Ananya Singhal
I build things, write code, and decode brains.
I'm currently an AI Researcher at GE Healthcare in the Advanced Technology group where I work on medical image analysis. I am interested in the practice and theory of diffusion tensor imaging analysis, including correlating diffusion-weighted imaging signals, estimating principal components of diffusion tensors using deep neural networks, and denoising high b-value data in diffusion MRI.
Prior to GE, I was an Undergraduate student at Shiv Nadar Institute of Eminence, pursuing a Bachelor's degree in Computer Science and Engineering with a minor in Mathematics. At SNIoE, I closely collaborated with Dr. Saurabh J. Shigwan on reseach in diffusion MRI, particularly in the early diagnosis of neurodegenerative diseases. During my Undergraduate years I have also interned at Dell Technologies, National University of Singapore, IIIT Hyderabad, CVIT Lab and ZKTeco.
Beyond technical skills, I've been recognized with awards including the Generation Google Scholarship (APAC) and the Diversity, Equity & Inclusion Travel Grant.
B.Tech, Computer Science & Engineering
Minor in Mathematics | Honors: Dean's List
Shiv Nadar Institute of Eminence (SNIoE)
Received the Advancing Inclusion Scholarship from AnitaB.org India, providing the opportunity to attend the Grace Hopper Celebration India (GHCI) in Bengaluru, India's largest gathering of women and nonbinary technologists. This scholarship includes year-round AnitaB.org India membership with access to career workshops, networking opportunities, and mentorship from industry role models.
Enhanced analytical denoising methods for high b-value diffusion data by implementing a gradient-based kernel allocation system that automatically preserves anatomical structures, applying targeted smoothing based on regional complexity for superior detail retention.
Developed a tract-based classification method leveraging sparse diffusion measurements for early detection of Alzheimer's disease, improving early diagnostic accuracy and reducing the need for extensive data acquisition.
Developed a machine learning model to estimate order fulfillment status and predict sales likelihood based on product configurations. Utilized SQL Server for data analysis to identify low-selling configurations, reducing idle inventory and improving warehouse efficiency. Enhanced skills in MySQL and Python during this 5-month on-site internship in Bengaluru.
Presented our research paper virtually titled, "Deep Learning Framework using Sparse Diffusion MRI for Diagnosis of Frontotemporal Dementia," at the 15th Asian Conference on Machine Learning. Collaborated with co-authors Dr. Saurabh Shigwan, Dr. Rajeev Kumar Singh, and Abhishek Tiwari on this research.
Presented our research paper, "Deep Learning Framework using Sparse Diffusion MRI for Diagnosis of Frontotemporal Dementia," at the BioImage Computing workshop. The work introduces an innovative framework that utilizes sparse diffusion MRI to enable efficient diagnosis while reducing scan time without compromising accuracy. Collaborated with co-authors Dr. Saurabh Shigwan, Dr. Rajeev Kumar Singh, and Abhishek Tiwari on this research.
Completed a three-month on-site software development engineering internship at Dell Technologies in Bengaluru. During this experience, I worked on projects involving Natural Language Processing (NLP) and Large Language Models (LLM) while gaining practical exposure to industry-standard defect and order management systems.
Served as an Undergraduate Research Fellow under Dr. Saurabh J. Shigwan, where I investigated the application of Transformer-based deep learning techniques to accelerate diffusion tensor imaging (DTI) processing. Working with the ADNI dataset, I developed models that leveraged attention mechanisms to generate quantitative measures of fractional anisotropy (FA), axial diffusivity (AxD), and mean diffusivity (MD) using only 5 and 21 diffusion directions. This research contributed to improving early Alzheimer's disease diagnosis while reducing clinical scanning times by more than half, making the diagnostic process more efficient and accessible.
Completed a two-month on-site internship at ZKTeco Inc in Bengaluru, where I worked on an advanced 3D-Structured Light Facial Recognition model. My primary focus was developing anti-spoofing capabilities by implementing systems that project speckle light spots to accurately calculate facial depth. This experience enhanced my skills in biometric security systems, computer vision applications, and 3D reconstruction techniques while providing valuable industry exposure in a leading security technology company.
Participated in the prestigious Microsoft Engage program as a mentee where I developed a real-time facial recognition system for web authentication. I implemented face detection using MTCNN and leveraged FaceNet with LinearSVC for accurate facial recognition and classification. To enhance security, I integrated liveness detection features to prevent forced login attempts. The final model achieved an impressive 93% accuracy rate. This experience strengthened my skills in OpenCV, computer vision, CNN, web development, and machine learning while receiving valuable guidance from Microsoft professionals.
Started a two-month on-site research internship at the National University of Singapore where I designed and developed a real-time violence detection system for surveillance cameras. I optimized an LRCN model combining CNN and LSTM architectures, trained on a novel dataset of 300 fight/non-fight videos. The project strengthened my skills in computer vision, deep learning and image processing.
Served as Vice Chair of the Association for Computing Machinery (ACM) chapter at Shiv Nadar University for a full year. During my tenure, I successfully organized and led a National level Hackathon called 'Front-a-thon' that attracted over 300 participants from across the country.
Honored to be awarded the Harvard WECode Scholarship issued by Harvard University. This prestigious scholarship covered the full cost of the conference ticket at the early bird rate and provided financial aid of $200 for travel and related expenses to attend the WECode 2025 conference.
Shared my knowledge and guided students through ML concepts, with a focus on classification algorithms, logistic regression, and SVM. Conducted a hands-on workshop where participants implemented a practical speaker recognition system.
Won the Feedback Star award at the Generation Google Scholars community forum.
Got promoted to Beta-level as Microsoft Learn Student Ambassador, where I actively engaged with Microsoft's educational ecosystem by mastering new technologies through their official channels and shared this knowledge with fellow students. My role involved distributing valuable Microsoft resources throughout the student community, championing the adoption of Microsoft Azure Cloud services, and raising awareness about Microsoft-hosted competitions and opportunities. Through these activities, I serve as a bridge between Microsoft's technological innovations and the student community, fostering technical growth and cloud adoption.
Recipient of the AWS Machine Learning Scholarship Program by Udacity, which provided access to the exclusive AWS MLE Foundations course. Selected from a competitive applicant pool based on technical aptitude and commitment to advancing machine learning skills.
Participated in the Generation Google Scholarship retreat, networking with fellow scholars and Google professionals.
Selected as a Generation Google Scholar and was awarded USD 1,000. One of only three first-year students selected among applicants from 14 countries.
Conducted my inaugural workshop on "Introduction to AI on Azure" as an Alpha-level Microsoft Learn Student Ambassador.
Awarded place on the Dean's List for academic excellence in first semester.
Thrilled to begin my first research internship at the prestigious Computer Vision and Information Technology (CVIT) Lab at IIIT Hyderabad under Dr. Anoop Namboodiri. I worked on mitigating bias in facial recognition systems, creating datasets, and analyzing correlation between facial recognition accuracy and demographic factors. This opportunity runs through December 2021 and marks an exciting step in my research journey.
Joined the MLSA program that encourages contributing to community engagement, workshops, and mentorship.
Created cryptic challenges for Breeze, the annual Inter-College Techno-Cultural Fest of Shiv Nadar University.
Started my Bachelor of Technology in Computer Science @Shiv Nadar University.