About

L. Jeff Hong is a Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. Prior to joining the University of Minnesota, he held academic positions at the Hong Kong University of Science and Technology from 2004 to 2014, City University of Hong Kong from 2014 to 2018, and Fudan University from 2018 to 2024. His research interests include stochastic simulation, stochastic optimization, statistical machine learning, and financial risk management. Professor Hong is currently an associate editor of Management Science and ACM Transactions on Modeling and Computer Simulation. He has also served as the Simulation Area Editor of Operations Research from 2018 to 2023 and was the President of the INFORMS Simulation Society from 2020 to 2022.

Research topics

• Computer Science
• Environmental science
• Medicine
• Materials science
• Physics
• Environmental health
• Chemistry
• Telecommunications
• Engineering
• Mechanics
• Simulation
• Biomedical engineering
• Nanotechnology
• Acoustics
• Geography
• Virology
• Atmospheric sciences
• Process engineering
• Meteorology
• Internal medicine
• Radiology

Selected publications

• A Regularized Inverse Holographic Volume Reconstruction (RIHVR) GUI Program for Digital Inline Holography (DIH)

  Journal of Open Research Software · 2026-01-01

  articleOpen accessSenior author

  Digital inline holography is a well-established experimental technique used to capture three-dimensional (3D) information from a scene onto a two-dimensional image using a single camera. The retrieval of the 3D scene from the recorded hologram is an ill-posed inverse problem. One of the methods to compute the 3D scene from the recorded hologram is via an iterative inversion of the regularized variational formulation of the holographic reconstruction problem. However, such an iterative inversion is complex to implement and could be prohibitively computationally expensive. This has led to a high barrier to entry, resulting in limited adoption. Here, we present an open-source iterative inverse holographic volume reconstruction algorithm with compute unified device architecture (CUDA) accelerated computations that can handle large reconstruction volumes while being computationally efficient. The developed program can get 3D particle tracks from holographic particle data, enabling 3D particle tracking velocimetry and microbial tracking. For ease of use, the program has a graphical user interface (GUI) along with integrated pre-/post-processing utilities.

  PublisherDOI

• Circulating tumor cell detection in cancer patients using in-flow deep learning holography

  npj Biosensing · 2026-04-14

  preprintOpen accessSenior authorCorresponding

  Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate significantly higher CTC counts in patients with late-stage prostate cancer (n=13) compared to healthy controls (n=8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.

  PublisherOA PDFDOI

• A liquid biopsy-based proteomic assay to identify circulating tumor cell (CTC) –derived druggable targets in cancer patients.

  Journal of Clinical Oncology · 2025-02-10 · 1 citations

  article

  258 Background: Cancers are often thought of as a disease of a specific tissue; however, the drugs designed to fight them are tissue agnostic. These drugs function by typically targeting mutant or amplified proteins such as kinases, transcription factors, or other cell surface proteins. An ever-growing body of research shows that many well characterized protein targets are commonly found across many cancer types and can serve as potential targets for pan-cancer therapies. Despite this, enrollment criteria for clinical trials often do not include any test to measure the presence of a particular protein drug target, and almost universally do not attempt to quantify the target. As a result, patient-specific protein profiles should be utilized to not only minimize patient harm but to categorize patients so that drug trials can be more intentional in identifying only the patients most likely to benefit from a selected drug. To that end, we developed a liquid biopsy-based proteomic assay to measure multiple proteins of interest simultaneously from enriched circulating tumor cells (CTCs) in blood and to detect druggable protein targets. Methods: CTCs were isolated using Astrin Bioscience’s proprietary enrichment system yielding matched CTC and white blood cell (WBC) fractions. Samples were then processed in parallel following a custom developed protein aggregation capture protocol followed by trypsin digestion to yield peptide samples for bottom-up targeted peptide analysis by mass spectrometry on a FAIMS equipped Exploris 480 instrument. Results: We prospectively enrolled 20 heavily-pretreated metastatic castration resistant prostate cancer (mCRPC) patients from whom matched CTC and WBC profiles were analyzed. The median age of these patients was 74 years, 80% had Gleason GG4-5, 30% had visceral metastases, median number of prior systemic therapies was 5, and median PSA level was 135 ng/mL. The mean CTC count was 10.4 cells/mL with a median of 4.9 CTCs/mL. After CTC enrichment, we observed vastly diverse intra- and inter-patient proteomic profiles. Highly relevant proteins to mCRPC were identified and quantified from CTCs in 100% of patients for androgen receptor (AR) and B7 homolog 3 (B7-H3), 57% of patients for trophoblast cell surface antigen-2 (TROP2), 43% of patients for prostate specific membrane antigen (PSMA), 28% of patients for programmed cell death antigen 1 (PD-L1), and 14% of patients for neuroendocrine markers chromogranin A (CHGA) and delta like ligand 3 (DLL3). Conclusions: The clear differences seen between the CTC and WBC fractions, combined with expected proteins observed in the CTC fraction, supports the successful isolation of CTCs. Together, this demonstrates a first of its kind assay able to probe multiple highly relevant proteins from CTCs that could be crucial to help minimize patient harm and better design precision therapeutics.

  PublisherDOI

• A review of 3D particle tracking and flow diagnostics using digital holography

  Measurement Science and Technology · 2025-01-20 · 13 citations

  reviewOpen accessSenior author

  Abstract Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using digital holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning (ML)-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of ML in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.

  PublisherDOI

• Research on the Effects of Vane Geometry Parameters on Cyclone Pump Performance

  Journal of Applied Fluid Mechanics · 2025-06-07

  articleOpen access1st authorCorresponding

  The thickness, width, and outlet deflection angle of the cyclone pump vane are selected as research objects to enhance overall cyclone pump performance and minimize its energy loss. Numerical simulations of the flow field are conducted for five groups of impellers with different structures based on the realizable k–ε turbulence model. The analysis incorporates the velocity and pressure field distributions under various operating conditions to demonstrate how the primary geometrical parameters of the vane affect the cyclone pump’s performance. The results show that although increasing the thickness of the vane can boost the pump’s maximum efficiency within a limited range, excessive thickness narrows the flow channels between the vanes. This results in the pump reaching its lowest efficiency at a thickness of 4.4 mm. To prevent efficiency loss, the blade width should remain within a certain range. A slight increase in blade width improves the cyclone pump’s flow stability, and its effect on the head is less than its impact on efficiency. Additionally, as the deflection angle at the vane’s outlet increases, the low-pressure zone at the impeller’s inlet slightly expands, and the pressure in the volute outlet flow passage slightly increases. These changes enhance flow stability and result in a more consistent pressure distribution in the volute pump’s flow passage.

  PublisherOA PDFDOI

• Development of a Digital Inline Holography Technique to Characterize Airborne Supercooled Water Droplets and Ice Crystals for Aircraft Icing Studies

  2025-01-03

  articleSenior author

  Atmospheric icing on aircraft surfaces represents a significant aviation hazard that compromises flight safety and aerodynamic performance in cold weather conditions. Precise measurement of supercooled water droplets and ice crystal characteristics in cold environments is essential for accurate ice formation prediction. This study presents a comprehensive experimental investigation to develop an advanced digital inline holography (DIH) system capable of differentiating and characterizing supercooled water droplets and ice crystals within an icing research tunnel. The DIH system measures critical particle characteristics including liquid water content (LWC), median volume diameter (MVD), ice water content (IWC), and particle size/shape distribution. System validation was performed using standard NIST particles. Additionally, we propose a novel method for distinguishing between supercooled water droplets and ice crystals based on DIH measurements.

  PublisherDOI

• Abstract P5-04-30: Holographic and molecular characterization of early disseminated cells from breast cancer patients

  Clinical Cancer Research · 2025-06-13

  article

  Abstract Growing evidence suggests that cancer cells disseminate into blood vessels at an early stage, seeding metastatic sites in breast cancer. These early-stage tumor cells that lodge or extravasate at metastatic sites can enter dormancy, marking a potential source of late recurrence and therapy resistance. Thus, the presence of early disseminated cells poses risks to patients but also holds potential benefits for early detection and opportunities for possibly curative interventions. We evaluated this in a cohort of women with newly diagnosed early-stage breast cancer (Stage 0, 1, 2). Blood samples were collected prior to initiation of therapy and analyzed by Astrin Biosciences' AI-empowered proprietary holographic imaging platform combined with in-flow protein marker expression for the presence of disseminated tumor cells. The platform was previously trained on holographic signatures (encoding both optical and morphological signatures) of 100 million+ individual cells and could differentiate healthy from cancer cells with greater than 99% accuracy. Preliminary data from this study revealed that blood samples from the majority of early breast cancer patients exhibited disseminated cancer cells. Gene expression patterns from the enriched cancer cells were further profiled via quantitative PCR using a selective gene panel consisting of breast-specific and cancer-specific genes. We were able to identify selective sets of breast and cancer-specific genes in these patients confirming breast origin with cancer-like features. In summary, we utilized holographic imaging coupled with proprietary deep learning approaches to identify early disseminated cells in women who are undergoing screening for breast cancer. Molecular analyses of these cells confirmed breast cancer origin. Combined, this work enables Astrin Biosciences to develop a two-pronged assay consisting of holographic plus molecular characterization of disseminated cells for early detection of breast cancer. Citation Format: Justin M. Drake, Kaylee Judith Kamalanathan, Catalina Galeano-Garces, Song Yi Bae, Nathaniel R. Bristow, Kevin Mallery, Alexa Hesch, Grant Schaap, Yash Travadi, Yulia Olimpiadi, Sarah Peterson, Jiarong Hong, Jayant Parthasarathy, Badri R. Konety. Holographic and molecular characterization of early disseminated cells from breast cancer patients [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-04-30.

  PublisherDOI

• Instance segmentation from particle holograms

  Measurement Science and Technology · 2025-02-12

  articleOpen accessSenior author

  Abstract Holographic microscopy has emerged as a low-cost and highly compact technique for 3D imaging of microscopic particles in suspension. However, its broad application is largely limited by the inclusion of multiple steps in extracting the particles from the hologram image, which can be computationally expensive and often involves human intervention. We introduce HoloDINO, a transformative model that leverages instance segmentation for streamlined, end-to-end particle detection and contour extraction. By pre-training a data-intensive transformer model on synthetic particle contours and fine-tuning it on experimental data, our approach demonstrates robust performance across synthetic holograms of varying particle concentration, morphology, and optical properties, as well as experimental holograms of dental aerosols and water spray droplets. HoloDINO surpasses conventional methods, which typically involve multiple steps—such as reconstruction, autofocusing, and segmentation—by consolidating these into a single, efficient process that delivers precise morphological data for each particle in one forward pass. This advancement not only facilitates real-time applications but also significantly enhances the generalization capabilities across diverse settings, paving the way for broader adoption of holography in particle analysis.

  PublisherOA PDFDOI

• Integrated CTC- and EV-based detection of PSMA protein and efficacy of ¹⁷⁷ Lu-PSMA-617 radioligand therapy.

  Journal of Clinical Oncology · 2025-05-28 · 1 citations

  article

  5083 Background: Blood-based predictive biomarkers of sensitivity to 177 Lu-PSMA-617 are lacking, and may facilitate clinical decisions. Here, we studied whether integrated PSMA protein detection in circulating tumor cells (CTCs) and extracellular vesicles (EVs) is associated with outcomes in patients receiving 177 Lu-PSMA therapy. Methods: We enrolled 100 metastatic castrate-resistant prostate cancer (mCRPC) pts who were candidates for 177 Lu-PSMA into a prospective biomarker trial. Blood samples were collected for CTC and EV analysis at baseline, at the time of response, and at progression. Baseline characteristics included serum PSA, alkaline phosphatase (ALP), hemoglobin, albumin, and radiographic tumor burden. PSMA+ CTCs were enumerated using an AI-empowered holographic imaging platform combined with in-flow protein marker analysis (Astrin Biosciences, St. Paul, MN); PSMA protein was quantified in plasma EVs using shotgun proteomics via mass spectrometry (Arafa et al., Cancers 2024; 16: 4261). We assessed the impact of PSMA+ CTCs and EV-derived PSMA protein on PSA 50 responses, PFS, and OS. Multivariable Cox regressions were used to adjust for baseline PSA, ALP, and hemoglobin. Exploratory analyses of other EV-derived proteins were also conducted. Results: Of 100 enrolled pts, 47% had Gleason sum 9-10, 62% had >10 bone mets, 12% had visceral mets, 72% had received ≥3 prior systemic therapies, and median PSA was 57 (range 1.5–5,000) ng/mL. High PSMA+ CTC counts (> median) were associated with shorter overall survival (OS) (HR 2.71, 95%CI 1.18–6.21, p=0.02). PSA 50 response rates were similar for those with high and low PSMA+ CTC counts (39% vs 42%, p=0.8). Shotgun proteomics from plasma EV samples identified >11 000 unique proteins, of which 12% represented the cell surfaceome. EV-PSMA protein correlated with baseline PSA, ALP, and tumor burden (all p<0.05). High EV-PSMA protein (> median) was associated with worse OS (1.81, 95%CI 0.97–3.35, p=0.06). PSA 50 response rates were similar for those with high and low EV-PSMA protein (48% vs 42%, p=0.5). After multivariate adjustment, nonsignificant trends for shorter OS persisted for pts with high PSMA+ CTCs (HR 1.71, 95%CI 0.72–4.05) and high EV-PSMA levels (HR 1.49, 95%CI 0.78–2.84). Worse OS was also observed in pts with high EV levels of B7-H3 (HR 2.85, 95%CI 1.58–5.14, p=0.002), Trop-2 (HR 2.23, 95%CI 1.22–4.05, p=0.008), and STEAP1 (HR 1.69, 95%CI 0.93–3.06, p=0.08) proteins. Conclusions: In mCRPC pts receiving 177 Lu-PSMA, high PSMA+ CTC counts and high EV-derived PSMA levels portended poor survival. PSMA protein may be a novel blood-based biomarker of 177 Lu-PSMA sensitivity, facilitating treatment decisions, with relevance for other PSMA-targeting strategies. The robust detection and prognostic impact of additional cell-surface proteins ( e.g. B7-H3, Trop-2, STEAP1) may fuel the development of alternative novel therapeutics.

  PublisherDOI

• Novel Release Mechanism of Microplastics and Nanoplastics by Environmentally Relevant Sand Abrasion

  Environmental Science & Technology · 2025-09-19 · 7 citations

  article

  ). Beyond suspended debris, we discovered that MPs/NPs' (sub- to low-micron) can transfer onto sand grains- a novel and major release mechanism that could serve as a new source of MPs/NPs. Simultaneously, a dynamic layer of sand minerals was deposited on LDPE. Such mutual transfer is hypothesized to be stochastic and to interfere with the subsequent MP/NP release. Our findings highlight that MP/NP release and fate from environmentally relevant sand abrasion are more complex than our previous understanding. Correlating the input power with harmonized degradation rates indicated that solid abrasion releases debris more efficiently than fluid shear.

  PublisherDOI

Frequent coauthors

• Siyao Shao

  TandemLaunch (Canada)

  102 shared

• Michele Guala

  University of Minnesota

  92 shared

• Jiaqi Li

  Saint Anthony College of Nursing

  86 shared

• Aliza Abraham

  National Renewable Energy Laboratory

  84 shared

• Roger E. A. Arndt

  69 shared

• Ashish Karn

  68 shared

• Linyue Gao

  Nankai University

  55 shared

• Kevin Mallery

  43 shared

Education

• PhD, Mechanical Engineering

  Johns Hopkins University

  2011

• MS, Mechanical Engineering

  Johns Hopkins University

  2008

Awards & honors

• INFORMS Simulation Society President (2020-2022)
• Operations Research Simulation Area Editor (2018-2023)