PhD Student Advice | Livestream Q&A with a first year PhD Student

A first-year PhD student’s livestream Q&A turned into a practical playbook for surviving the day-to-day realities of research: managing reading, writing, teaching duties, motivation, and productivity—especially while working from home. The most consistent through-line was that progress comes from tight feedback loops: set specific goals for each work block, write notes soon after reading, and build systems (timers, organization tools, and earlier deadlines) that reduce decision fatigue.

On research workflow, the student described running multiple projects in parallel, with a clear seasonal rhythm: use autumn for analysis on one project while preparing two others, then move into the next phase. Data collection is split across projects—some require cleaning existing datasets, while others are still determining the best way to collect new data for a target timeline (around December). For a marathon-running + machine learning project, COVID-19 disrupted the usual pipeline: cancelled events reduce opportunities for new data collection and make live user trials harder because marathons may not return on schedule. Even so, the work remains feasible because much of the research can be done remotely using anonymized app data and voluntary runner data, pending ethics approval.

Reading and note-taking advice focused on speed without losing retention. Instead of reading broadly “for the sake of it,” the student reads for a specific project, then converts notes into thesis-ready text quickly—often formatting into a related-work section once a reading batch is done. Notes are captured in GoodNotes (highlighting plus side annotations), then typed up after reading. To prevent reading from becoming a time sink, timers like Pomodoro-style cycles (25 minutes work/5 minutes break, or longer 52/17 patterns) are used to force focus and create predictable breaks.

Several questions addressed workload management and teaching responsibilities. The student did not do full teaching assistantship in the first year, but did “demo hours,” a lighter lab-assistant role: helping in computer labs, answering questions, and preparing with a TA through a prep meeting. They reported balancing demo hours (about nine hours weekly at most) with classes and projects by scheduling demo work in the afternoon—protecting the morning for high-focus research.

Motivation and mental health came up repeatedly. For procrastination and home distractions, the advice was concrete: use work timers, turn off notifications or the phone itself, and consider website blockers. For imposter syndrome, the student said it often doesn’t fully disappear, but it becomes easier after early wins like getting a first paper accepted; the key is remembering there’s a reason someone was admitted and continuing to prove capability through consistent work. Writing improvement recommendations included taking writing courses, forming writing groups for peer feedback, doing peer review, and writing small amounts daily.

Across the Q&A, research productivity was treated less like a talent and more like a system: categorize papers to avoid getting lost, quantify daily progress with to-do lists and word counts, and set deadlines earlier than the real due date so stress doesn’t spike at the finish line. The result is a grounded message for new PhD students: build structure, reduce friction, and keep moving forward in measurable increments.