UC Berkeley CTML

Who Are We? The Center for Targeted Machine Learning and Causal Inference, at UC Berkeley, is an interdisciplinary research center for advancing, implementing, and disseminating methodology to address problems arising in public health and clinical medicine. The Center brings the rigor and power of statistical theory together with advances in machine learning and causal inference to generate robust evidence for advancing health. Find out more at our website linked in the bio. #machinelearning #causalinference #ctml #ucberkeley

Huge congratulations to Wenxin Zhang and Yi Li on successfully completing their Ph.D.s! 🎓🎉 Your hard work, dedication, and brilliant contributions have been a tremendous asset to our center. On behalf of everyone at the Center for Targeted Machine Learning and Causal Inference (CTML), we are incredibly proud of your achievements. We cannot wait to see what groundbreaking research and innovations you both share with the world next.

AIPM 2026 Registration and Poster Submission portals now open! 📢 We are thrilled to announce that the call for poster submissions and early bird registration are now officially open for the 2026 Annual Symposium on Risks and Opportunities of AI in Pharmaceutical Medicine! Join us at UC Berkeley this October as we dive into the intersection of machine learning, causal inference, and the next generation of drug development. Conference Dates: October 13 - 14, 2026 Early Bird Registration (through June 30, 2026): /meetings/aipm/2026/registration.cfm Poster Submissions (due June 30, 2026): /forms/d/e/1FAIpQLSfu0YXUfYsXvVH_sC_v1uMGTDgqg9DuDTsJBG35Y3HuUBAbAg/viewform Location: University of California, Berkeley

Excited to announce that the Center for Targeted Machine Learning (CTML) will be well-represented at the American Causal Inference Conference 2026! 📍 Our Graduate Student Researchers will be in Salt Lake City from May 11–14, sharing cutting-edge research on experimental design, semiparametric theory, and machine learning applications in causal inference. Swipe ➡️ to see the full schedule of our short courses, research spotlights, and poster sessions. We look forward to seeing you in Utah! #ACIC2026 #CausalInference #MachineLearning #CTML

Join us for the Biostatistics & Epidemiology Research Showcase ⚡️ Come hear our brilliant presenters share their cutting-edge research in fast-paced, engaging sessions. Check out the incredible lineup of speakers and their titles below! 👇 📅 Date: Friday, May 1, 2026 ⏰ Time: 2:30 PM - 4:30 PM 📍 Location: Berkeley Way West, 1st Floor | Room 1102 Presenters: Kaitlyn Lee - Improving Precision through Covariate Adjustment in RCTs with Binary Outcomes Nick Williams - Improving Reproducibility by Controlling Random Seed Stability in Machine Learning Based Estimation via Bagging John Halifax - Estimating Causal Effects of Community Drivers of Individual Health in the Presence of Community-Specific Confounding in Large Data Settings Alissa Gordon - The Average Mixed Derivative: A Nonparametric Framework of Interactivity Joy Nakato - Choices and Consequences of Alternative Estimands in Time-to-event Safety Analyses Noel Pimentel - MASH Placebo-Arm Database Project Joyce Huiyu Hu - Beyond the Treated Child: Spillover Effects of Mass Azithromycin on Infant Antimicrobial Resistance Clara Voong - Does Where You Live and Move Matter? Ethnic Enclaves and Gestational Diabetes Risk in Asian American Subgroups Emily Hou - Efficient Estimation of Restricted Mean Survival Time under Data Integration Wenxin Zhang - Causal Inference and Adaptive Design for Evaluating Effectiveness of Medical Tests and Devices Mingxun Wang - Highly Adaptive Principal Component Regression Kaiwen Hou - From Iterative Targeting to One-Step Updates: Affine Universal Least Favorable Models in Convex-Dual Coordinates Check out their abstracts on our website! Link in bio 🔗 Accessibility: For accessibility accommodations please contact [email protected]

Congratulations to #CTML Senior Project Data Analyst and #School of Public Health's (SPH) Lecturer Andrew Mertens on being awarded the SPH's extremely competitive 2026 Zak Sabry Mentorship Award. The award was established "to recognize faculty with distinguished records of mentorship." The award was established in 2004 to recognize faculty with distinguished records of mentorship. Dr. Mertens will be sharing the honor this year with Coco Auerswald. The award will be presented at the School of Public Health graduation ceremonies on Tuesday, May 19, 2026.

We’re excited to close out our Spring CTML Seminar Series next week with a dynamic set of research talks from Stellarus! Join us as three speakers share their latest work: Yun Hu (Data Scientist Consultant) will present “Early Prenatal Initiative: Risk Stratification Prototype,” Rupali Roy (Data Scientist) will share “A Two-Stage LLM Pipeline for Classifying Non-Network Request Authorization Notes,” and Roanne Toretsky (Data Scientist) will discuss “Operationalizing Targeted Maximum Likelihood Estimation in Practice.” This seminar will take place on Wednesday, April 22, from 12:00–1:30 PM in Berkeley Way West, 5th Floor, Room 5101. Be sure to check out their talk abstracts and bios on our website 👉 https://ctml.berkeley.edu/42226-stellarus-research-talks

CTML Graduate Student Researchers Sylvia Cheng and Wenxin Zhang are featured today at the Frontiers in Computational Health Conference (FiCPH)! Don’t miss them at the FiCPH Poster Session, where they’ll showcase research on innovative statistical and machine learning approaches to improve clinical decision-making and precision health. 💡📊

Continuing our CTML Seminar Series is CTML GSR Nolan Gunter! He will be discussing "Aging Out of the Blue: Region-Specific Epigenetic Clock Calibration for a Blue Zone with the DNAm SuperLearner." This seminar will take place on Wednesday, April 15 at 12:00 PM in BWW, 5th Fl, Rm 5401. Abstract: This study addresses the limitations of standard epigenetic clocks when applied to unique populations like the Nicoya "Blue Zone" with data from the Costa Rican Longevity and Healthy Aging Study (CRELES). We propose an ensemble machine learning approach - the SuperLearner - which leverages its Oracle property to asymptotically outperform individual candidate predictors by training a weighted combination of algorithms on a non-Nicoya Costa Rican reference group. We constructed a calibrated hypothesis test to compare residual age distributions between the blue zone and comparison population. Calibration improved consistency in aging estimates among all clocks, with the calibrated SuperLearner estimating a two-year aging advantage in Nicoya (-1.96, 95% CI [-2.56,-1.37]).

CTML Graduate Student Researchers (GSRs) will be represented at the 2026 European Causal Inference Meeting (EUROCIM) in Oxford, UK! Get ready to dive into some groundbreaking research with our incredible lineup of student researchers. Catch CTML's GSRs Alissa Gordon, Kaitlyn Lee, Kirsten Landsiedel, and Nick Williams as they share their latest insights during the poster and presentation sessions on April 15th and 16th. 📊✨ If you're attending, be sure to stop by and support our incredible GSRs 🎉

We’re proud to recognize Prof. Laura Balzer, PhD, for serving as a keynote speaker at the International Workshop on HIV and Hepatitis Observational Databases (IWHOD). Her presentation on Causal Inference & Machine Learning for HIV Prevention highlighted the powerful intersection of analytics and public health, demonstrating how rigorous methodology can drive more effective, data-informed strategies in HIV prevention. Prof. Balzer’s work continues to advance the frontiers of causal inference, machine learning, and public health, driving evidence-based strategies to improve HIV prevention worldwide.

Happening This Week! Don't miss out on our Biostatistics and Epidemiology Career Panel! 🌟 Join us for an engaging session with leading experts in biostatistics and epidemiology, be sure to check out our website to read more about our esteemed chair Laura Balzer and panelists Courtney Schiffman, Lauren Dang, Lina Montoya, and Milena Gianfrancesco. Get to know them before the event and see what exciting insights they’ll bring through the link here 👉 https://ctml.berkeley.edu/4826-biostatistics-and-epidemiology-career-panel-spring-2026 Date: Wednesday, April 8th Time: 12:00pm-1:30pm (12pm sharp) Location: Berkeley Way West, Rm 5101