Let’s imagine that you are at the top of the Beaver Stadium, which is home to one of the prestigious college football programs in the nation — Penn State. And you want to figure out how many people attended the game without manually clicking a tally counter — seriously, that would be too much.

With the help of ongoing development in computer vision, detecting multiple people in an image is not even a real problem these days. In fact, there are numerous pre-trained models available to the public for them to easily integrate on their device without having to train…


This excerpt is based on the paper we turned in at the 35th AAAI Student Abstract and Poster Program [7].

In recent years, wildfire has become an unavoidable natural disaster that continues to threaten fire-prone communities. Due to the ongoing climate change, global warming, and fuel drying, the frequency of devastating wildfires increases every year [1]. The consequences of massive wildfires are brutal. For instance, in 2003, wildfires that occurred in San Diego County burned over 376,000 acres and 3,241 households. This sums up to approximately $2.45 billion in terms of total economic costs [2]. Traditional, physics and empirically-based wildfire…


National Football League (NFL) is the biggest sport industry in the United States. Due to its highly competitive nature, the NFL attracts a massive audience around the globe. In particular, during the Super Bowl, more than 100 million viewers watch the game and the cost of a 30-second commercial is around 5 million dollars.

To win the Super Bowl, players and coaches must commit a year-long effort. Nowadays, advanced equipment and advanced data analysis make it NFL teams easier to find useful information from accessible data to help players improvise on their game.

In this project, we conducted a simple…


In today’s world, data is ubiquitous. The quantity of data increases exponentially, but the size of the data keeps on growing. Often, processing such big data becomes a bottleneck when running a deep learning model. For this topic, I would like to discuss how to speed up preprocessing speed by running the same task in parallel.

In this article, a brief introduction to multiprocessing is laid out, followed by a case study of implementing multiprocessing to slice a whole slide image into patches

Before I go any deeper, I would like to briefly talk about how Python manages incoming data.

Samuel Sung

AI enthusiast who is currently on the quest for exploring new insights and ideas

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