Evan Druskin is excited to see how he can utilize AI to solve previously unsolvable problems. 

Can you share the most interesting part of your career journey?

The most interesting part of my career is the nontraditional path I took to get to Deloitte. As a computer science major, I never thought about what opportunities my major could offer me besides coding from behind a desk all day.

Emerging technology was a new field I wanted to integrate myself into, and Deloitte was the best way to interact with AI technology.

Having worked as a full stack developer for an identity management provider, I realized I needed to do more. Emerging technology was a new field I wanted to integrate myself into, and Deloitte was the best way to interact with AI technology. Within the first week of starting at the firm I was working on a project to code a robot for a high profile client. 

What excites you most about working with data and AI?

Empowering end users excites me the most about working with data and AI. Despite the numerous conversations about whether AI will replace workers, I personally think it will empower them. If we utilize AI to automate the mundane day-to-day tasks someone may be working on, we can elevate their experiences.

I am excited to see how we can utilize AI to solve previously unsolvable problems such as P vs. NP.

I believe the possibilities of AI are endless and will lead to more innovation than ever before. I am excited to see how we can utilize AI to solve previously unsolvable problems such as P vs. NP. If problems of this nature can be solved, it will revolutionize algorithmic design and produce elegant solutions to problems deemed unsolvable. All these reasons will lead to completely new applications of algorithms that will fundamentally change how computing is done.

Describe an interesting project that you have worked on.

The most interesting project I worked on was the Iris application for the Smart Factory. Iris was designed to be an application that facilitates autonomous dispatching of assets for quality checks and inspections on the factory floor. In an effort to meet the project goal, I was given a robot to work with. The Spot robot needed to interface with the app and plan autonomous routes to critical points on the floor, which are designated by the app. After getting the robot to respond to the front-end component of the application, we partnered with a vision vendor to implement sophisticated computer vision algorithms in order to identify part types. Once the parts were identified, a quality check began. If the confidence threshold was less than 90%, end users were prompted to take action. Otherwise, the inspection concluded without any end user involvement.

This project gave me an opportunity to bring different AI/ML concepts from the classroom to a client-facing environment.

This is a very interesting project to me because it combined robotics, AI, and machine learning into a deliverable package. While we are still in the demo phase of this initiative, the next phase includes integrating more autonomous robots such as different kinds of drones. I will also be working on a graph-searching algorithm to help the robots find a recognizable landmark when they get lost. The Spot robot does not perform well in open space, and the algorithm I am working on will address that concern. This project gave me an opportunity to bring different AI/ML concepts from the classroom to a client-facing environment.