HUNG CHAU's blog

1. They don’t work in their comfort zone. 2. They don’t do without first learning. 3. They don’t fear asking for advice. 4. They don’t get lost in the small details. 5. They don’t multitask. 6. They don’t lie to themselves. 7. They don’t procrastinate in asking for feedback. 8. They don’t follow, they lead. 9. They don’t let the past dictate their future. 10. They don’t hang around negative people. http://www.lifehack.org/articles/productivity/10-habits-successful-people-give-increase-their-productivity.html

Shuting Wang, 5th year PhD student, from Computer Science department, Penn State University had the talk "Concept Map Extraction from Textbooks" on Dec 05, 2016. In the talk, she presented three parts of her work: extracting concept hierarchy from textbook, using prerequisite to extract concept maps from textbook, and using the concept maps for automatic assessment. First of all, Shuting talked about how to extract concept hierarchy from textbook. In her work, she extracted important concepts in each book chapter using Wikipedia as a resource and then construct a concept hierarchy for that book. She presented the process to construct a concept hierarchy as follows: Build a concept dictionary from Wikipedia entities related to the topic of the book. Select concept candidates in the concept dictionary based on title and content similarity between a section (subchapter) in the book and Wikipedia articles. Construct the concept hierarchy from the table of content order of t...

Dr. Pavel Dmitriev, a Principal Data Scientist, from Microsoft's Analysis and Experimentation team had a talk about "A/B Testing at Scale" on Thursday, 2016/12/08. The talk was about the introduction of a controlled experiment, four real experiments that Microsoft had been running, and 5 challenges about testing at scale. Dr. Pavel started the talk with a brief introduction of controlled experiments, aka A/B tests. A/B testing is a method of comparing two versions of a webpage or app against each other to determine which one performs better. A/B testing is also used to evaluate a new feature of an application. If the feature has an effect on users, the result will show the significant difference (p<0.05); the lack of different is called null hypothesis. With the evolving product development process, Dr. Pavel presented the motivation for A/B testing. In classical software development, a product is usually designed, developed, tested and then released. However, in c...

Dr. Marcus Rohrbach from University of California, Berkeley made a talk "Explain and answer: Intelligent systems which can communicate about what they see" on Friday, 2016//12/02. In the talk, Marcus presented the models which can answer questions but at the same time are modular and expose their semantic reasoning structure, and showed how to generate explanations given only image captions as training data. To begin the talk, Marcus showed the motivation of how to make the computer able to talk to about the visual world. He introduced two components in a successful communication: (1) the ability to answer natural language questions about the visual world, and (2) the ability of the system to explain in natural language, allowing a human to trust and understand it. to deal with tasks such as visual question answering, he emphasized that it is important to integrate the representation of textual and visual information together.  Marcus described the whole process of the sy...

Huma Hashemi, ISP graduate student, University of Pittsburgh had a talk about "Parse Tree Fragmentation of Ungrammatical Sentences" on Friday, 2016/11/18. She presented an evaluation of Parser Robustness for ungrammatical sentences. Huma started the talk by giving a introduction about natural language processing (NLP) that brings about a motivation for her proposal. One of the most challenging issues that NPL has to deal with is "noisier" texts such as English-as-a-second language and machine translation. For many NLP applications that requires a parser, the sentences may not be well-formed, for instance, information extraction, question answering and summarization systems. Therefore, to build a good NLP application, a parser should be able to parse ungrammatical sentences.   Huma's research focuses on answering the question "how much parser's performance degrades when deal with grammar mistake?" and evaluation of a parser on ungrammatical sent...

Yann LeCun, Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, held a talk about unsupervised learning, the next frontier in AI, on Friday, 2016/11/18 at CMU. At the beginning of the talk, prof. LeCun introduced Neuroscience, supervised learning, deep learning, multi-layer neural nets, convolutional network architecture, very deep convNet architectures, Memory-augmented networks. He presented different kinds of application using machine learning such as image recognition and question answering.  The main part of the talk presented by prof. LeCun was about obstacles to AI. The challenge of the next several years is to let machines learn from raw, unlabeled data, such as video or text. This is known as unsupervised learning. AI systems today do not possess "common sense", which humans and animals acquire by observing the world, acting in it, and understanding t...

Dr. Ying Ding from School of Informatics and Computing, Indiana University gave the talk "Data-Driven Science of Science" on November, 04, 2016 at School of Information Science, University of Pittsburgh. In the talk. Dr. Ding presented an overview about Data Science and the current layers of bibliometrics, which are macro level in complex network, meso level in bibliometrics, and micro level in collaboration and team science. Currently, most of research work has been focusing on analyzing data from complex network and bibliometrics. Dr. Ding suggested that collaboration and team science, as a micro level, should be a new trend to have the attention of data scientists. In addition, Dr. Ding concisely summarized her work related to Data Science which is beyond the bibliometrics. The three following are the main of her research: Data-Driven Discovery: entity metrics, computational hypothesis generation, and digital innovation (e.g. machine reading) Data-Driven Decision Ma...