203 citations · 224 across the 17 of their papers we have counts for
9 papers · 1 filter
Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models
Zehao Liu, Weijieying Ren, Jipeng Zhang +4
Vision--language models (VLMs) have recently shown promise for assisting clinical reasoning in dermatological diagnosis. However, their trustworthiness and clinical utility remain…
CombiGraph-Vis: A Curated Multimodal Olympiad Benchmark for Discrete Mathematical Reasoning
Hamed Mahdavi, Pouria Mahdavinia, Alireza Farhadi +7
State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 o…
RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
Hamed Mahdavi, Pouria Mahdavinia, Samira Malek +7
State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 o…
A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models
Weijieying Ren, Jingxi Zhu, Zehao Liu +2
Artificial intelligence (AI) has demonstrated significant potential in transforming healthcare through the analysis and modeling of electronic health records (EHRs). However, the i…
DiaLLMs: EHR Enhanced Clinical Conversational System for Clinical Test Recommendation and Diagnosis Prediction
Weijieying Ren, Tianxiang Zhao, Lei Wang +2
Recent advances in Large Language Models (LLMs) have led to remarkable progresses in medical consultation. However, existing medical LLMs overlook the essential role of Electronic…
Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics
Hamed Mahdavi, Alireza Hashemi, Majid Daliri +6
Recent advances in large language models (LLMs) have shown impressive progress in mathematical reasoning tasks. However, current evaluation benchmarks predominantly focus on the ac…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions
Weijieying Ren, Tianxiang Zhao, Yuqing Huang +1
Tabular data remains one of the most prevalent data types across a wide range of real-world applications, yet effective representation learning for this domain poses unique challen…
C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference
Abhishek Dalvi, Neil Ashtekar, Vasant Honavar
We address the problem of estimating causal effects from observational data in the presence of network confounding, a setting where both treatment assignment and observed outcomes…