Moloco is a Silicon-valley-based ad-tech startup that provides cutting-edge ML and big-data processing solutions and services to businesses worldwide. Moloco’s core technical competence and success in business derive from i) our highly scalable and cost-efficient big-data processing infrastructure, and ii) our best-in-class DNN models and the infrastructure that trains and serves those models. My job at Moloco, as a VP of Engineering, is primarily helping passionate, talented, and exceptionally capable engineers and scientists by identifying, creating, and offering opportunities that can create industry-wide technical or business impacts.

In this “general” talk, I will give a technical, but high-level overview of what Moloco does. This will be by no means a deep research talk focusing on a particular subject matter, but I intend to touch upon some technical aspects of our engineering projects by summarizing a few core challenges we face and roughly how we are addressing them. During the talk I will also inevitably give a sketch of what I have worked on in my previous jobs. In doing so, I hope I could clarify the relevance of what Moloco does for the audience and also show how we can draw upon experiences from one sub-field in our broad industry -- or research domain -- to another.

I am VP of Engineering at Moloco, an ad-tech startup that provides cutting-edge ML and big-data processing solutions and services to businesses. I work with passionate, talented, and exceptionally capable engineers and scientists at Moloco by leading the infrastructure and ML software development groups, along with the data engineering, ML ops, and data scientist groups.

I am also teaching and advising students at the CS department of Stanford University. Up until early 2021, I worked as CTO of Applications at Barefoot Division in Intel, and an Intel Fellow. I had also worked actively for P4.org, where I led various engineering and research projects regarding fully-programmable high-speed networking devices and their applications. Before getting involved with P4.org and Barefoot Networks, I had worked at Windows Azure, Microsoft’s cloud-service division and had led engineering and research projects on the architecture, performance, and management of datacenter networks.

I have knack of having interest in and working on a variety of topics, including large-scale data-processing systems, applications of DNNs, ML infrastructure, programmable networking, domain-specific machine architectures, application acceleration, and debugging and diagnosis of large-scale distributed systems. Many of my engineering and research contributions — including In-band Network Telemetry, Tiny Packet Programs, VL2, Seawall, EyeQ, Ananta, and SEATTLE — are adopted in large production systems and services.

With my collaborators I received a few awards, including best paper awards from top-notch conferences, such as SIGCOMM, NSDI, and FAST. I was the recipient of Microsoft Rockstar Award 2013, an annual recognition for the strongest networking contributions Microsoft-wide. I received PhD in Computer Science at Princeton University, and MS/BS in Computer Engineering at Seoul National University.