Dr. Yi Sun received the B.S. and M.S. degrees from the Shanghai Jiao Tong University, China, and the Ph.D. from the University of Minnesota, Minneapolis, in 1997, all in electrical engineering. Dr. Sun was a lecturer at the Shanghai Jiao Tong University from 1985 to 1993, a visiting scientist at the Concordia University in the summer of 1993, a postdoctoral research fellow at the University of Utah from March to September 1997, and a postdoctoral research associate at the University of Connecticut from October 1997 to August 1998. Since September 1998, Dr. Sun has been an Assistant Professor and then Associate Professor in the Department of Electrical Engineering at the City College of City University of New York. 

PhD, Department of Electrical Engineering, University of Minnesota, Minneapolis, MN, 1997

MSEE, Shanghai Jiao Tong University, Shanghai, P. R. China, 1985

BSEE, Shanghai Jiao Tong University, Shanghai, P. R. China, 1982

Undergraduate 

• ENGR 20400 Electrical Circuits 
• EE 31200 Communication Theory 
• EE 42200 Communication Lab 
• EE 46000 Computer Communications 
• EE 51003 Independent Study/Project 
• EE 59866 Senior Design I 
• EE 59867 Senior Design II 
• Bridge Program 

Graduate 

• EE F6300 Wireless Communications 
• EE G6500 Neural Networks and Deep Learning
• EE I7100 Statistical Communications 
• EE I7200 Spread Spectrum 
• EE I7300 Digital Communications I 
• EE I7400 Digital Communications II  
• EE I9600 Master Report 
• EE I9700 Graduate Project 
• EE I9900 Master Thesis 

Graduate (at Columbia University) 

• ELEN E6201 Linear System Theory 
• ELEN E4702 Digital Communications 

Dr. Sun's research interests include system modeling, theoretical foundation, algorithm development, and performance analysis with applications in various fields, in the past including wireless communications, neural network, magnetic resonance imaging (MRI) and angiography (MRA), image processing, pattern recognition, robotics, and optical biopsy. Currently, my research is focused on single molecule localization microscopy (SMLM), 6G-future wireless communications, artificial intelligence (AI), and intimate relationship modeling. 

Research Accomplishments 

• Single molecule localization microscopy 
  • Discovered and proved the spatiotemporal resolution - the uncertainty principle of single molecule localization microscopy (SMLM) 
  • Derived Markov chain models of emitter photoactivation processes
  • Developed the universal models for data frames and data movies, respectively 
  • Analyzed Cramer-Rao lower bound (CRLB) for multiple emitters 
  • Defined information-theoretical signal-to-noise ratio (SNR) 
  • Proposed information-sufficient segmentation of data movie 
  • Proposed unbiased Gaussian information achieving (UGIA) estimators for data frames and a data movie, respectively 
  • Developed image quality metrics of root mean square minimum distance (RMSMD), RMSMD with partition (RMSMD-P), and root mean square error with partition (RMSE-P) 
  • Developed expectation maximization (EM) localization algorithm and 3D maximum likelihood localization algorithm 
• Wireless communications and network
  • Based on the Hopfield neural network (HNN), developed the family of likelihood ascent search (LAS) detectors that is extensive applied in multi-in multi-out (MIMO) channels, including code division multiaccess (CDMA), spectrotemporal random spreading code, and multiantenna channels 
  • Developed the family of local maximum likelihood (LML) detectors 
  • Discovered and proved the LML characteristic of large random channels. It laid the theoretical foundation that a linear-complex local search algorithm can approach the NP-hard global maximum likelihood (GML) performance in large random channels. This result prophesied extensive development and success of local search algorithms in large MIMO channels, in particular large multiantenna channels. 
  • Obtained the closed-form bit error rate (BER) and proved the optimality of LML detectors in large MIMO channels 
  • Discovered and proved an upper bound of BER and a lower bound of asymptotic multiuser efficiency for the LAS and LML detectors, respectively 
  • Developed the bandwidth-efficient orthogonal frequency-division multiplexing (OFDM) 
  • Disproved the long-standing simplex conjecture 
  • Developed an algorithmic approach to optimal signal set design 
  • Analyzed spectral efficiency of random-access channel 
  • Analyzed capacity of retransmission channels 
  • Defined and analyzed the transport capacity of ad hoc networks
• Neural networks
  • Developed the modified Hopfield neural network (MHNN) 
  • Discovered the generalized updating rule for MHNN and proved its stability in any sequence of updating modes
  • Applied MHNN to image restoration and reconstruction and analyzed performance
  • Analyzed performance of Kohonen self-organization mapping (SOM) neural network 
  • Developed feedforward neural network approach to target recognition 
• Magnetic resonance imaging 
  • Analyzed performance of maximum intensity projection (MIP) 
  • Developed the local maximum mean approach to micro-vessel detection 
  • Proposed a model-based approach to neovascularization assessment of tumor and atherosclerosis in dynamic contrast enhanced (DCE) MRI 
• Robotic source search, optical biopsy, signal detection, signal analysis, pattern recognition, etc. 
  • Proposed an adaptive approach to robotic source search 
  • Developed the biomarker spectral subspace approach to cancer detection 
  • Developed a local maximum mean (LMM) approach to point target detection 
  • Analyzed power spectrum of pulse train over random time scaling 
  • Developed the approach of visible and invisible region centers to shape classification 

Recruit Students and Postdoc in Research

• PhD and Postdoc

I am recruiting new PhD students and Postdoc to participate in federal funded research projects. If you are interested, please send me your CV and transcripts. 

• Master and Undergraduate 

For master or undergraduate students, if you are interested in research, please send me your CV and transcripts. 

Current Students in Research Supported by Federal Funding

• PhD 
  • Zhiqiang Tang
  • Mona Sharifi
• Master 
  • Kofi Bosompim, K-12 teacher
• Undergraduate
  • Nafiul Mia
  • Amina Shamraze
  • Qudrat E. Khoda

Past Three-Year Students in Research 

• Undergraduate 
  • Christian Chu Yang, Fall 2023, Spring, Summer and Fall 2024
  • Jasia Meher Chowdhury, Fall 2023, Spring 2024
  • Batoul El Sayed Mohamad, Fall 2023, Spring 2024
  • Kisholoy Saha, Fall 2023
  • Nicholas J Podias, Fall 2023 
  • Jumana Azar, Spring 2023 
  • Tazdid Hossain, Spring 2023
  • Maggie Liang, Spring 2023
  • Abdou Niang, Spring and Summer 2022
  • Ming En Wu, Summer 2022 
  • Petr Lelikov, Summer 2022 
  • Peining Huang, Summer 2022 
  • Brian Lee, Summer 2022 
  • Andrea Li, Summer 2022 
  • Tony Lin, Summer 2022
  • Puja Saha, Spring 2022 
  • Maisha Tapti, Spring 2022
  • Yeiry Chaverra, Spring 2022 
  • Eric Iliyev, Spring 2022 
  • Scarlett Solis Lozano, Spring 2022
  • Michal Kropiewnicki, Summer and Fall 2021 
  • Michelle A Orna Capito, Summer 2021 
  • Juan C Zhanay, Summer 2021
  • Michael C Zhanay, Summer 2021

Journal papers

1. Y. Sun, "The Family of LML Detectors and the Family of LAS Detectors for Massive MIMO Communications," in Innovation in MIMO Systems (ISBN 978-0-85014-793-3), London, UK: IntechOpen, 2024. (See: arXiv:2407.19709) (Invited). 
2. Y. Sun, "Markov chain models of emitter activations in single molecule localization microscopy," Optics Express, vol. 32, no. 19, pp. 33779-33794, Sept. 2024. 
3. Y. Sun, "Partition of estimated locations: an approach to accurate quality metrics for stochastic optical localization nanoscopy," JOSA A, vol. 39, no. 12, pp. 2307-2315, Dec. 2022.
4. Y. Sun and Y. Guan, "Effect of unknown emitter intensities on localization accuracy in stochastic optical localization nanoscopy using single frames," JOSA A, vol. 38, no. 12, pp. 1830-1840, Nov. 19, 2021. 
5. M. Sun and Y. Sun, "Information sufficient segmentation and signal-to-noise ratio for 3D astigmatism stochastic optical localization nanoscopy," Electr. Letters, vol. 58, no. 2, Oct. 28, pp. 58-60, 2021. 
6. Y. Sun, "Information sufficient segmentation and signal-to-noise ratio in stochastic optical localization nanoscopy," Optics Letters, vol. 45, no. 21, pp. 6102-6105, Nov. 1, 2020. 
7. Y. Sun, "Potential quality improvement of stochastic optical localization nanoscopy images obtained by frame by frame localization algorithms," Scientific Reports, vol. 10, no. 1, pp. 11824, July 16, 2020.
8. J. Fan, Y. Sun, Y. Xia, J. M. Tarbell, and B. M. Fu. "Endothelial surface glycocalyx (ESG) components and ultra-structure revealed by stochastic optical reconstruction microscopy (STORM)," Biorheology, vol. 56, no. 2-3, pp. 77-88, 2019. 
9. Y. Sun, "Root Mean square minimum distance as a quality metric for stochastic optical localization nanoscopy images," Scientific Reports, vol. 8, no. 1, pp. 17211, Nov. 21, 2018. 
10. Y. Sun, L. Zheng, P. Zhu, and X. Wang, "On optimality of local maximum-likelihood detectors in large-scale MIMO channels," IEEE Trans. Wireless Commun., vol. 15, no. 10, pp. 7074-7088, Nov. 2016. 
11. X. Zhang, Y. Sun, and J. Xiao, "Adaptive source search in a gradient field," Robotica, vol. 33, no. 8, pp. 1589-1608, 2015. 
12. Y. Sun, "Distribution of intravascular and extravascular extracellular volume fractions by total area under curve for neovascularization assessment by DCE-MRI," J. Medical Signals & Sensors, vol. 4, no. 3, pp. 159-170, July-Sept. 2014. 
13. Y. Sun, and X. Wang, "Power spectral density of pulse train over random time scaling," IET Signal Processing, vol. 8, no. 6, pp. 601-605, 2014. 
14. L. Shi, M. Zeng, Y. Sun, and B. M. Fu, "Quantification of blood-brain barrier solute permeability and brain transport by multiphoton microscopy," J. Biomech. Engr., vol. 136, pp. 031005, 2014.  
15. Y. Sun, "Localization precision of stochastic optical localization nanoscopy using single frames," J. Biomedical Optics, vol. 18, no. 11, pp. 111418, Oct. 2013. 
16. J. Shi, Y. Sun, X. Zhang, and J. Xiao, "Random access channel with retransmission gain," IEIE Trans. on Smart Processing and Computing, vol. 2, no. 3, pp. 148-159, 2013 (invited). 
17. Y. Sun, and J. Xiao, "Multicode sparse-sequence CDMA: approach to optimum performance by linearly complex WSLAS detectors," Wireless Personal Communications, vol. 71, pp. 1049-1056, 2013. 
18. C.-H. Liu, Y. Zhou, Y. Sun, J. Y. Li, L. X. Zhou, S. Boydston-White, V. Masilamani, K. Zhu, Y. Pu, and R. R. Alfano, "Resonance Raman and Raman spectroscopy for breast cancer detection," Technology in Cancer Research & Treatment, vol. 12, no. 4, pp. 371-382, 2013. 
19. X. Zhang, Y. Sun, and J. Xiao, "A novel radio source search algorithm using force field vectors and received signal strengths," Int. J. Mechatronics Automation, vol. 3, no. 1, pp. 25-35, 2013. 
20. Y. Zhou, C.-H. Liu, Y. Sun, Y. Pu, S. Boydston-White, Y. Liu, and R. R. Alfano, "Human brain cancer studied by resonance Raman spectroscopy," J. Biomedical Optics, vol. 17, no. 11, pp. 116021, 2012. 
21. Y. Sun, Y. Pu, Y. Yang, R. R. Alfano, "Biomarkers spectral subspace for cancer detection," J. Biomedical Optics, vol. 17, no. 10, pp. 107005, 2012. 

Conference papers

1. A. Shamraze, Q. E. Khoda, and Y. Sun, "KAN: A new architecture of neural network for AI," 2024 Summer Undergraduate Research Symp., CCNY, New York, NY, Aug. 8, 2024. 
2. N. Mia, C. C. Yang, and Y. Sun, "Successive interference cancellation approach with brightest pixel picker for data movie," 2024 Summer Undergraduate Research Symp., CCNY, New York, NY, Aug. 8, 2024. 
3. Y. Sun, "Markov chain model of emitter activations in superresolution microscopy," 9th Int. Conf. Signal Image Proc., ICSIP 2024, Nanjing, China, July 12-14, 2024. 
4. Y. Sun, "Modeling of data movie in single molecule localization microscopy," Modeling, Data Analy. & AI in Eng. Conf., MadeAI 2024, Porto, Portugal, July 2-5, 2024. 
5. N. Mia, C. C. Yang, and Y. Sun, "Successive interference cancellation approach to single molecule localization for superresolution imaging," CCNY GSoE Industry Day Symp., New York, NY, May 14, 2024. 
6. A. Niang, M. E. Wu, P. Lelikov, and Y. Sun, "Making a low-cost superresolution microscope," 2022 STEM Summer Research Symp., CCNY, New York, NY, Aug. 11, 2022. 
7. Y. Sun, "Spatiotemporal resolution as an information theoretical property of stochastic optical localization nanoscopy," 2020 Quantitative BioImaging Conf., QBI 2020, Oxford, UK, Jan. 6-9, 2020.
8. Y. Sun, "Temporal correlation approach to quality improvement of frame by frame localization nanoscopy images," 2019 IEEE 4th Int. Conf. Signal & Image Proc., ICSIP 2019, Wuxi, China, July 19-21 2019.
9. B. M. Fu, J, Fan, Y. Sun, and J. M. Tarbell. "Endothelial surface glycocalyx revealed by stochastic optical reconstruction microscopy (STORM)." In FASEB JOURNAL, vol. 32, no. 1. 2018.
10. J. Fan, Y. Sun, Y. Xia, J. M. Tarbell, and B. M. Fu, "Endothelial surface glycocalyx (ESG) components and ultra-structures revealed by stochastic optical reconstruction microscopy (STORM)," Joint meet. Europ. Soc. Clinic. Hemorh. & Microcirc, The International Society of Clinical Hemorheology and the International Society of Biorheology, Krokow, Poland, July 2-6, 2018.
11. J. Fan, Y. Sun, Y. Xia, J. M. Tarbell, and B. M. Fu, "Ultra-structure of endothelial surface glycocalyx revealed by stochastic optical reconstruction microscopy (STORM)," 8th World Congr. Biomech, Dublin, Ireland, July 8-12, 2018.
12. Y. Sun, Y. Zeng, W. Yen, J. Tarbell, and B. Fu, "Expectation maximization can outperform Cramer-Rao lower bound in stochastic optical localization nanoscopy," 2014 Conference on Quantitative BioImaging, Albuquerque, NM, Jan. 9-11, 2014.