The Center for Targeted Machine Learning and Causal Inference (CTML) at UC Berkeley is a vibrant hub where students from different disciplines come together to push the boundaries of public health and clinical medicine research. With over 15 Graduate Student Researchers (GSR), CTML is at the forefront of blending statistical theory with the latest in machine learning and causal inference to create strong, evidence-based advancements in health research. CTML GSRs have led projects on novel methods on the integration of observational and randomized data, on targeted learning treatment heterogeneity for research partners such as Novo Nordisk, Gates and NIH.
When you support CTML, you're investing in students who not only excel academically but are also passionately committed to making a difference in public health research. Your donation helps more GSRs to share their innovative work and collaborate with peers at statistical conferences worldwide, enriching their learning experience and expanding their research impact.
Take a look into an interview with our GSR Lauren Liao, sharing insights from her conference experience:
How has working in CTML enhanced your educational experience and research?
“Working within CTML has been an enriching experience as I am able to engage with fellow students and top-researchers in targeted learning. I especially enjoy the presentations at weekly seminars to learn about cutting-edge research in statistical methodology and novel applications. Outside of presenting at the weekly seminar, I was fortunate to attend conferences to share and disseminate my work across US and internationally. The collaborative environment and networking opportunities contribute to my career and personal growth as a biostatistics PhD candidate.”
Can you share any insights from a recent travel and what was significant about this conference experience?
“Attending conferences is one of my favorite parts of being a researcher. The opportunities to meet other researchers, listen to their presentations, and participate in discussion are so valuable to feel the sense of belonging in the greater statistical community. It also allowed me to travel to places I have never been before. It was an eye-opening experience to broaden my perspective on different areas of statistics and data science.”
In what ways has this conference experience made a difference for you and your work?
“Being at the conference always inspires me to continue a career in research. The exchange of ideas and meeting statisticians with similar interests help solidify and envision a future furthering the field. I also aspire to travel more and open my horizons to differences in statistical approaches and cultural differences. I recommend any graduate student to attend conferences!”