Anisha Pal

I am a first year Masters student in the Department of Computer Science at Georgia Tech . I completed my undergraduate studies in Electronics & Communication Engineering at Manipal Institute of Technology in Manipal, Karnataka.

My research interests lie in developing safe, robust, and autonomous solutions to handle issues involving climate change, conservation, and sustainability using Computer Vision and Deep learning techniques.

Prior to this, I worked for two years as a Machine Learning Engineer at Hyperverge, a Fintech and Geospatial startup based out of India. At Hyperverge, I developed various Computer Vision applications in the geospatial and facial spoof detection sector. Parallelly, I have also pursued my interests in environmental conservation by contributing to Prof. Ryan Kastner's group UCSD Engineers for Exploration to develop the pipeline for animal behavior analysis.

In my free time, I love going trekking, camping, and pursuing adventure sports. I am also an avid reader and enjoy discussions related to music spanning various genres, languages, and cultures.

If you have any questions / want to collaborate / discuss research, feel free to send me an email at

CV  /  Google Scholar  /  LinkedIn  /  Github

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project_img Graduate Student Researcher at Hoffman Lab, Georgia Tech | Jan 2023 - Present

Generated a synthetic aerial imagery dataset with different weather and layout variations

Conducted ablation studies on leveraging diverse simulated aerial data to improve visual recognition accuracy and robustness under differing real world conditions on both synthetic and real aerial imagery.

project_img Graduate Research Assistant at CQRD, Georgia Tech | Aug 2022 - Present

Analyzing environmental risk due to warehouse construction & freight transportation in Henry County, Georgia using geospatial analytics and visualization

Designing the decision support tool to quantify the impact of various development scenarios while channeling growth towards social, environmental and economic resilience

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

Developed custom passive video liveness solution for Face PAD(Presentation Attack Detection) robust across different ethnicities using vision transformer based approach and achieved accuarcy of >99% on spoof and live classes across different benchmarks.

Led the AI efforts of the Geospatial team towards designing the change detection and classification system using a combination of segmentation models, automatic imagery alignment adjustment techniques, vectorization and shape enhancement methods on satellite/aerial imagery to simplify and improve the efficiency of the property tax collection system.

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

Enabled FY20 (Financial Year 2020) GTM (Go to Market) strategy for Microsoft Public Sector Department by conducting a thorough analysis of the smart city landscape in India and suggesting a roadmap for efficient integration of cloud and AI services.

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 Satellite Imagery based Shoreline Monitoring System

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 Aye-Aye monitoring

At University of California, San Diego Engineers for Exploration worked in collaboration 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 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 /

Improved upon the previous SegFast approach by introducing kernel factorization that further reduced the number of parameters resulting in 10x faster speed compared to the state-of-the-art segmentation algorithms without compromising segmentation performance.

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 /

Designed a computationally efficient deep learning-based semantic segmentation algorithm for self- driving cars that achieved 8.7x faster speed, 5x less memory consumption, and 223x 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 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