About Me

Hi! This site is...old, and purple...

My new personal site is rosanneliu.com (well if it says coming soon that means I am lazy.)

I recently (December 2016) obtained my PhD in Computer Engineering from Northwestern University!

Also since April 2016 I have joined a fabulous startup, Geometric Intelligence, as a Machine Learning researcher. We play with neural networks, Bayesian, both big and sparse data, towards building general artificial intelligence. The startup is later acquired by Uber to become Uber AI Labs, and I was luckily a founding member of it.

During PhD I was supervised by Prof. Alok Choudhary, and was a member of the Center for Ultra-scale Computing and Information Security (CUCIS). Prior to joining Northwestern, I obtained a bachelor's degree in EE at Fudan University.

My research interests span in the areas of machine learning, deep learning, natural language processing, data mining in social and scientific applications, high performance computing, parallel algorithms, and big data analytics. I also have work and research experience in dynamic driving control, intelligent driving systems, materials informatics, energy management and quantitative finance.

"So we let machines learn and extend our bandwidth of intelligence to compensate for our own humanness."



Publications (Google Scholar)

Journal articles, refereed
  • R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary. A Predictive Machine Learning Approach for Microstructure Optimization and Materials Design. Scientific Reports, 5, 11551; doi: 10.1038/srep11551. 2015. (pdf) 

  • R. Liu, Y. Yabansu, A. Agrawal, S. Kalidindi, and A. Choudhary. Machine learning approaches for elastic localization linkages in high-contrast composite materials. Integrating Materials and Manufacturing Innovation, 2015, 4:13; doi:10.1186/s40192- 015-0042-z (pdf) 

  • H. Xu, R. Liu, A. Choudhary, and W. Chen. A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures. Journal of Mechanical Design, 137(5):051403–051403, ASME, May 2015. (pdf) 
Conference papers, refereed
  • C. Jin, R. Liu, Z. Chen, W. Hendrix, A. Agrawal, and A. Choudhary. A Scalable Hierarchical Clustering Algorithm Using Spark. In Proceedings of The IEEE International Conference on Big Data Computing and Applications (BigDataService 2015), San Francisco Bay, USA, March 2015. (pdf) 

  • R. Liu, A. Agrawal, W. Liao, and A. Choudhary. Search Space Preprocessing in Solving Complex Optimization Problems. In Proceedings of the IEEE International Conference on Big Data, October 2014. (pdf) 

  • R. Liu, A. Agrawal, W. Liao, and A. Choudhary. Enhancing Financial Decision-Making Using Social Behavior Modeling. In the Workshop on Social Network Mining and Analysis, held in conjunction with the SIGKDD Conference on Knowledge Discovery and Data Mining, August 2014. (pdf) 

  • H. Xu, R. Liu, A. Choudhary, and W. Chen. A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures, (Design Automation (DAC) Best Paper). In the ASME International Design Engineering Technical Conferences, August 2014. (pdf) 

  • R. Liu, S. Xu, C. Fang, Y. Liu, Y.L. Murphey, and D.S. Kochhar. Statistical Modeling and Signal Selection in Multivariate Time Series Pattern Classification. In 21st International Conference on Pattern Recognition (ICPR), pp.2853–2856, 11–15 November 2012. (pdf) 

  • R. Liu, H. Yu, R. McGee, and Y.L. Murphey. Driving Course Prediction for Vehicle Handling Maneuvers. In American Control Conference (ACC), pp.2096–2101, 27–29 June 2012. (pdf) 

  • S. Xu, R. Liu, D. Li, and Y.L. Murphey. A Hybrid System Ensemble Based Time Series Signal Classification on Driver Alertness Detection. In The 2011 International Joint Conference on Neural Networks (IJCNN), pp.2093–2099, July 31 2011&nash;Aug. 5 2011. (pdf) 

  • R. Liu, S. Xu, J. Park, Y.L. Murphey, J. Kristinsson, R. McGee, M. Kuang, and T. Phillips. Real Time Vehicle Speed Prediction Using Gas-Kinetic Traffic Modeling. In 2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS). pp.80–86, 11–15 April 2011. (pdf) 

  • R. Liu, Y.L. Murphey. Time-series Temporal Classification Using Feature Ensemble Learning. In The 2010 International Joint Conference on Neural Networks (IJCNN). pp.1–5, 18–23 July 2010. (pdf) 
Published abstracts
  • R. Liu, Z. Chen, T. Fast, S. Kalidindi, A. Agrawal, and A. Choudhary. Predictive Modeling in Characterizing Localization Relationships. In the TMS Annual Meeting & Exhibition, Symposium of Data Analytics for Materials Science and Manufacturing, February 2014.

  • R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan, and A. Choudhary. A Data Mining Approach in Structure-Property Optimization. In the TMS Annual Meeting & Exhibition, Symposium of Data Analytics for Materials Science and Manufacturing, February 2014.

  • L. Ward, R. Liu, A. Krishna, V. Hegde, A. Agrawal, A. Choudhary, and C. Wolverton. A General-Purpose Toolkit for Predicting the Properties of Materials using Machine Learning. In Computational Materials Discovery and Optimization: From 2D to Bulk Materials, TMS 2016. 17 February 2016.

  • L. Ward, R. Liu, A. Krishna, V. Hegde, A. Agrawal, A. Choudhary, and C. Wolverton. Accurate Models of Formation Enthalpy Created using Machine Learning and Voronoi Tessellations. In Predicting and Classifying Materials via High-Throughput Databases and Machine Learning, APS March 2016, 15 March 2016.


Awards and Honors

  • Second place, poster presentation at EECS Student Poster Fair Award, 2015.
  • Second place, poster competition at the Symposium of Multidisciplinary Computer-Aided Design and Simulation-Based Optimization - Recent Applications & Future, Evanston IL, December 2014.
  • Best paper award: H. Xu, R. Liu, A. Choudhary, and W. Chen, "A Machine Learning-Based Design Representation Method for Designing Heterogeneous Microstructures", ASME 2014 International Design Engineering Technical Conferences, Computers and Information in Engineering Conference, IDETC2014-34570, August 17-20, Buffalo, New York, 2014.
  • ATPESC (Argonne Training Program on Extreme-Scale Computing) Award, 2014.
  • Predictive Science and Engineering Design (PS&ED) Fellowship, 2013-2014.
  • BPDM (Broadening Participation in Data Mining) Scholarship, 2013.
  • First place, Kaggle Competition on Driving Alertness Detection, 2011.


Teaching Experience