Dimitry Gorinevsky Industrial AI Engineer Researcher Educator Founder CEO IEEE Life Fellow Phone: (650) 400-3172 gorinevsky (at) ieee.org

Papers

Short bio, longer CV in html, long CV with a publication list in PDF

My work is in AI for Industrial Applications and Data Intelligence.

Founder CEO:

I am currently leading an AI startup, Mitek Analytics, in the rapidly growing Data Intelligence area. The AI applications to supply chain and sustainment operations focus on costs that dominate lifecycle in physical asset fleets. Impact of our AI app on effectiveness of sustainment of Aerospace & Defense systems has been documented at the scale of hundreds of millions of dollars.

Stanford Professor:

Research Focus and Interests:

My research has been recognized for its practical engineering applications. Common theme is Explainable AI for mission critical decisions based on limited available data. The problems in Machine Learning (ML) and AI inference involve applied math methods related to Statistics, Optimization, Signal Processing, Decision & Control, and Operations Research. Many applications are in sustainability and sustainment processes. Selected examples are below

Time Series Data Analysis

• General methods for data trending
• Deterioration trending in time series and in image sequence

Probabilistic Risk Analysis

• Extreme events risk analysis. Showing that the risk of 100-year heat waves in US has doubled over the last 40 years
• Demand/supply risk and capacity planning in power grid with renewable energy sources

ML for Explainable Models

• ML of process models for advanced control and industrial applications
• Federated learning of multilevel models and application to aircraft fleet flight operations data

Anomaly Detection and Diagnostic Inference

• Fault isolation in multivariate processes
• Mixed data fusion for fault detection and isolation
• Estimation of faults in DC electrical power system

Earlier AI-related Work:

Learning Control

• Iterative learning control
• Neural Network learning of motion control and application to robotics

Neural Networks

• Generalization in recursive learning
• Application to automated parking

Robotics

• Force control of robotics systems
• Legged locomotion
• Fuzzy logic control

Biological Motor Control

Learning of motor programs