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Amir Golnabi

Assistant Professor, Mathematical Sciences

Office:
Conrad J. Schmitt Hall 374
Email:
golnabia@montclair.edu
Phone:
973-655-7226
Degrees:
BS, Montclair State University
PhD, Dartmouth College
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Profile

I studied Mathematics and Computer Engineering at the University of Salamanca in Spain. After completing 4 years of coursework, I transferred to Montclair State University where I graduated with a Bachelor of Science in Mathematics in 2007. Thereafter, I started my graduate studies at the Thayer School of Engineering at Dartmouth College, and worked on the "Computational Aspect of Microwave Imaging for Biomedical Applications." In June 2012, I completed my Ph.D. degree in Biomedical Engineering, and joined the Pulmonary Imaging and Bioengineering Laboratory at the Massachusetts General Hospital (MGH) and Harvard Medical School. During my postdoctoral research fellow training, I worked on "Complex Systems Approach to Bronchoconstriction in Asthma."

I have a track record of peer-reviewed publications, conference presentations, and awards, including a featured article in the September 2019 issue of the IEEE Transactions on Biomedical Engineering (TBME), the Sylvia Sorkin Greenfield Award for the Best Paper Published in Medical Physics in 2013, and the groundbreaking study entitled “Tri-Ponderal Mass Index vs Body Mass Index in Estimating Body Fat During Adolescence”, which was published in the Journal of the American Medical Association (JAMA) Pediatrics. I was also the recipient of the prestigious IEEE Microwave Theory and Techniques Society (MTT-S) Graduate Fellowship for Medical Applications in 2010. My research interest includes mathematical modeling, numerical methods, inverse problems, medical image processing, and image reconstruction algorithms. I hold a patent in System and Method for Collection and Use of Magnetic Resonance Data and Microwave Data to Identify Boundaries of Interest.

I am also interested in undergraduate mathematics education. I am currently a Co-PI on an NSF funded project entitled “Adjunct Mathematics Instructor Resources and Support: Improving Undergraduate Precalculus Teaching and Learning Experience.”

I am very passionate about teaching and I am currently with the Department of Mathematical Sciences as an Assistant Professor. I teach a variety of lower- and upper-level mathematics courses, and I am very interested in implementing novel techniques to engage students in classroom, promoting deep learning, and improving students’ learning experience through shifting pedagogical practices.

Specialization

Biomedical Engineering: Mathematical Modeling, Medical Imaging, Image Reconstruction Algorithms, Data Analysis, and Image Processing
Undergraduate Mathematics Education: Deep learning, Undergraduate Research Experience

Office Hours

Spring

Tuesday
2:00 pm - 3:00 pm
Friday
10:30 am - 11:30 am
2:00 pm - 3:00 pm

Links

Research Projects

Mathematical Modeling and Medical Imaging

I am interested in the science of biomedical imaging, how data can be collected, processed, assembled, and presented as an image, how it may be visualized, and how it may be interpreted. In addition, my research focuses on the application of biomedical imaging for new methods, techniques, and tools to further understanding of biological mechanisms of human diseases.

Adjunct Mathematics Instructor Resources and Support: Improving Undergraduate Precalculus Teaching and Learning Experience

Undergraduate students' persistence in STEM is heavily influenced by their classroom experiences, particularly in entry-level mathematics courses. Considerable research has been conducted showing that improved instruction by full-time faculty can increase student motivation and persistence in mathematics courses. However, entry-level courses, such as PreCalculus, are often taught by adjunct and other part-time instructors, who traditionally have tended to be offered only minimal professional development and support for teaching undergraduate mathematics. Thus, there is a pressing need to focus on developing instructional practices among adjunct and other part-time instructors. This project will build a model of course coordination and adjunct instructor support to improve the teaching and learning of PreCalculus.

While some work has been done to understand the benefits of supports for part-time instructors at the undergraduate level, they have focused mostly on graduate teaching assistants rather than adjunct instructors. Our project extends this work to adjunct instructors and will contribute to the research base the adjunct instructor population. By building a model of adjunct instructor resources and support, this project contributes to deeper understanding of how such efforts impact (1) adjunct instructor knowledge and instructional practices, (2) adjunct instructor job satisfaction, and (3) student academic success and retention in STEM majors. This understanding could help other departments and institutions with similar instructor populations to better support their adjunct faculty, with the goal of improving student learning and persistence in PreCalculus and beyond.

Synthesizing 3D Ultrasound Images of Different Fetal Structures for Automated Semantic Segmentation

Over the past few year, different machine learning algorithms have significantly advanced our ability to automatically classify and segment various medical images. However, the remarkable capability of an effective machine learning model heavily relies on the learning from sufficient amount of training samples. Such large data may be available in some imaging modalities and for certain clinical applications. However, ultrasound images of different fetal structures for further development and use of machine learning techniques are scarce, mainly due to the challenging nature of acquiring routine clinical US data and the fact that annotating a large set of US images is an extensive task. The lack availability of such data provides us a motivation to develop novel image synthesis techniques to generate 3D US images for training deep learning models in order to assess fetal health and growth.

This project focuses on better understanding how the size of different fetal structures at different stages of pregnancy is correlated with fetal health and growth. Towards attaining this goal, the overall objective of the project is to develop a reliable technique to synthesize realistic 3D ultrasound images of different fetal structures, which then could be used to train deep learning models.

Analyzing vessel diameter and Doppler blood flow from ultrasound videos

This collaborative work focuses on developing a graphical user interface (GUI) to analyze vessel diameter and Doppler blood flow from ultrasound videos. Currently, there are software that do this, but they are often proprietary or not very efficient to use (loading and analyzing ultrasound videos in certain formats could be very slow).

Research Assistant Opportunity

Research assistant wanted with an interest in medical image reconstruction. This position is ideal for anyone considering future graduate study/career in medical imaging and/or image processing.

Responsibilities include regular importation and quality assurance of remote biomedical image data using MATLAB, and implementation of new mathematical techniques for image reconstruction.

Required Skills: Programming experience in MATLAB.

Anyone interested in the position should write to me (with an attached CV) at: golnabia@montclair.edu.

Independent Study

If you are a science major interested in taking an independent study course related to a mathematics or biomedical topic, please write to me (with an attached unofficial transcript) at: golnabia@montclair.edu.

Undergraduate and Graduate Research Opportunity

I am constantly looking for motivated undergraduate and graduate students who are interested in doing research in applied mathematics and have a GPA greater than 3.5/4. If you are qualified, just stop by my office or send me an email including your CV.