# Publications — Scott Clark

1,200+ citations · h-index 16 · Google Scholar: https://scholar.google.com/citations?user=mwWbhAUAAAAJ

### Parallel Bayesian Global Optimization of Expensive Functions

Jialei Wang, **Scott C. Clark**, Eric Liu, Peter I. Frazier. *Operations Research*, 2020.

[Link](https://pubsonline.informs.org/doi/10.1287/opre.2019.1966)

DOI: 10.1287/opre.2019.1966

We develop parallel Bayesian global optimization methods for expensive black-box functions, providing both theoretical analysis and practical algorithms that enable efficient optimization across multiple parallel evaluations.

### Bayesian Optimization for Machine Learning: A Practical Guidebook

Ian Dewancker, Michael McCourt, **Scott C. Clark**. *arXiv preprint*, 2016.

[Link](https://arxiv.org/abs/1612.04858)

We present a practical guide to Bayesian optimization for machine learning practitioners, covering the core concepts, common pitfalls, and best practices for hyperparameter tuning and model selection.

### Evaluation System for a Bayesian Optimization Service

Ian Dewancker, Michael McCourt, **Scott C. Clark**, Patrick Hayes, Alexandra Johnson, George Ke. *arXiv preprint*, 2016.

[Link](https://arxiv.org/abs/1605.06170)

We describe the evaluation system used to benchmark and validate a production Bayesian optimization service, covering metrics, test functions, and evaluation methodologies.

### A Strategy for Ranking Optimization Methods using Multiple Criteria

Ian Dewancker, Michael McCourt, **Scott C. Clark**, Patrick Hayes, Alexandra Johnson, George Ke. *AutoML Workshop at ICML 2016 (PMLR Vol. 64)*, 2016.

[Link](https://proceedings.mlr.press/v64/dewancker_strategy_2016.html)

We propose a multi-criteria strategy for ranking optimization methods, enabling principled comparison across diverse benchmark problems and performance metrics.

### A Stratified Analysis of Bayesian Optimization Methods

Ian Dewancker, Michael McCourt, **Scott C. Clark**, Patrick Hayes, Alexandra Johnson, George Ke. *arXiv preprint*, 2016.

[Link](https://arxiv.org/abs/1603.09441)

We present a stratified analysis of Bayesian optimization methods, comparing their performance across different problem types and dimensionalities to provide guidance for practitioners.

### Adaptive Sequential Experimentation Techniques for A/B Testing and Model Tuning

**Scott C. Clark**. *The Web Conference (WWW 2015)*, 2015.

DOI: 10.1145/2740908.2743063

We present adaptive sequential experimentation techniques that improve upon traditional A/B testing by dynamically allocating resources to the most promising alternatives.

### ALE: A Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies

**Scott C. Clark**, Rob Egan, Peter I. Frazier, Zhong Wang. *Bioinformatics*, 2013.

[Link](https://academic.oup.com/bioinformatics/article/29/4/435/199222)

DOI: 10.1093/bioinformatics/bts723

We present ALE, a generic assembly likelihood evaluation framework that assesses the accuracy of genome and metagenome assemblies using a probabilistic model of read placement, providing a reference-free quality metric.

### Parallel Machine Learning Algorithms in Bioinformatics and Global Optimization

**Scott C. Clark**. *PhD Dissertation, Cornell University*, 2012.

This thesis develops parallel machine learning algorithms for two domains: bioinformatics (genome assembly evaluation) and global optimization (Bayesian optimization with parallel evaluations), with applications to real-world computational problems.

### Solving Genomic Jigsaws

**Scott C. Clark**. *DEIXIS Magazine, DOE CSGF*, 2010.

[Link](https://www.krellinst.org/csgf/profile/clark2008)

A feature article in the DOE CSGF DEIXIS magazine describing computational approaches to genome assembly and the development of tools for evaluating assembly accuracy.

### Left-Handed Beta Helix Models for Mammalian Prion Fibrils

K. Kunes, **Scott C. Clark**, Daniel L. Cox, Rajiv R. P. Singh. *Prion 2(2):81–90*, 2008.

[Link](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2634523/)

DOI: 10.4161/pri.2.2.7059

Statistical analysis of left-handed beta helix structural models for mammalian prion protein fibrils, applying computational biophysics methods to study protein misfolding. Output of an NSF REU at UC Davis under Prof. Daniel Cox.

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Source: https://scottclark.io/publications