Joseph D. Butner, Ph.D.
Department of Radiation Oncology, Division of Radiation Oncology
About Dr. Joseph D. Butner
Dr. Butner is an Assistant Professor working in the fields of computational biology, deep machine learning, and physical oncology with a focus on personalized prediction of disease outcomes with and without clinical intervention. Over the last seven years, he has worked closely alongside highly respected teams of research scientists and physicians to apply many of the most modern techniques in the fields of physical oncology and mathematical modeling of biological systems to gain understanding of the mechanistic underpinnings of disease development and treatment, and to develop clinically translatable tools to address current clinical needs. He recently applied mechanistic modeling descriptions of key biophysical factors and processes in checkpoint inhibitor immunotherapy intervention to large patient-derived clinical datasets, and discovered mathematically-derived biomarkers that are predictive of patient response to therapy and that provide quantitative assessment of efficacy of individual drug-disease treatments, resulting in a series of high-impact publications that have been favorably received by the scientific community.
He is now leveraging the knowledge gained in these projects with deep machine learning and artificial intelligence methods to more completely capture the complexity of the immune-tumor interaction, with the goal of finding ways to engineer optimal, unique treatment protocols for each patient. These predictive mathematical relationships between measurably biological quantities and patient outcome can be used to identify the specific, mechanistic causes underlying immunotherapy treatment outcomes on a per-patient basis at early times under the current immunotherapy intervention paradigm, with the significant goal of allowing clinicians to customize individual treatment protocols and ancillary treatment strategies to bring patients into a biological state where immunotherapy has the highest likelihood of being successful. Ongoing research in the Butner laboratory includes mathematical modeling studies to better understand the effects of high- and low-dose radiotherapy on immune priming and stromal alterations in patients receiving checkpoint inhibitor immunotherapies, and studies on how deep-learning platforms may be leveraged to predict per-patient treatment failure and survival.
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He is now leveraging the knowledge gained in these projects with deep machine learning and artificial intelligence methods to more completely capture the complexity of the immune-tumor interaction, with the goal of finding ways to engineer optimal, unique treatment protocols for each patient. These predictive mathematical relationships between measurably biological quantities and patient outcome can be used to identify the specific, mechanistic causes underlying immunotherapy treatment outcomes on a per-patient basis at early times under the current immunotherapy intervention paradigm, with the significant goal of allowing clinicians to customize individual treatment protocols and ancillary treatment strategies to bring patients into a biological state where immunotherapy has the highest likelihood of being successful. Ongoing research in the Butner laboratory includes mathematical modeling studies to better understand the effects of high- and low-dose radiotherapy on immune priming and stromal alterations in patients receiving checkpoint inhibitor immunotherapies, and studies on how deep-learning platforms may be leveraged to predict per-patient treatment failure and survival.
Present Title & Affiliation
Primary Appointment
Assistant Professor, Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
Adjunct Professor, Houston Methodist Academic Institute, Houston, TX
Adjunct Professor of Informatics, Department of Master in Clinical Translation Management Program, The Cameron School of Business, University of St. Thomas, Houston, TX
Dual/Joint/Adjunct Appointment
Assistant Professor, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
Adjunct Professor, Houston Methodist Academic Institute, Houston, TX
Adjunct Professor of Informatics, Department of Master in Clinical Translation Management Program, The Cameron School of Business, University of St. Thomas, Houston, TX
Education & Training
Degree-Granting Education
| 2017 | University of New Mexico, Albuquerque, New Mexico, US, Biomedical Engineering, Ph.D |
| 2016 | University of New Mexico, Albuquerque, New Mexico, US, Biomedical Engineering, M.S |
| 2012 | University of New Mexico, Albuquerque, New Mexico, US, Mechanical Engineering, M.S |
| 2008 | University of New Mexico, Albuquerque, New Mexico, US, Mechanical Engineering, BS |
Postgraduate Training
| 2017-2018 | Postdoctoral Associate, Physical Oncology and Biomedical Research, University of Texas Health, Houston, Texas |
Experience & Service
Academic Appointments
Instructor, Department of Computational Biology in Cardiovascular Sciences, Houston Methodist Academic Institute, Houston, TX, 2023 - 2023
Faculty Fellow, Houston Methodist Research Institute, Houston, TX, 2021 - 2023
Research Associate, Houston Methodist Research Institute, Houston, TX, 2018 - 2021
Other Appointments/Responsibilities
Regular Member, The University of Texas Health Science Center at Houston, 2024 - Present
Affiliate, The University of Texas MD Anderson Cancer Center, 2023 - Present
Honors & Awards
| 2023 | President's Award for Excellence in Peer-Reviewed Publication, Houston Methodist Research Institute |
| 2021 | President's Award for Excellence in Peer-Reviewed Publication, Houston Methodist Research Institute |
| 2021 | Wolfram Innovators Award |
| 2020 | Feature Article in IEEE TBME. May |
| 2017 | PhD awarded "with distinction", University of New Mexico |
| 2013 | New Mexico Lottery Scholarship |
| 2013 | New Mexico Technical Institute Silver Scholarship |
| 2002 | National Merit Semifinalist |
Selected Publications
Peer-Reviewed Articles
- Butner, JD, Dogra, P, Chung, C, Koay, EJ, Welsh, J, Hong, DS, Cristini, V, Wang, Z. Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy. npj Systems Biology and Applications 10(1), 2024. e-Pub 2024. PMID: 39143136.
- Dogra, P, Shinglot, V, Ruiz Ramírez, J, Cave, J, Butner, JD, Schiavone, C, Duda, DG, Kaseb, A, Chung, C, Koay, EJ, Cristini, V, Ozpolat, B, Calin, GA, Wang, Z. Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer. Molecular cancer 23(1), 2024. e-Pub 2024. PMID: 39095771.
- Dogra P, Butner JD, Cristini V, Wang Z. Development of a Multiscale Mechanistic Model for Predicting Tumor Response to Anti-miR-155. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2024. e-Pub 2024.
- Cave J, Shinglot V, Butner JD, Cristini V, Ozpolat B, Calin GA, Dogra P, Wang Z. Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer. Annu Int Conf IEEE Eng Med Biol Soc 2023:1-4, 2023. e-Pub 2023. PMID: 38083518.
- Butner JD, Farhat M, Cristini V, Chung C, Wang Z. Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy. STAR Protoc 3(4):101886, 2022. e-Pub 2022. PMID: 36595890.
- Sun K, Xu Y, Zhang L, Niravath P, Darcourt J, Patel T, Teh BS, Farach AM, Guerrero C, Mathur S, Sultenfuss MA, Gupta N, Schwartz MR, Haley SL, Nair S, Li X, Nguyen TTA, Butner JD, Ensor J, Mejia JA, Mei Z, Butler EB, Chen SH, Bernicker EH, Chang JC. A Phase 2 Trial of Enhancing Immune Checkpoint Blockade by Stereotactic Radiation and In Situ Virus Gene Therapy in Metastatic Triple-Negative Breast Cancer. Clin Cancer Res 28(20):4392-4401, 2022. e-Pub 2022. PMID: 35877117.
- Wang CX, Elganainy D, Zaid MM, Butner JD, Agrawal A, Nizzero S, Minsky BD, Holliday EB, Taniguchi CM, Smith GL, Koong AC, Herman JM, Das P, Maitra A, Wang H, Wolff RA, Katz MHG, Crane CH, Cristini V, Koay EJ. Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer. Int J Radiat Oncol Biol Phys 114(1):163-172, 2022. e-Pub 2022. PMID: 35643254.
- Butner JD, Dogra P, Chung C, Ruiz-Ramírez J, Nizzero S, Plodinec M, Li X, Pan PY, Chen SH, Cristini V, Ozpolat B, Calin GA, Wang Z. Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study. Cell Death Dis 13(5):485, 2022. e-Pub 2022. PMID: 35597788.
- Dogra P, Ramírez JR, Butner JD, Peláez MJ, Chung C, Hooda-Nehra A, Pasqualini R, Arap W, Cristini V, Calin GA, Ozpolat B, Wang Z. Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer. Pharm Res 39(3):511-528, 2022. e-Pub 2022. PMID: 35294699.
- Dogra P, Ramirez JR, Butner JD, Pelaez MJ, Cristini V, Wang Z. A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor Inhibition. Annu Int Conf IEEE Eng Med Biol Soc 2021:4230-4233, 2021. e-Pub 2021. PMID: 34892157.
- Butner JD, Martin GV, Wang Z, Corradetti B, Ferrari M, Esnaola N, Chung C, Hong DS, Welsh JW, Hasegawa N, Mittendorf EA, Curley SA, Chen SH, Pan PY, Libutti SK, Ganesan S, Sidman RL, Pasqualini R, Arap W, Koay EJ, Cristini V. Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling. Elife 10, 2021. e-Pub 2021. PMID: 34749885.
- Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden. Nat Biomed Eng 5(4):297-308, 2021. e-Pub 2021. PMID: 33398132.
- Dogra P, Ruiz-Ramírez J, Sinha K, Butner JD, Peláez MJ, Rawat M, Yellepeddi VK, Pasqualini R, Arap W, Sostman HD, Cristini V, Wang Z. Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model. ACS Pharmacol Transl Sci 4(1):248-265, 2021. e-Pub 2021. PMID: 33615177.
- Anaya DA, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayouri M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramírez JR, Plodinec M, Sidman RL, Cavenee WK, Pasqualini R, Arap W, Fleming JB, Cristini V. A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel) 13(3):1-18, 2021. e-Pub 2021. PMID: 33503971.
- Dogra P, Butner JD, Ramirez JR, Cristini V, Wang Z. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. Annu Int Conf IEEE Eng Med Biol Soc 2020:2447-2450, 2020. e-Pub 2020. PMID: 33018501.
- Butner JD, Elganainy D, Wang CX, Wang Z, Chen SH, Esnaola NF, Pasqualini R, Arap W, Hong DS, Welsh J, Koay EJ, Cristini V. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. Sci Adv 6(18):eaay6298, 2020. e-Pub 2020. PMID: 32426472.
- Butner JD, Fuentes D, Ozpolat B, Calin GA, Zhou X, Lowengrub J, Cristini V, Wang Z. A Multiscale Agent-Based Model of Ductal Carcinoma In Situ. IEEE Trans Biomed Eng 67(5):1450-1461, 2020. e-Pub 2020. PMID: 31603768.
- Dogra P, Butner JD, Ruiz Ramírez J, Chuang YL, Noureddine A, Jeffrey Brinker C, Cristini V, Wang Z. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput Struct Biotechnol J 18:518-531, 2020. e-Pub 2020. PMID: 32206211.
- Dogra P, Chuang YL, Butner JD, Cristini V, Wang Z. Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors. Annu Int Conf IEEE Eng Med Biol Soc 2019:2852-2855, 2019. e-Pub 2019. PMID: 31946487.
- Butner JD, Cristini V, Wang Z. Understanding Ductal Carcinoma in Situ Invasion using a Multiscale Agent-Based Model. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2018-July:5846-5849, 2018. e-Pub 2018.
- Sarkar D, Baboly MG, Elahi MM, Abbas K, Butner JD, Pinon D, Ward TL, Hieber T, Schuberth A, Leseman ZC. Determination of etching parameters for pulsed XeF2 etching of silicon using chamber pressure data. Journal of Micromechanics and Microengineering 28(4), 2018. e-Pub 2018.
- Butner JD, Cristini V, Wang Z. Development of a three dimensional, multiscale agent-based model of ductal carcinoma in situ. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS:86-89, 2017. e-Pub 2017.
- Butner JD, Cristini V, Wang Z. Development of a three dimensional, lattice-free multiscale model of the mammary terminal end bud. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2016-October:6134-6137, 2016. e-Pub 2016.
- Butner JD, Chuang Y, Simbawa E, Al-Fhaid AS, Mahmoud SR, Cristini V, Wang Z. A hybrid agent-based model of the developing mammary terminal end bud. Journal of Theoretical Biology 407:259-270, 2016. e-Pub 2016.
- Wang Z, Kerketta R, Chuang Y, Dogra P, Butner JD, Brocato TA, Day A, Xu R, Shen H, Simbawa E, Al-Fhaid AS, Mahmoud SR. Theory and Experimental Validation of a Spatio-temporal Modell of Chemotherapy Transport to Enhance Tumor Cell Kill. PLoS Computational Biology 12(6), 2016. e-Pub 2016.
- Butner JD, Cristini V, Wang Z. A modeling approach to study the normal mammary gland growth process. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2015-November:1444-1447, 2015. e-Pub 2015.
- Mousavi AK, Abbas K, Elahi MMM, Lima E, Moya S, Butner JD, Pinon D, Benga A Mousavi BK, Leseman ZC. Pulsed vacuum and etching systems: Theoretical design considerations for a pulsed vacuum system and its application to XeF2 etching of Si. Vacuum 109:216-222, 2014. e-Pub 2014.
- Pascal J, Ashley CE, Wang Z, Brocato TA, Butner JD, Carnes EC, Koay EJ, Brinker CJ, Cristini V. Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. ACS Nano 7(12):11174-11182, 2013. e-Pub 2013.
- Butner JD, Leseman ZC. The effect of temperature on the etch rate and roughness of surfaces etched with XEF2. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) 10:565-568, 2010. e-Pub 2010.
Other Articles
- Butner JD, Wang Z Creating a Mathematical Model to Help the Fight Against Cancer. Scientia, 2024.
- Syed M, Cagely M, Dogra P, Hollmer L, Butner JD, Cristini V, Koay EJ Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models. Wiley Interdiscip Rev Nanomed Nanobiotechnol 15(2):e1855, 2023. PMID: 36148978.
- Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z Mathematical modeling of cancer immunotherapy for personalized clinical translation. Nat Comput Sci 2(12):785-796, 2022. PMID: 38126024.
- Butner JD, Wang Z, Cristini V Personalized prediction of immunotherapy efficacy: improving clinical approaches via mechanistic mathematical modeling. Open Access Government:116-117, 2021.
- Dogra P, Butner JD, Nizzero S, Ruiz Ramírez J, Noureddine A, Peláez MJ, Elganainy D, Yang Z, Le AD, Goel S, Leong HS, Koay EJ, Brinker CJ, Cristini V, Wang Z Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. Wiley Interdiscip Rev Nanomed Nanobiotechnol 12(5):e1628, 2020. PMID: 32314552.
- Dogra P, Butner JD, Chuang YL, Caserta S, Goel S, Brinker CJ, Cristini V, Wang Z Mathematical modeling in cancer nanomedicine: a review. Biomed Microdevices 21(2):40, 2019. PMID: 30949850.
- Curley SA, Dogra P, Butner JD, wang Z, Cristini V Non-invasive radiofrequency hyperthermia. Open Access Government:78-79, 2018.
- Butner JD, Cristini V, Wang Z Ductal Carcinoma in Situ: Gaining new Biological Insights from Multiscale Mathematical Modeling. Open Access Government:136-137, 2018.
- Wang Z, Butner JD, Cristini V, Deisboeck TS Integrated PK-PD and agent-based modeling in oncology. Journal of Pharmacokinetics and Pharmacodynamics 42(2):179-189, 2015.
- Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS Simulating cancer growth with multiscale agent-based modeling. Seminars in Cancer Biology 30:70-78, 2015.
Editorials
- Butner JD, Wang Z. Predicting immune checkpoint inhibitor response with mathematical modeling. Immunotherapy Volume 13(Number 14):pages 1151-1155, 2021. PMID: 34435504.
- Wang Z, Butner JD, Cristini V. Behind the Paper: Towards providing physicians with a quantitative tool for optimizing immunotherapy treatment protocols for each individual patient. Bioengineering & Biotechnology, 2021.
Abstracts
- Silalahi ARJ, Chung C, Welsh JW, Wang Z, Butner JD. Mathematical modeling-based optimization of gamma knife surgery after checkpoint blockade in melanoma brain metastases. Clin Cancer Res 31(2_Supplement):B019, 2025. e-Pub 2025.
- Prakash G, Wang Z, Butner JD. Hybridizing CT Tumor Volume Measurements with Standard of Care Clinical Measures for Immunotherapy Response Prediction using Mechanistic Modeling and Machine Learning. The Co-Clinical Imaging Research Resource Program (CIRP) Annual Hybrid Meeting 2024, 2024. e-Pub 2024.
- Butner JD, Wang Z. Predicting cancer patient survival by hybridizing mechanistic mathematical modeling and deep learning methods. Joint Mathematics Meeting 2024, 2024. e-Pub 2024.
- Farhat M, Butner J, Wang Z, Shanker M, Talpur W, Thrower S, Erickson L, Bronk J, Langshaw H, Lucky Tran B, Yadav D, Elliott A, Wang C, Tawbi HA, Cristini V, Chung C. Evaluating novel imaging-based mathematical modeling prediction of immunotherapy response by individual melanoma brain metastasis and patient prognosis. Journal of Clinical Oncology Volume 41(Number 16):e14013, 2023. e-Pub 2023.
- Dogra P, Cave J, Butner JD, Cristini V, Wang Z. Multiscale Modeling-Identified Synergistic Combinations of Anti-microRNA-155 and Standard-of-Care Drugs for Improved Outcomes in Non-small Cell Lung Cancer. AAPS PharmSci 360 Conference, 2023. e-Pub 2023.
- Farhat M, Butner JD, Thrower S Erickson L, Wang Z, Cristini V, Yadav D, Tran B, Bronk J, Langshaw H, Elliot A, Chung C. Growth Kinetic Mathematical Modeling to Predict Individual Melanoma Brain Metastasis Response to Immunotherapy. Radiological Society of North America 108th Scientific Assembly and Annual Meeting, 2022. e-Pub 2022.
- Dogra P, Palaez MJ, Ramirez JR, Sinha K, Butner JD, Cristini V, Wang Z. Investigating the Role of Innate Immunity in the Control of SARS-CoV-2 Infection Using a Mathematical Model. AAPS PharmaSci 360, 2021. e-Pub 2021.
- Dogra P, Ramirez JR, Palaez MJ, Butner JD, Cristini V, Wang Z. Translational mechanistic modeling for treatment optimization of nanoparticle-delivered miRNA-22 therapy in triple negative breast cancer. 9th Annual Houston Methodist Cancer Symposium, 2021. e-Pub 2021.
- Anaya DA, Wilkes JG, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayourim M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramirez JR, Fleming JB, Cristini V. Tumor-Site Chemotherapy Concentration Predicts Response To Treatment In Patients With Colorectal Liver Metastasis: A New Paradigm For Individualized Cancer Care. HPB Volume 22:page S10, 2020. e-Pub 2020.
- Dogra P, Pelaez MJ, Ramirez JR, Butner JD, Cristini V, Wang Z. Identifying parameters to improve the pharmacokinetics and tumor delivery efficiency of nanomedicine. Special poster presentation session at the American Association of Pharmaceutical Scientists PharmSci 360 Conference, 2020. e-Pub 2020.
- Dogra P, Chuang YL, Butner JD, Cristini V, Wang Z. A multiscale mathematical model to study nanomedicine delivery in solid tumors. American Association of Pharmaceutical Scientists (AAPS) PHARMSCI 360 Conference, 2019. e-Pub 2019.
- Butner JD, Cristini V, Wang Z. Multiscale Modeling of Ductal Carcinoma in Situ. Biophysical Journal 116(3):322a-323a, 2019. e-Pub 2019.
- Wang CX, Elganainy D, Zaid MM, Butner JD, Agrawal A, Nizzero S, Minsky BD, Holliday EB, Taniguchi CM, Smith GL, Koong AC, Herman JM, Das P, Maitra A, Wang H, Wolff RA, Katz MHG, Crane CH, Cristini V, Koay EJ. Mass Transport Model of Radiation Response: Calibration and Application to Chemoradiation for Pancreatic Cancer. International Journal of Radiation Oncology, Biology, Physics 103(5):p. E48-E49, 2019. e-Pub 2019.
- Kerketta R, Butner JD, Brocato TA, Dogra P, Day A, John J, Chuang YL, Wang Z, Curley SA, Cristini V. Predicting chemotherapeutic outcomes in patients with colorectal cancer liver metastases. The Eighth q-bio Conference, 2014. e-Pub 2014.
Book Chapters
- Butner JD, Dogra P, Cristini V, Deisboeck TS, Wang Z. Computational Approaches for Multiscale Modeling. In: Encyclopedia of Cell Biology (Second Edition). Elsevier, pages 251-260, 2023.
- Noureddine A, Butner JD, Zhu W, Naydenkov P, Pelaez MJ, Goel S, Wang Z, Brinker CJ, Cristini V, Dogra P. Emerging Lipid-Coated Silica Nanoparticles for Cancer Therapy. In: Cancer Nanotheranostics. Springer,Cham, pages 335-361, 2021.
- Cristini V, Koay EJ, Wang Z, Butner JD. Developing More Successful Cancer Treatments with Physical Oncology. In: An Introduction to Physical Oncology. CRC Press, pages 13-30, 2017.
- Cristini V, Koay EJ, Wang Z, Butner JD. Mathematical Modeling of Drug Response. In: An Introduction to Physical Oncology. 1st Edition. CRC Press, pages 53-74, 2017.
Selected Presentations & Talks
Local Presentations
- 2024. Session: Data Science and Digital Twins. Panelist. 2024 Imaging Physics retreat. Houston, Texas, US.
- 2024. Predicting cancer patient survival by hybridizing mechanistic mathematical modeling and deep learning methods. Conference. Digital Twin Summit 2024: Computational Modeling in Radiation Oncology. Houston, TX, US.
- 2024. Data Science and Computational Modeling for Precision Medicine Program: Overview and Case Studies. Invited. Radiation Oncology Chairs and Chiefs Retreat 2024. Houston, Texas, US.
National Presentations
- 2025. A Mechanistic approach to optimizing radio-immunotherapy synergy in cancer treatment (speaker: Alex Silalahi). Invited. AMS Special Session on Computational Biomedicine: Emerging Methods and Applications. Seattle, Washington, US.
- 2024. Hybridizing CT Tumor Volume Measurements with Standard of Care Clinical Measures for Immunotherapy Response Prediction using Mechanistic Modeling and Machine Learning. Poster. The Co-Clinical Imaging Research Resource Program (CIRP) Annual Hybrid Meeting 2024. Bethesda, MD, US.
- 2024. Predicting cancer patient survival by hybridizing mechanistic mathematical modeling and deep learning methods. Invited. Special session on Computational Biomedicine: Methods-Models-Applications. San Francisco, CA, US.
- 2022. Growth Kinetic Mathematical Modeling to Predict Individual Melanoma Brain Metastasis Response to Immunotherapy. Invited. Radiological Society of North America 108th Scientific Assembly and Annual Meeting: Empowering Patients and Partners in Care. Chicago, IL, US.
- 2022. Spatial patterns of microenvironmental biomarkers drive long-term breast cancer outcome. Conference. American Associations of Cancer Research Annual Meeting 2022. New Orleans, Lousiana, US.
- 2021. Investigating the role of innate immunity in the control of SARS-CoV-2 infection using a mathematical model. Poster. American Association of Pharmaceutical Scientists PharmSci 360 Conference. Philadelphia, Pennsylvania, US.
- 2020. Identifying parameters to improve the pharmacokinetics and tumor delivery efficiency of nanomedicine. Poster. American Association of Pharmaceutical Scientists PharmaSci 360 Conference. Virtual, US.
- 2019. Emerging mechanistic biomarkers of cancer chemo-radiation and immunotherapy from mathematical bio-physics. Invited. American Physiological Society 2019. Interface of Mathematical Models and Experimental Biology: Role of the Microvasculature. Scottsdale, AZ, US.
International Presentations
- 2024. Development of a Multiscale Mechanistic Model for Predicting Tumor Response to Anti-miR-155. Conference. 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Sydney, AU.
- 2023. Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer. Conference. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Sydney, AU.
- 2022. Developing Mathematical Biomarkers for Predicting Cancer Immunotherapy Outcomes. Invited. The Society for Industrial and Applied Mathematics (SIAM) Conference on Life Sciences (LS22). Pittsburgh, US.
- 2021. A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor Inhibition. Conference. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Guadalajara, MX.
- 2021. Interfacing Mathematics and Medicine: Predicting Cancer Patient Response to Checkpoint Inhibitor Immunotherapy. Invited. The Wolfram Virtual Technology Conference 2021, US.
- 2020. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. Conference. 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Montreal, CA.
- 2018. Understanding Ductal Carcinoma in SITY Invasion using a Multiscale Agent-Based Model. Conference. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Honolulu, US.
- 2017. Development of a Three Dimensional, Multiscale Agent-Based Model of Ductal Carcinoma in Situ. Conference. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju Island, KR.
- 2016. Development of a three dimensional, lattice-free multiscale model of the mammary terminal end bud. Conference. 38th Annual International Conference of the IEE Engineering in Medicine and Biology Society. Orlando, US.
- 2015. A Modeling Approach to Study the Normal Mammary Gland Growth Process. Conference. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Milan, IT.
Formal Peers
- 2024. Predicting cancer patient survival by hybridizing mechanistic mathematical modeling and deep learning methods. Visiting. Houston Methodist Research Institute. Houston, TX, US.
- 2024. Personalized patient survival prediction by hybridizing mechanistic mathematical modeling and deep learning methods. Visiting. MD Anderson Cancer Center. Houson, TX, US.
- 2023. The Receiver Operator Characteristics Plot: the rest of the story. Visiting. Houston Methosdist Research Institute. Houston, TX, US.
- 2023. Mathematical Modeling of Biological Systems for Oncology Applications: From Basic Science to Clinically Translatable Methods. Visiting. MD Anderson Cancer Center. Houston, TX, US.
- 2023. Predicting Personalized Immunotherapy Outcomes using Mathematical Biomarkers. Visiting. Houston Methodist Research Institute. Houston, TX, US.
- 2022. R Coding Bootcamp, Day 4: Importing & fitting data with built-in and user-defined equations. Visiting. Houston Methodist Research Institute. Houston, TX, US.
- 2022. R Coding Bootcamp, Day 3: Boolean logic, loops, and writing functions. Visiting. Houston Methodist Research Institute. Houston, TX, US.
- 2021. Modeling/Simulation in Clinical Translation. Visiting. University of St. Thomas. Houston, TX, US.
- 2021. Predicting Patient Response to Checkpoint Inhibitor Immunotherapy with Mathematical Modeling. Visiting. Weill Cornell Graduate School PBSB Program Faculty Research Presentations, US.
Grant & Contract Support
| Date: | 2025 - 2027 |
| Title: | Identifying non-small cell lung cancer lesions that can benefit from radiotherapy alongside checkpoint inhibitor immunotherapy using mathematical modeling |
| Funding Source: | 1R21CA305373-01 |
| Role: | PI |
| Date: | 2025 - 2025 |
| Title: | Identifying non-small cell lung cancer lesions that can benefit from radiotherapy alongside checkpoint inhibitor immunotherapy using mathematical modeling |
| Funding Source: | Cancer Research Institute (CRI) Technology Impact Award letter of intent (LoI) |
| Role: | PI |
| Date: | 2024 - 2025 |
| Title: | Identifying brain metastasis lesions that can benefit from radiotherapy alongside checkpoint inhibitor immunotherapy using mathematical modeling |
| Funding Source: | Andrew M. McDougall Brain Metastasis Clinic and Research Program FY25 BRAIN METASTASIS RESEARCH AWARD |
| Role: | PI |
| ID: | Brain Mets RFA FY25-009 |
| Date: | 2024 - 2025 |
| Title: | Automation of a tumor growth rate measurement to assess immunotherapy response |
| Funding Source: | QIAC QPR Award |
| Role: | Co-I |
| ID: | FY2024 |
| Date: | 2017 - 2020 |
| Title: | Collaborative research: A new multiscale methodology and application to tumor growth modeling |
| Funding Source: | NSF |
| Role: | Postdoctoral Researcher |
| ID: | DMS-1930583 |
| Title: | Development of KRAS-targeted siRNA- or miRNA-mediated nanotherapeutics for pancreatic ductal adenocarcinoma |
| Funding Source: | NIH/R01 |
| Role: | Co-I |
| Title: | Novel therapeutic approaches for co-targeting of two oncogenic kinases in Triple negative breast cancer |
| Funding Source: | NIH/R01 |
| Role: | Co-I |
| Title: | Predicting immunotherapy outcomes from noninvasive blood measures before start of treatment |
| Funding Source: | Golfers Against Cancer |
| Role: | Co-I |
| Title: | Integration of multimodal imaging and biofluid laboratory measurements for mathematical prediction of checkpoint inhibitor immunotherapy outcome in patients with melanoma brain metastases |
| Funding Source: | NIH/R01 |
| Role: | Co-I |
| Title: | Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy |
| Funding Source: | NIH |
| Role: | PI |
| Title: | Designing a predictive clinical tool to improve patient treatment strategies: integration of multiparametric imaging and mathematical modeling to predict patient outcome in melanoma brain metastases |
| Funding Source: | NIH |
| Role: | PI |
| Title: | Optimizing novel miR-873 nanotherapeutics for targeted cancer treatment using a combination of experimental and mechanistic modeling approaches |
| Funding Source: | NIH/R01 |
| Role: | Co-I |
| Title: | Development of a clinically applicable tool for physicians to predict checkpoint inhibitor immunotherapy outcome in patients with melanoma brain metastases |
| Funding Source: | NIH/U01 |
| Role: | Co-I |
Patient Reviews
CV information above last modified March 03, 2025