Elisa Long is an expert in statistics, data analytics, and mathematical modeling. Professor Long focuses on topics related to healthcare operations management, epidemiology, predictive analytics, and machine learning, with application across various industries.
Professor Long’s statistical and data-analytic expertise is central to litigation and regulatory investigations involving healthcare and life sciences. Her work in advanced analytics, machine learning, and artificial intelligence (AI) is well-suited to matters involving data privacy, class certification, algorithmic decision-making, and healthcare reimbursement.
Expert testimony in pharmaceutical and epidemiological matters
As an expert witness, Professor Long has submitted expert reports and has testified in deposition.
She has served as an expert on statistical issues in pharmaceutical industry matters and has provided expert testimony responding to an opposing party’s epidemiological model. She has also analyzed predictive models of drug utilization.
Class certification and statistical sampling
Professor Long applies statistical sampling to class certification matters to assess whether an aggregate statistical model conceals variation among putative class members. Serving as the statistician in a Telephone Consumer Protection Act (TCPA) matter involving a financial institution, she analyzed sample data and concluded that identifying members of the proposed class would require individualized inquiry.
In a pharmaceutical industry matter, Professor Long opined that the opposing expert’s statistical analysis failed to reliably demonstrate that the proposed class members were injured.
Machine learning and predictive modeling in healthcare
In her research, Professor Long has applied machine learning and AI methods to predict long-term opioid use, forecast infectious disease spread, and optimize the Food and Drug Administration’s (FDA’s) approval process to capture disease severity and the pace of innovation. This work positions her to evaluate how algorithmic and AI-driven systems are built, trained, and validated, and whether their outputs support the claims made about them, issues that arise in disputes over how these systems are deployed.
Professor Long combines mathematical modeling, econometric analysis, and medical decision-making under uncertainty to analyze large and complex datasets. She uses these tools to assess the value and quality of health interventions and the effective allocation of limited resources. She has conducted empirical studies of patient length-of-stay inside a hospital intensive care unit (ICU) and COVID-19 transmission among skilled nursing facilities.
Publications, regulatory citations, and teaching
Professor Long’s work has been widely published in academic journals and cited by regulatory agencies. The Federal Register cited her research in connection to COVID-19 vaccine mandates for healthcare workers. The Centers for Disease Control and Prevention (CDC) featured an online tool she developed that enables skilled nursing facilities using shared staff to alert contacts following a COVID-19 outbreak. Professor Long has written for the Washington Post and given numerous talks and presentations, including at the CDC.
At the UCLA Anderson School of Management, Professor Long teaches courses on data and decision-making, healthcare analytics, and sports analytics. She has received numerous awards for excellence in research and teaching, including the Neidorf “Decade” Teaching Award recognizing excellence in teaching over a ten-year period.
Previously, Professor Long served on the faculty at Yale School of Management, teaching probability modeling and statistics, healthcare operations, and decision analysis courses.
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