Analysing data with precision: How Aishwarya Asesh’s algorithm is making anomaly detection faster and easier

The success of Aishwarya’s algorithm is evident in its adoption by many companies, his algorithms are used by fortune 500 companies like Nike, Walmart, CVS, amongst others.

Analysing Data, Aishwarya Asesh, Aishwarya Asesh Algorithm, anomaly detection, science

By Alexander Statnikov

Aishwarya Asesh is a Senior Data Scientist, who is known for his research contributions in the field of Data Science, Time Series Analysis, and Forecasting. He is well-known for his innovative Anomaly Detection algorithm, which considers automated data segmentation and data breakdown into seasonality, error, and trend series. In a recent interview, he shared his journey to becoming a leading researcher in the field and how his algorithm is helping many companies reap the benefits of anomaly detection.

Aishwarya obtained his Master’s in Computer Science from the University of Utah in 2018. He began his professional career by filing a commercial patent in his first year, which has now become a multi-patented technology. His dedication and expertise have made him a judge for various conferences and research forums. Recently he will also be speaking as a Keynote Speaker at the 11th International Symposium on Digital Forensics and Security (ISDFS 2023) being held in Tennessee, USA. He shared he will be sharing insights about one of his recent research – “How Artificial Intelligence is driven Anomaly Detection can help in Data Security”, a topic of utmost importance with the growing number of digital devices in the world.

When asked about his contributions to the research field, Aishwarya said “My research has been focused on finding a better way to detect anomalies in time series data. Anomaly detection has been a major challenge for Machine Learning for a long time and my algorithm is a significant advancement in this area.” He further explained how his algorithm identifies seasonality, which is often neglected in traditional anomaly detection approaches.

Speaking about the benefits of his algorithm, Aishwarya said “My algorithm can help organizations identify unusual trends in their data and take preventive measures against potential risks. For example, if a company notices an unusual spike in customer complaints, it can take proactive measures to address the issue and prevent it from snowballing. This can also help companies identify potential opportunities and optimize their strategies to make the most of them.”

Aishwarya believes that the success of his algorithm lies in its simplicity. He said “My algorithm is easy to understand and implement, which makes it an ideal choice for companies looking to leverage anomaly detection. Moreover, my algorithm is highly customizable and can be used for different kinds of data, making it a versatile solution for many organizations.”

The success of Aishwarya’s algorithm is evident in its adoption by many companies, his algorithms are used by fortune 500 companies like Nike, Walmart, CVS, amongst others. It is being used in industries ranging from finance to healthcare to identify anomalies and take preventive measures. Aishwarya believes that anomaly detection has the potential to revolutionize the way organizations analyze and act on data, and his algorithm is a major step towards achieving that goal.

Written by Alexander Statnikov, a Machine Learning leader. He has worked with many business division, and helped improve business modeling, customer experience across North America and Europe.

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First published on: 17-03-2023 at 02:08 IST
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