Seminar

Dietary Assessment and Interventions using AI, Wearables and LLM
Dietary Assessment and Interventions using AI, Wearables and LLM

This T32 Spring 2026 seminar covers AI-assisted dietary assessment and intervention pipelines using food photos, continuous glucose monitoring, wristband wearables, counterfactual AI, and LLM-RAG meal recommendations.

May 5, 2026
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

MOIRAI-MOE emphasizes that feature specific specialization in timeseries foundation model cannot help generalize input signals and is an hindrance in zero-shot prompting. MOIRAI-MOE underscores that processing mut be done at token level inside the transformer modules with a mixture of experts (FFNs) and a gating algorithm to decide which togen goes to which expert.

Feb 11, 2026
Chronos-2: From Univariate to Universal Forecasting
Chronos-2: From Univariate to Universal Forecasting

Chronos 2 is the first ever multivariate time-series forecasting foundation model. It leverages time attention (across time axis) and group attention (across different signals within group) to predict quantiles instead of point estimates.

Dec 17, 2025
UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting
UniCast: A Unified Multimodal Prompting Framework for Time Series Forecasting

Unicast is a timeseries forecasting foundation model. It leverages multi-modal data (visual and textual information) from a timeseries through embedding generators and aligns the output of the embedders to forecasting task using parameter efficient soft prompting.

Oct 8, 2025
A decoder-only foundation model for time-series forecasting
A decoder-only foundation model for time-series forecasting

TimesFM is a decoder-only foundation model trained in a supervised way. It has a very high out-of-the-box zero-shot performance and can be used with any context size.

Aug 27, 2025
A General Search-Based Framework for Generating Textual Counterfactual
A General Search-Based Framework for Generating Textual Counterfactual

This paper runs a 48-week long clinical trial with Type 2 diabetic patients to assess the effectiveness of a digital health platform .

Jul 2, 2025
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction

LEAD is an explainable AI approach that perturbs synthetic critical samples to generate consistent, sparse and robust explanations for disease classification.

May 28, 2025
A General Search-Based Framework for Generating Textual Counterfactual
A General Search-Based Framework for Generating Textual Counterfactual

This paper is a search based but fast counterfactual generation method for textual data. It proposes three operators to conduct the search in an anytime algorithm. Therefore, the more time it gets the better qulaity countefactual it delivers.

May 21, 2025
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers

This paper introduces new constraints in the optimization method to generate realistic and feasible counterfactual explanations.

Feb 26, 2025
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments
Towards Unifying Evaluation of Counterfactual Explanations: Leveraging Large Language Models for Human-Centric Assessments

Since evaluation of counterfactual explanations is a difficult task, his paper tries to automate the process by fine-tuning large language models (LLMs) on human ratings for counterfactuals.

Jan 15, 2025