Meet the QCon San Francisco Committee Members

What are your areas of expertise? What have you been working on?

I've been working in the space of edge computing. A lot of people ask what is edge computing? I like to use the Linux Foundation’s definition from The State of the Edge Report from a couple of years ago. It focuses on the notion of the last mile, that last bit of compute that a telco might give an end-user. That's things like your cell phone; that's things like IoT devices; that's things like cars - the devices that operate between the last mile and the cloud. There are 200,000 cell phone towers in the United States, for example, cell phone towers can actually host the Kubernetes cluster.

The area I’ve been working on is in this edge space looking at shaping traffic, shifting traffic, inferencing, and training models back at the cloud, but running the actual model at the edge is as close to the user as possible to reduce the back home offloaded. I've been focused on running Kubernetes at the edge.

This has been my focus for the last 15 months or so. 

What was your first experience at QCon?

My origin actually goes back to a JavaOne 13 or 14 years ago, believe it or not, with a gentleman I used to work with named Jamie Ridgeway. We went to a Birds of a Feather late-night session after the main conference. I can't recall what the particular topic was, but there were only four people there: the person running the session, Jamie, myself, and a guy named Floyd Marinescu. Floyd was filming the session, and I asked "What are you doing?" 

This was a long time ago, back before InfoQ was a thing. He said: "I'm filming talks. I'm going to put them up on this website and make them available for developers along with news." Fast forward about eight years later, I did a talk at a conference in the middle of the country that was on gamification - designing applications with principles of gameful design. In that talk, I wanted everybody to have the feel of full gameplay. I had about 150 people in the room all link hands together in a massive round of multi-person thumb wrestling. This all was videoed and it happened to get picked up by InfoQ. Floyd saw it and if you know Floyd, he loves interactive things and he loves engagement. He got really excited and sent me a message saying that he enjoyed it. While I was thanking him, I also said, "You know, I actually met you about eight years ago." And I described what happened. He said, “I remember that!” Lo and behold, that kicked off some conversations. I joined the QCon San Francisco Programming Committee, ran a track, and was invited to take the reins of the product of QCon.

What topics are you excited about for QCon San Francisco 2020?

Machine learning, Kubernetes, and Cloud-Native are the types of things that are at the forefront of my mind. But one that I think is interesting is what are the ramifications of COVID-19 and software today? What does it mean for working in an office versus working remotely? What does it mean for strategic decisions? What does it mean for the developer experience and operating with smaller teams? What are the realities over the next 12, 18, or 24 months of how we're going to deliver software, because of the realities of COVID-19?

To be honest, this is not one of the things I’d really even considered before going into our committee meeting. I was just thinking about things like edge computing, Kubernetes, and AI/ machine learning, those kinds of things. I really think that looking at software in the light of COVID-19 will provide a unique take on what you might traditionally see in software development.

What do you think is the next big disruption in software?

Every time I’ve asked that people say, ‘Man, that's an unfair question!’ Now it's reversed. So for everyone who I've asked that question, I truly apologize.

My big disruption is one that a lot of people might repeat or say exists, but it's machine learning. Every time I hear about new advancements and new things that are happening, machine learning just continues to get pushed further and further. I particularly like operating learning inferencing at the edge. 

If you think about just all of the IoT devices that are out there, the massive amounts of data that are being pulled up and transferred up to the cloud - it's a huge undertaking. Being able to run machine learning at an edge closer to a user and to be able to make decisions on the data closer represents a huge advancement. Being able to do things like federated learning where you can aggregate some things and ship the model back so as to protect privacy, reduce backhaul, all of this represents a major change.  I don't know if it's the next big disruption because it's already here. But I think we're in the middle of that disruption. And I think it's going to change a lot about how we view software, where we operate software, and what it means for us now.