I just submitted another paper to SOSP 2025, and it’s hard to believe it’s been nearly four years since I started my PhD. A lot has changed since my last post about my PhD journey—looking back, I seemed pretty desperate then.

So here I am, reflecting on the past few years. I feel far more confident now—not just in my research decisions but in navigating the space of SysML in general.

When I started, I wasn’t sure about pretty much anything. But one thing I was certain about was ML inference. Admittedly, I didn’t grasp its full complexity or what compelling research directions existed, if at all. But I remembered reading in INFaaS that inference workloads account for 90% of ML infrastructure costs in AWS. That fact alone gave me hope—if inference drives such high traffic, it must be, or will become, important in the future.

Yet I kept wondering: Is there anything I can do at the model level? Many systems ML papers treated models as fixed-sized black boxes with deterministic execution latency and resource consumption. This assumption felt limiting.

That period was rough. Tons of methods for dynamic DNNs had already been proposed—early exit strategies, Mixture of Experts (MoE), model ensembles—but there just wasn’t a clear justification for designing systems specifically to optimize these approaches.

Honestly, I’m not sure how I made it through besides furiously searching ‘‘dynamic neural network’’, hoping to find something worth pursuing. Of course, the rise of ChatGPT changed everything, but that’s another story.

Everything shifted after my first paper was accepted to EMNLP. That was the moment I realized publishing isn’t as impossible as it seemed. Before, I kept trying to build an end-to-end system, a process that consumed far too much time.

Instead, I learned that starting with a clear motivation and simply writing it down first is a much better approach. Writing helps untangle confusion—it forces clarity.

The next few projects moved at a much faster pace. What changed? Honestly, the biggest shift was that I stopped fixating on the future. The long-term uncertainty used to kill my productivity—it felt overwhelming. But I realized that instead of worrying about whether a proposed method might fail, it’s far more productive to just write the next paragraph in Overleaf and move forward. After, if you write a paper, there exists a conference that is willing to accept it.

I’ll stop here and revisit this topic once I fully recover from the SOSP grind. For now, time to rest.