1 citations · 1 across the 8 of their papers we have counts for
8 papers
Population-Robust Feature Selection via Generalized Welfare Optimization
Ruiqi Lyu, Alistair Turcan, Bryan Wilder
Choosing which features to collect is a deployment decision: the same limited questionnaire, test panel, or sensor set may need to serve several heterogeneous populations. Standard…
SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
Ruiqi Lyu, Alistair Turcan, Bryan Wilder
Accurate epidemic forecasting is crucial for public health response, resource allocation, and outbreak intervention, but remains difficult with sparse, noisy, and highly non-statio…
SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
Shuaike Shen, Wenduo Cheng, Mingqian Ma +3
Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remain…
Combining digital data streams and epidemic networks for real time outbreak detection
Ruiqi Lyu, Alistair Turcan, Bryan Wilder
Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating informa…
Improving constraint-based discovery with robust propagation and reliable LLM priors
Ruiqi Lyu, Alistair Turcan, Martin Jinye Zhang +1
Learning causal structure from observational data is central to scientific modeling and decision-making. Constraint-based methods aim to recover conditional independence (CI) relat…
Predicting Language Models' Success at Zero-Shot Probabilistic Prediction
Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño +7
Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or…
TusoAI: Agentic Optimization for Scientific Methods
Alistair Turcan, Kexin Huang, Lei Li +1
Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such tools is costly and time-consuming…
Explaining Concept Shift with Interpretable Feature Attribution
Ruiqi Lyu, Alistair Turcan, Bryan Wilder
Concept shift occurs when the distribution of labels conditioned on the features changes between domains, which can make even a well-tuned ML model miscalibrated on a new domain. I…