MENU
Sky View Through an Archway on Campus

Li, Xianqi

Editing Help

Please reference the Faculty Profile Editing Guide if you have any questions or issues updating your profile. If you receive any error notices please contact webservices@fit.edu.

Xianqi Li

Assistant Professor | College of Engineering and Science: Department of Mathematics and Systems Engineering

Affiliate Faculty | College of Engineering and Science: Biomedical Engineering and Science

Program Chair of Operations Research

Contact Information

xli@fit.edu
(321) 674-6076
CRF202A
Google Scholar
Research Website

Expertise

Generative AI Models; Linear/Nonlinear Optimization Methods; Statistical Inference ; Medical Image Analysis

Personal Overview

I am an Assistant Professor in the Department of Mathematics and Systems Engineering at the Florida Institute of Technology, where I also serve as Program Chair of Operations Research. I am additionally affiliated with the Department of Biomedical Engineering and Science. Before joining Florida Tech in 2021, I was a Postdoctoral Research Fellow in the Department of Radiology at Harvard Medical School from 2018 to 2021, and earlier a Graduate Research Fellow in the Informatics Institute at the University of Florida. I earned my Ph.D. in Applied Mathematics from the University of Florida in 2018, an M.S. in Electrical and Computer Engineering from the University of Florida in 2015, an M.S. in Mathematical and Statistical Sciences from The University of Texas Rio Grande Valley in 2009, and a B.S. in Computational Mathematics from Liaoning University in 2007.

My research lies at the intersection of AI, applied mathematics, optimization, inverse problems, statistical learning, and computational modeling, with particular emphasis on healthcare applications. My work focuses on designing mathematically grounded models and algorithms with theoretical performance guarantees for challenging problems in imaging informatics, medical image analysis, data analytics, and scientific machine learning. My research has been supported by the collaborative NSF Division of Mathematical Sciences project Data-driven Realization of State-space Dynamical Systems via Low-complexity Algorithms.

My publications span a broad range of leading venues in applied mathematics, signal processing, imaging, and computational medicine, including Radiology, IEEE Transactions on Signal Processing, Pattern Recognition, Neuro-Oncology Advances, Computers in Biology and Medicine, Journal of Imaging Informatics in Medicine, NMR in Biomedicine, Inverse Problems and Imaging, Journal of Magnetic Resonance Imaging, Journal of Materials Science, IEEE Journal of Radio Frequency Identification, Linear Algebra and its Applications, and Theoretical and Mathematical Physics. 

At Florida Tech, my teaching spans applied mathematics, operations research, statistics, and data science, with particular emphasis on optimization, mathematical modeling, and AI-driven computational methods. I have taught courses such as Linear Optimization, Nonlinear Optimization, Probability and Statistics, Mathematical Statistics, Graph Neural Networks, Deep Generative AI Models, and special topics courses in Spatial-temporal Deep Learning Models and Geometric Deep Learning for Brain Health & Cognitive Aging. 

 

 

Educational Background

Ph.D.,  Applied Mathematics,  University of Florida, Gainesville, FL, 2018

M.S.,   Electrical & Computer Engineering, University of Florida, Gainesvile, FL, 2015

M.S.,   School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, TX, 2009

B.S.,    Computational Mathematics, Liaoning University, Shenyang, China, 2007

Professional Experience

2021.08 - present:    Assistant Professor, Department of Mathematics and Systems Engineering, Florida institute of Technology

2018.07 - 2021.08:   Postdoctoral Research Fellow, Department of Radiology, Harvard Medical School

2016.01 - 2018.06:   Graduate Research Fellow, Informatics Institute, University of Florida

 

Current Courses

 

Spring, 2026

  • MTH 5335 - Nonlinear Optimization: Models and Methods
  • MTH 2401 - Probability and Statistics

 

  • ORP 5091 - Special Topics in OR 2: Geometric Deep Learning for Brain Health and Cognitive Aging

 

Previous Teaching

Fall 2025: 

  • MTH 5225 - Linear Optimization: Models and Methods 
  • MTH 5051 - Applied Discrete Math (Graph Theory and Graph Neural Network) 
  • ORP5090 - Special Topics in OR 1: Integrating Physics-Based Modeling with Deep Learning
  • MTH 4990 - Undergraduate Research

Summer 2025: 

  • ORP 5090 - Special Topics in OR 1: Theoretical Perspectives on Deep Generative Models

Spring 2025: 

  • MTH 5335 - Nonlinear Optimization: Models and Methods 
  • MTH 5051 - Applied Discrete Math (Graph Theory and Graph Neural Network) 
  • ORP 5091 - Special Topics in OR 2: Deep Generative AI Models
  • ORP 5003 - Operations Research Practice in Data Science (online)
  • MTH 4990 - Undergraduate Research

Fall, 2024: 

  • MTH 5225 - Linear Optimization: Models and Methods 
  • MTH 5051 - Applied Discrete Math

Summer, 2024:

  • ORP 5090 -  Special Topics in OR 1: Spatial-temporal Deep Learning Models
  • MTH 2401 - Probability and Statistics

Spring, 2024:

  • ORP 5003 - Operations Research Practice in Data Science
  • MTH 5335 - Nonlinear Optimization
  • MTH 2401 - Probability and Statistics

Fall, 2023:  

  • MTH 5411 - Mathematical Statistics I 
  • MTH 5225 - Linear Optimization: Models and Methods 

Summer, 2023: 

  • MTH 5051 - Applied Discrete Math

Spring, 2023:

  • ORP 5003 - Operations Research Practice in Data Science
  • MTH 5051 - Applied Discrete Math

Fall, 2022:

  • MTH 3107 - Optimization
  • MTH 5051 - Applied Discrete Math

Spring, 2022:

  • MTH 5335 - Nonlinear Optimization: Models and Methods

Fall, 2021:

  • MTH 5225 - Linear Optimization: Models and Methods

Selected Publications

(^*:Corresponding Author; ^**: Co-first Author)

1. Y Shokrollahi, S Yarmohammadtoosky, MM Nikahd, P Dong, X Li*, L Gu*, "A comprehensive review of generative AI in healthcare", in review, 2024

2. TH Pham, X Li, KD Nguyen, "SeUNet-trans: A simple yet effective UNet-transformer model for medical image segmentation", IEEE Access, 2024

3. Y Shokrollahi, P Dong, C Zhou, X Li*, L Gu*, "Deep Learning-based prediction of stress and strain maps in arterial walls for improved cardiovascular risk assessment", Applied Sciences, 12/2023

4. X. Li, O. C. Andronesi, et al., "Deep learning super-resolution magnetic resonance spectroscopic imaging of brain metabolism and mutant isocitrate dehydrogenase glioma", Neuro-oncology advances, 05/2022

5. X. Li, O. C. Andronesi, et al., "Super-resolution whole-brain 3D MR spectroscopic imaging for mapping d-2-hydroxyglutarate and tumor metabolism in isocitrate dehydrogenase 1–mutated human gliomas", Radiology, 03/2020

6. S Basu**, X Li**, G Michailidis, "Low rank and structured modeling of high-dimensional vector autoregressions", IEEE Transactions on Signal Processing, 01/2019

7. Y Chen, X Li^*, Y Ouyang, E Pasiliao, "Accelerated Bregman Operator Splitting with Backtracking", Inverse Problems and Imaging, 12/2017

8. Z. Qiao, X. Li, "An integrable equation with nonsmooth solitons", Theoretical and Mathematical Physics, 05/2011

Recognition & Awards

External and Internal Support

  1. Collaborative Research: Data-driven Realization of State-space Dynamical Systems via Low-complexity Algorithms, the Division of Mathematical Sciences (DMS), the National Science Foundation (award number 2410676), USA, Aug 2024-July 2027 (Collaborative PIs: Sirani Perera (Lead PI), Kshitij Khare at UF with the award number 2410677, Aaron Welters (50%) and Xianqi Li (50%) at FIT, with the award number 2410678).
  2. Traveling Support ($3500), COES, Florida Tech, 01/2025 -05/2025.
  3. Provost Seed Grant ($5000), Provost Office, Florida Tech, 2025.
  4. Summer Research Experience (SRE) Award ($2000), Burrell College of Health Sciences and Florida Tech, 06/2025 - 08/2025

 

Current Ph.D students:

  • Michael Trimboli 
  • Sri Madhavareddy (coadvise with Prof. Ke-gang Wang)
  • Wenxi Liu

 

Graduated Master and Ph.D students Career: 

(^*: collaborative student)

  • Alexander Bugielski (MS, 2024) ------ Machine Learning Engineer, Northrop Grumman Corporation, FL.
  • Mohammed Alsubaie (Ph.D, 2024) ------ Assistant Professor, Taif University, Saudi Arabia. 
  • Yasin Shokrollahi^* (Ph.D, 2023) ------ Senior Data Scientist, MD Anderson,  TX.  (Advisor: Prof. Linxia Gu)
  • Tan Hanh Pham^* (Ph.D. 2025) ------ Postdoctoral Researcher, Harvard Medical School, MA. (Advisor: Prof. Kim-Doang Nguyen)

 

Journal Editorial Member:

  • Journal of Nonlinear Mathematical Physics (JNMP)

 

Research

  • Generative AI Models
  • Accelerated First-order Optimization Methods 
  • Vector Autoregressive Models
  • Medical Image Analysis
Edit Page