Devyani Maladkar

I am a Masters student at the University of Texas at Austin, where I am pursuing my Masters in Computer Science. I am interested in Machine Learning and Optimisation.

I completed my Undergraduate at the Indian Institute of Technology Goa in Computer Science and Engineering, wherein I graduated with 9.95/10 CGPA and was awarded the Institute Gold Medal.

I interned in Dolby in summer 2023 and interned at Mathworks where I worked on C++ software developement. I was also a Research Intern at intelligent Global Health Group, Machine Learning and Opimisaion Lab at EPFL.

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I'm interested in computer vision, machine learning, optimization. I am passionate about solving interesting problems using Mathematics and Computer Science.

Working under the supervision of Dr. Aryan Mokhtari at the WNCG Lab.

I worked on developing diagnosis tools for audio video real time streaming pipeline. Th work involved building tools to obtain and visualise network condition and performance metrics. The code was developed using C++,C, Python and Prometheus and Graphana tools to build diagnostic reports .

I worked on developing a C++ software component, alongside the AUTOSAR and SLRT prototyping team, for improving performance of existing Simulink Calibration Tool. The project involved software development in C++ and MATLAB.

Worked on a project to apply existing Contrastive Learning algorithms in Computer Vision for COVID detection. Successfully adapted and evaluated performance for Lung Ultrasound Videos to perform COVID-19 diagnosis. Analysed and designed experiments for using Lung Ultrasound Videos in contrastive learning settings for COVID diagnosis. Presented work virtually at the Geneva Health Forum. The theme of the conference was “COVID-19 Pandemic and Environmental Emergency: Reinventing Global Health in a Time of Global Change”.

Contributed to developing a visualisation tool for geo-spatial analysis, by collaborating with the interdisciplinary team at the Agro-Informatics Lab at IIT Bombay. Designed and built visualisation platform for drone-sensing outputs using OpenLayers javascript Library and performed data analysis using Python. The platform was developed to be user friendly for scientists and interactive for more flexibility in visualisation and analysis of the agriculture data.

Worked on Android Malware detection using Machine Learning. Developed a pipeline from collecting and processing android application data and classifying as benign or malware.