About

Ganapathy Mani is a highly skilled Senior Engineer at Qualcomm with over 7 years of relevant experience in the field of machine learning and deep learning. He holds a PhD in Computer Science from Purdue University, an MS in Computer Science from The George Washington University, and a Bachelor of Engineering in Electrical, Electronics and Communications Engineering from Anna University. During his time at Purdue University, Ganapathy conducted research to enhance autonomy in cyber systems by profiling applications through machine learning techniques with data obtained from hardware, OS, and applications. He implemented deep learning methodologies for malware and anomaly detection in classifying unknown malware and exploit attacks with data obtained from fixed interval opcode/memory bytes sampling. Ganapathy also developed a linear-time clustering algorithm using fuzzy logic based on perfect error correcting codes with fault-tolerant indexing. As a Machine Learning Intern at Qualcomm, Ganapathy developed a novel multimodal machine learning framework with Bidirectional Long Short-Term Memory (BLSTM) deep neural network model for predicting software vulnerabilities in code. He designed and developed a hotspot sampling algorithm to collect and assemble only relevant lines of code, which considerably reduced the time and resources used by brute force static rule-based analysis. Ganapathy also developed a new framework that automatically fine tunes the model to fit the data and gives options to set thresholds for false positives, negatives, and accuracy before making a prediction. Ganapathy's technical skills include ML, Deep Learning, Neural Networks, Research Scientist, and n-grams. He is a highly motivated and skilled engineer with a passion for solving complex problems through innovative machine learning techniques. Ganapathy is a valuable asset to any team and is committed to delivering high-quality results.