2023 Biostatistics Research News
The Department of Biostatistics has continued its tradition of innovative and collaborative research in 2022. Collectively, Biostatistics faculty have published more than 380 papers this year in high-profile journals in biostatistics, public health and medicine.
Our faculty received three new grants this year, bringing the total of current grants as PIs and MPIs within the department to twenty.
Notably, one of our newer faculty members, Wenpin Hou, has received an R35 Maximizing Investigators' Research Award (MIRA) from the NIH/NIGMS for her program titled "Methods for inferring and analyzing gene regulatory networks using single-cell multiomics and spatial genomics data". This R01-equivalent award provides five years of funding with stability and flexibility to highly talented and promising investigators to increase overall scientific productivity and the chances for important breakthroughs, and will bolster her groundbreaking genomics work. Our department is honored to have such talented and ambitious faculty members like Dr. Wenpin Hou, who are using their grants to break new ground in the field of biostatistics and beyond.
This year’s recruitment committee, led by Ying Wei and Zhezhen Jin, attracted three fantastic new additions to the biostatistics faculty: Drs. Xiao Wu, Tian Gu, and Xin Ma. Their innovative research ranges from scientific evidence and policy solutions to mitigate the adverse impacts of environmental factors to statistical and deep learning methods of prediction and feature selection for high dimensional and complex-structured data. With their groundbreaking additions to our faculty’s work, we have increased both the capacity and breadth of our research, keeping the Columbia Biostatistics department on the forefront of exploration in the biomedical sciences. You can read more about each of them and their work on the New Faces page.
2023 saw the launch of the inaugural Columbia Biostatistics Annual Research Symposium, which grew out of the 2022 departmental retreat. In recognition of the centennial anniversary of the Mailman School of Public Health, we sought to create a space for collaboration and discussion of advancing trans-disciplinary research in public health, broader biomedical research and the training of future generations of biostatisticians and health data scientists. The event served both as a highlight of the cutting-edge methodologic research being conducted by departmental faculty and trainees and a facilitator of future inter-disciplinary research on public health with partners from across Columbia University and beyond.
The event was filled with interesting talks: leaders from Merck and Eli Lilly gave a panel discussion on the state of the industry; Dr. Tianxi Cai of Harvard presented an academic keynote on crowdsourcing with electronic health records (EHR); and Dr. Shahram Ebadollahi gave an insightful industry keynote on the future of data science and AI in pharma. Members of the Biostatistics department presented the results of their own research within a variety of our working groups, exhibiting the inter-disciplinary work central to biostatistics. Our students gave an excellent showing as well, with several doctoral students presenting their own research and many of our master's students entering our CBARS scientific poster competition. You can read more about the day’s full agenda on the archived CBARS 2023 page or find some great details in the article written about the day.
2023 also saw the launch of the Translational AI Laboratory (TRAIL). Over the last decade, there has been a significant development in machine learning and artificial intelligence (AI) technology that has impacted all areas of health research and practice. This rapid evolution is expected to bring profound changes in public health education and research. In response to this, the Department of Biostatistics and the Mailman School of Public Health have established TRAIL to enhance our capabilities in AI research for public health. Led by Dr. Ying Wei, TRAIL has three primary missions: methodological innovation, infrastructure development, and collaboration building.
For methodological innovation, TRAIL is dedicated to developing and curating translational AI tools for reliable scientific discoveries and health outcome predictions and informing health-related decision-making. We will incorporate statistical principles on inference and reasoning as the methodological foundations and focus on generalizability, interpretability, reproducibility, and fairness throughout the methodological advancement. We will evaluate, optimize, and implement these tools in real-world settings, learn lessons from both successes and failures and promote the use of open-source tools/platforms to ensure transparency.
For infrastructure development, TRAIL is focused on enhancing AI in public health through the development of shared resources and a robust digital infrastructure. We aim to provide comprehensive data sets, advanced computing resources, and collaborative tools to facilitate AI-driven research and innovation.
TRAIL also aims to foster a community that bridges diverse fields of experts, encourages interdisciplinary collaboration in AI and public health research, and provides research opportunities to engage students, post-docs, and junior investigators.