Institution profile

University of Texas at Austin

Academic institutionnorthamerica · us
Official website
Research library2,066linked papers
Opportunities0open roles
Selected work

Representative Papers

A Primer on Zadoff Chu Sequences

Nov 10, 2022arXiv.org

This paper addresses the widespread neglect by communication engineers of the critical role of Zadoff–Chu (ZC) sequences in cellular system overhead channels. We systematically uncover their universal design principles across LTE and 5G NR—spanning synchronization, random access, uplink control, sounding reference signals (SRS), and pilot design. Leveraging complex unit-magnitude modeling, DFT symmetry analysis, and rigorous derivation of zero autocorrelation and ideal cross-correlation properties, we establish the mathematical necessity and engineering superiority of ZC sequences over PN or Walsh sequences. For the first time, we unify the construction principles, time–frequency mapping rules, and robustness foundations of ZC sequences from a practical implementation perspective, bridging a long-standing gap between theoretical properties and physical-layer realization. The work provides a reusable theoretical framework and design guidelines for low-overhead, high-reliability sequence design in 6G systems.

13 citations1 influentialRead paper

From hanging out to figuring it out: Socializing online as a pathway to computational thinking

May 21, 2020New Media & Society

This study addresses a key challenge in educational platforms: effectively transforming adolescents’ online social interactions into opportunities for computational thinking development. Focusing on over 14,000 comments from the Scratch platform, the research introduces the novel concept of “participatory debugging,” wherein users cultivate computational thinking through collaborative troubleshooting. Employing a mixed-methods approach that integrates inductive analysis, content analysis, and longitudinal qualitative analysis, the study identifies three critical social antecedents that support this practice: sustained community engagement, identifiable problems, and topical permeability. The findings not only demonstrate the prevalence of participatory debugging but also establish a theoretical framework linking interest-driven social interaction to computational thinking learning, offering empirical grounding and design implications for creating socially engaging, learning-oriented platforms.

10 citations2 influentialRead paper

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Jan 17, 2026

This work addresses the challenge that existing AI agent benchmarks inadequately evaluate performance on real-world, complex, and long-horizon command-line tasks. To bridge this gap, the authors introduce a novel evaluation benchmark comprising 89 high-difficulty terminal tasks, all derived from authentic workflows and accompanied by isolated execution environments, human-authored reference solutions, and automated verification tests. The benchmark is designed to ensure realism, verifiability, and diversity, substantially narrowing the disparity between practical scenarios and current model evaluation paradigms. Experimental results demonstrate that even state-of-the-art agents achieve success rates below 65% on this benchmark. The paper further provides comprehensive error analysis and publicly releases the dataset and evaluation toolchain to support future research in this domain.

9 citations1 influentialRead paper

Separate Exchangeability as Modeling Principle in Bayesian Nonparametrics

Dec 14, 2021

This paper addresses the underutilization of separate exchangeability in Bayesian nonparametric (BNP) modeling, noting that existing partially exchangeable models—such as nested Dirichlet process variants—often neglect multidimensional experimental structures (e.g., matrix-valued data, multiple treatment groups), leading to misalignment between prior specification and experimental design. To resolve this, the paper introduces the first systematic BNP framework grounded in separate exchangeability, proposing two novel models: nested random partitioning and ANOVA-type dependent Dirichlet processes (ANOVA-DDP). Both explicitly encode hierarchical experimental structure, ensuring theoretical consistency between prior construction and statistical inference. Empirical evaluation on real-world datasets demonstrates substantial improvements in regression prediction accuracy and clustering interpretability. The proposed framework establishes the first BNP paradigm that simultaneously satisfies rigorous theoretical foundations and practical applicability for complex experimental designs.

8 citations2 influentialRead paper

Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs"Difficult"Downstream Tasks in LLMs

Sep 29, 2023

This work challenges the prevailing assumption that small-magnitude weights in large language models (LLMs) are redundant, proposing instead the “Junk DNA Hypothesis”: low-magnitude weights encode essential knowledge for solving difficult downstream tasks. Method: We conduct systematic magnitude-based pruning—both structured and unstructured—alongside multi-granularity task difficulty quantification (e.g., reasoning depth, distribution shift, generalization gap), validated across model scales (7B–70B) and diverse benchmarks (MMLU, GSM8K, HumanEval). Contribution/Results: Pruning induces irreversible, monotonic performance degradation strictly correlated with task difficulty—degradation persists even after extensive fine-tuning—whereas quantization exhibits no such effect. This is the first study to empirically establish the functional necessity of small-magnitude weights from a task-difficulty perspective. We further propose novel, quantifiable cross-task difficulty metrics and demonstrate a strong negative correlation between optimal pruning ratio and task difficulty.

7 citations1 influentialRead paper
Recent publications

Latest Papers