Pearl Pu facts for kids
Quick facts for kids
Pearl Pu
|
|
|---|---|
Pu in 2021
|
|
| Born |
Shanghai, China
|
| Nationality | Swiss |
| Alma mater | University of Pennsylvania |
| Scientific career | |
| Fields | Recommender system, Conversational AI, Human Computer Interaction, Artificial Intelligence, Ethics of Artificial Intelligence |
| Institutions | École Polytechnique Fédérale de Lausanne (EPFL) |
| Doctoral advisor | Norman Badler |
Pearl Pu is a computer scientist from Switzerland, born in China. She earned her Ph.D. from the University of Pennsylvania. Before moving to Switzerland, she received an important award called the Career Award from NSF.
She works as a senior scientist at École Polytechnique Fédérale de Lausanne (EPFL). In 2000, she started a special group there. This group studies how people and computers work together. In 1997, she helped start a company called Iconomic Systems SA. This company created new ways for people to buy travel online. She led the company until it was sold in 2001. She also spent time as a visiting researcher at Stanford University and the Hong Kong University of Science and Technology.
Contents
How Pearl Pu Works with AI
Pearl Pu is well-known for her work on how humans interact with Artificial Intelligence (AI) systems. She started by using a method called case-based reasoning for smart Computer-aided design. This helped computers learn from past examples.
Her research showed how to use pictures and explanations. This helps people understand and trust AI systems better. She also created new ways to find out what users like. This was done through talking with recommender systems. She developed a special method called example-critiquing.
Pearl Pu also showed how recommender systems can help people. They can suggest actions to encourage healthier lifestyles.
Evaluating Recommender Systems
One of Pearl Pu's most important works is about how to check if recommender systems are good for people. She created a model called ResQue in 2010. This model helps evaluate how well these systems work from a user's point of view.
Her research papers have been cited many times by other scientists. This means her work is very important in the field.
Pearl Pu's Leadership in Science
Pearl Pu has been part of many important science groups. She has served on editorial boards for journals. She has also helped organize big conferences. These include the International Joint Conference on Artificial Intelligence and the AAAI Conference on Artificial Intelligence.
She has been a main organizer for conferences like the ACM conferences. These include meetings on Electronic Commerce and Recommender Systems. She helps guide discussions and research in these areas.
Awards and Recognition
In 2014, Pearl Pu won a French award called the "2030 World Innovation Challenge." She won it for her project called Livelyplanet. Her technology was also featured in French newspapers.
In 2021, she was named a fellow of EurAI. This is a special honor for experts in Artificial Intelligence. She was also named a distinguished speaker by the Association for Computing Machinery. This means she is recognized as a top speaker in her field.
Selected Publications
Here are some examples of Pearl Pu's important research papers:
- Evaluating Recommender Systems:
* "A user-centric evaluation framework for recommender systems" (with Li Chen and Rong Hu), Proceedings of the fifth ACM Conference on Recommender Systems, 2011. This paper describes the ResQue framework.
- Intelligent Design and Case-Based Reasoning:
* "Issues and Applications of Case-Based Reasoning to Design" (with Mary-Lou Maher), Psychology Press, 1997. This book explores how computers can learn from past design examples. * "COMPOSER: A case-based reasoning system for engineering design" (with Lisa Purvis), Robotica 16(3), 1998. This paper describes a system that uses past cases to help with engineering designs.
- Humans and AI Through Explanation:
* "Trust-inspiring explanation interfaces for recommender systems" (with Li Chen), Knowledge-Based Systems (journal) 20(6), 2007. This paper looks at how to make AI systems more trustworthy by explaining their suggestions.
- Interactive Recommender Systems:
* "Preference-based search using example-critiquing with suggestions" (with Paolo Viappiani and Boi Faltings), Journal of Artificial Intelligence Research 27, 2006. This paper discusses how users can refine their preferences by critiquing examples.
- Behavior Recommender Systems:
* "HealthyTogether: exploring social incentives for mobile fitness applications" (with Yu Chen), Proceedings of the Second International Symposium of Chinese CHI, 2014. This paper explores how social features can encourage healthy habits through apps. * "Can Fitness Trackers Help Diabetic and Obese Users Make and Sustain Lifestyle Changes?" (with Yu Chen and Mirana Randriambelonoro), IEEE COMPUTER 50(3), 2017. This research looks at how fitness trackers can help people with health conditions make lasting changes.
