Tie-Yan Liu is a principal researcher of Microsoft Research Asia, leading the machine learning group. His research interests include artificial intelligence, machine learning, information retrieval, data mining, and computational economics. As a researcher in an industrial lab, Tie-Yan is making his unique contributions to the world. On one hand, many of his technologies have been transferred to Microsoft’s products and online services, such as Bing, Microsoft Advertising, and Azure. He has received many recognitions and awards in Microsoft for his significant product impacts. On the other hand, he has been actively contributing to the academic community. He is an adjunct professor at CMU and several universities in China, and an honorary professor at Nottingham University. He is frequently invited to chair or give keynote speeches at major machine learning and information retrieval conferences. He is a fellow of the IEEE, a distinguished member of the ACM, as well as a senior member, distinguished speaker, and academic committee member of the CCF.
Tie-Yan Liu is a pioneer in machine learning for Web search and online advertising.
His seminal contribution to the field of learning to rank has been widely recognized (https://en.wikipedia.org/wiki/Learning_to_rank). He invented several highly impactful algorithms and theories, including the listwise approach to ranking, relational ranking, and statistical learning theory for ranking. He is an advocator of learning to rank as a self-contained research discipline – he gave the first batch of keynote speeches and tutorials, organized the first series of workshops, and wrote the very first book on this topic (among top-10 Springer computer science books written by Chinese authors). He is the creator of LETOR benchmark dataset (http://research.microsoft.com/en-us/um/beijing/projects/letor/), which has become a must-have experimental platform for the research on learning to rank. With his deep research and social efforts, learning to rank has become a fundamental technology in major search engines today, and it continues to be one of the most important directions in the related research communities.
He has also done impactful work on large scale machine learning. As early as in 2005, Tie-Yan has developed the largest text classifier in the world, which can categorize over 250,000 categories on 20 machines (published at SigKDD Explorations). Recently, Tie-Yan and his team developed many other large-scale machine learning tools, including the fastest and largest topic model in the world (LightLDA, with one million topics, published at WWW 2015) and the largest word embedding model. Some of these models were open-sourced in Microsoft Distributed Machine Learning Toolkit (http://www.dmtk.io/), which has attracted millions of visitors, hundreds of thousands of downloads, and thousands of stars at GitHub.
He has conducted innovative research on mechanism design for online advertising. In order to bridge theory and practices, he introduced many practical constraints into auction mechanism design (e.g., bounded rationality, budget constraints), and proposed a data-driven framework called “game-theoretical machine learning” for ad auction optimization. This framework learns the bounded rationality model from data, and optimizes the action parameters based on the learned model using a simulation-based framework. The framework extends algorithmic game theory due to the introduction of data, and extends machine learning by considering the strategic (non-i.i.d.) behaviors behind data generation.
Over the years, Tie-Yan and his team have been recognized as one of the global powerhouses and trendsetter in machine learning for Web search and online advertising. He and his team have contributed hundreds of high-impact papers at top conferences – a good indicator of their influence and impact. His top ten papers have been cited over 4000 times in refereed conferences and journals. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), and the research break-through award at Microsoft Research (2012). He has been invited to serve as general chair, PC chair, or area chair for a dozen of top conferences including SIGIR, WWW, NIPS, IJCAI, AAAI, KDD, ACL, ICTIR, as well as associate editor/editorial board member of ACM Transactions on Information Systems, ACM Transactions on Web, Information Retrieval Journal, Neurocommputing, and Foundations and Trends in Information Retrieval. Tie-Yan Liu and his works have been reported by many International media, including National Public Radio, CNET, MIT Technology Review, and PCTech Magazine.
We are organizing a workshop on distributed machine learning at AAAI 2017. Please visit https://distributedml2017.wordpress.com/ for more information.
We are going to give a tutorial on distributed machine learning at AAAI 2017.
Dataset and Toolkit Release
Microsoft Graph Engine (https://www.graphengine.io/), 2016 – the most powerful graph engine in the world!
Microsoft Distributed Machine Learning Toolkit (http://www.dmtk.io/), 2015 – Attracted millions of page views, hundreds of thousands of downloads, and thousands of stars at GitHub; including record-keeping machine learning algorithms like LightLDA and LightGBM.
LETOR Benchmark Dataset for Learning to Rank (http://research.microsoft.com/en-us/um/beijing/projects/letor/), 2007 – A must-have experimental platform for research on learning to rank. According to incomplete statistics, more than half of the papers on learning to rank published at major conferences and journals have used this dataset for their evaluations in the past ten years.
We Are Hiring!
We are hiring at all levels (especially senior researchers)! If your major is machine learning (especially deep learning and distributed machine learning), and you have the passion to change the world, please send your resume to email@example.com.