Monica Munnangi

I am a third year PhD student at Khoury College of Computer Science, advised by Silvio Amir and Byron Wallace. I had a fun summer '23 interning at Allen Institute for AI advised by Aakanksha Naik.

I graduated from UMass Amherst, with a Masters in Computer Science and as a Graduate Student I was a part of Information Fusion Lab advised by Prof. Madalina Fiterau and by Abhyuday Jagannatha Additionally, I co-advised a group of graduate students on a project with GE Healthcare on Tiny-ChexNet.

While I was at UMass, I interned at GE Healthcare as a Data Scientist where I worked on computer vision for medical imaging. I was fortunate to be advised by Katelyn Nye. Prior to UMass Amherst, I've done my undergrad at Vellore Institute of Technology, Chennai where I was introduced to Artificial Intelligence and Machine Learning by Prof. Priyadarshini J.

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Machine Learning, Computer Vision and Clinical Natural Language Processing, Multi-Modal data and Deep Learning for Medicine

Worked on classification and regression problems with recurrent neural networks on time series data of ICU patients and visualizing the patterns in data with sophisticated techniques.

Worked on a natural language processing model to classify patient physician communication and to improve message triage.

Implemented a novel forecasting framework which utilizes a CNN to extract features from a patient's brain MRIs which we then fused with patient data and use RNN to track progression.

Showed that the inclusion of these customised/patient-specific features increases the F1-score of 0.4644, with recall at 0.4974 and precision of 0.4355 of forecasting the disease stages.

Developed a Neural Network classifier to identify if a chest tube is present in an X-Ray image and achieved an accuracy of 95% trained on 6000+ images, to help radiologists make better decisions which is waiting on FDA approval.

Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases. The paper explores the evolution of NER from a rule based approach, supervised to unsupervised learning approaches.

Medical report generation could be error-prone, long and tedious task for most physicians. To address this issue we have worked on automatic image captioning for medical images, exclusively Chest X-Ray images as they have large repository of publicly available dataset with captions and also datasets which have comprehensive reports.

Attempted to predict the onset of Sepsis in patients using historical patient data. Precisely we used ICU and timestamp data of over 40,000 patients and used baseline logistic regression and random forests to predict the onset of Sepsis as a baseline.

Developed a Long Short - Term Memory (LSTM) model using PyTorch which led to F1 score of 0.82. Our model could predict the onset of Sepsis 6 hours before its clinical occurrence to assist patients with high risks of sepsis for early intervension.

Teaching Assistant : Database Management Systems, assisted Prof. Muralidhar

• Student Grant for NeurIPS 2020
• Student Grant for EMNLP 2020
• Central Board of Secondary Education Excellence award for outstanding performance in AISSE 2014
• City topper, Science Olympiad Foundation - National Science Olympiad 2012

• Steve Jobs by Walter Issacson
• The Gene : An Intimate History by Siddhartha Mukherjee
• The Emperor of All Maladies: A Biography of Cancer by Siddhartha Mukherjee
• Asura: Tale of the Vanquished by Anand Neelakantan
• Shiva Trilogy by Amish Tripathi
• An Uncertain Glory: India and its Contradictions by Jean Drèze and Amartya Sen
• Rich Dad Poor Dad by Robert T. Kiyosaki
• The Bhagavad Gita by A. C. Bhaktivedanta Swami Prabhupada
• Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

SOF is an Educational Organization popularizing academic competition and assisting development of competitive spirit among school children. I was awarded the School Topper in Grade 10.

Software Developer - 2016 to 2017 ; Team Technocrats is VIT Chennai’s official robotics team. Technocrats incorporates students from different branches who share a common passion for robotics and each one showcases their talent and skill in their particular field.

Participated in the MIT Hacking Medicine - May 2019 and co-developed OraNet which is a mobile application which would assist a clinician to screen patients for oral cancer in a quick and cost effective way.Participated in the HackHer413 and co-developed an algorithm to detect Wild animals in the images and gives information about the animals.

Represented Spain in the United Nations Development Program and was an honorable mention in a 4 day Model United Nations in Hyderabad in August 2013.

Represented Spain in the United Nations Environmental Program and was an honorable mention in a 3 day Model United Nations in BITS Goa in February 2015.

Part of an event at DESIRE Society, Hyderabad - Serving children affected with HIV/AIDS.
Volunteer of an event at Sivananda Rehabilation Home, Hyderabad - To serve the needs of people affected by leprosy.
Volunteered a fund raising event, organized a 5K run in Hyderabad.

Credits : Jon Barron for the template.