HUNG CHAU's blog

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

By reading the first chapte "Becoming an anthor" of the book  "Authoring a PhD: How to plan, draft, write and finish a doctoral thesis or dissertation", I found some useful information about the differences between the classical and taught PhD model, which provides us with a significant attention about what knowledge and skills we should focus on more and allocate our time efficiently. For example, in "classical model", the thesis requirement is to write a big book thesis integrating set of chapters from 80,000 to 100,000 words; on the other hand, that of the "taught PhD model" is a papers model dissertation including four or five publishable quality papers, around 60,00 words. My school's dotoral program is a taught PhD model that includes coursework, examinations and a dissertation. Therefore, practicing writing papers is crucial for us, even at our very first year. It helps us build up our academic writting  and authoring sk