Anisha Pal

Update: I am actively looking for full-time positions in the industry starting May 2024. Please reach out to me at if you think I am a good fit for your team.

I am a second year MS student in the Department of Computer Science at Georgia Tech. I am currently pursuing my Graduate Research under Dr.Judy Hoffman, where I am working on problems related to robustness and domain adaptation in computer vision.

My primary research focus revolves around the development of robust and efficient deep learning-driven computer vision algorithms for geospatial data with applications in climate change, conservation, and sustainability .

My background features various projects and internships, focusing on computer vision, geospatial applications, and sustainability. In the summer of 2023, I interned at Corteva Agriscience's R&D Data Science team, enhancing low-res satellite imagery through super-resolution for improved downstream computer vision tasks.

Additionally, I have two years of work experience as a Machine Learning Engineer at HyperVerge, a Fintech and Geospatial startup based out of India developing computer vision solutions for geospatial analysis and face spoof detection.

In my free time, I enjoy trekking, camping, adventure sports, reading, and discussing music across genres and cultures.

CV  /  Google Scholar  /  LinkedIn  /  Github

profile photo
project_img SkyScenes: A Synthetic Dataset for Aerial Scene Understanding
Sahil Khose*, Anisha Pal*, Aayushi Agarwal*, Deepanshi*, Judy Hoffman Prithvijit Chattopadhyay
* Indicates equal contribution
Under Submission  
pdf / link / code / data / website /
project_img SegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving
Swarnendu Ghosh, Anisha Pal, Shourya Jaiswal, Nibaran Das, Mita Nasipuri
International Journal of Machine Learning and Cybernetics, 2019  
pdf / link / code /
project_img SegFast: A Faster SqueezeNet based Semantic Image Segmentation Technique using Depth-wise Separable Convolutions
Anisha Pal, Shourya Jaiswal, Swarnendu Ghosh, Nibaran Das, Mita Nasipuri
ICVGIP, 2018  
pdf / link / code /
project_img Graduate Student Researcher at Hoffman Lab | Jan 2023 - Present

Working under the supervision of Prof.Judy Hoffman

Exploring synthetic-to-real generalization of computer vision applications for aerial imagery and analyzing the effects of intra domain shifts

project_img Machine Learning Intern Corteva Agriscience | May 2023 - Aug 2023

Designed and implemented image super-resolution algorithms on Sentinel-2 satellite imagery using transformer architecture for the downstream task of improving field boundary detection on agricultural land

Achieved a PSNR value of 36 dB and SSIM value of 0.81 on the curated test set

Performed data cleaning and satellite image preprocessing such as tile splitting, merging and georeferencing, generating feasible Sentinel2 composites with minimum cloud cover

Conducted a study on the feasibility of using GANs and Diffusion Models for the specific problem set

project_img Graduate Research Assistant at CQGRD | Aug 2022 - May 2023

Designed the objective criteria and performed suitability analysis to rank parcels within Henry County based on their suitability for freight related construction and transportation by taking into account environmental, socio-economic, logistical and transportation factors

Designed the decision support tool using ArcGIS to quantify the impact of freight development while channeling growth towards social, environmental and economic resilience

project_img Machine Learning Engineer at Hyperverge | Dec 2019 - July 2022

Led the development of a custom passive video liveness solution incorporating a vision transformer-based, multi-channel architecture with optimized frame sampling techniques for detecting Face Presentation At- tacks and achieving a consistent score of over 99% on research benchmarks and in production environments..

Led the AI efforts of the Geospatial team, designing a unique change detection and classification system on large-scale satellite/aerial imagery. Implemented a hybrid approach utilizing segmentation models, automatic imagery alignment, vectorization, and shape enhancement techniques, achieving a consistent F1 score of over 90% across various geographies and significantly improving property tax collection efficiency.

project_img Solution Specialist Intern at Microsoft, India | May 2019 - July 2019

Enabled the FY20 GTM (Go to Market) strategy for Microsoft Public Sector Department by conducting a comprehensive analysis of the smart city landscape in India, performing market research on cloud and AI services adoption, and suggesting a growth-maximizing roadmap targeting industry leaders

Facilitated easy adoption of smart city solutions by understanding the niche technical requirements of each smart city element and designing prototypes using Microsoft products and services.

project_img Research Intern at Jadavpur University, India | May 2018 - June 2018

Designed a computationally efficient deep learning-based semantic segmentation algorithm for self- driving cars that achieved 10x faster speed, 5x less memory consumption, and 224x fewer parameters with similar accuracy compared to the state-of-the-art segmentation algorithms by introducing the concept of transposed depthwise separable convolutions and spark module.

project_img NAS Latency Predictor

Creating the first dataset of its kind for training HW-NAS latency predictors by expanding the NATS-Bench dataset to include hardware-specific latency measurements and descriptors across various GPUs, CPUs, and Edge devices.

Developing a latency predictor that generalizes to unseen hardware and model architectures without sample measurements on the critical inference path

project_img Medical VQA

Analyzed the importance of different VQA components and designed an easy to use lightweight framework that is able to achieve results comparable to state of the art on VQA-RAD dataset

The work highlights the importance of encoder selection and offers a practical solution for VQA in the medical domain

project_img Aye-Aye monitoring

As a member of UC, San Diego, Engineers for Exploration collaborated with San Diego Zoo to develop a real-time machine learning system to monitor and analyze behavioral cues in Aye-Aye for early detection of health issues.

project_img Black Box Optimization

Conducted extensive survey and experimentation on the different Hyperparameter Optimization techniques such as Turbo, Hyperopt, PySOT, and Nevergrad as part of the BBO challenge.

Identified the advantage of trust-region based exploration-exploitation provided by Turbo and the superior candidate selection method (TPE) used by Hyperopt to design a hybrid approach.

project_img Adversarial Defense

Trained neural networks that are robust to post-hoc pruning using Targeted Dropout. The idea was to cut off the snow-ball effect of error buildup in the network’s activations by removing the impact of most weights.

Tested the feasibility of this technique by experimenting with FSGM and PGD attacks on CIFAR 10/100 datasets implemented on ResNet models augmented with Targeted Dropout.

project_img Satellite Imagery Based Shoreline Monitoring System

Attempt at developing an automated system to monitor shifting shoreline trends in high-impact regions of the world using satellite imagery (Landsat, SPOT) and deep learning based segmentation techniques.

Achieved an accuracy of 97% in delineating shorelines in sandy beaches worldwide through semi-supervised learning techniques on a custom dataset using a 3 class approach (sea, land, and runup) to counter tidal noise.

project_img Master of Science in Computer Science

Georgia Institute of Technology, Atlanta | Aug 2022 - Present

Specialization in Machine Learning

project_img Bachelor of Technology in Electronics & Communication Engineering

Manipal Institute of Technology, Manipal | July 2016 - June 2020

Minor Specialization in Data Science