Dr. Haoda Fu is Head of Exploratory Biostatistics in Amgen, before that he was an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS-B, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and also served as an associate editor for JASA theory and method from 2023, and JASA application and case study from 2025-2027.
Xiaotong T. Shen is the John Black Johnston Distinguished Professor, the School of Statistics at the University of Minnesota. He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics. He was formerly the Co-editor of Statistica Sinica and is currently an Action Editor for The Journal of Machine Learning Research. Additionally, he serves as an Associate Editor for Theory and Methods and Applications in the Journal of the American Statistical Association and for The Annals of Statistics. His areas of interest include machine learning and data science, high-dimensional inference, nonparametric and semiparametric inference, causal relations, graphical models, explainable machine intelligence (MI), personalization, recommender systems, natural language processing, generative modeling, and nonconvex minimization. His current research focuses on advancing causal and constrained inference, developing generative inference and prediction methods for black-box learners, and exploring diffusion processes, normalizing flows, and summarization techniques. His targeted application areas span biomedical sciences, artificial intelligence, and engineering.
This short course will equip sponsors, regulators, data scientists, statisticians, and students with the knowledge and skills to effectively evaluate the output of Generative AI (GenAI) pipelines in the context of drug development. Via use cases, we will explore a range of methodologies for assessment, including quantitative metrics, qualitative assessments, and expert reviews. Participants will learn the different elements of GenAI pipelines and how adjustments impact generated output. Participants will gain a deeper understanding of the challenges and considerations involved in assessing the credibility of AI-generated output, including scientific validity, accuracy, reliability, and safety of GeAI-generated outputs.
With the rapid advancement of machine learning (ML) and deep learning (DL) methodology in the last decade, the performances of prediction tasks in many computer science fields (e.g., natural language processing) have been greatly improved. However, the impact of ML/DL in the field of pharmaceutical development has been relatively limited. Hence, we would like to propose a short course to motivate and encourage the use of ML/DL in pharmaceutical development. The course starts with an overview of ML/DL methodology evolution over time and the related key concepts (e.g., back-propagation, hyperparameter tuning, etc.). Then the latest developments in image processing and natural language processing are introduced, together with their novel applications in pharmaceutical development from our recent projects and submitted papers. In terms of the course outline, the materials of the course are divided into three sections:
General ML/DL methodology:
Image processing and applications: deep convolutional neural networks (DCNN), object detection and segmentation, Region-based CNN (R-CNN), YOLO, and applications (e.g. psoriasis area and severity prediction)
Natural language processing and applications: word embeddings (word2vec), recurrent neural networks and language models, self-attention and transformers, pre-train and fine-tune paradigm and applications (e.g., adverse drug event prediction)
Li Wang: Dr. Li Wang is currently Senior Director and Head of Statistical Innovation group in AbbVie. Li is leading Design Advisory which provides strategic and quantitative consulting as requested to all Development teams in all Therapeutic Areas to facilitate innovative thinking and complex innovative design evaluation. Li also co-leads Development Advanced Analytics capability in AbbVie to drive Machine Learning and Advanced Analytics research and application in Development. Prior to this senior leadership role, he led Immunology and Solid Tumor statistical design and strategy discussions and multiple ML, RWE and Bayesian innovation projects from 2017 to 2019. From 2006 to 2017, he contributed to and subsequently led several NDAs and SNDAs including blockbusters Eliquis, Onglyza and Rinvoq. He is enthusiastic in teaching statistical courses to non-statisticians, and investigating/ promoting novel statistical and machine learning methodologies.
Yunzhao Xing: Dr. Yunzhao Xing is the Associate Director of Statistical Innovation at AbbVie, with a PhD in Material Science from the University of North Carolina at Chapel Hill and a background in Physics. Prior to AbbVie, he served as a senior scientist at Halliburton, focusing on sensor modeling and simulation. Since joining AbbVie in 2018, Yunzhao has led numerous successful projects in machine learning, deep learning, and image processing. His skill set encompasses web scraping, simulation modeling, and interactive web application development, making him a pivotal contributor to AbbVie's Statistical Innovation Group. Yunzhao is recognized for his commitment to pushing the boundaries of statistical innovation.
Sheng Zhong: Dr. Sheng Zhong is the Director of Statistics at AbbVie Inc. He received his Ph.D. in Statistics from the University of Chicago. At AbbVie, he led multiple innovative predictive modeling projects across different fields such as clinical trial enrollment duration forecasting, virtual controls based on targeted learning in single-arm trials, and predictive clinical safety monitoring based on structured and text data. His recent works have led to multiple publications and manuscripts under review. Before joining AbbVie in 2016, Dr. Zhong worked at a big data analytics start-up for heavy machine equipment maintenance, where his work led to 3 US patents.
The landscape of natural language processing (NLP) has been significantly transformed by recent advancements in Large Language Models (LLMs). In the biomedical domain, LLMs-based approaches and solutions have demonstrated its potential to revolutionize biomedical research and clinical practice. This short-course will provide lectures on developing biomedical LLM models and software tools, as well as their applications to important real-world healthcare and life science problems, such as real-world studies, literature review, and drug discovery. Additionally, we will delve into the valuable insights gleaned from employing LLM-based approaches in biomedical applications.
Dr. Hua Xu is Robert T. McCluskey Professor and Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science at Yale School of Medicine (YSM), as well as Assistant Dean for Biomedical Informatics at YSM. He received his Ph.D. in Biomedical Informatics from Columbia University. His primary research interests include biomedical natural language processing (NLP) and data mining, as well as their applications in secondary use of electronic health records data for clinical and translational research. His research is funded by multiple agencies (i.e., NLM, NCI, NIGMS, NIA, AHA, and CPRIT), and methods/tools developed in his lab have been widely used to support diverse biomedical applications. He served as the Chair of the NLP Working Group at American Medical Informatics Association (AMIA) and now leads the Observational Health Data Sciences and Informatics (OHDSI) NLP Working Group. Dr. Xu is a fellow of both the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).
Dr. Yifan Peng, Ph.D., FACMI, is an Associate Professor in the Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis. He has published in major AI and healthcare informatics venues, including ACL, NAACL, CVPR, and MICCAI, as well as medical venues, including Nature Medicine, Nucleic Acids Research, npj Digital Medicine, and JAMIA. His research has been funded by federal agencies, including NIH and NSF and industries such as Amazon and Google. He received the AMIA New Investigator Award in 2023. He was elected as a fellow of the American College of Medical Informatics (ACMI) in 2024.