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Posts by CTML - Center for Targeted Machine Learning and Causal Inference

Happening this Wednesday! 🌟 Don't miss the Stellarus research talks at our CTML Seminar.

Hear Yun Hu, Rupali Roy, and Roanne Toretsky share their work on risk stratification, LLM pipelines, and TMLE.

📅 Wednesday, April 22 | 12:00–1:30 PM
📍 Berkeley Way West, Room 5101

Link in bio! 👉

2 hours ago 1 0 0 0
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For our last Spring CTML Seminar, we are excited to host Yun Hu (Data Scientist Consultant), Rupali Roy (Data Scientist), and Roanne Toretsky (Data Scientist) from Stellarus to each present their respective talks! This seminar will take place on Wed. April 22, from 12–1:30 PM in BWW, Room 5101.

6 days ago 1 1 0 1
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For our last Spring CTML Seminar, we are excited to host Yun Hu (Data Scientist Consultant), Rupali Roy (Data Scientist), and Roanne Toretsky (Data Scientist) from Stellarus to each present their respective talks! This seminar will take place on Wed. April 22, from 12–1:30 PM in BWW, Room 5101.

6 days ago 1 1 0 1
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CTML GSR's 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! 💡📊

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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.

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CTML Graduate Student Researchers (GSRs) are heading to the 2026 European Causal Inference Meeting (EUROCIM) in Oxford, UK!

Catch Alissa Gordon, Kaitlyn Lee, Kirsten Landsiedel, and Nick Williams at the poster and presentation sessions on April 15-16. Stop by and support our incredible GSRs! 📊✨🎉

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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).

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Don't miss out on our Biostatistics and Epidemiology Career Panel! 🌟 Join us for an engaging session with leading experts in biostatistics and epidemiology on Wednesday, April 8th between 12-1:30 pm @ Berkeley Way West, Room 5101.

👉 ctml.berkeley.edu/4826-biostat...

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Thank you to all the speakers, panelists, and attendees who joined us for the 4th Annual FIORD Workshop (2026).

We look forward to continuing the conversation at FIORD 2027!

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CTML t-shirts and long sleeves are available for purchase through our online store until Friday, March 27th!

CTML Short sleeve: www.customink.com/g/rgc0-00d1-...
CTML Long Sleeve: www.customink.com/g/rgc0-00d1-...

4 weeks ago 0 0 0 0
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A community-based project in Kenya & Uganda reduced new HIV infections about 70% by bringing testing & prevention directly to communities.

🔗 www.science.org/content/arti...

1 month ago 1 0 1 0
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CTML appreciates Vanessa Rodriguez of University College London for joining the JICI Lab Seminar Series to present her talk, "Efficient Estimation and Inference for Generalizing CATEs". If you would like further details, please scan the QR code or visit ctml.berkeley.edu/jici-current....

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Calling all UC Berkeley Biostatistics and Epidemiology graduate students! Got exciting research to share? Join us for the Biostatistics and Epidemiology Research Showcase—your chance to present to faculty, peers, and special guests! This event will take place on Friday, May 1st from 2:30PM to 4:30PM at Berkeley Way West, 1st Floor, Room 1102 & 1104. Choose your format: ⚡ Lightning Talk (3-min + Q&A) 📌 Poster Presentation 💡 Or do both! Visit our bio for full details on presentation formats 🔗 📣 Interested in presenting? Email Jessica Angell (jessica.angell@berkeley.edu) to sign up. #BerkeleyBiostat #BerkeleyEpi #BerkeleyCTML #BerkeleyPublicHealth #ResearchShowcase

Calling all UC Berkeley Biostatistics and Epidemiology graduate students! Got exciting research to share? Join us for the Biostatistics and Epidemiology Research Showcase—your chance to present to faculty, peers, and special guests! This event will take place on Friday, May 1st from 2:30PM to 4:30PM at Berkeley Way West, 1st Floor, Room 1102 & 1104. Choose your format: ⚡ Lightning Talk (3-min + Q&A) 📌 Poster Presentation 💡 Or do both! Visit our bio for full details on presentation formats 🔗 📣 Interested in presenting? Email Jessica Angell (jessica.angell@berkeley.edu) to sign up. #BerkeleyBiostat #BerkeleyEpi #BerkeleyCTML #BerkeleyPublicHealth #ResearchShowcase

Calling all UC Berkeley Biostat and Epi graduate students! Got exciting research to share? Join us for the Biostat and Epi Research Showcase—your chance to present to faculty, peers, and special guests! Visit the link in our bio for full details on presentation formats 🔗

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Don't miss the next session of the CTML - Center for Targeted Machine Learning and Causal Inference Seminar Series on March 4th, where CTML GSR Alissa Gordon will discuss "Average Mixed Derivative: A Nonparametric Framework of Interactivity." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.

Don't miss the next session of the CTML - Center for Targeted Machine Learning and Causal Inference Seminar Series on March 4th, where CTML GSR Alissa Gordon will discuss "Average Mixed Derivative: A Nonparametric Framework of Interactivity." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.

Abstract: Abstract: There are many ways that multiple theoretically intervenable variables can synergize or antagonize to affect an outcome. We review several definitions of "interaction" and propose a new estimand called the average mixed derivative (AMD) that captures a scalar notion of interactivity within a nonparametric framework. We identify the AMD under standard causal assumptions and develop regression, weighting, and doubly-robust type estimators, focusing on continuous exposures and outcomes.

Abstract: Abstract: There are many ways that multiple theoretically intervenable variables can synergize or antagonize to affect an outcome. We review several definitions of "interaction" and propose a new estimand called the average mixed derivative (AMD) that captures a scalar notion of interactivity within a nonparametric framework. We identify the AMD under standard causal assumptions and develop regression, weighting, and doubly-robust type estimators, focusing on continuous exposures and outcomes.

Don't miss the next session of the CTML Seminar Series on March 4, where CTML GSR Alissa Gordon will discuss "Average Mixed Derivative: A Nonparametric Framework of Interactivity." This talk will take place from 12:00PM-1:00PM at Berkeley Way West, 5th Floor, Room 5401.

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Support the Future of Public Health Innovation—Donate to CTML for UC Berkeley’s Big Give on March 12th! With over 15 Graduate Student Researchers, CTML is leading discoveries that improve health outcomes locally and globally.
Learn more 👉 ctml.berkeley.edu/join-us-big-...

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Hear from a leading expert in the field! Join us next Wednesday, February 25th for our Center for Targeted Machine Learning and Causal Inference (CTML) Seminar with Andrew Mertens, presenting "Spatial Superlearner for Subnational Micronutrient Deficiency Prediction and Proxy Identification in Data-sparse Settings." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. *Speaker will present remotely.*

Hear from a leading expert in the field! Join us next Wednesday, February 25th for our Center for Targeted Machine Learning and Causal Inference (CTML) Seminar with Andrew Mertens, presenting "Spatial Superlearner for Subnational Micronutrient Deficiency Prediction and Proxy Identification in Data-sparse Settings." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. *Speaker will present remotely.*

Hear from a leading expert in the field! Join us next Wednesday, February 25th for our CTML Seminar with Andrew Mertens, Ph.D. *Speaker will present remotely.*

Check out the abstract on our website linked here👉 tinyurl.com/39sjym53

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Next week’s seminar brings another thought-provoking discussion! Join us on February 11th to hear from CTML GSR Mingxun (Michael) Wang presenting his talk on "Highly Adaptive Principal Component Regression: Fast HAL/HAR via Outcome-Blind Kernel PCA." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.

Next week’s seminar brings another thought-provoking discussion! Join us on February 11th to hear from CTML GSR Mingxun (Michael) Wang presenting his talk on "Highly Adaptive Principal Component Regression: Fast HAL/HAR via Outcome-Blind Kernel PCA." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.

The Highly Adaptive Lasso (HAL) has strong rate guarantees under minimal smoothness assumptions, but can be computationally prohibitive in moderate to high dimensions due to its enormous basis expansion. We introduce PCHAL and PCHAR, which perform outcome-blind dimension reduction by projecting the highly adaptive kernel onto its leading principal components, yielding simple closed-form ridge solutions and a lasso solution that reduces to soft-thresholding in an orthogonal score space. The resulting estimators substantially accelerate fitting and cross-validation while matching the empirical predictive performance of HAL/HAR, and are implemented in the hapc R package.

The Highly Adaptive Lasso (HAL) has strong rate guarantees under minimal smoothness assumptions, but can be computationally prohibitive in moderate to high dimensions due to its enormous basis expansion. We introduce PCHAL and PCHAR, which perform outcome-blind dimension reduction by projecting the highly adaptive kernel onto its leading principal components, yielding simple closed-form ridge solutions and a lasso solution that reduces to soft-thresholding in an orthogonal score space. The resulting estimators substantially accelerate fitting and cross-validation while matching the empirical predictive performance of HAL/HAR, and are implemented in the hapc R package.

Next week’s seminar brings another thought-provoking discussion! Join us on February 11 to hear from CTML GSR Mingxun Wang presenting his talk on "Highly Adaptive Principal Component Regression: Fast HAL/HAR via Outcome-Blind Kernel PCA." The seminar will take place at 12PM in BWW, 5th Fl, Rm 5401.

2 months ago 1 0 0 0
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We’re pleased to welcome Toru Shirakawa, CTML GSR and CPH PhD Student, to next week’s CTML Seminar on Wednesday, February 4th, presenting on “A Conformalized Inference on Unobservable Variables.”The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Abstract: Quantifying uncertainty in predicted unobservable variables is a critical area of research in statistics, artificial intelligence, and empirical science. Most scientific studies assume a specific structure involving unobservable variables for the data-generating process and draw inferences from a parameter of interest within that framework. Conformal prediction is a popular model-agnostic method for constructing prediction intervals for new observations. However, it typically requires observed true labels to build the prediction interval, making it unsuitable for unobserved latent variables. We propose a method to construct a prediction interval by leveraging sample-splitting of the training data and analyzing the discrepancy between two independently trained models. To ensure the identifiability of the distribution of this conformity score, we introduce a few assumptions regarding the distribution of the residuals of the predictions. Furthermore, we propose a residual orthogonalization to satisfy these assumptions with a coordinating regularization term. The performance of the proposed method was evaluated using both simulation and large language model experiments.

We’re pleased to welcome Toru Shirakawa, CTML GSR and CPH PhD Student, to next week’s CTML Seminar on Wednesday, February 4th, presenting on “A Conformalized Inference on Unobservable Variables.”The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Abstract: Quantifying uncertainty in predicted unobservable variables is a critical area of research in statistics, artificial intelligence, and empirical science. Most scientific studies assume a specific structure involving unobservable variables for the data-generating process and draw inferences from a parameter of interest within that framework. Conformal prediction is a popular model-agnostic method for constructing prediction intervals for new observations. However, it typically requires observed true labels to build the prediction interval, making it unsuitable for unobserved latent variables. We propose a method to construct a prediction interval by leveraging sample-splitting of the training data and analyzing the discrepancy between two independently trained models. To ensure the identifiability of the distribution of this conformity score, we introduce a few assumptions regarding the distribution of the residuals of the predictions. Furthermore, we propose a residual orthogonalization to satisfy these assumptions with a coordinating regularization term. The performance of the proposed method was evaluated using both simulation and large language model experiments.

We’re pleased to welcome Toru Shirakawa, CTML GSR and CPH PhD Student, to next week’s CTML Seminar on February 4th! The seminar will take place at 12PM in BWW, 5th Fl, Rm 5401.

Click here for the abstract 👉 tinyurl.com/5ab59c85

2 months ago 2 0 0 0
As we gear up in anticipation for this wonderful workshop, we'd like to highlight it once again to give every member of our community a chance to participate! CTML Co-Director Mark van der Laan’s talk, "The Causal Roadmap, Targeted Learning and TMLE: What Is That All About?", will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop this Thursday, on January 29th, 2026, from 10:00 AM–11:00 AM (EST). The talk will be available online via Zoom. Click the link in our bio to learn more about the talk and register today!

As we gear up in anticipation for this wonderful workshop, we'd like to highlight it once again to give every member of our community a chance to participate! CTML Co-Director Mark van der Laan’s talk, "The Causal Roadmap, Targeted Learning and TMLE: What Is That All About?", will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop this Thursday, on January 29th, 2026, from 10:00 AM–11:00 AM (EST). The talk will be available online via Zoom. Click the link in our bio to learn more about the talk and register today!

As we gear up in anticipation for this wonderful workshop, we'd like to remind members of our community to endeavor to participate!

CTML Co-Director Mark van der Laan’s talk will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop this Thursday from 10:00 AM–11:00 AM (EST).

2 months ago 1 0 0 0
Connect with the Center for Targeted Machine Learning and Causal Inference (CTML) community! Next week’s seminar on Wednesday, January 28th, will feature CTML GSR Kaitlyn Lee presenting her talk, "Improving Precision through Covariate Adjustment in RCTs with Binary Outcomes." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Please note that this week’s session will be open exclusively to the CTML and UC Berkeley community.

Connect with the Center for Targeted Machine Learning and Causal Inference (CTML) community! Next week’s seminar on Wednesday, January 28th, will feature CTML GSR Kaitlyn Lee presenting her talk, "Improving Precision through Covariate Adjustment in RCTs with Binary Outcomes." The seminar will take place at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401. Please note that this week’s session will be open exclusively to the CTML and UC Berkeley community.

Abstract: Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its recent guidance when baseline variables are prognostic for the primary outcome. In this talk, we review the principles underlying covariate adjustment, with a focus on standardization, a method highlighted in the guidance for estimating the marginal treatment effect. We concentrate on settings with binary outcomes, describing practical implementation of the estimator and discussing open questions related to variance estimation and finite-sample performance.

Abstract: Covariate adjustment is a general method for improving precision when estimating treatment effects in randomized trials and is recommended by the FDA in its recent guidance when baseline variables are prognostic for the primary outcome. In this talk, we review the principles underlying covariate adjustment, with a focus on standardization, a method highlighted in the guidance for estimating the marginal treatment effect. We concentrate on settings with binary outcomes, describing practical implementation of the estimator and discussing open questions related to variance estimation and finite-sample performance.

Connect with the CTML community! Next week’s seminar on Wed, Jan 28, will feature CTML GSR Kaitlyn Lee. The seminar will take place at 12 PM in BWW, 5th Fl, Rm 5401. Please note that this week’s session will be open exclusively to the CTML and UC Berkeley Community.

2 months ago 1 1 0 0
The Center for Targeted Machine Learning and Causal Inference (CTML) Spring Seminar Series kicks off next Wednesday, January 21, with a talk by CTML GSR Andy Kim: “Predicting Loss to Follow-Up Under Resource Constraints: Leveraging Registry-Linked Mobile Health Data in Trauma Care.” Join us at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.

The Center for Targeted Machine Learning and Causal Inference (CTML) Spring Seminar Series kicks off next Wednesday, January 21, with a talk by CTML GSR Andy Kim: “Predicting Loss to Follow-Up Under Resource Constraints: Leveraging Registry-Linked Mobile Health Data in Trauma Care.” Join us at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.

Abstract: Traumatic injury remains a leading cause of morbidity and mortality in sub-Saharan Africa, with a substantial proportion of adverse outcomes occurring after hospital discharge due to missed follow-up care. Leveraging linked-data from the Cameroon Trauma Registry (CTR) and Mobile Health (mHealth) follow-up system, I use predictive ensemble super learning methods to construct risk scores for loss to follow-up and evaluate models using recall-based metrics most relevant under resource constraints (i.e. who to prioritize calling given limited funding, staff, etc.). By framing loss to follow-up as a prioritization problem rather than a classification task, this work highlights how context-driven choices of loss functions and performance metrics shape predictive modeling strategies in applied settings.

Abstract: Traumatic injury remains a leading cause of morbidity and mortality in sub-Saharan Africa, with a substantial proportion of adverse outcomes occurring after hospital discharge due to missed follow-up care. Leveraging linked-data from the Cameroon Trauma Registry (CTR) and Mobile Health (mHealth) follow-up system, I use predictive ensemble super learning methods to construct risk scores for loss to follow-up and evaluate models using recall-based metrics most relevant under resource constraints (i.e. who to prioritize calling given limited funding, staff, etc.). By framing loss to follow-up as a prioritization problem rather than a classification task, this work highlights how context-driven choices of loss functions and performance metrics shape predictive modeling strategies in applied settings.

The CTML Spring Seminar Series kicks off next Wednesday, January 21, with a talk by CTML GSR Andy Kim: “Predicting Loss to Follow-Up Under Resource Constraints: Leveraging Registry-Linked Mobile Health Data in Trauma Care.” Join us at 12:00 PM in Berkeley Way West, 5th Floor, Room 5401.

3 months ago 1 0 0 0
We’re excited to kick off our first newsletter for the Spring 2026 semester coming next Wednesday, January 21! Subscribe today to stay in the loop with the CTML community: http://eepurl.com/iLgQIw

We’re excited to kick off our first newsletter for the Spring 2026 semester coming next Wednesday, January 21! Subscribe today to stay in the loop with the CTML community: http://eepurl.com/iLgQIw

We’re excited to kick off our first newsletter for the Spring 2026 semester coming next Wednesday, January 21!

Subscribe today to stay in the loop with the CTML community 👉: eepurl.com/iLgQIw

3 months ago 0 0 0 0
Huge thank you to Dr. Alejandro Schuler, Assistant Professor in Residence at UC Berkeley Biostatistics and CTML faculty member, for his continued efforts with CTML and for his talk at the Gilead Health Equity Partnership (GHEP) Seminar Series titled " Increasing the Efficiency of Randomized Trials with Machine Learning." Stay tuned for more cutting-edge conversations in our ongoing CTML × Gilead Health Equity Partnership (GHEP) Series in the coming weeks and on our website!

Huge thank you to Dr. Alejandro Schuler, Assistant Professor in Residence at UC Berkeley Biostatistics and CTML faculty member, for his continued efforts with CTML and for his talk at the Gilead Health Equity Partnership (GHEP) Seminar Series titled " Increasing the Efficiency of Randomized Trials with Machine Learning." Stay tuned for more cutting-edge conversations in our ongoing CTML × Gilead Health Equity Partnership (GHEP) Series in the coming weeks and on our website!

Trials enroll a large number of subjects in order to attain power, making them expensive and time-consuming. However, advancements in machine learning can make adjusted trial analyses more efficient, yielding smaller confidence intervals and p-values without sacrificing control of false positives. Adjustment works by explaining away within-treatment-group variability in the outcome using associated variability in baseline covariates, similar to stratification. Therefore, the key parameter that determines power and confidence of an adjusted analysis is how predictive the baseline covariates are for the outcome. Machine learning models often predict better than linear models and therefore they boost power. The power gain is predictable if we can accurately anticipate model performance, which allows us to trade power gains with the same sample size for smaller trials with equal power. In some settings, strongly predictive baseline information like images and free text are captured but never exploited for adjustment because they are not tabular data. Use of these data could therefore hugely increase power or decrease sample sizes, which I propose to enable using multimodal foundation models.

Trials enroll a large number of subjects in order to attain power, making them expensive and time-consuming. However, advancements in machine learning can make adjusted trial analyses more efficient, yielding smaller confidence intervals and p-values without sacrificing control of false positives. Adjustment works by explaining away within-treatment-group variability in the outcome using associated variability in baseline covariates, similar to stratification. Therefore, the key parameter that determines power and confidence of an adjusted analysis is how predictive the baseline covariates are for the outcome. Machine learning models often predict better than linear models and therefore they boost power. The power gain is predictable if we can accurately anticipate model performance, which allows us to trade power gains with the same sample size for smaller trials with equal power. In some settings, strongly predictive baseline information like images and free text are captured but never exploited for adjustment because they are not tabular data. Use of these data could therefore hugely increase power or decrease sample sizes, which I propose to enable using multimodal foundation models.

Huge thank you to Dr. Alejandro Schuler for his continued efforts with CTML and for his talk at the Gilead Health Equity Partnership (GHEP) Seminar Series! Stay tuned for more cutting-edge conversations in our ongoing CTML × GHEP Series in the coming weeks and on our website.

4 months ago 1 0 0 0
Explore everything CTML - Center for Targeted Machine Learning and Causal Inference has planned for our Spring Seminar Series! The full schedule is available by scanning the QR code on this post or by clicking the link in our bio. This spring, you can look forward to an engaging lineup of events—including a career panel featuring professionals in both biostatistics and epidemiology, as well as a panel discussion with experts from Blue Shield who will share insights from industry practice. Join us starting January 21st, 2026 to learn, connect, and grow with the CTML community!

Explore everything CTML - Center for Targeted Machine Learning and Causal Inference has planned for our Spring Seminar Series! The full schedule is available by scanning the QR code on this post or by clicking the link in our bio. This spring, you can look forward to an engaging lineup of events—including a career panel featuring professionals in both biostatistics and epidemiology, as well as a panel discussion with experts from Blue Shield who will share insights from industry practice. Join us starting January 21st, 2026 to learn, connect, and grow with the CTML community!

Explore everything CTML has planned for our Spring 2026 Seminar Series! The full schedule is available by scanning the QR code on this post or by clicking the link to our website 👉 ctml.berkeley.edu/spring-2026-...

4 months ago 2 0 0 0
CTML Co-Director Mark van der Laan’s talk will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop on Jan 29th, 2026, from 10AM–11AM (EST). The talk will be available online via Zoom. Click the link to register today! 🔗: https://www.phuse-events.org/attend/frontend/reg/tOtherPage.csp?pageID=80014&eventID=106&traceRedir=4

CTML Co-Director Mark van der Laan’s talk will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop on Jan 29th, 2026, from 10AM–11AM (EST). The talk will be available online via Zoom. Click the link to register today! 🔗: https://www.phuse-events.org/attend/frontend/reg/tOtherPage.csp?pageID=80014&eventID=106&traceRedir=4

CTML Co-Director Mark van der Laan’s talk will be presented at the Pharmaceutical Users Software Exchange (PHUSE) Workshop on Jan 29th, 2026, from 10AM–11AM (EST). The talk will be available online via Zoom. Click the link to register today! 🔗: www.phuse-events.org/attend/front...

4 months ago 0 0 0 0
Former CTML Postdoc David McCoy will be presenting his and CTML Postdoc Zach Butzin-Dozier's research at the Neural Information Processing Systems (NeurIPS) 2025 Conference in Mexico City. If attending the conference, please stop by and check out their poster!

Former CTML Postdoc David McCoy will be presenting his and CTML Postdoc Zach Butzin-Dozier's research at the Neural Information Processing Systems (NeurIPS) 2025 Conference in Mexico City. If attending the conference, please stop by and check out their poster!

Former CTML Postdoc David McCoy will be presenting his and CTML Postdoc Zach Butzin-Dozier's research at the Neural Information Processing Systems (NeurIPS) 2025 Conference in Mexico City. If attending the conference, please stop by and check out their poster!

4 months ago 1 0 0 0
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Thank you to Antonio Remiro Azócar for presenting his talk "Data Fusion for Indirect Treatment Comparisons in Health Technology Assessment" as part of the JICI Lab Seminar Series on 12/2/2025. To learn more about Antonio's work, please scan the QR code or visit lnkd.in/ge_qruVY.

4 months ago 0 0 0 0
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CTML Postdoc Marie Charpignon, will be presenting her poster at the AHLI 2025 ML4H Symposium next Monday, December 1st. If you’ll be attending AHLI, we welcome you to stop by Poster #4 to learn more about her work on federated target trial emulation using EHR data spanning multiple health systems!

CTML Postdoc Marie Charpignon, will be presenting her poster at the AHLI 2025 ML4H Symposium next Monday, December 1st. If you’ll be attending AHLI, we welcome you to stop by Poster #4 to learn more about her work on federated target trial emulation using EHR data spanning multiple health systems!

CTML Postdoc Marie Charpignon, will be presenting her poster at the AHLI 2025 ML4H Symposium next Monday, December 1st. If you’ll be attending AHLI, we welcome you to stop by Poster #4 to learn more about her work on federated target trial emulation using EHR data spanning multiple health systems!

4 months ago 4 0 0 0
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T-shirt only: We’re reopening Professor Art Reingold’s commemorative t-shirt store for the last time! 🎉

Celebrate his remarkable career and legacy with an exclusive t-shirt designed just for this occasion.

📦Order here: www.customink.com/g/rgc0-00cz-...

📅 The store will close on November 30th.

4 months ago 3 0 0 0
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Interest in AI for social impact? Join us on December 2nd as CTML’s Dr. Laura Balzer presents her latest research during #BerkeleyPublicHealth’s virtual Latest in Public Health Research series.

To register for zoom: lnkd.in/gXT-k2Ve

For more info: lnkd.in/girU-8Cf

4 months ago 2 1 0 0