Pang Wei Koh (original) (raw)

I'm interested in making machine learning systems more useful, responsible, and reliable in the real world. For example:

I received my PhD in Computer Science from Stanford, advised by Percy Liang. Before that, I was the 3rd employee and Director of Partnerships at Coursera. I was also an undergraduate at Stanford, advised by Andrew Ng and Daphne Koller.

I'm part of the UW ML and NLP groups, and I'm also a visiting research scientist at AI2. If you're interested in joining our group, please read this. This cycle, I'm also looking for prospective students/postdocs interested in AI for science.

Current students

Publications

* = equal contribution.

DataDecide: How to predict best pretraining data with small experiments

Ian Magnusson, Nguyen Tai, Ben Bogin, David Heineman, Jena D Hwang, Luca Soldaini, Akshita Bhagia, Jiacheng Liu, Dirk Groeneveld, Oyvind Tafjord, Noah A Smith, Pang Wei Koh, and Jesse Dodge

arXiv 2025

OLMoTrace: Tracing language model outputs back to trillions of training tokens

Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, YenSung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Cheng, Karen Farley, Sruthi Sreeram, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, and Jesse Dodge

arXiv 2025

EvalTree: Profiling language model weaknesses via hierarchical capability trees

arXiv 2025

ParaPO: Aligning language models to reduce verbatim reproduction of pre-training data

Tong Chen, Faeze Brahman, Jiacheng Liu, Niloofar Mireshghallah, Weijia Shi, Pang Wei Koh, Luke Zettlemoyer, and Hannaneh Hajishirzi

arXiv 2025

Large-scale data selection for instruction tuning

Hamish Ivison, Muru Zhang, Faeze Brahman, Pang Wei Koh, and Pradeep Dasigi

arXiv 2025

S4S: Solving for a diffusion model solver

Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J Ratliff, and Sewoong Oh

arXiv 2025

2 OLMo 2 Furious

Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, Nathan Lambert, Dustin Schwenk, Oyvind Tafjord, Taira Anderson, David Atkinson, Faeze Brahman, Christopher Clark, Pradeep Dasigi, Nouha Dziri, Michal Guerquin, Hamish Ivison, Pang Wei Koh, Jiacheng Liu, Saumya Malik, William Merrill, Lester James V. Miranda, Jacob Morrison, Tyler Murray, Crystal Nam, Valentina Pyatkin, Aman Rangapur, Michael Schmitz, Sam Skjonsberg, David Wadden, Christopher Wilhelm, Michael Wilson, Luke Zettlemoyer, Ali Farhadi, Noah A. Smith, and Hannaneh Hajishirzi

arXiv 2025

Metabolically purified human stem cell-derived hepatocytes reveal distinct effects of Ebola and Lassa viruses

Joseph B Prescott, Kevin J Liu, Angelika Lander, Nicole Min Qian Pek, Sawan Kumar Jha, Marcel Bokelmann, Manali Begur, Pang Wei Koh, Henry Yang, Bing Lim, Kristy Red-Horse, Irving L Weissman, Kyle M Loh, Lay Teng Ang

bioRxiv 2025

OLMoE: Open Mixture-of-Experts language models

Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A Smith, Pang Wei Koh, Amanpreet Singh, and Hannaneh Hajishirzi

ICLR 2025

Language models scale reliably with over-training and on downstream tasks

Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, Rui Xin, Marianna Nezhurina, Igor Vasiljevic, Jenia Jitsev, Luca Soldaini, Alexandros G. Dimakis, Gabriel Ilharco, Pang Wei Koh, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, and Ludwig Schmidt

ICLR 2025

Group-robust sample reweighting for subpopulation shifts via influence functions

Rui Qiao, Zhaoxuan Wu, Jingtan Wang, Pang Wei Koh, and Bryan Kian Hsiang Low

ICLR 2025

Use large language models to promote equity

Emma Pierson*, Divya Shanmugam*, Rajiv Movva*, Jon Kleinberg*, Monica Agrawal, Mark Dredze, Kadija Ferryman, Judy Wawira Gichoya, Dan Jurafsky, Pang Wei Koh, Karen Levy, Sendhil Mullainathan, Ziad Obermeyer, Harini Suresh, and Keyon Vafa

NEJM AI 2025

ICONS: Influence Consensus for Vision-Language Data Selection

Xindi Wu, Mengzhou Xia, Rulin Shao, Zhiwei Deng, Pang Wei Koh, and Olga Russakovsky

arXiv 2025

Establishing task scaling laws via compute-efficient model ladders

Akshita Bhagia, Jiacheng Liu, Alexander Wettig, David Heineman, Oyvind Tafjord, Ananya Harsh Jha, Luca Soldaini, Noah A Smith, Dirk Groeneveld, Pang Wei Koh, Jesse Dodge, and Hannaneh Hajishirzi

arXiv 2024

Negative Token Merging: Image-based adversarial feature guidance

Jaskirat Singh, Lindsey Li, Weijia Shi, Ranjay Krishna, Yejin Choi, Pang Wei Koh, Michael F Cohen, Stephen Gould, Liang Zheng, and Luke Zettlemoyer

arXiv 2024

OpenScholar: Synthesizing scientific literature with retrieval-augmented LMs

Akari Asai, Jacqueline He*, Rulin Shao*, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D'arcy, David Wadden, Matt Latzke, Minyang Tian, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Dan Weld, Doug Downey, Wen-tau Yih, Pang Wei Koh, and Hannaneh Hajishirzi

arXiv 2024

Scaling retrieval-based language models with a trillion-token datastore

Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, and Pang Wei Koh

NeurIPS 2024

The unmet promise of synthetic training images: Using retrieved real images performs better

Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan, Matthew Wallingford, Chun-Liang Li, Pang Wei Koh, and Ranjay Krishna

NeurIPS 2024

MEDIQ: Question-asking LLMs for adaptive and reliable clinical reasoning

Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan Ilgen, Emma Pierson, Pang Wei Koh, and Yulia Tsvetkov

NeurIPS 2024

Uncertainty of Thoughts: Uncertainty-aware planning enhances information seeking in large language models

Zhiyuan Hu, Chumin Liu, Xidong Feng, Yilun Zhao, See-Kiong Ng, Anh Tuan Luu, Junxian He, Pang Wei Koh, and Bryan Hooi

NeurIPS 2024

Multilingual diversity improves vision-language representations

Thao Nguyen, Matthew Wallingford, Sebastin Santy, Wei-Chiu Ma, Sewoong Oh, Ludwig Schmidt, Pang Wei Koh, and Ranjay Krishna

NeurIPS 2024

DataComp-LM: In search of the next generation of training sets for language models

Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, and Vaishaal Shankar

NeurIPS (Datasets and Benchmarks Track) 2024

JPEG-LM: LLMs as image generators with canonical codec representations

Xiaochuang Han, Marjan Ghazvininejad, Pang Wei Koh, and Yulia Tsvetkov

arXiv 2024

Exploring how generative MLLMs perceive more than CLIP with the same vision encoder

Siting Li, Pang Wei Koh, and Simon Shaolei Du

arXiv 2024

MoSH: Modeling multi-objective tradeoffs with soft and hard bounds

Edward Chen, Natalie Dullerud, Thomas Niedermayr, Elizabeth Kidd, Ransalu Senanayake, Pang Wei Koh, Sanmi Koyejo, and Carlos Guestrin

arXiv 2024

CopyBench: Measuring literal and non-literal reproduction of copyright-protected text in language model generation

Tong Chen, Akari Asai, Niloofar Mireshghallah, Sewon Min, James Grimmelmann, Yejin Choi, Hannaneh Hajishirzi, Luke Zettlemoyer, and Pang Wei Koh

EMNLP 2024

Data-centric AI in the age of large language models

Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, and Bryan Kian Hsiang Low

EMNLP Findings 2024

Merge to Learn: Efficiently adding skills to language models with model merging

Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Pang Wei Koh, Jesse Dodge, Pradeep Dasigi

EMNLP Findings 2024

Annotation alignment: Comparing LLM and human annotations of conversational safety

Rajiv Movva, Pang Wei Koh, and Emma Pierson

EMNLP 2024

Information-theoretic distillation for reference-less summarization

Jaehun Jung, Ximing Lu, Liwei Jiang, Faeze Brahman, Peter West, Pang Wei Koh, and Yejin Choi

COLM 2024

PLeaS--Merging models with permutations and least squares

Anshul Nasery, Jonathan Hayase, Pang Wei Koh, and Sewoong Oh

arXiv 2024

Using unlabeled data to enhance fairness of medical AI

Rajiv Movva, Pang Wei Koh, and Emma Pierson

Nature Medicine 2024

Reliable, adaptable, and attributable language models with retrieval

Akari Asai, Zexuan Zhong, Danqi Chen, Pang Wei Koh, Luke Zettlemoyer, Hannaneh Hajishirzi, and Wen-tau Yih

arXiv 2024

Instructional fingerprinting of large language models

Jiashu Xu, Fei Wang, Mingyu Derek Ma, Pang Wei Koh, Chaowei Xiao, and Muhao Chen

NAACL 2024

The generative AI paradox: "What it can create, it may not understand"

Peter West*, Ximing Lu*, Nouha Dziri*, Faeze Brahman*, Linjie Li*, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, and Yejin Choi

ICLR 2024

Leveraging domain relations for domain generalization

Huaxiu Yao*, Xinyu Yang*, Xinyi Pan, Shengchao Liu, Pang Wei Koh, and Chelsea Finn

ICLR 2024

Impossibility theorems for feature attribution

Blair Bilodeau, Natasha Jaques, Pang Wei Koh, and Been Kim

Proceedings of the National Academy of Sciences (PNAS) 2024

Retrieval-based language models using a multi-domain datastore

Rulin Shao, Sewon Min, Luke Zettlemoyer, and Pang Wei Koh

NeurIPS Workshop on Distributution Shifts (DistShift) 2023

OpenFlamingo: An open-source framework for training large autoregressive vision-language models

Anas Awadalla*, Irena Gao*, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, and Ludwig Schmidt

arXiv 2023

FActScore: Fine-grained atomic evaluation of factual precision in long form text generation

Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi

EMNLP 2023

DataComp: In search of the next generation of multimodal datasets

Samir Yitzhak Gadre*, Gabriel Ilharco*, Alex Fang*, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, and Ludwig Schmidt

NeurIPS (Datasets and Benchmarks Track) 2023

Proximity-informed calibration for deep neural networks

Miao Xiong, Ailin Deng, Pang Wei Koh, Jiaying Wu, Shen Li, Jianqing Xu, and Bryan Hooi

NeurIPS 2023

Are aligned neural networks adversarially aligned?

Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Anas Awadalla, Pang Wei Koh, Daphne Ippolito, Katherine Lee, Florian Tramer, and Ludwig Schmidt

NeurIPS 2023

On the trade-off of intra-/inter-class diversity for supervised pre-training

Jieyu Zhang*, Bohan Wang*, Zhengyu Hu, Pang Wei Koh, and Alexander Ratner

NeurIPS 2023

Out-of-distribution robustness via targeted augmentations

Irena Gao*, Shiori Sagawa*, Pang Wei Koh, Tatsunori Hashimoto, and Percy Liang

ICML 2023

Wild-Time: A benchmark of in-the-wild distribution shift over time

Huaxiu Yao*, Caroline Choi*, Yoonho Lee, Pang Wei Koh, and Chelsea Finn

NeurIPS (Datasets and Benchmarks Track) 2022

Extending the WILDS benchmark for unsupervised adaptation

Shiori Sagawa*, Pang Wei Koh*, Tony Lee*, Irena Gao*, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, and Percy Liang

ICLR 2022

WILDS: A benchmark of in-the-wild distribution shifts

Pang Wei Koh*, Shiori Sagawa*, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang

ICML 2021

Just Train Twice: Improving group robustness without training group information

Evan Zheran Liu*, Behzad Haghgoo*, Annie S. Chen*, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn

ICML 2021

Accuracy on the line: On the strong correlation between out-of-distribution and in-distribution generalization

John Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt

ICML 2021

Supporting COVID-19 policy response with large-scale mobility-based modeling

Serina Chang, Mandy L. Wilson, Bryan Lewis, Zakaria Mehrab, Komal K. Dudakiya, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, Madhav Marathe, Jure Leskovec

KDD (Applied Data Science track) 2021

On the opportunities and risks of foundation models

Rishi Bommasani, Drew A. Hudson, ..., Pang Wei Koh, ..., and Percy Liang (116 authors, alphabetical within ellipses)

arXiv 2021

Selective classification can magnify disparities across groups

Erik Jones*, Shiori Sagawa*, Pang Wei Koh*, Ananya Kumar, and Percy Liang

ICLR 2021

Stronger data poisoning attacks break data sanitization defenses

Pang Wei Koh*, Jacob Steinhardt*, and Percy Liang

Machine Learning 2021

Mobility network models of COVID-19 explain inequities and inform reopening

Serina Y Chang*, Emma Pierson*, Pang Wei Koh*, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec

Nature 2021

Concept bottleneck models

Pang Wei Koh*, Thao Nguyen*, Yew Siang Tang*, Steve Mussmann, Emma Pierson, Been Kim, and Percy Liang

ICML 2020

An investigation of why overparameterization exacerbates spurious correlations

Shiori Sagawa*, Aditi Raghunathan*, Pang Wei Koh*, and Percy Liang

ICML 2020

ExpBERT: Representation engineering with natural language explanations

Shikhar Murty, Pang Wei Koh, and Percy Liang

ACL 2020

Toward trustworthy AI development: Mechanisms for supporting verifiable claims

Miles Brundage*, Shahar Avin*, Jasmine Wang*, Haydn Belfield*, Gretchen Krueger*, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, ..., Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, and Markus Anderljung

arXiv 2020

Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization

Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang

ICLR 2020

On the accuracy of influence functions for measuring group effects

Pang Wei Koh*, Kai-Siang Ang*, Hubert H. K. Teo*, and Percy Liang

NeurIPS 2019

Temporal FiLM: Capturing long-range sequence dependencies with feature-wise modulations

Sawyer Birnbaum*, Volodymyr Kuleshov*, Zayd Enam, Pang Wei Koh, Stefano Ermon

NeurIPS 2019

Inferring multi-dimensional rates of aging from cross-sectional data

Emma Pierson*, Pang Wei Koh*, Tatsunori B. Hashimoto*, Daphne Koller, Jure Leskovec, Nicholas Eriksson, and Percy Liang

AISTATS 2019

Certified defenses for data poisoning attacks

Jacob Steinhardt*, Pang Wei Koh*, and Percy Liang

NeurIPS 2017

Understanding black-box predictions via influence functions

Pang Wei Koh and Percy Liang

ICML 2017

Localized hepatic lobular regeneration by central-vein-associated lineage-restricted progenitors

Jonathan M. Tsai, Pang Wei Koh, Ania Stefanska, Liujing Xing, Graham G. Walmsley, Nicolas Poux, Irving L. Weissman, and Yuval Rinkevich

Proceedings of the National Academy of Sciences (PNAS) 2017

An atlas of transcriptional, chromatin accessibility, and surface marker changes in human mesoderm development

Pang Wei Koh*, Rahul Sinha*, Amira A. Barkal, Rachel M. Morganti, Angela Chen, Irving L. Weissman, Lay Teng Ang, Anshul Kundaje, and Kyle M. Loh

Scientific Data 2016

Mapping the pairwise choices leading from pluripotency to human bone, heart, and other mesoderm cell types

Kyle M. Loh*, Angela Chen*, Pang Wei Koh, Tianda Z. Deng, Rahul Sinha, Jonathan M. Tsai, Amira A. Barkal, Kimberle Y. Shen, Rajan Jain, Rachel M. Morganti, Ng Shyh-Chang, Nathaniel B. Fernhoff, Benson M. George, Gerlinde Wernig, Rachel E.A. Salomon, Zhenghao Chen, Hannes Vogel, Jonathan A. Epstein, Anshul Kundaje, William S. Talbot, Philip A. Beachy, Lay Teng Ang, and Irving L. Weissman

Cell 2016

Denoising genome-wide histone ChIP-seq with convolutional neural networks

Pang Wei Koh*, Emma Pierson*, and Anshul Kundaje

Intelligent Systems for Molecular Biology (ISMB) / Bioinformatics 2017

Dissecting an online intervention for cancer survivors

Zhenghao Chen, Pang Wei Koh, Philip L. Ritter, Kate Lorig, Erin O'Carroll Bantum, and Suchi Saria

Health Education & Behavior 2014

Peer and self assessment in massive online classes

Chinmay Kulkarni, Pang Wei Koh, Huy Le, Daniel Chia, Kathryn Papadopoulos, Justin Cheng, Daphne Koller, and Scott Klemmer

ACM Transactions on Computer-Human Interaction 2013

Identifying genetic drivers of cancer morphology

Pang Wei Koh, Andrew Beck, and Daphne Koller.

Undergraduate honors thesis 2012

Sparse filtering

Jiquan Ngiam, Pang Wei Koh, Zhenghao Chen, Sonia Bhaskar, and Andrew Y. Ng

NeurIPS 2011

Learning deep energy models

Jiquan Ngiam, Zhenghao Chen, Pang Wei Koh, and Andrew Y. Ng

ICML 2011

On random weights and unsupervised feature learning

Andrew Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y. Ng

ICML 2011

Tiled convolutional neural networks

Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang Wei Koh, and Andrew Y. Ng

NeurIPS 2010

Lower bound on the time complexity of local adiabatic evolution

Zhenghao Chen, Pang Wei Koh, and Zhao Yan

Physical Review A 2006