Dr. Yongchao Liu is currently a Staff Engineer in Ant Financial (from 07/2017 to present), working on distributed machine learning (online and graph learning) and heterogeneous computing platforms for deep learning (neural network compilers). Prior to that, he worked as a Research Scientist II (a research faculty member) in the School of Computational Science & Engineering, Georgia Institute of Technology (USA) (from 01/2015 to 07/2017), and as a Postdoctoral Researcher in the Institute of Computer Science, University of Mainz (Germany) (from 11/2011 to 01/2015). He earned his Ph.D degree in computer engineering from Nanyang Technological University (Singapore) in 2012 (supervised by Dr. Bertil Schmidt and Dr. Douglas Maskell). Prior to that, he earned the Master and Bachelor degrees in computer science and technology from Nankai University (China) in 2008 and 2005, respectively.
He is an internationally recognized researcher in parallel computing and bioinformatics. He demonstrated outstanding contributions to scientific communities by his novel parallel algorithms and software tools for large-scale biological data analysis. These parallel algorithms and tools streamline fundamental and computationally challenging biological issues with parallel computing, via in-depth exploration of current high performance computing techniques and technologies such as hardware accelerators (e.g. GPUs and Intel Xeon Phis) and clusters (e.g. CPU/GPU/Xeon Phi clusters). On the other hand, these algorithms and tools investigate a set of related critical issues in bioinformatics and have actually established an innovative and unique analysis platform for large-scale biological datasets, especially next-generation sequencing reads. To address big data challenges, he proposed a new concept of computing, i.e. Compact Computing, which centers around data and enables full-stack computation by tightly coupling algorithms with systems. His research interests focus on parallel and distributed algorithm design for bioinformatics, heterogeneous computing with accelerators (GPUs and Xeon Phis), high performance computing on big data, and parallelized machine learning.
He has released a set of software tools for reproducible research and public use, most of which are open-source, associated with his paper publications. Among these algorithms, three CUDA-based open-source software tools, i.e. CUDASW++, mCUDA-MEME and CUSHAW, are rated by NVIDIA Corporation as popular GPU-accelerated applications, while DecGPU (the first parallel and distributed error correction algorithm for high-throughput short reads) was reported by GenomeWeb. Furthermore, some of his other open-source tools are also leading in their respective areas, including MSAProbs (multiple protein sequence alignment), Musket (Illumina reads error correction), CUSHAW2 (NGS base-space read alignment), CUSHAW3 (NGS base-space and color-space read alignment), PASHA (NGS de novo genome assembly), SNVSniffer (NGS germline and somatic SNV calling), SWAPHI (Xeon-Phi-based protein database search), SWAPHI-LS (Xeon-Phi-based pairwise DNA sequence alignment), ParaBWT (parallel construction of Burrows-Wheeler transform and suffix array), LightSpMV (CUDA-based sparse matrix-vector multiplication), and LightPCC (parallel correlation computation on Xeon Phi clusters).
He won two Best Paper Awards from IEEE International Conference on Application-specific Systems, Architectures and Processors (IEEE ASAP) in 2009 and 2015, got one paper recommended for Best Paper Award from IEEE International Conference on Cluster Computing (IEEE Cluster) in 2014 and won the Program to Empower Partnerships with Industry Award from U.S. South Big Data Hub in 2016. He was listed in the reputable Who's Who in America (Marquis research) in 2016. He was awarded Innovative Talent (in artificial intelligence) of Hangzhou 521 Program for Global Talents Introduction in 2019 and was recognized as Hangzhou High-Level Talents of Category C in 2020. He co-chaired the first Workshop on Parallel Software Libraries for Sequence Analysis (pSALSA) in 2015 and founded the Workshop on Accelerator-Enabled Algorithms and Applications in Bioinformatics (WACEBI) in 2016. In addition, he serves as a reviewer for some top journals, such as Nature Methods, Nature Communications, Bioinformatics, TPDS and TCBB, and as a program committee member for some leading conferences such as IPDPS and CCGrid.
- AI Smart Phone Recycling Robot (AI智能手机回收机器人)
- My first U.S. patent has got granted: "Fast computation of a convolutional neural network".04/2020.
- A China patent has got granted: "Chip and data processing method based on chip." 06/2020.
- Our preliminary research on learning-to-compiling for deep learning is avaiable now: Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations. 08/2020.