About

Rong Tong is an Associate Professor in the Department of Chemical Engineering at Virginia Tech. He earned his Ph.D. from the University of Illinois at Urbana-Champaign in 2010 and his B.S. from Fudan University in 2005. His research interests include polymers and materials chemistry, biomaterials and nanoparticles, and drug delivery systems, including cancer nanomedicine. He is actively involved in research related to these areas and is associated with the Goodwin Hall at Virginia Tech. His work focuses on the development and application of advanced materials for biomedical and nanotechnology purposes.

Research topics

• Combinatorial chemistry
• Organic chemistry
• Computer Science
• Materials science
• Chemistry
• Pharmacology
• Internal medicine
• Medicine
• Oncology
• Biochemistry
• Cancer research
• Nanotechnology
• Immunology
• Polymer chemistry
• Biomedical engineering

Selected publications

• A systematic approach to develop and characterize a compositionally equivalent buprenorphine in situ forming implant (Sublocade®)

  Journal of Controlled Release · 2026-03-04

  article
  PublisherDOI

• Predict the ceiling

  Chem · 2026-04-14

  articleSenior author
  PublisherDOI

• Robust Speech Recognition for Visual Acuity Testing in Multi-Speaker Clinical Environments

  2025-08-03

  articleSenior author

  Automatic Speech Recognition (ASR) in multispeaker clinical environments remains a significant challenge due to overlapping speech, ambient noise, and the lack of reference audio for speaker identification. This paper presents a robust, target-aware ASR system tailored for visual acuity testing, where accurate transcription of patient responses is critical. We finetune the Whisper model on a domain-specific clinical dataset to improve transcription accuracy and reduce latency. To address crosstalk and overlapping speech, we evaluate two state-of-the-art speech separation models, MossFormer2 and SepFormer, and integrate reference-free Target Talker Identification (TTI) strategies based on signal-to-noise ratio (SNR) and cosine similarity. Our evaluation on synthetic and real clinical datasets demonstrates that the fine-tuned Whisper model, when combined with MossFormer2 and cosine similarity-based TTI, achieves the lowest Word Error Rate (WER) across two-speaker scenarios. These results highlight the importance of integrating robust separation and speaker identification methods to enable accurate, efficient ASR in clinical settings. The system provides a scalable foundation for future work in more complex environments with three or more speakers.

  PublisherDOI

• LLM-Enhanced Spoken Named Entity Recognition Leveraging ASR N-Best Hypotheses

  2025-08-03

  articleSenior author

  Identifying Personally Identifiable Information (PII) from spoken documents is crucial for privacy preservation in speech processing. Unlike written text, spoken language exhibits greater variability due to factors such as accent, emotion, hesitation, and vocabulary choice, which can complicate PII detection. A standard approach involves using Automatic Speech Recognition (ASR) followed by Named Entity Recognition (NER) to identify PII from speech input. However, the accuracy of ASR is pivotal for effective PII discovery, and the inherent complexities of speech production can lead to ASR errors, hindering PII detection. To address this limitation, we propose a novel method that integrates an LLM-based module after ASR to perform error correction and PII tagging, leveraging the richer contextual information available in the n-best outputs from the ASR system. We systematically investigate various prompting strategies, including Zero-shot, Few-shot, and Chain-of-Thought prompting, to guide the LLM. Our experimental results demonstrate that the LLM-based error correction yields a substantial F1 improvement on PII tagging. Furthermore, incorporating the n-best list consistently improves the F1 score, and Chain-of-Thought prompting outperforms other strategies like Zero-shot and Few-shot prompting.

  PublisherDOI

• Cyclic Polyesters: Synthetic Methods and Perspectives

  Chemistry - An Asian Journal · 2025-12-01 · 1 citations

  reviewSenior authorCorresponding

  Cyclic polymers exhibit a distinctive topology that significantly differs from their linear counterparts due to their circular structure and absence of chain ends. These structural characteristics confer unique physico-mechanical properties, positioning the development of degradable and environmentally benign cyclic polyesters at the forefront of macromolecular chemistry. In this review, we examine recent advances in synthetic strategies of various cyclic polyesters and provide an overview of these cyclic polyesters' properties. Challenges in both the synthesis and practical application of these cyclic polyesters are also discussed.

  PublisherDOI

• Functionalized Cyclic Poly(α‐Hydroxy Acids) via Controlled Ring‐Opening Polymerization of O‐Carboxyanhydrides

  Angewandte Chemie · 2025-04-07

  articleSenior author

  Abstract Linear poly(α‐hydroxy acids) are important degradable polymers, and they can be efficiently prepared by ring‐opening polymerization of O‐carboxyanhydrides with pendant functional groups. However, attempts to prepare cyclic poly(α‐hydroxy acids) have been plagued by side reactions, including epimerization and uncontrolled intramolecular chain transfers or termination, that prevent the synthesis of high‐molecular‐weight stereoregular cyclic polyesters. Herein, we report a scalable method for the synthesis of high‐molecular‐weight (>100 kDa) stereoregular functionalized cyclic poly(α‐hydroxy acids) by means of controlled polymerization of O‐carboxyanhydrides using a catalytic system consisting of a lanthanum complex with a sterically bulky ligand and a manganese silylamide. Additionally, using this system, we could readily prepare cyclic block poly(α‐hydroxy acids) by means of sequential addition of O‐carboxyanhydrides. The obtained cyclic polyesters and their cyclic block copolyesters exhibit distinctive physicochemical properties—including elevated phase transition temperature, improved toughness, and reduced viscosity—compared to their linear counterparts.

  PublisherDOI

• Machine Learning for Developing Sustainable Polymers

  Chemistry - A European Journal · 2025-04-23 · 7 citations

  reviewSenior authorCorresponding

  Sustainable polymers from renewable resources have been gaining importance due to their recyclability and reduced environmental impact. However, their development through conventional trial-and-error methods remains inefficient and resource-intensive. Machine learning (ML) has emerged as a powerful tool in polymer science, enabling rapid prediction, and discovery of new chemicals and materials. In this review, we examine emerging trends in ML applications for sustainable polymer development, focusing on catalyst discovery, property optimization, and new polymer design. We analyze unique challenges in applying ML to sustainable polymers and evaluate proposed solutions, providing insights for future development in this rapidly evolving field.

  PublisherDOI

• Nanoparticle Adjuvant Design Enhances Germinal Center Responses Targeting Conserved Subdominant Epitopes for Pan‐Coronavirus Vaccine Development

  Advanced Science · 2025-11-25 · 1 citations

  articleOpen access

  Current SARS-CoV-2 vaccines primarily elicit antibodies targeting the variable receptor-binding domain in the S1 subunit of the spike protein, resulting in limited cross-reactivity and short-lived immunity against emerging variants. The conserved S2 subunit presents a promising vaccine target for broad and durable protection, but the immunodominance in vaccine-induced germinal center (GC) responses hinders effective antibody generation against S2. Here, a polymeric toll-like receptor 7 agonist nanoparticle (TLR7-NP) adjuvant is reported, well designed to enhance lymph node targeting and more efficiently activate S2-specific B cells. When combined with Alum-adsorbed SARS-CoV-2 HexaPro spike protein, TLR7-NP promotes early GC recruitment of S2-specific B cells and overcomes the immunodominance, leading to early and robust S2-specific antibody responses. Compared to conventional TLR7-Alum adjuvanted subunit vaccine and clinically used SARS-CoV-2 mRNA vaccine, TLR7-NP adjuvant induces stronger humoral immune responses across sarbecoviruses and betacoronaviruses and promotes long-lived plasma cell and memory B cell formation. These findings present a direct B cell-activating adjuvant approach for effective pan-coronavirus vaccine development.

  PublisherOA PDFDOI

• SegReConcat: A Data Augmentation Method for Voice Anonymization Attack

  2025-10-22

  article

  Anonymization of voice seeks to conceal the identity of the speaker while maintaining the utility of speech data. However, residual speaker cues often persist, which pose privacy risks. We propose SegReConcat, a data augmentation method for attacker-side enhancement of automatic speaker verification systems. SegReConcat segments anonymized speech at the word level, rearranges segments using random or similarity-based strategies to disrupt long-term contextual cues, and concatenates them with the original utterance, allowing an attacker to learn source speaker traits from multiple perspectives. The proposed method has been evaluated in the VoicePrivacy Attacker Challenge 2024 framework across seven anonymization systems, SegReConcat improves de-anonymization on five out of seven systems <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Code can be found at https://github.com/monkeyDarefeen/SegReConcat_augmenter. Xiaoxiao Miao is the corresponding author and this work was conducted while she was at SIT..

  PublisherDOI

• Robust Audio-Visual Speech Recognition in Noisy Clinical Environments

  2025-10-22

  articleSenior author

  Visual acuity (VA) testing is a foundational clinical procedure for assessing vision clarity. Traditionally, VA testing requires patients to read optotypes aloud from a distance, with an assistant present to record the responses. Speech-enabled automation has been explored, however these solutions are highly susceptible to performance degradation from ambient noise and speech crosstalk: a common challenge in real-world, multilane clinical environments. In this work, we introduce a novel multimodal pipeline system that combines audio and visual speech recognition to enable robust and accurate VA transcription even under noisy conditions. By integrating synchronized microphone and camera inputs, our architecture employs advanced noise and crosstalk detection, and utilizes a dynamic decision module to intelligently select between audio-based ASR and lip-reading outputs based on real-time environmental analysis. To further improve accuracy under crosstalk conditions, we introduce a lip-guided audio masking mechanism that suppresses non-target speech by detecting when the patient is actively speaking. This multimodal pipeline represents a significant advancement for automated vision screening, offering enhanced transcription accuracy and reliability where single modality systems fail. Preliminary results demonstrate that the addition of lip-reading achieves a 33% and 16% relative reduction in word error rate (WER) compared to the audio-only baseline on the three-speaker and two-speaker crosstalk visual acuity test sets, respectively.

  PublisherDOI

Recent grants

• Photoredox Polymerization of O-Carboxyanhydrides for Functionalized Polyesters

  NSF · $430k · 2018–2021

Frequent coauthors

• Daniel S. Kohane

  Harvard University

  105 shared

• Jianjun Cheng

  University of Illinois Urbana-Champaign

  71 shared

• Sahadev Shankarappa

  Amrita Institute of Medical Sciences and Research Centre

  45 shared

• Homer H. Chiang

  42 shared

• Joseph B. Ciolino

  Harvard University

  36 shared

• Liqiang Wang

  36 shared

• Jonathan H. Tsui

  36 shared

• Xiaojian Yang

  National University of Defense Technology

  29 shared

Education

• Ph.D., Materials Science and Engineering

  University of Illinois

  2010

• B.S., Macromolecule Science and Engineering

  Fudan University

  2005