I'm Aditya Mohan - a Computer Science graduate and a Software Engineer. I'm currently enrolled in the Master's of Science in Computer Science from Arizona State University, and parallelly, working as a part-time Research Assistant. Most recently, I worked with Amazon Web Services in the AWS Proactive Security organization to build a highly secure, optimized and scalable tool to facilitate on-demand network scans throughout the extensive AWS internal network. Prior to that, I worked at Oracle Corporation as a Software Engineer in the Oracle Analytics division.
I'm interested in large-scale cloud platforms and machine learning and
I love to work in areas pertaining to cloud infrastructure and machine learning. I firmly believe in the importance of such scalable
systems given the "data explosion" and the need to securely maintain and make sense of valuable information from disorganized data.
Apart from my research interests, I enjoy taking up the occasional side projects and venturing into new realms of Computer Science.
Outside of work, I enjoy spending time playing instruments (particularly the guitar), following/playing football, travelling and dabbling in some trivia.
Work
Volcanic anomaly detection in remote sensing
Automating the analysis of volcanic features in time-series ASTER satellite imagery to identify subtle thermal anomalies for over 7k volcanic time-series data points using Local RX and PyTorch-based data parallel CNN models.
Consistent Multi- and Single-view HDR Image Reconstuctions from Single Exposures using Deep Learning Techniques
Worked on reconstructing multi-view HDR images from single exposure LDR images using a Deep Learning algorithm
while achieving consistency among multiple views. A poster for this paper has been accepted to Eurographics '22!
Enhancing a Performance Prediction Tool for CUDA kernels
Built a performance prediction Java based tool using a combination of static and dynamic analysis of the CUDA Program and obtained features for performance prediction using the NVIDIA Visual Profiler. Improved the GPU execution time prediction by building memory access penalty models using a Java application.
Genius PDF
A PDF Renderer desktop application with special functionalities for a client. The renderer includes an annotation side-bar which displays the definition of a word by looking up previous occurrences of the same word through local database transactions.
Hyperspectral Image Segmentation
This project was carried out as part of the "Pixxel Open Data challenge". The task was to segment hyperspectral satellite imagery using an unsupervised classification technique.
The 242 bands of satellite imagery data was collected by NASA's EO1 program.
Computational Photography Techniques
An online repository for various image processing and manipulation techniques applied on some photos captured by myself. Looking forward to expanding the repository to incorporate more techniques in Computational Photography.
Automatic Leather defect classification
This project was part of my internship. Apart from a ton of literature survey on employing ML techniques, I came up with a simple ML classifier which makes use of the Random Forest Classifier to classify leather samples into defective and non-defective samples.
News
- May '23 Joined Amazon Web Services as a SWE Intern in the AWS Security Organization
- Feb '23 Won first place in ASU's SpaceHack for sustainability hackathon
- Aug '22 Appointed as a Graduate Research Assistant in Dr. Hannah Kerner's lab at ASU
- Aug '22 Enrolled in Arizona State University's M.S. in Computer Science program
- Apr '22 Awarded People's Choice Award for our project at Orahacks '22
- Aug '21 Joined Oracle's Analytics Cloud as a Software Engineer
- May '21 Graduated from BITS Pilani Goa
- Apr '21 Published a poster at Eurographics '22
- Jan '21 Joined Dr. Celine Loscos's team at the University of Reims Champagne-Ardenne as a Research Intern