While our lab has world class facilities and consistently employs cutting edge techniques, it is really the people who work here that makes this a premiere place to work and do research. The Hemodynamics & Vascular Biology Lab is comprised a diverse yet cohesive group of individuals, which are organized into two maing groups: One principaly investigates the biological mechanisms the vasculature during hemodynamic insult, and the other simulates related events using advanced computer technology. Together, the people at the Hemodynamics & Vascular Biology Lab are some of the most friendly, intelligent, capable people working together towards common goals.



Dr. MengHui Meng, Ph. D.
Department of Mechanical & Aerospace Engineering

Department of Biomedical Engineering
Department of Neurosurgery
Co-Director, Toshiba Stroke and Vascular Research Center

For more information click here.

Dr. KolegaJohn Kolega, M.Phil., Ph.D.
Associate Professor
Department of Pathological & Anatomical Sciences

My primary research interest is the behavior of endothelial cells, which form the inner lining of blood vessels and are key players in the remodeling events that occur during wound healing, aneurysm formation, tumor growth, and a wide variety of disease conditions. In my own lab we look at how endothelial cells sense and respond to their mechanical environment. Here at the TSRC, I mentor students in the use of cell culture and whole animal systems to examine how endothelial cells respond to specific hemodynamic micro-environments in order to understand the mechanism and regulation of flow-induced remodeling, especially as it relates to cerebral aneurysms.



Ph.D. Students

Nikhil Paliwal
Mechanical Engineering, Ph.D. Candidate

Flow diversion has become the preferred endovascular treatment option for intracranial aneurysms (IAs), but this treatment modality might not be successful for every aneurysm. My research focuses on predicting the clinical outcome of IAs after the implantation of flow diverters (FDs). We have developed a computational analysis workflow that uses patients' clinical images as input and predicts the long-term outcome of FD-treated aneurysms. The computational workflow includes aneurysmal morphology assessment, device modeling and CFD simulations to obtain relevant morphological, FD-related and pre- and post-treatment hemodynamic parameters. These parameters are used to train a neural network algorithm, which can predict the 6-month follow-up outcome of FD-treated aneurysms with high accuracy.


Robert Damiano
Mechanical Engineering, PhD Candidate

Endovascular device interventions such as flow diversion and embolic coiling are the primary treatment therapies for intracranial aneurysm, but they are not always successful at healing aneurysms in the long-term. We hypothesize that aneurysmal hemodynamics plays a key role in treatment outcomes of endovascularly-treated aneurysms. My research focuses on developing physics-based computer models of endovascular interventions and investigating how these interventions affect aneurysmal hemodynamics, and thus treatment outcomes. The ultimate goal of my research is to build and test machine learning models that predict treatment outcomes of coil-treated aneurysms using hemodynamic and other aneurysm-related factors.


Hamidreza Rajabzadeh-Oghaz
Mechanical Engineering, Ph.D. Candidate

Management of intracranial aneurysms (IAs) requires comprehensive evaluation of relevant aneurysm- and patient-risk factors. Our main goal is to use large database of aneurysms – which includes morphometrics, hemodynamics, and related clinical information of previously ruptured and unruptured IAs – along with pattern recognition algorithms to aid clinicians with: A. Identification of the ruptured IA in patients with multiples when hemorrhage pattern is not definitive. B. Evaluation of newly discovered unruptured IA when decision-making is challenging and requires additional information.


Kerry Poppenberg
Bioengineering, Ph.D. Candidate

Intracranial aneurysms are typically asymptomatic, so many individuals experience rupture, often with fatal consequences, with no warning. A method to screen for aneurysms could allow individuals to have elective treatment before rupture. My research focuses on developing blood-based prediction models for IA using neutrophil and whole blood transcriptomes. In the future, these models could be translated to a blood test to screen individuals for IA. We also perform differential expression and bioinformatics analyses on the transcriptomes to investigate the biological mechanisms underlying the disease.


Saeb Ragani Lamooki
Mechanical Engineering, Ph.D. Candidate

Flow-diverters and embolic coils are currently the dominant endovascular devices for treatment of intracranial aneurysms (IAs). Development of virtual intervention algorithms has been an ongoing effort for in-silico placement of such devices in patient-specific IAs. The ultimate goal of such algorithms is to integrate to the clinical settings for bed-side assessment of different treatment modalities prior to the real treatment. My research is focused on investigating the accuracy and efficiency of different virtual intervention algorithms. To that end, we evaluate the accuracy of such virtual algorithms in terms of device geometry and post-treatment hemodynamics when used along with CFD, against PIV measurements.


Tatsat Rajendra Patel
Mechanical Engineering, Ph.D. Candidate

Image-based computational tools have helped gain insights into clinical diagnosis and treatment decisions for cerebrovascular diseases like intracranial aneurysms (IAs). However, these tools have failed to translate into the clinical workflow to aid the clinicians in decision-making. One of the big bottlenecks for the lack of translation is the segmentation of patient’s medical image to create 3D vascular model. My primary research aims at developing Artificial Intelligence (AI) algorithms for automatic segmentation of cerebral vasculature with IAs from patient specific images. This work will help achieve automatic, objective and computationally efficient image segmentation.


Sricharan Veeturi
Mechanical Engineering, Ph.D. Student

Cerebral Intracranial aneurysms result in 500,000 deaths a year. Morphology and hemodynamics of previously ruptured or unruptured aneurysms can be used to quantify the similarity of new unruptrued anueurysms to the past ruptured cohorts. My research primarily focuses on using a large database of aneurysms to quantify this type of similarity using advanced machine learning algorithms. An additional part of my research includes quantification of accuracy and computational cost of the Computational Fluid Dynamics solvers being used in our lab.






Nicole Varble, Ph.D. (Student)
Vincent Tutino, Ph.D. (Student)
Jianping Xiang, Ph.D. (Student and Post Doctoral Associate)
Rahul Sanal, M.S. (Student)
Christopher Martensen, M.S. (Student)
Jessica Utzig, M.S. (Student)
Jason Kushner, M.S. (Student)
Hoon Choi, M.D., M.S. (Student)
Ying Zhang, M.D. (Visiting Scholar)
Ding Ma, Ph.D. (Student)
Nicholas Liaw, M.D., Ph.D. (Student)
Jennifer Dolan, Ph.D. (Student and Post Doctoral Associate)
Markus Tremmel, Ph.D. (Post Doctoral Associate)
Ling Gao, Ph.D.
(Post Doctoral Associate)
Eleni Metaxa, Ph.D. (Post Doctoral Associate)
Yiemeng Hoi, Ph.D. (Student)
Zhijie Wang, Ph.D. (Student)
Max Mendelbaum, M.D., Ph.D. (Student)
Dayle Hodge, M.S. (Student)
Sujan Dhar, M.S.
Sukhjinder Sing, M.S.
Madhu Vellakal, M.S.
Shashikanth Kaluvala, M.S. (Student)