Anjali Balagopal
San Jose,CA
Summary
Seasoned Machine Learning Engineer with experience in designing, developing and deploying machine learning algorithms. Strong skills in statistical analysis, data mining and generative AI. Demonstrated success in improving system performance through innovative machine learning methods. Proficient at synthesizing complex datasets, defining problems accurately and delivering intelligent solutions that improve business outcomes.
Overview
11
11
years of professional experience
Work History
Senior Machine Learning Engineer
01.2022 - Current
- Optimized existing models to improve accuracy and reduce computational time.
- Conducted research on new algorithms in the field of deep learning.
- Collaborated with data scientists to develop a strategy for deploying ML and AI solutions at scale across an organization.
- Designed experiments to test hypotheses about customer behavior and product features.
- Performed experimental research for improving model performance.
- Developed tools for automating the training process of machine learning models.
- Worked closely with stakeholders to define requirements for ML products.
- Analyzed user feedback from deployed ML products in order to identify areas of improvement.
- Provided technical guidance on best practices related to building robust ML systems.
- Investigated state-of-the-art approaches in Machine Learning literature and implemented them as needed.
- Designed and deployed machine learning models into product stack in support of product development.
- Researched state-of-the-art NLP techniques and technologies in order to improve existing models.
Graduate Researcher
UT Southwestern Medical Center
Dallas, USA
08.2017 - 12.2021
- Company Overview: A leading academic medical center dedicated to the health sciences
- Trained and deployed clinically acceptable deep learning-based tumor and organs-at-risk segmentation networks
- Developed few shot adaptation model for multi-institutional deployment
- Designed and executed large clinical studies for deep learning network evaluation
- Executed clinical translation and quality assurance of deep learning frameworks
- Investigated the effectiveness of using Bayesian approximations and ensembling for uncertainty estimation
- Improved on current models for Deformable Registration
- Implemented novel loss functions for effective multi-modality deep learning-based segmentation
- Authored multiple first author publications
- A leading academic medical center dedicated to the health sciences
AI/ML PhD Intern
GE Healthcare
San Ramon, USA
06.2020 - 08.2020
- Company Overview: A global medical technology and life sciences company
- Deep Learning for Image-to-Image translation
- Worked directly with stakeholders to develop an image quality improvement model for Ultrasound
- A global medical technology and life sciences company
Research Trainee
M D Anderson Cancer Research Center
Houston, USA
02.2016 - 07.2017
- Company Overview: A leading cancer research and treatment center
- Investigated the effectiveness of Tc99m MAA planning for Y90 dose determination for Hepatocellular carcinoma
- Validated Monte Carlo Y90 voxel-based dosimetry model with the Y90 dosimetry model used in MIM SurePlan Y90 dosimetry tool
- A leading cancer research and treatment center
Intern
Livanova PLC
Houston, USA
05.2016 - 08.2016
- Company Overview: A global medical technology company focused on neuromodulation
- Design and testing with the electrical engineering team for Vagus Nerve Simulation device used for treating drug-resistant epilepsy
- A global medical technology company focused on neuromodulation
Systems Engineer, Edison Engineering Development Program
GE Healthcare
Bangalore, India
06.2013 - 07.2015
- Company Overview: A global medical technology and life sciences company
- Development and evaluation of Calcium scoring application on CT contrast cardio scan images
- Successfully designed a common software platform for automating the verification of around 300 MRI applications
- Owned and tested components of an I/O board processor for a new cardiovascular system to attain a much higher processing speed than the current system
- Mentored interns for MRI verification team
- A global medical technology and life sciences company
Education
PhD - Biomedical Engineering
University of Texas Southwestern Medical Center
Dallas, TX12.2021
Masters - Bioengineering
RICE University
Houston, TX12.2016
Bachelor of Engineering (Hons.) - Electronics & Instrumentation
Birla Institute of Technology & Science
Pilani, India12.2013
Skills
- Deep Learning
- Computer Vision
- Natural Language Processing (NLP)
- Data Analysis
- Python
- PyTorch
- DICOM
- Clinical Imaging
- Data Structures
- Algorithms
- AWS/GCP
- Machine Learning
- Algorithm development
- Deep learning
- Data Analytics
- Model Development
Certifications and awards
- RSNA Imaging AI Certificate Program faculty
- Distinguished Referee for Medical Physics Journal, American Association of Physicists in Medicine, 2021 and 2022
- AI for medical diagnosis, Coursera, 05/2020
- The Complete Neural Networks Bootcamp: Theory, Applications, Udemy, 08/2020
- Edison Engineering Development Program, GE Healthcare, 07/2015
- GE Healthcare CEO Award, 2013, For the development of MRI applications automation platform
- Systems Thinking Award, 2013, From MRI team for the accomplishment of MRI automation project in a very short time without any prior experience
- Above & Beyond Award, 2014, From Detection & Guidance Solutions Robotics Hardware team
Publications
- A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy, Medical Image Analysis, 72, 2021, https://doi.org/10.1016/j.media.2021.102101
- PSA-Net: Deep learning-based physician style-aware segmentation network for postoperative prostate cancer clinical target volumes, Artificial Intelligence in Medicine, 121, 2021, https://doi.org/10.1016/j.artmed.2021.102195
- Cone-Beam Computed Tomography (CBCT) Segmentation by Adversarial Learning Domain Adaptation, Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019
- Bai T, Balagopal A, Dohopolski M, et al. A proof-of-concept study of artificial intelligence-assisted contour editing. Radiol Artif Intell. 2022; 4(5):e210214.
- Mashayekhi M, Tapia IR, Balagopal A, et al. Site-agnostic 3D dose distribution prediction with deep learning neural networks. Med Phys. 2022; 49(3): 1391-1406. doi:10.1002/mp.15461
- Balagopal A, Dohopolski M, Suk Kwon Y, et al. Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer. Phys Imaging Radiat Oncol. 2024 Apr 15;30:100577. doi: 10.1016/j.phro.2024.100577. PMID: 38707629; PMCID: PMC11068618.
- Fully automated organ segmentation in male pelvic CT images, Phys. Med. Biol., 63, 2018
- Dosimetric impact of physician style variations in contouring CTV for postoperative prostate cancer: A deep learning-based simulation study, Journal of Artificial Intelligence for Medical Sciences, 2, 1-2, 2021, https://doi.org/10.2991/jaims.d.210623.001
- A comparison of Monte Carlo dropout and bootstrap aggregation on the performance and uncertainty estimation in radiation therapy dose prediction with deep learning neural networks, Physics in Medicine & Biology, 66, 5, 2020
- Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning, Biomedical Physics & Engineering Express, 4, 5, 2018
- Characterization of 90Y-SPECT/CT self-calibration approaches on the quantification of voxel-level absorbed doses following 90Y-microsphere selective internal radiation therapy, Medical Physics, 45, 2, 2018, 10.1002/mp.12695
- Hepatocellular carcinoma tumor dose response after 90Y radioembolization with glass microspheres using 90Y-SPECT/CT-based voxel dosimetry, International Journal of Radiation Oncology Biology Physics, 102, 2, 2018, 10.1016/j.ijrobp.2018.05.062
- A Proof-of-Concept Study of Artificial Intelligence Assisted Contour Revision, arXiv:2107.13465
- Site-agnostic 3D dose distribution prediction with deep learning neural networks, Medical Physics, March, 2022, https://doi.org/10.1002/mp.15461
- Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer, arXiv:2302.01493, 2023
- Prior Guided Deep Difference Meta-Learner for Fast Adaptation to Stylized Segmentation, arXiv:2211.10588, 2022
Timeline
Senior Machine Learning Engineer
01.2022 - Current
AI/ML PhD Intern
GE Healthcare
06.2020 - 08.2020
Graduate Researcher
UT Southwestern Medical Center
08.2017 - 12.2021
Intern
Livanova PLC
05.2016 - 08.2016
Research Trainee
M D Anderson Cancer Research Center
02.2016 - 07.2017
Systems Engineer, Edison Engineering Development Program
GE Healthcare
06.2013 - 07.2015
PhD - Biomedical Engineering
University of Texas Southwestern Medical Center
Masters - Bioengineering
RICE University
Bachelor of Engineering (Hons.) - Electronics & Instrumentation
Birla Institute of Technology & Science
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