Mononito Goswami

@mononitogoswami.bsky.social

Ph.D. Student at Carnegie Mellon, Student Research at Google Formerly Applied Science Intern Amazon, Undergrad at Delhi Technological University 📈 Foundation Models for Structured Data (Time Series, Tabular), applications in healthcare.

All AI evaluations embed assumptions about what "good" behavior looks like. Our #FAccT2026 paper explores how we can center the perspectives of impacted communities - specifically, subjects of AI-generated media - in designing LLM-as-a-judge evaluation rubrics.

A flyer teasing our research paper. The flyer contains a visualization of our approach: First, community members participate in creating an evaluation rubric, visualized as a list of criteria. The rubric is then given as input to an LLM judge.

🚀 Interested in time series 📈📉 and tabular data? Then our @icmlconf.bsky.social’25 Workshop on Foundation Models for Structured Data is exactly what you are looking for 😁! Send us your best work and join us in Vancouver 🇨🇦🍁

Nick Erickson@nickerickson.bsky.social · last yr.

We are excited to announce #FMSD: "1st Workshop on Foundation Models for Structured Data" has been accepted to #ICML 2025! Call for Papers: icml-structured-fm-workshop.github.io

🚀 Excited to share that MOMENT has been downloaded 1.3M+ times on HuggingFace, with over 280K downloads in just the last month! MOMENT is the BERT for time series. It can solve many tasks across different domains, including healthcare! Best part? It's completely open source!

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Excited to be at NeurIPS'24, where I'll be presenting at several workshops! Looking forward to chatting about (time series & tabular) foundation models, data science agents, or ML for healthcare! Also, I'm also on the industry job market, looking forward to connect 😁!

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Excited to be at NeurIPS'24, where I'll be presenting at several workshops! Looking forward to chatting about (time series & tabular) foundation models, data science agents, or ML for healthcare! Also, I'm also on the industry job market, looking forward to connect 😁!

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The question that a reviewer should ask themselves is: Does this paper take a gradient step in a promising direction? Is the community better off with this paper published? If the answer is yes, then the recommendation should be to accept.

Medically adapted foundation models (think Med-*) turn out to be more hot air than hot stuff. Correcting for fatal flaws in evaluation, the current crop are no better on balance than generic foundation models, even on the very tasks for which benefits are claimed. arxiv.org/abs/2411.04118

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pret...

arxiv.org