Under United States securities laws corporations must disclose material risks to their operations. Human rights issues, especially in authoritarian countries, rarely show up in the information that data providers offer to investors, in part due to the risks to those subject to these abuses. The result is a dearth of data on human rights materiality and the tendency of investors to overlook human rights risks of the companies that they finance.
Humanity thrives along major rivers – this is as true now as it was ages ago. Our dependence on rivers for agriculture and electricity, as well as the need to control its flow because of our proximity, has resulted in dramatic changes to the nature of the rivers. What were once great perennial rivers are now mere trickles during the summer months. This puts the livelihood of many people, especially poor farmers, in jeopardy. How can we monitor and document changes to the flow through rivers over time? Since river gauge measurements are rare or non-existent, any way in which we can use freely available satellite imagery (Landsat, Sentinel) to determine the changes in flow patterns of rivers over time would be extremely useful. One such tool is Rivamap – it uses OpenCV to analyze satellite imagery to extract information about rivers, especially for large rivers. What about smaller ones – it does not seem to work as well. In this project, the student(s) will have to develop machine-learning based methods (or extend the capabilities of Rivamap) to study satellite images to extract information about the path and dimensions of rivers of different flow rates and flow patterns. Comparison with ground-truth data will be needed.
Data visualization, statistics, and analysis of translation entries online. More details will be furnished upon request.
The talent students will be given search entries, topics, or terms and will be required to analyze the algorithms of search results across various engines, languages. More information and details upon request.
Our lab is using clinical notes to phenotype COVID patient outcomes. The aim is to better understand the sequela of COVID-19 from clinical notes.
The question we ask is whether online echo-chambers on social media networks enhance the anxiety and depression of individuals during the COVID19 outbreak. More specifically we want to measure the intensity of the communication about COVID-19 within the echo-chamber of individuals on Twitter and investigate the impact on their subsequent tweets in terms of the level of anxiety and signs of depressive language in their Tweets. We measure echo-chambers by the number of users in the social network that tweeted about COVID-19. We build on an extensive dataset of Twitter users for whom we have identified a large number of demographic and geographic variables (such as the gender, age, ethnicity, location by state, political affiliation) as well as their social network.
I am conducting studies on lifestyle behaviors, in particular diet, sleep behaviors, and circadian rest-activity rhythms in relation to cardiometabolic outcomes (hypertension, type 2 diabetes, and obesity). Sample sizes of my studies range from n=100 to n=16,000.
