Siddharth Satpathy, Ph.D.

Siddharth Satpathy, Ph.D. has extensive experience in machine learning and research. Siddharth is currently working as a Senior Staff Machine Learning Engineer at Element Energy, where they are responsible for developing and deploying a deep convolutional network-based platform for predicting states of degradation and remaining useful life in large-scale lithium-ion battery fleets. Prior to this, they worked as a Senior Machine Learning Engineer at Deepfence Inc, where they built an innovative anomaly detection pipeline for detecting malicious traffic in complex environments. Siddharth also has experience as a Machine Learning Engineer at Cisco, where they developed self-healing networks and designed machine learning algorithms for anomaly detection.

In addition to their industry experience, Siddharth has conducted research at the Machine Learning Department at CMU. Siddharth'sresearch projects include building a semi-supervised deep learning architecture for detecting Diabetic Retinopathy, training convolutional neural networks to identify electronic components in printed circuit boards, and designing deep neural networks for predicting potential dark matter maps and cell types from gene data.

Before their industry and research positions, Siddharth worked as a Graduate Teaching Assistant at Carnegie Mellon University, where they taught physics and mathematics courses. Siddharth also served as a Research Assistant, conducting research on lensing potential maps, galaxy correlations, and the structure of two-dimensional materials.

Siddharth's academic background includes a Ph.D. and a research internship in the field of materials science from Michigan State University. Siddharth also has a research internship at the Tata Institute of Fundamental Research, where they investigated electron impact ionization.

Overall, Siddharth Satpathy, Ph.D. has a strong background in machine learning, research, and teaching, with expertise in various applications such as battery degradation prediction, anomaly detection, and medical image analysis.

Siddharth Satpathy, Ph.D., has an extensive education history in the field of physics. Siddharth began their academic journey in 2007 at the National Institute of Science Education and Research, where they pursued a BS/MS Dual Degree in Physics. After completing their undergraduate studies in 2012, they furthered their education at Carnegie Mellon University from 2013 to 2015, obtaining a Master's Degree in Physics.

Motivated to deepen their knowledge and contribute to the field of physics, Siddharth pursued a Doctor of Philosophy (Ph.D.) at Carnegie Mellon University from 2015 to 2019. Siddharth'sresearch focus during this time was in the field of physics. Additionally, they demonstrated a keen interest in machine learning, which led him to the Machine Learning Department at CMU, where they pursued a Master's Degree in Machine Learning from 2018 to 2019.

Siddharth Satpathy's educational background showcases their strong commitment to advancing their understanding of physics and exploring the intersection of physics and machine learning through their studies at Carnegie Mellon University.