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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
iSense: AI-Based Model and App to Predict Depressive and Destructive Sentiment
Mobile application powered by an Artificial Intelligence model to securely identify linguistic biomarkers of mood disorders in a teen’s outgoing SMS messages
Development of a Novel Enzyme Inhibitor for the Cdc14 Enzyme in Verticillium longisporum
Computationally developed an inhibitor for the enzyme Cdc14 in Molecular Operating Environment to serve as the basis for the development of a novel fungicide.
Hero: Automated Detection System for Prescription Drug Overdose
Multi-factor automated overdose detection system designed to operate in a teen’s living and working space. Involves sweat composition analysis, AI-based mood swing detection, and measurement of vital signs.
Computer Vision Based Gesture Recognition Interface for Automobile Control System
Interconnected embedded system to accurately recognize a set of hand gestures for media, navigation, and temperature control in automobile
Win2Win: Social App to Promote STEM Participation
Mobile application built in Android Studio where students can scroll through a feed of local STEM events, view what peers are enrolled in, and work towards increasing their STEM-Score with participation.
publications
Hero: Automated Detection System for Prescription Stimulant Overdose via AI-Based Emotion Inference, Metabolite Detection, and Biometric Measurement
Published in Open Journal of Applied Sciences, 2020
Over the past year, approximately 10,000 Americans have died by psychostimulant overdose, and over 50% of these deaths were caused by prescription stimulant misuse. A comprehensive approach to detect a drug overdose in the environment where it occurs is imperative to reduce the number of prescription stimulant overdose-related deaths. Teenagers are at the highest risk for prescription stimulant overdose, so this study proposes a multi-factor overdose detection system named Hero which is designed to noninvasively operate within the context of a teen’s life. Hero monitors five factors that indicate stimulant abuse: extreme mood swings, presence of amphetamine metabolite in sweat excreted from the fingertip, heart rate, blood pressure, and respiration rate.
Recommended citation: Nori, Divya. (2020). "Hero: Automated Detection System for Prescription Stimulant Overdose via AI-Based Emotion Inference, Metabolite Detection, and Biometric Measurement." Open Journal of Applied Sciences.
Development and implementation of enzymatic and volatile compound-based approaches for instantaneous detection of pathogenic Staphylococcus aureus
Published in Journal of Emerging Investigators, 2021
Staphylococcus aureus (S. aureus) has a mortality rate of up to 30% in developing countries. In contrast, the mortality rate of S. aureus in the U.S. is less than 5%. One reason for the six-fold increase in fatality in underdeveloped communities is lack of access to a rapid, accurate, and user-friendly diagnostic method to enable timely detection of these infections. The purpose of this experiment was to determine if enzymatic and volatile compound-based approaches would perform more quickly in comparison to existing S. aureus diagnostic methods and to evaluate these novel methods on accuracy. Further, we sought to implement them in a reusable 3D-printed instantaneous detection device.
Recommended citation: Nori, Divya. (2021). "Development and implementation of enzymatic and volatile compound-based approaches for instantaneous detection of pathogenic Staphylococcus aureus." Journal of Emerging Investigators.
Real-Time Intervention Framework for Nicotine Poisoning via Identification of Alternate Compound and a Smart E-Cigarette Device
Published in MIT Undergraduate Research Journal, 2021
There has been a 200-fold increase in nicotine poisoning-related hospitalizations over the past decade which can be attributed to the rising popularity of electronic cigarette use or “vaping.” E-cigarettes are battery-powered devices designed to vaporize a liquid that consists of lung-damaging additives, flavorings, and nicotine. Due to the potency and palatability of this vapor, users can easily ingest a dangerous level of nicotine and experience symptoms such as difficulty breathing, dizziness, and seizures. This paper proposes a smart e-cigarette device capable of tracking nicotine release with respect to time, stopping traditional vape liquid release when a threshold is reached, and switching to a safer compound.
Recommended citation: Nori, Divya. (2021). "Real-Time Intervention Framework for Nicotine Poisoning via Identification of Alternate Compound and a Smart E-Cigarette Device." MIT Undergraduate Research Journal. p. 39-43.
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.