Jiangsheng Yu
Distinguished Scientist of Machine Learning and Applied Mathematics at FutureWei
- Role
- Distinguished Scientist of Machine Learning and Applied Mathematics at Futurewei Technologies, Inc.
- Location
- Santa Clara, CA, US
- LinkedIn followers
- 500 followers
About Jiangsheng Yu
Solid background of applied mathematics and computer science- 20+ years R&D experience in artificial intelligence (AI), statistical machine learning, pattern recognition, natural language processing (NLP), image processing, etc- 3 books on AI/ML, and 6 translated books on AI/causal inference/complexity/risk- 26 filed US patents of algorithms, and some others in progress- Strong publications in leading journals like Bioinformatics, Trans. NN, etc- Professional programming skills in numerical/statistical high performance computing, big data analysis, stochastic simulation, etc- Hands-on experiences in predictive modelling, and proficient in related algorithms.MATHEMATICS EXPERTISEProbability Theory and Statistics, Machine Learning/Pattern Recognition, Stochastic Simulation, Stochastic Calculus, Approximation and Optimization, Numerical Methods, Differential Geometry, Differential Equations, Linear Algebra, Abstract Algebra, Topology, Algorithms, etc.COMPUTER SKILLS- Languages & Software: Fortran, C/C++, R/S-Plus, MatLab/Octave, Python, Perl, Lisp, Maxima, JAGS/BUGS, OpenMP/MPI, Hadoop, shell programming, etc- Operating Systems: 20+ years experience of UNIX-like operating systems, e.g, FreeBSD/OpenBSD, Linux, etc.
Experience
Distinguished Scientist of Machine Learning and Applied Mathematics
Aug 2015 — Present
Autonomous driving, driverless simulation, Bayesian/causal inference, LiteAI/machine learning, auto tuning, blockchain, big data analysis, intelligent operations, cognitive computing, feature engineering, deep/reinforcement/incremental/ensemble learning, knowledge discovery/reasoning/computing, natural language processing (NLP), time series analysis, tensor analysis/decomposition, non-convex optimization, image processing, facial recognition, techniques for sparse matrices, algorithms in logistics, data cloning, risk management, etc- Smart speaker- Intelligent service/operations- Intent identification and chatbot- AI chips- Auto tuning of models- LiteAI and light-weight machine learning- High-order AI and auto tuning- Face recognition and hallucination- Power saving models- Churn prediction by time series analysis- R & Python in Spark- Topic models based on singular value decomposition (SVD)- Intelligent information retrieval- Stereoscopic learning- Recommendation algorithms- CNNs for image processing- Machine learning algorithms for query optimization- Vectorization of concepts- Online/offline outlier detection- SVD of large sparse matrices- Vehicle routing problem- Bin packing problem- Warehouse optimization- Import price risk management- Query optimization- OCR and form information extraction- Statistics-based multidimensional data cloning
Education
Peking University
PhD
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