[RESEARCH HIGHLIGHTS]

Our research group is deeply engaged in a multifaceted exploration at the intersection of statistics, geometry, and real world data, seeking to unravel complex problems through a series of innovative projects and seminars.

 

At the heart of our work in the Interaction of Statistics and Geometry, we explore manifold fitting and estimation, developing state-of-the-art methods for constructing and evaluating smooth submanifolds for data set approximation, inspired by the geometric complexity of the Whitney problem. We extend our geometric investigation through the study of principal submanifolds and classification challenges on Riemannian manifolds, developing sophisticated statistical techniques to exploit the unique properties of manifold structures for data analysis, including the development of principal currents, submanifolds and nonlinear classifiers. Our research also ventures into the geometrical intricacies of Calabi-Yau manifolds, aiming to decode the geometry of their moduli spaces. We are also pioneering the development of graphical models and classification strategies in high-dimensional settings, introducing a novel two-stage classification approach that uses innovative thresholding and quadratic discriminant analysis to navigate the complexities of high-dimensional data. Apart from advancing theoretical understanding, these methods have been applied to the analysis of complex, real-world datasets, such as single-cell RNA sequencing and UK Biobank data, highlighting the practical impact of our work.

 

Under the umbrella of Statistical Inference for Inverse Problem with Singularity, our projects address pressing questions in brain imaging, using advanced techniques to investigate time-varying source distributions in magnetoencephalography (MEG) and to develop predictive behavioral models based on MEG/EEG data. At the same time, we are pioneering non-traditional methods to address the inverse problem in tomographic reconstruction, to revolutionize 3D particle reconstruction from 2D random projections in cryo-electron microscopy (cryo-EM). Our group's work is characterized by a relentless pursuit of knowledge at the intersection of disparate fields, driven by the ambition to provide groundbreaking solutions to some of the most challenging problems in science today.

 

 

[PUBLICATIONS]

Multidimensional Physical Fitness is Associated with Reduced Dementia Risk through Proteomic and Neuroimaging Pathways: a Prospective Cohort Study of the UK Biobank (with Yiqing Sun, Runyu Lin, Jiayue Qin, Feiyue Pan, Bingjie Li) (2026) [Manuscript][Platform]

 

Routine Blood Biomarkers Reveal a Preclinical Continuum of Multiple Myeloma Risk (with Bingjie Li, Jiadai Xu, Yiqing Sun, Feiyue Pan, Shing-Tung Yau and Peng Liu) (2026) [Manuscript][Platform]

 

Manifold Fitting: A Review of Methods and Applications (with Jiaji Su) (2026) [Manuscript]

 

A Principal Submanifold-Based Approach for Clustering and Multiscale RNA Clash Correction (with Menghao Wu) (2026). Annals of Applied Statistics, In press. [PDF][Manuscript]

 

Distinguishing Calabi-Yau Threefolds: Topology and Machine-learning (with Yang-Hui He and Shing-Tung Yau) (2026) Harvard Data Science Review, 8.1 [PDF] [Manuscript]

 

Estimation of Riemannian Quantities from Noisy Data via Density Derivatives (with Junhao Chen and Ruowei Li) (2026). [Manuscript]

 

Curvature-driven Manifold Fitting under Unbounded Isotropic Noise (with Ruowei Li) (2026). [Manuscript]

 

Differentially Private Manifold Denoising (with Jiaqi Wu and Yiqing Sun) (2025). [Manuscript][PDF]

 

Questionnaire Non-response: A Warning Sign of Cognitive Decline and Age-related Chronic Disease (with Bingjie Li and Yukun Lu) (2025). [Manuscript]

 

Enhanced High-Dimensional Data Visualization through Adaptive Multi-Scale Manifold Embedding (with Tianhao Ni and Bingjie Li) (2025). [Manuscript]

 

Principal Decomposition with Nested Submanifolds (with Jiaji Su) (2025). [Manuscript]

 

Manifold Fitting Reveals Metabolomic Heterogeneity and Disease Associations in UK Biobank (with Bingjie Li, Jiaji Su, Runyu Lin and Shing-Tung Yau) (2025). Proceedings of the National Academy of Sciences of the United States of America, 122 (22) e2500001122 [PDF][Manuscript][Code]

 

CellScope: A High-Performance Cell Atlas Builder with Tree-Structured Representation (with Bingjie Li, Runyu Lin, Tianhao Ni, Guanao Yan, Mannix Burns and Jingyi Jessica Li) (2024). Nature Communications, 11, 1130 [PDF][Manuscript][Code][Tutorial]

 

Single-Cell Analysis via Manifold Fitting: A New Framework for RNA Clustering and Beyond (with Bingjie Li, Yukun Lu and Shing-Tung Yau) (2024). Proceedings of the National Academy of Sciences of the United States of America, 121 (37) e2400002121 [PDF][Manuscript][Code][NUS News][Tsinghua News]

 

Manifold Fitting with CycleGAN (with Jiaji Su and Shing-Tung Yau) (2023). Proceedings of the National Academy of Sciences of the United States of America, 121 (5) e2311436121 [PDF][Manuscript][Code][NUS News][Tsinghua News]

 

Uncertainty Quantification in Cryo-EM: A Framework for Continuous Variability (with Bertrand Sim) (2023). [Manuscript][Code]

 

Fitting Manifolds on Manifolds (2023). [Manuscript]

 

Manifold Fitting (with Jiaji Su, Bingjie Li and Shing-Tung Yau) (2023). [PDF][Manuscript][Code]

 

Hunting Principal Sub-manifolds: New Theories and Methods (with Bingjie Li, Do Tran and Zhenyue Zhang) (2023). [Manuscript][Code]

 

Random Fixed Boundary Flows (with Yuqing Xia and Zengyan Fan) (2023). Journal of the American Statistical Association, To appear. [PDF][Code]

 

A Statistical Approach of Estimating Adsorption Isotherm Parameters in Gradient Elution Preparative Liquid Chromatography (with Jiaji Su, Cheng Li and Ye Zhang) (2022). Annals of Applied Statistics, In press. [PDF][Manuscript][Code]

 

High Dimensional Quadratic Discriminant Analysis: Optimality and Phase Transitions (with Wanjie Wang and Jingjing Wu) (2021). [PDF][Manuscript][Code]

 

Manifold Fitting and Projection via Quadratic Approximation (with Zheng Zhai) (2021). [PDF][Manuscript][Code]

 

Manifold Fitting by Ridge Estimation: A Subspace-constrained Approach (with Zheng Zhai) (2020). [PDF][Manuscript][Code]

 

Manifold Fitting under Unbounded Noise (with Yuqing Xia) (2019). Journal of Machine Learning Research, 26, 1-55, 2025 [PDF][Manuscript][Code]

 

Community Detection Based on the L∞ Convergence of Eigenvectors in DCBM (with Yan Liu et al.) (2019). [PDF][Manuscript]

 

Manifold Learning in Ambient Space (with Bingjie Li and Wee Chin Tan) (2019). [PDF][Manuscript]

 

Quantifying Time-Varying Sources in Magnetoencephalography – A Discrete Approach (with Zengyan Fan, Masahito Hayashi and William F. Eddy) (2020). Annals of Applied Statistics, 14, 1379-1408 [PDF][Supplementary Material]

 

Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach (with Ye Zhang, Patrik Forssen and Fornstedt Torgny) (2019). Annals of Applied Statistics, 13, 2011-2042 [PDF][Supplementary Material]

 

Principal Boundary on Riemannian Manifolds (with Zhenyue Zhang) (2020). Journal of the American Statistical Association, 115 1435-1448 [PDF] [Supplementary Material]

 

Sufficient Dimension Reduction for Classification (with Xin Chen, Jingjing Wu and Jia Zhang) (2018). Statistica Sinica, Accepted in 2024 [PDF]

 

A Level Set Based Variational Principal Flow Method for Nonparametric Dimension Reduction on Riemannian Manifolds (with Hao Liu, Shingyu Leung and Tony F. Chan) (2017). SIAM Journal on Scientific Computing, 39, A1616–A1646 [PDF]

 

Principal Sub-manifolds (with Benjamin Eltzner and Tung Pham) (2016). Statistica Sinica, Accepted in 2024 [PDF][Manuscript][Supplementary Material]

 

Estimating the Number of Sources in Magnetoencephalography Using Spiked Population Eigenvalues (with Ye Zhang, Zhidong Bai and William F. Eddy) (2018). Journal of the American Statistical Association, 113 505-518 [PDF][Supplementary Material]

 

Partial Correlation Screening for Estimating Large Precision Matrices, with Applications to Classification (with Shiqiong Huang, Jiashun Jin) (2016). Annals of Statistics, 44 (Peter Hall Memorial Issue) 5 2018-2057 [PDF][Manuscript]

 

A Statistical Approach to the Inverse Problem in Magnetoencephalography (with William F. Eddy) (2014). Annals of Applied Statistics 8 1119-1144 [PDF][Manuscript]

 

Principal Flows (with Victor Panaretos, Tung Pham) (2014). Journal of the American Statistical Association 109 424-436 [PDF]

 

Optimal Classification in Sparse Gaussian Graphic Model (with Jiashun Jin, Yingying Fan) (2013). Annals of Statistics 41 2537-2571 [PDF][Supplementary Material]

 

A Comparison of the Lasso and Marginal Regression (with Christopher R. Genovese, Jiashun Jin and Larry Wasserman) (2012). Journal of Machine Learning Research 13 2107-2143 [PDF]

 

 

[GRANTS]

Our work has been supported by Singapore Ministry of Education (MOE) Academic Research Fund grants --

·       MOE Tier 1 (Startup), Modern Statistical Methods for Complex Data, 2014-2017,

·       MOE Tier 2, Magnetoencephalography (MEG) Inverse Problem, 2017-2020,

·       MOE Tier 2, Learning in MEG/EEG imaging-- from localization to behaviour prediction, 2021-2024,

·       MOE Tier 2, Cryo-Electron Microscopy: Reconstruction and Uncertainty Quantification via Manifold Learning, 2023-2027,

·       MOE Tier 2, Manifold Landscape in Data Science: Learning beyond Euclidean Space (with Shing-Tung Yau), 2024-2028 (pending),

and by various grants from the National University of Singapore.

 

[RECRUITING]

Ø  PhD Candidates: Interested students are encouraged to contact me directly for possible funded positions. Preferences will be given to those with solid mathematical/statistical background and who are willing to work hard on challenging problems. Any exceptional student without TOEFL/GRE scores will also be considered.

For NUS students, I normally expect at least one semester of positive research interaction before discussing joining the group.

Ø  Postdocs: Candidates interested in these open positions are encouraged to get in touch directly.

 

 

[EDITORIAL BOARD/SERVICE]

Ø  Associate Editor,  Journal of the Royal Statistical Society, Series B (Statistical Methodology), 2026-present  

Ø  Guest Editor,  Harvard Data Science Review, 2024-present

Ø  Editor, International Journal of Data Science in the Mathematical Sciences, 2024-present

 

[RECENT/UPCOMING SEMINARS]

     

 

 

1.     Hausdorff Research Institute for Mathematics (Nov-Dec 2026)

2.     IMSI, Chicago (Dec 2026)

3.     Seoul National U (June 2026)

4.     Harvard CMSA Colloquium (Nov 2025)

5.     HKUST Business School (Nov 2025)

6.     NTU Math, Singapore (Oct 2025)

7.     Annual Review of Mathematics in China, Beijing (Aug 2025)

8.     JSM Invited Talk, Nashville (Aug 2025, cancelled)

9.     Invited Lecture (1-hour), The International Congress of Basic Science, Beijing (July 2025)

10.  Statistics, Seoul National University (March 2025)

11.  Plenary Lecture (1-hour), International Congress of Chinese Mathematicians (ICCM), Shanghai (Jan 2025)

12.  Second Symposium of Geometry and Statistics in China, Sanya/Shanghai (Jan 2025)

13.  Shanghai Institute for Mathematics and Interdisciplinary Sciences (Dec 2024)

14.  Statistical Methods for Network Analysis, Sanya (Dec 2024)

15.  Sixth Conference on Computational and Mathematical Bioinformatics and Biophysics, Sanya (Dec 2024)

16.  TOPS (Tsinghua Open Problems Seminar) Lecture, Qiuzhen College, Tsinghua (Nov 2024)

17.  IMS, Singapore (Sep and Oct 2024)

18.  The International Congress of Basic Science, Beijing (July 2024)

19.  Peking U (July 2024)

20.  Shanghai Institute for Mathematics and Interdisciplinary Sciences (June 2024)

21.  SIAM Conference on Applied Linear Algebra, Paris (May 2024)

22.  Plenary Talk, Current Developments in Mathematics and Physics, Beijing (April 2024)

23.  YMSC, Tsinghua (Feb 2024)

24.  Invited Lecture (45-min), International Congress of Chinese Mathematicians (ICCM), Shanghai (Jan 2024)

25.  Kent, LSE and Cambridge, under London Mathematical Society and Royal Statistical Society (Nov 2023)

26.  First Symposium of Geometry and Statistics in China, Beijing (July 2023)

27.  Pujiang Innovation Forum, hosted by Ministry of Science and Technology of China, Shanghai Municipal Government (July 2023)

28.  The Inaugural International Congress of Basic Science, Guest of Honor, People's Hall of China, Beijing (July 2023)

29.  YMSC, Tsinghua (July 2023)

30.  YMSC, Tsinghua (June 2023)

31.  Statistics Centre, Peking U (May 2023)

32.  Applied/Computational Mathematics/Statistics, Notre Dame (April 2023)

33.  Mathematics, Duke (March 2023)

34.  Statistics and Geometry Conference, Harvard (Feb 2023)

35.  Probability Seminar, Harvard (Feb 2023)

36.  Mathematics, Berkeley (Feb 2023)

37.  Biostatistics, Berkeley (Feb 2023)

38.  Statistics/Biostatistics, Stanford (Jan 2023)

39.  Simons Center, Stony Brook (Nov 2022)

40.  Statistics, George Mason (Nov 2022)

41.  Statistics, Columbia (Oct 2022)

42.  Statistics, UW-Madison (Oct 2022)

43.  CMSA, Harvard (Oct 2022)

44.  Statistics, Carnegie Mellon (Oct 2022)

45.  Statistics, Pitt (Sep 2022)

46.  Mathematical Sciences, WPI (Sep 2022)

47.  Statistics, Harvard (Sep 2022)

                                                                            

 

[CONFERENCES & WORKSHOPS]

     

 

 

Ø  Meeting the Statistical Challenges in High Dimensional Data and Complex Networks, Singapore, February 5-16, 2018 (Co-organizer) [IMPRINTS]

Ø  Interactions of Statistics and Geometry (ISAG I), February 14-18, 2022 (Co-organizer)

Ø  The First Harvard Conference on Geometry and Statistics, Center of Mathematical Sciences and Applications, Harvard University, Feb 27-March 1, 2023 (Organizer) [Poster]

Ø  The First Symposium of Geometry and Statistics in China, Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Yau Center at Tsinghua University, Beijing, July 29-31, 2023 (Organizer)

Ø  Summer Seminar Series: Statistics + Geometry + X, Shanghai Institute for Mathematics and Interdisciplinary Sciences, June 1-July 31, 2024 (Organizer)

Ø  Interactions of Statistics and Geometry (ISAG II), Singapore, October 14-25, 2024 (Co-organizer)

Ø  Winter Seminar Series: Statistics + Geometry + X, Shanghai Institute for Mathematics and Interdisciplinary Sciences, June 1-July 31, 2024 (Organizer)

Ø  The Second Symposium of Geometry and Statistics in China, A Satellite Conference of 2024 Annual Meeting of International Congress of Chinese Mathematicians, Shanghai (SIMIS) and Sanya (TSIMF), January 8-12, 2024 (Organizer)

Ø  Symposium on Advancing Data Science, Shanghai, October 11, 2025 (Organizer)

Ø  The Second Harvard Conference on Geometry and Statistics, Center of Mathematical Sciences and Applications, Harvard University, November 17-19,  2025 (Organizer) [Poster]

Ø  Interface of Data Science and Geometry (IDSG) Summer Series, Shanghai Institute for Mathematics and Interdisciplinary Sciences, May 20- July 31, 2026 (Organizer)

Ø  Dual Trimester Program on Geometric Statistics: Theory, Application, and Computation, Hausdorff Research Institute for Mathematics, scheduled in 2026 (Co-organizer)

Ø  Interactions of Statistics and Geometry (ISAG III), Bonn, 2026 (Co-organizer)

 

[PROFESSIONAL SOCIETY]

     

 

Elected Member, International Statistical Institute (ISI), 2018-present

 

[UNIVERSITY SERVICE]

     

 

Member,  University Research Committee (URC) Expert Panel, 2024-present

Member,  University Research Committee (URC) Expert Panel (Informatics and Mathematics), 2022-2023

 

 

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