Lab Members

Kristy K. Brock, Ph.D.

Principal Investigator, Morfeus Lab
Professor, Depts of Imaging Physics and Radiation Physics
Executive Director, Image Guided Cancer Therapy Research Program

Tien T. Tang, Ph.D.

Research Assistant Professor

Tien joined the Morfeus lab in 2023 as a Research Assistant Professor. She received her Ph.D. in Bioengineering from Rice University. Her doctoral work focused on identifying functional imaging marker of radiation-induced damage in the brain and predicting radiation therapy response.

Tien’s current research is on using machine learning and diagnostic imaging to predict treatment outcome.

Mais Al Taie, M.D., Ph.D.

Research Investigator

Mais is a Research Investigator in the Morfeus lab who joined the lab in April 2022. She received her Ph.D. in Radiology and Oncology from Tokyo Medical and Dental University and is a skilled and compassionate radiologist with broad clinical and research experience. Her doctoral work focused on analyzing the long-term complications of brachytherapy in patients with mobile tongue cancers. Her research found that brachytherapy is an acceptable treatment option, with low rates of long-term toxicity. In her free time, Mais enjoys spending time with family and friends, exercising, shopping, and cooking a diverse array of foods. 

Her current research in the Morfeus Lab involves manual contouring of liver images and vessels annotation detection on CT images towards developing robust AI segmentation algorithms.

Muhammad Awais, Ph.D.

Postdoctoral Fellow

Muhammad Awais joined the Morfeus Lab as a Postdoctoral Fellow April 2022. He completed his Ph.D. in Biomedical Engineering from Fudan University in China and his dissertation focused on developing unobtrusive-based approaches for neonatal sleep monitoring, utilizing computer vision and Artificial Intelligence models.

Muhammad conducts pioneering research in the Morfeus lab, aimed at implementing a real-time and highly accurate approach for liver vasculature detection using Intraoperative Ultrasounds (IOUS) videos, and subsequent registration of these 2D IOUS frames onto preoperative CT slices. He is also working on the Mammography, Early detection biomarkers, Risk assessment, and Imaging Technologies (MERIT) project. One of the key objectives of this project is the AI-based spatiotemporal breast cancer risk prediction (AI-SRP), which seeks to facilitate the breast dynamic evaluation of mammographic microcalcifications utilizing current and prior mammographs. This aims to significantly improve the ability to distinguish between benign and malignant conditions, ultimately leading to improved patient outcomes.

Amirreza Heshmat, Ph.D.

Postdoctoral Fellow

Amirreza is a Postdoctoral Fellow in the Morfeus lab who joined the lab in October 2022. He received his PhD in Engineering Science from the Vienna University of Technology (Austria). His doctoral work focused on computational modeling and simulation using the finite element method.  

Amirreza's current research in the Morfeus lab involves percutaneous liver tumor ablation by employing finite element modeling and simulation from image data of patients treated with the ablation approach to gain more precise and patient-specific treatment.

McKell Woodland, BS

Rice University Ph.D. Student 

McKell is a Ph.D. candidate in the Computer Science Program at Rice University. She received her B.S. in Applied and Computational Mathematics with a minor in Computer Science from Brigham Young University. She has interned in data science roles at Microsoft, Lawrence Livermore National Laboratory, 3M Health Information Systems, and the National Security Agency. In her free time, she enjoys outdoor adventures and tutoring K-12 students in coding through the non-profit organization The Coding School. 

Deep learning models typically fail on inputs that are far from the model’s training distribution. McKell’s research focuses on utilizing generative adversarial networks (GANs) to detect these out-of-distribution inputs. Her research has applications in warning clinicians when a deep learning-based segmentation model likely failed, flagging which images should be reviewed when large amounts of data are automatically segmented, determining which images in large cohorts of data would have the most utility to label, and improving datasets and model capabilities over time by diversifying data. 

Xinyue Zhang, MA

Rice University Ph.D. Student 

Xinyue is a Ph.D. student in the Computer Science program at Rice University. She received her M.A. and B.A. in Quantitative Economics with a minor in Computer Science from San Diego State University. In her free time, she enjoys traveling to different cities and visiting zoos.

Xinyue's research interest is in Surgical AI. Her current research focuses on CT image registration and deformation vector field prediction with a 3D deep learning model (3D UNet). The goal of the project is to use limited image data to predict the deformation vector field accurately and efficiently in real time during surgery. 

Aashish Gupta, MS

GSBS Ph.D. Student

Prior to joining the lab, he obtained his B.S. and M.S. from Texas Tech University, and the UTHealth Houston MD Anderson Cancer Center Graduate School of Biomedical Sciences, respectively. In his free time, he likes to play tennis, Gaelic football and do weightlifting.  

After 3-6 months of radiotherapy, there is an increase and decrease in the volume of liver in the region of low and high radiation dose, respectively. Aashish’s Ph.D. project focuses on developing AI based anatomical models to describe the response of liver to radiation. He plans to accomplish three major milestones in his project. Firstly, he is developing AI based contouring infrastructure to auto-contour liver segments on CT and MRI scans to quantify the volumetric change. Secondly, he plans to develop both machine and deep learning models to predict the longitudinal volumetric response of liver segments after radiotherapy. Thirdly, he plans to investigate how functional imaging modalities such as SPECT and DCE-MRI could help describe physiological and volumetric change in liver when radiation is administered. Once the segmental response of liver is clearly known, clinicians will be able to better spare the sensitive parts of liver which would decrease the risk of hepatic failure and increase the survival rate post-therapy.  

Erin Snoddy

GSBS Ph.D. Student

Erin is a Medical Physics Ph.D. student in the GSBS (Graduate School of Biomedical Sciences) program. She received her B.A. in Astrophysics with a minor in English Literature from Swarthmore College. In her free time, she enjoys practicing yoga, reading, watching movies, and going to concerts with friends. 

Erin’s research interests are centered on correlative medical and diagnostic imaging (especially non-ionizing imaging modalities). Her current research focuses on the correlation of MR imaging with histology to determine anatomical characteristics of the breast. The goal is to use this information to improve breast reconstructive techniques for breast cancer patients who have had mastectomy procedures. 

Brandon Reber, B.S.

GSBS Ph.D. Student

Brandon is a Medical Physics Ph.D. student in the Morfeus Lab. He obtained his Bachelor of Science degree in Physics at the University of British Columbia in Vancouver, Canada. His undergraduate research project focused on validating a machine learning technique for the identification of Parkinson's disease in individuals using PET images. He feels like project was a great introduction to medical imaging and machine learning, and how they can be used to solve problems in the clinic. In his spare time, he currently enjoys exploring Houston and trying out the many restaurants the city has to offer. 

Brandon's Ph.D. project uses machine learning methods to predict osteoradionecrosis (ORN) resulting from head and neck cancer radiation therapy. These machine learning methods include traditional techniques such as support-vector machines as well as more recent methods such as convolutional neural networks (CNNs). First, his project will look at using data acquired prior to the start of radiation treatment for ORN prediction. This data can include radiation treatment plans and treatment planning computed tomography (CT) images. Next, he will examine functional imaging for this task. These imaging modalities can capture physiological changes in patients associated with ORN. Finally, the last portion of his project will examine using changes in imaging features from different treatment timepoints for ORN prediction. At the conclusion of this project, he hopes to improve the early identification of ORN resulting from head and neck cancer radiation therapy, allowing for earlier interventions, alternative treatment strategies, and improved patient quality of life post-treatment.

Austin Castelo, BS

Associate Systems Analyst

Austin is an Associate Systems Analyst in the Morfeus Lab. Before joining in March 2022, he received his undergraduate degree in Financial Mathematics and Statistics from University of California, Santa Barbara and worked in web-based computer vision and machine learning engineering. In his free time, he enjoys reading, playing video game with friends, and playing poker.

His current research project involves automated liver segmentation during liver ablation for use in image guided liver ablation. He also works within the lab to provide computational and machine learning support to other projects.

Emma McCollum, BS

Research Assistant II

Emma is a Research Assistant II in the Morfeus Lab. She earned a B.S. in Biomedical Engineering from Washington University in St. Louis. Previously, she participated in the CPRIT CURE Summer Undergraduate Program during the summers of 2019 and 2020 in the Department of Imaging Physics. Her research interests include AI, machine learning, biomechanical modeling, image processing, and surgical device innovation. Emma also enjoys cycling, walking dogs, and eating Italian food.  

Her current research projects include assessing accumulated dose delivery of intensity modulated photon therapy in prostatic adenocarcinoma patients and ongoing collaborations with Dr. Justin Bird as part of the Surgery Innovations initiative at MD Anderson. 

Nihil Patel, MS

Research Assistant II

Nihil is a Research Assistant II at the Morfeus Lab. Before joining in October of 2022, he obtained his Bachelor of Science in Computer Science at Trinity University in San Antonio and his Master's of Science in Computer Science at the University of Houston. Nihil's research interests are in deep learning and transfer learning. During his free time, he likes to build model airplanes and play video games. 

Nihil's current projects involve exploring methods of unsupervised transfer learning between CT and MRI.

Androniki Mitrou, MS

Physics Assistant

Androniki is a Physics Assistant in the Morfeus Lab. Before joining in November 2021, she completed her undergraduate degree in Physics at Grinnell College in Iowa and then received an MS in Medical Physics from University of Massachusetts Lowell, where she conducted biophotonics research for breast cancer detection. She is interested in clinical Medical Physics, image guided therapy research, and science education. Androniki loves swimming, hosting at her home, and Grinnell College alumni activities.

Her current research project focuses on methods for adaptive radiotherapy in head and neck cancer, including conversion of cone beam CTs for daily dose calculations and dose accumulation.

Caleb O'Connor, MS

Data Scientist

Before joining  the Morfeus Lab in July 2021, Caleb worked as an Image Scientist for L3Harris for five years. He completed his undergraduate degree in physics at University of Louisiana at Lafayette and then received an MS in physics, with a focus in signal processing. In his free time, he likes to watch basketball, play videos games and participate in local sporting leagues. 

His current research projects focus on deformable image registration techniques, AI implementation, and RayStation automation. He is actively involved with the COVER-ALL and STEROLAB clinical trials.

Jun Hong, Ph.D.

Imaging Physics Resident

Jun is an Imaging Physics Fellow in the Department of Imaging Physics. She received her Ph.D. in nuclear physics from Michigan State University. She became very interested in applying physics into medicine and had postdoctoral training on modeling cancer metastasis at Yale University, as well as on AI assistance for adaptive radiotherapy and toxicity analysis at Memorial Sloan Kettering Cancer Center. In her free time, she enjoys playing games with her family, oil painting and cheering for her favorite college football team.

Her current research project involves image guided liver ablation using imaging technologies to achieve more accurate and personalized treatment for the patients. The project aims to help establish the artificial Intelligence workflow with biomechanical modeling, assess the automatic segmentation of critical regions of interest and image registration performance, and analyze the clinical outcome. 

Olubunmi Lebimoyo

Undergraduate Research Student

Olubunmi (Bunmi) Lebimoyo is currently an undergraduate student at Texas Southern University (TSU) in Houston, Texas.  She was introduced to Physics in a prerequisite course during her undergraduate studies and was interested in learning more about opportunities within the field of study.  Olubunmi joined the Morfeus Lab in May 2022; her current project involves adaptive radiotherapy in head and neck cancer patients.

She is actively involved in MAPS (Medical Affairs Professional Society), the STEM (Science Technology Engineering and Math) Trio Program, and HOSA (Health Occupations Students of America)- Future Professions organization.  She is currently a volunteer at Houston Methodist Hospital and the Houston Humane Society. In her spare time, Bunmi enjoys playing the flute, exercising, and trying new cultural dishes. She will be earning her bachelor's of science in biology in May 2023 and plans to become a medical doctor serving underrepresented communities.

Stephanie Robles

Undergraduate Research Student

Stephanie is an Undergraduate Research Student in the Department of Imaging Physics. She is a full-time student-athlete at Texas Southern University, where she plans to graduate top of her class with a bachelor’s degree in physics with a Pre-Medical Concentration. Before joining the lab in May 2022, Stephanie was a summer intern for the Perelman School of Medicine at the University of Pennsylvania (UPENN). At UPENN, her research focus was working with dual-energy computed tomography scans. Her current research focus in the Morfeus Lab involves manual contouring of pelvic floor images towards developing an artificial intelligence model.

She is interested in both clinical and translational research in Medical Physics. During her free time, Stephanie enjoys playing soccer, exploring the outdoors, and volunteering with her Society of Physics club.

Kari Brewer Savannah, Ph.D.

Director, Image Guided Cancer Therapy Research Program 

Kari Brewer Savannah, Ph.D., joined the IGCT in Fall 2020 and serves as the Program Director. Dr. Savannah holds a BS in Chemistry from Hillsdale College and a Ph.D. in Biochemistry and Biomedical Sciences from the MD Anderson UTHealth GSBS. She previously served as Assistant Professor at the University of Texas at Brownsville (Biomedicine Dept) and Houston Baptist University (Biology Dept). Dr. Savannah has significant experience in the design, management, and assessment of research training programs, educational programming, and in initiatives to expand diversity, equity, and inclusion for underrepresented minorities in STEM fields. Dr. Savannah brings a strong background in research administration, scientific project management, and program management to her role in the IGCT and Morfeus Lab. She manages large, multidisciplinary scientific projects, assists in multi-investigator grant preparation and management, and manages IGCT/Morfeus Lab classified staff.