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Ash Pahwa

  • Adjunct Professor of Data Sciences and Operations

Ash Pahwa, Ph.D., is an educator, author, entrepreneur, and technology visionary with three decades of industry and academic experience. He has founded several successful technology companies during his career.

His expertise includes Machine Learning, Deep Learning, Natural Language Processing, Digital Image Processing, and data storage technologies.

In Industry, Dr. Pahwa has worked for General Electric, AT&T Bell Laboratories, Xerox Corporation, and Oracle.

In Academia, Dr. Pahwa is an Adjunct Professor at the University of Southern California. He is also affiliated with Caltech and the University of California system which includes UC Irvine and UC San Diego.

Ash Pahwa
Press Inquiries: COMMUNICATIONS@MARSHALL.USC.EDU

Departments

Data Sciences + Operations

COURSES

Spring 2024
  • DSO-562
    Fraud Analytics
    Fraud detection model systems; identify normal vs. outlying behavior; malicious adversaries; complex datasets; supervised and unsupervised fraud statistical models; measures of model efficacy.
  • DSO-560
    Text Analytics and Natural Language Processing
    Acquire, analyze, visualize and perform natural language processing (NLP) on text data. Apply Python, machine learning packages, statistical methodology and computer code to business decision-making.
Fall 2023
  • DSO-545
    Statistical Computing and Data Visualization
    Data cleaning and reshaping; good vs. bad graphics; univariate, bivariate, trivariate, hypervariate and time series graphics; interactive graphics; web-related computing.
  • DSO-560
    Text Analytics and Natural Language Processing
    Acquire, analyze, visualize and perform natural language processing (NLP) on text data. Apply Python, machine learning packages, statistical methodology and computer code to business decision-making.
  • DSO-562
    Fraud Analytics
    Fraud detection model systems; identify normal vs. outlying behavior; malicious adversaries; complex datasets; supervised and unsupervised fraud statistical models; measures of model efficacy.