Research
Peer-reviewed publications, granted patents, and academic work. Google Scholar lists 1,200+ citations and an h-index of 16; ~20 granted US patents as named inventor. A subset is shown below; the most up-to-date list is on Google Scholar.
- 2020Parallel Bayesian Global Optimization of Expensive Functions
Jialei Wang, Scott C. Clark, Eric Liu, Peter I. Frazier · Operations Research
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.
- 2016Bayesian Optimization for Machine Learning: A Practical Guidebook
Ian Dewancker, Michael McCourt, Scott C. Clark · arXiv preprint
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.
- 2016Evaluation System for a Bayesian Optimization Service
Ian Dewancker, Michael McCourt, Scott C. Clark, Patrick Hayes, Alexandra Johnson, George Ke · arXiv preprint
We describe the evaluation system used to benchmark and validate a production Bayesian optimization service, covering metrics, test functions, and evaluation methodologies.
- 2016A 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)
We propose a multi-criteria strategy for ranking optimization methods, enabling principled comparison across diverse benchmark problems and performance metrics.
- 2016A Stratified Analysis of Bayesian Optimization Methods
Ian Dewancker, Michael McCourt, Scott C. Clark, Patrick Hayes, Alexandra Johnson, George Ke · arXiv preprint
We present a stratified analysis of Bayesian optimization methods, comparing their performance across different problem types and dimensionalities to provide guidance for practitioners.
- 2015Adaptive Sequential Experimentation Techniques for A/B Testing and Model Tuning
Scott C. Clark · The Web Conference (WWW 2015)
We present adaptive sequential experimentation techniques that improve upon traditional A/B testing by dynamically allocating resources to the most promising alternatives.
- 2013ALE: 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
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.
- 2012Parallel Machine Learning Algorithms in Bioinformatics and Global Optimization
Scott C. Clark · PhD Dissertation, Cornell University
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.
- 2010Solving Genomic Jigsaws
Scott C. Clark · DEIXIS Magazine, DOE CSGF
A feature article in the DOE CSGF DEIXIS magazine describing computational approaches to genome assembly and the development of tools for evaluating assembly accuracy.
- 2008Left-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
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.