2021年4月1日，联想集团副总裁李国庆、上海大区总经理徐筝一行访问了上海药物所。药物所党委书记叶阳、生物信息平台负责人黄宇教授、信息化管理处处长黄子福等接待来访。
黄宇、黄子福陪同李国庆一行等参观了药物所所史展示馆，党委书记叶阳向他们介绍了药物所的历史沿革、研究领域布局和重大科研进展等情况，黄宇讲述了数据中心对人工智能新药研究的重要性，黄子福介绍了药物所信息化发展现况，以及药物所科研与信息化进一步融合的需求。随后，双方就药物所数据中心和信息化建设事宜举行会谈。会上，李国庆表示，希望能与药物所开展多维度合作，全方位促进科研发展。
过去一年多名新同学加入团队，看到同学们的模型功夫蒸蒸日上，深感欣慰。希望来年大家继续努力，研发出更厉害的模型！
最近发布了内部使用的适用超算的大规模分布式计算库，Pegaflow。We recently released our internal HPC (high-performance computing) workflow package, Pegaflow, to the public. It allows a developer to connect dependent computing jobs into a DAG (Directed Acyclic Graph) and run the DAG on a computing cluster (rather a single node) in parallel.
It differs from Pegasus's own python2 API.
- Pegaflow is Python3-only.
- An abstract class https://github.com/polyactis/pegaflow/blob/master/pegaflow/Workflow.py that simplifies workflow coding significantly, for OO (Object-Oriented) programmers.
- APIs in https://github.com/polyactis/pegaflow/blob/master/pegaflow/__init__.py that simplifies workflow coding for non-OO programmers.
The ready-to-install pip package is at https://pypi.org/project/Pegaflow/. Install via "pip3 install pegaflow".
Code is at https://github.com/polyactis/pegaflow.
For examples, check https://github.com/polyactis/pegaflow/tree/master/pegaflow/example.
又到新年party的时间了，感谢各位同学过去一年的付出， 实验室在算法模型开发、数据库建设等都有长足进展。 期待2020我们发现新的pattern， 新的模型。
PS： 某同学的poker概率功夫有待提高 ：）
读博士时开始合作的好朋友严锡峰教授（美国加州大学圣塔芭芭拉分校计算机系终身教授, 2019年美国工业与应用数学会数据挖掘国际会议主席）受邀来作深度学习的讲座。他团队研发出来的DeepMotif比经典算法MEME快1万倍；时间序列预测算法AGA（Attention-Guided Autoregression）取得了当前最好的预测准确度，击败了Amazon的DeepAR和Google的Transformer算法。感谢郑老师，殷老师，和各位同学百忙中聆听讲座，以后多多交流。
Xifeng Yan, Professor at UCSB Computer Science, a friend and long-term collaborator, came to give a Talk: Deep Learning in Motif Finding and Time Series Forecasting.
DeepMotif, developed by his group, is 10,000 times faster than MEME. AGA (Attention-Guided Autoregression), a time-series prediction algorithm, also by his group, is currently the best prediction algorithm, beating DeepAR of Amazon, Transformer of Google. Thanks to the colleagues and students that find time to attend this lecture!
Xifeng Yan, Venkatesh Narayanamurti Chair of Computer Science, University of California at Santa Barbara
Location: 4-426 at Haike 501 (conference room on the east side)
Time: 2:30pm, Thursday, Oct 17th, 2019
Abstract
Sequences and time series data are ubiquitous. In this talk, I will introduce the recent advance we achieved in analyzing sequential data, specifically, motif finding and time series forecasting. Traditional statistical models often have to sacrifice speed for accuracy in order to handle large volumes of sequential data generated by new devices. I will discuss how to leverage deep learning techniques to solve this issue and make new breakthroughs in these two areas.
Bio
Xifeng Yan is a professor at the University of California at Santa Barbara, holding the Venkatesh Narayanamurti Chair of Computer Science. He received his Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign in 2006 and was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008. His work is centered on modeling, mining, and searching data. His contribution can be found in data mining, database systems, natural language processing, etc. His works were extensively referenced, with over 18,000 citations per Google Scholar and thousands of software downloads. He received NSF CAREER Award, IBM Invention Achievement Award, ACM-SIGMOD Dissertation Runner-Up Award, and IEEE ICDM 10-year Highest Impact Paper Award.
