Institution profile

University of Minnesota

Academic institutionnorthamerica · us
Official website
Research library850linked papers
Opportunities0open roles
Selected work

Representative Papers

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples

Sep 07, 2022

This work systematically investigates the underexplored problem of adversarial robustness in Spiking Neural Networks (SNNs). We find that white-box attacks against SNNs heavily rely on surrogate gradient techniques and exhibit severely limited transferability of adversarial examples across architectures (e.g., between SNNs and ViTs/CNNs). To address this, we first uncover a strong coupling mechanism between SNNs’ adversarial vulnerability and surrogate gradient estimation. Building on this insight, we propose Auto-SAGA—a cross-architecture universal white-box attack method that jointly optimizes adaptive self-attention gradient estimation and surrogate gradient approximation. Evaluated on CIFAR-10, CIFAR-100, and ImageNet, Auto-SAGA achieves a 91.1% improvement in attack success rate on SNN-ViT ensembles and attains three times the effectiveness of Auto-PGD on adversarially trained SNN ensembles, significantly outperforming existing baselines.

13 citationsRead paper

Designing Health Technologies for Immigrant Communities: Exploring Healthcare Providers' Communication Strategies with Patients

Apr 25, 2025International Conference on Human Factors in Computing Systems

This study addresses the lack of clear mechanisms for effective communication between healthcare providers and culturally diverse immigrant patients in high-income countries—a gap that hinders the design of culturally appropriate health technologies. Through semi-structured interviews with 15 healthcare practitioners serving immigrant communities, the research systematically identifies four key cultural competence strategies: recognition, community engagement, incremental care, and adaptive communication. Building on these insights, the work proposes a contextualized and actionable design framework for health technologies tailored to immigrant populations. This framework offers human-computer interaction (HCI) researchers and practitioners principled guidance and practical implications for developing culturally sensitive digital health tools that meaningfully support cross-cultural care delivery.

4 citationsRead paper

AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science

May 25, 2025arXiv.org

Large language models (LLMs) struggle to critically leverage external domain knowledge in automated data science. Method: We introduce AssistedDS—the first benchmark for domain-knowledge-assisted evaluation—comprising synthetic and real Kaggle datasets paired with beneficial or adversarial domain documents. Our “interpretable synthesis + real-world scenarios” dual-track framework employs multi-stage prompting to assess end-to-end capabilities: document retrieval, knowledge filtering, code generation, and execution validation. Contribution/Results: We uncover a critical “blind adoption” flaw in LLMs: they fail significantly in time-series modeling, cross-fold consistency, and categorical variable handling. Experiments show state-of-the-art models suffer sharp performance degradation under adversarial documents; beneficial knowledge fails to mitigate harmful information, exposing severe deficiencies in domain knowledge discrimination and robust application.

3 citations1 influentialRead paper

Benchmarking GPT-5 for biomedical natural language processing

Aug 28, 2025arXiv.org

This study presents the first systematic evaluation of GPT-5’s zero- to five-shot generalization across a comprehensive biomedical NLP (BioNLP) multitask benchmark. We assess performance on six core tasks—named entity recognition, relation extraction, document classification, question answering, summarization, and text simplification—spanning 12 standard datasets. A unified prompt template, fixed decoding parameters, and standardized batched inference ensure fair comparison against GPT-4o and GPT-4. Results show that GPT-5 achieves a macro-average F1 of 0.557 in the five-shot setting, significantly surpassing prior models. It attains 94.1% accuracy on MedQA—exceeding the best supervised model by over 50 percentage points—and establishes new state-of-the-art results on chemical NER (F1 = 0.886) and ChemProt relation extraction (F1 = 0.616). This work provides critical empirical evidence on large language models’ domain-specific reasoning capabilities and few-shot adaptability in biomedicine.

3 citationsRead paper

Attribute-Based Robotic Grasping With Data-Efficient Adaptation

Jan 04, 2025IEEE Transactions on robotics

Rapid robotic grasping of unknown objects in cluttered scenes remains challenging—particularly under data-scarce conditions where labeled grasp annotations are limited. Method: This paper proposes an attribute-driven, end-to-end vision-language grasping framework. It introduces a novel object-attribute-guided cross-modal embedding learning mechanism, integrating gated attention fusion, self-supervised contrastive learning, and adversarial domain adaptation. Additionally, it designs two data-efficient transfer strategies: single-grasp adaptation and adversarial adaptive adaptation. Results: The method achieves over 81% instance-level grasping success on unseen objects in both simulation and real-world robotic platforms, requiring only a single demonstration grasp or unlabeled images for adaptation to new environments. It significantly outperforms state-of-the-art few-shot and transfer learning baselines, demonstrating robust generalization with minimal supervision.

3 citationsRead paper
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