is collected and, in some cases, subsequently distributed by researchers without consent, leading to potential misuse and exacerbating harm against the subjects depicted. We argue that the distribution of nonconsensually collected nude images by researchers perpetuates image-based sexual abuse and that the machine learning community should stop the nonconsensual use of nude images in research. To characterize the scope and nature of this problem, we conducted a systematic review of papers published in computing venues that collect and use nude images. Our results paint a grim reality: norms around the usage of nude images are sparse, leading to a litany of problematic practices like distributing and publishing nude images with uncensored faces, and intentionally collecting and sharing abusive content. We conclude with a call-to-action for publishing venues and a vision for research in nudity detection that balances user agency with concrete research objectives. You can check out the paper here: openreview.net/pdf?id=Ev5xwr3vWh