I am currently a phd student at ECE Department, Northeastern University. My advisor is Dr. David Kaeli. I am a member of Northeastern University Computer Architecture Research Group (NUCAR).
I received my B.Eng. degree of Computer Science from Beijing University of Posts and Telecommunications (BUPT) in 2010.
My research interests are parallel computing on different platforms, including GPU (Graphic Processing Unit) and distributed systems. I have worked on various GPU-accelerated algorithms in the domains of speech recognition, medical ultrasound imaging and recommender system.
I am working on several projects where machine learning techniques are used in various domains, such as understanding environmental contamination factors linked to pre-term birth and reducing carbon-nanotube experiment design space.
Recently I started to work on accelerating general machine learning tasks using GPUs, Hadoop and Spark. I am currently working on the integration of GPUs in Spark to utilize the power of GPU and the scalability of Spark for large-scale machine learning tasks.
Medical Ultrasound Imaging on GPUs
I have transferred Synthetic Aperture Sequential Beamforming in ultrasound image processing onto GPU when I was in Technical University of Denmark as a trainee researcher.
I have also worked with BK Medical on processing ultrasound images on GPU.
Data Analysis on Spectral Biological Sample Data
I have used various machine learning models such as clustering, dimension reduction and regression to analyze biological sample data from Puerto Rico with the goal of establishing linkages between environmental pollutants and preterm birth.
Data Mining on Carbon-nanotube Fusion Process Design (ongoing)
I am using data mining techniques to reduce Carbon-nanotube design space to accelerate the Carbon-nanotube fusion process.
Sports Prediction using Gaussian Processes (ongoing)
I am mentoring four undergraduate students to develop a framework that uses Gaussian process to predict the margin of victory for NFL games.
NICE (Northeastern University Clustering Eigine) Project (ongoing)
I am the mentor of 9 undergraduate students mostly funded by REU program. Our group is developing a framework that supports interactive big data visulization in web interfaces to help reserchers from bioinformatics and environmental engineering domains better understand and explore their data.
I have worked in AMD as an intern on a project that executes the compute-intensive part of Mahout on APUs in heterogeneous clusters.
Hetero-Mark, A Benchmark Suite for CPU-GPU Collaborative Computing
Yifan Sun, Xiang Gong, Amir Kavyan Ziabari, Leiming Yu, Xiangyu Li, Saoni Mukherjee, Carter McCardwell, Alejandro Villegas, David Kaeli
In IISWC 2016
A Framework for Big Metabolomic Data Management and Analysis
Xiangyu Li, Leiming Yu, David Kaeli, Yuanyuan Yao, Poguang Wang, Roger Giese, Vicent Yusa and Akram Alshawabkeh
In International Journal On Advances in Software, v 9 n 1&2 2016
Mystic: Predictive Scheduling for GPU Based Cloud Servers using Machine Learning
Yash Ukidave, Xiangyu Li, and David Kaeli
In IPDPS 2016
Big Data Analysis on Puerto Rico Testsite for Exploring Contamination Threats
Xiangyu Li, Leiming Yu, David Kaeli, Yuanyuan Yao, Poguang Wang, Roger Giese, and Akram Alshawabkeh.
In The First International Conference on Big Data, Small Data, Linked Data and Open Data (ALLDATA 2015)
Mahout on Heterogeneous Clusters using HadoopCL
Xiangyu Li, Max Grossman, and David Kaeli
In Proceedings of the 2nd Workshop on Parallel Programming for Analytics Applications (PPAA) at PPoPP 2015
Accelerating the Training of HTK on GPU with CUDA
Zhihui Du, Xiangyu Li, and Ji Wu.
In Parallel and Distributed Processing Symposium Workshops PhD Forum (IPDPSW 2012)
xili "at" ece "dot" neu "dot" edu
Department of Electrical and Computer Engineering
140 The Fenway, Boston, MA