Services

Consulting
Use-inspired Translational R&D Leadership Management and Support
Academic-Industrial-Partnership Industry Collaboration Management and Strategic Analysis
Public-Private Partnerships Development and Coordination
Technology Commercialization Support
Innovation Strategic Advancement Support, Analysis, and Implementation
Product Realization Strategy and Implementation Management

Research Interests

Improve the predictive value of preclinical and translational cancer research:

Combined information fusion and imaging models to readily identify and predict efficacy and toxicities of drug-radiation combination,
Leveraging systemic Imaging that combines multiple imaging modalities with multimodal imaging agents for comprehensive characterization of diseases for the achievement of image-guided therapy, precise evaluation of therapeutic effects, and rapid development of novel pharmaceuticals,
Advanced AI-enabled microfluidics applications with imaging in radiobiology for cancer treatment development,
Personalized oncology integrates predictive models and omics analysis into fast and efficient solutions to deliver the best treatment for every patient.

Precision radiation oncology simulations using digital twin technology and quantum information science applications:

Advanced patient-tailored models incorporating multi-omic, clinical, environmental, and social data powered by quantum computing automation to predict individual patient trajectories that can inform shared decision-making between patients and doctors with data-integrated digital dashboards.
Develop automated health learning systems that continuously integrate new data and knowledge across spatiotemporal scales to iteratively improve the accuracy of predictions that can support quantitative imaging longitudinal studies.
Employ quantum computing simulations to optimize radiography operation for precise delivery of treatment beams on cancer-causing tissues.
Translate personalized digital twin simulation for treatment planning and measuring the response to therapy of novel radiation drug combinations to improve patient outcomes.

Intelligent radiotherapy and novel radiation drug combination development:

Translate technology-driven research into innovation for image guidance, adaptive radiotherapy, integration of AI, heavy-particle therapy, and “flash” ultra-high dose-rate radiotherapy.
Improving imaging tools for patient positioning and treatment adaption for proton and carbon ion therapy with AI and quantum information science.
Develop and implement automated real-time in-vivo range measurement of proton and heavier charge particle therapeutic beams.
Advance clinical acceptance for intelligent radiotherapy capability for novel radiation drug combination development and use-inspired translational R&D.

Digital proximity care for diagnostic imaging and response to therapy evaluation:

Data analytics: Research can focus on developing algorithms and techniques for analyzing large volumes of medical imaging data (database harboring pre- and post-treatment images) to identify patterns, predict treatment outcomes, and improve diagnostic accuracy.
Artificial intelligence (AI) and machine learning: Investigate the application of AI and machine learning models to assist in the automated interpretation of medical images, aiding in diagnosis and treatment evaluation.
Telemedicine and remote monitoring: Explore the effectiveness of telemedicine platforms and remote monitoring technologies in providing timely and accurate diagnostic and therapy response assessments.
Image-guided interventions: Study the integration of digital imaging technologies for targeted radiotherapy (photon, proton, and carbon) to enhance the precision, safety, and efficacy of therapies.
Quantitative imaging biomarkers: Research the development and validation of quantitative imaging biomarkers that can provide objective measures for therapy response assessment, aiding in personalized treatment planning.
Image sharing and collaborative platforms: Explore the development of secure and interoperable platforms for sharing medical images, enabling remote consultations and multidisciplinary collaborations.
Usability and user experience: Investigate the usability and user experience of digital proximity care tools and platforms to ensure efficient workflow integration and user satisfaction.
Data privacy and security: Focus on addressing privacy and security concerns related to storing, transmitting, and accessing sensitive medical imaging data in digital proximity care settings.

Cancer biology end-to-end solutions to improve treatment outcomes:

Targeted Therapies: Investigating the molecular mechanisms and genetic alterations involved in different types of cancer can help identify specific targets for drug development to augment with radiation or standard-of-care treatments. Research can focus on developing targeted therapies that selectively inhibit or modulate these targets to enhance combined modality treatment efficacy and minimize side effects.
Immunotherapy: Studying the interaction between cancer cells and the immune system can lead to the development of immunotherapeutic approaches. Research exploring novel immunotherapies such as radiation-induced immune modulation, immune checkpoint inhibitors, CAR-T cell therapy, and cancer vaccines to enhance the body’s immune response against cancer cells.
Biomarker Discovery: Identifying and validating biomarkers that predict treatment response or prognosis is crucial for personalized cancer medicine. Our interest underlies analyzing genomic, proteomic, or metabolomic data to identify potential biomarkers and validate their clinical utility.
Early Detection and Diagnosis: Developing sensitive and specific methods for early cancer detection can significantly improve treatment outcomes. One of the activities includes identifying novel biomarkers, developing innovative imaging techniques, or exploring liquid biopsies to detect cancer at its earliest stages.
Drug Resistance Mechanisms: Investigating the mechanisms underlying drug resistance in cancer cells can lead to developing strategies to overcome or prevent resistance. The objectives are to monitor cancer cells’ genetic and molecular changes during treatment and identify strategies to overcome resistance, such as combination therapies or targeting drug efflux pumps.
Systems Biology Approaches: Applying systems biology approaches, such as computational modeling and network analysis, can provide insights into the complex interactions within cancer cells and their microenvironment. The company is focused on understanding the dynamic behavior of cancer cells, identifying key signaling pathways, and predicting therapeutic responses.