I received my Ph.D. in Mathematics under Bernd Sturmfels in 2011 from the University of California, Berkeley, where I analysed singularities in statistical models over large data sets through the lens of modern algebraic geometry. This work was continued at Stanford University in a one-year DARPA postdoctoral collaboration with Andrew Ng’s lab to explore mathematical challenges in deep learning. In 2012, I returned to Singapore to join the Institute for Infocomm Research (A*STAR) where I started the Sense-making Group in the Sense and Sense-abilities (S&S) programme. The group focused on exploiting machine learning techniques in sensor networks to create resource-efficient algorithms that exhibit higher-order intelligence. Before joining SUTD, I oversaw deep science activities in S&S as the Deputy Head for Research.
Beyond basic research, I worked closely with government and industry partners such as NEA, HDB and Sky Greens in several urban projects. These projects received both local and international recognition such as the MTI Borderless Silver Award 2015 and the World Smart Cities Award Finalist 2014. I was also appointed by the Science and Engineering Research Council (SERC) in A*STAR to lead a multi-agency panel of experts in developing future roadmaps for Data-Driven Research and Future Computing Paradigms.
- Ph.D. Mathematics, University of California, Berkeley (2011)
- B.S. Mathematics (Honors with Distinction), Stanford University (2005)
My primary research goal is to understand how learning occurs within large systems. My areas of interest include algebraic geometry, statistical learning, neural networks, homotopy type theory and the Internet of Things. I am currently focused on two tasks. The first is to develop efficient distributed algorithms for deep reinforcement learning, by analyzing the statistics of neural networks through the lens of computation. The second is to design a scaleable formal language for knowledge exchange and collaboration between intelligent machines, by using techniques from linked data and univalent foundations.
- SUTD Brain Lab
- NVIDIA-SUTD Artificial Intelligence Lab
- C. J. Hillar, S. Lin, and A. Wibisono. “Inverses of symmetric, diagonally dominant positive matrices and applications”. submitted to SIAM Journal on Matrix Analysis and Applications, arXiv:1203.6812, 2017.
- Z. Liu, S. Lin, T. Q. S. Quek, and W. Zhang. “Robustifying Deep Heterogeneous Sensor Data Fusion”. submitted to Journal of Select Topics in Signal Processing, 2016.
- S. Lin. “Asymptotic approximation of marginal likelihood integrals”. accepted in Journal of Algebraic Statistics, arXiv:1003.5338, 2016.
- M. Drton,S. Lin, L. Weihs and P. Zwiernik, “Marginal likelihood and model selection for Gaussian latent tree and forest models,” arXiv:1412.8285, to appear in Bernoulli Journal, 2016.
- M. A. Alsheikh, D. Niyato, S. Lin, H.-P. Tan, and D. I. Kim. “Fast adaptation of activity sensing policies in mobile devices”. accepted in IEEE Transactions on Vehicular Technology, 2016.
- M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan. “Rate-Distortion Balanced Data Compression for Wireless Sensor Networks”. Sensors Journal, IEEE 16.12, pp. 5072–5083, 2016.
- M. A. Alsheikh, D. Niyato, S. Lin, H.-P. Tan, and Z. Han. “Mobile big data analytics using deep learning and Apache Spark”. Network, IEEE 30.3, pp. 22–29, 2016
- Z. Liu, S. Lin, T. Q. S. Quek, and W. Zhang. “Deep fusion of heterogeneous sensor data”. accepted in International Conference on Acoustics, Speech, and Signal Processing, 2016.
- M. A. Alsheikh, A. Selim, D. Niyato, L. Doyle, S. Lin, and H.-P. Tan. “Deep activity recognition models with triaxial accelerometers”. Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016.
- M. A. Alsheikh, S. Lin, H.-P. Tan, and D. Niyato. “Toward a robust sparse data representation for wireless sensor networks”. Proceedings of IEEE Conference on Local Computer Networks, 2015.
- M. A. Alsheikh, D. T. Hoang, D. Niyato, H.-P. Tan, and S. Lin. “Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey”. Communications Surveys Tutorials, IEEE 17.3, pp. 1239–1267, 2015
- M. Abu Alsheikh, S. Lin, D. Niyato, and H.-P. Tan. “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications”. Communications Surveys Tutorials, IEEE 16.4, pp. 1996–2018, 2014
- S. Lin, C. Uhler, B. Sturmfels and P. Bühlmann, “Hypersurfaces and their singularities in partial correlation testing,” Foundations of Computational Mathematics, 14 (5), 1079-1116, Oct. 2014.
- L. Z. Wong, H. Chen, D. C. Chen and S. Lin, “Imputing missing values in sensor networks using sparse data representations,” Proc. ACM MSWIM, 227-230, Sep. 2014.
- M. Abu-Alsheikh, P. K. Poh, S. Lin, D. Niyato and H. P. Tan. “Efficient Data Compression with Error Bound Guarantee in Wireless Sensor Networks,” Proc. ACM MSWIM, 307-311, Sep. 2014.
- M. Abu-Alsheikh, S. Lin, D. Niyato and H. P. Tan, “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies and Applications,” IEEE Comms Surveys and Tutorials, 16(4), 1996-2018, Apr. 2014.
- M. Abu Alsheikh, P. K. Poh, S. Lin, H.-P. Tan, and D. Niyato. “Efficient data compression with error bound guarantee in wireless sensor networks”. Proceedings of the 17th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems, pp. 307–311, 2014.
- P. Zhang, J. Y. Koh, S. Lin, and I. Nevat. “Distributed event detection under Byzantine attack in wireless sensor networks”. Intelligent Sensors, Sensor Networks and Information Processing, IEEE 9th International Conference on, pp. 1–6, 2014.
- G. Peters, I. Nevat, S. Lin, and T. Matsui. “Modelling threshold exceedence levels for spatial stochastic processes observed by sensor networks”. Intelligent Sensors, Sensor Networks and Information Processing, IEEE Ninth International Conference on, pp. 1–7, 2014.
- M. A. Alsheikh, D. Niyato, S. Lin, and H.-P. Tan. “Area coverage under low sensor density”. Proceedings of the 11th IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2014.
- V. I. Morgenshtern, E. Riegler, W. Yang, G. Durisi, S. Lin, B. Sturmfels, and H. Bölcskei, “Capacity pre-log of noncoherent SIMO channels via Hironaka’s theorem,” IEEE Trans. Inf. Th, vol. 59(7), 4213-4229, Jul. 2013.
- M. A. Cueto and S. Lin, “Tropical secant graphs of monomial curves,” Beiträge zur Algebra und Geometrie, 2012.
- S. Lin, “Algebraic Methods for Evaluating Integrals in Bayesian Statistics,” Ph.D. dissertation, UC Berkeley, May 2011.
- E. Riegler, V. I. Morgenshtern, G. Durisi, S. Lin, B. Sturmfels, and H. Bolcskei. “Noncoherent SIMO pre-log via resolution of singularities”. Information Theory Proceedings, IEEE International Symposium on, pp. 2020–2024, 2011.
- M. A. Cueto and S. Lin. “Tropical secant graphs of monomial curves”. FPSAC 2010, DMTCS Proceedings AN., pp. 669–680, 2010.
- S. Lin and B. Sturmfels, “Polynomial Relations among Principal Minors of a 4×4-Matrix,” J. Algebra, vol. 322(11), 4121-4131, Dec 2009.
- S. Lin, B. Sturmfels and Z. Xu, “Marginal Likelihood Integrals for Mixtures of Independence Models,” JMLR. 10, 1611-1631, Jul 2009.
- S. Lin, W. W. L. Ho and Y. C. Liang, “Block Diagonal Geometric Mean Decomposition (BD-GMD) for MIMO Broadcast Channels,” IEEE Trans. Wireless Comms., vol. 7(7), 2778-2789, Jul 2008.
- S. Lin, W. W. Ho, and Y.-C. Liang. “MIMO broadcast communications using blockdiagonal uniform channel decomposition (BD-UCD)”. Personal, Indoor and Mobile Radio Communications, IEEE 17th International Symposium on, pp. 1–5, 2006.
- S. Lin, W. W. Ho, and Y.-C. Liang. “Block-diagonal geometric mean decomposition (BD-GMD) for multiuser MIMO broadcast channels”. Personal, Indoor and Mobile Radio Communications, IEEE 17th International Symposium on, pp. 1–5, 2006.
- MTI Borderless Silver Award (2015)
- World Smart Cities Award Finalist (2014)
- A*STAR Borderless Award (2014)
- A*STAR TALENT Award (2014)
- A*STAR National Science Scholarship (Ph.D.) (2006)
- A*STAR Roll of Honour (2005)
- A*STAR Chairman’s Honours List (2003-2005)
- Stanford Mathematics Undergraduate Research Award (2005)
- William Lowell Putnam Mathematical Competition – Top 15 (2004)
- A*STAR National Science Scholarship (B.S.) (2002)