Leila Pirhaji, Ph.D., ReviveMed

Leila Pirhaji, Ph.D.

Dr. Pirhaji is Founder and CEO of ReviveMed with a Ph.D. in Biological Engineering from MIT. During her Ph.D. at Prof. Ernest Fraenkel Lab at MIT, Leila developed pioneering artificial intelligence (AI) platform to overcome difficulties of leveraging metabolomics for drug discovery, which was published in Nature Methods. She then founded ReviveMed to bring this technology to the market and make an impact in people’s lives. Currently, ReviveMed is a venture-backed startup, based in Cambridge MA, focusing on discovering therapeutics for metabolic diseases using AI.

Previously, Leila worked at top-notch research institutes, such as ETH Zurich, as well as large pharmaceutical companies, including Merck and Takeda pharmaceuticals.

Her innovative work has received several prestigious awards, and featured in media outlets including TechCrunch.


4:30 PM - 5:00 PM

Innovation Spotlight ChangeMaker Presentations

How much can you learn in five minutes? Quite a bit. Hear quick hitting presentations on the latest products, new releases, exciting innovations, and more. Each presenter will speak for 5 minutes and then will be available to answer questions during the reception.

David Wild, Ph.D. — Knowledge Graphs: A Transformative New Approach for Drug Discovery
  • Knowledge Graphs (KGs) are a transformative new way to think about drug discovery data
  • KG-based tools open up powerful new scientific and decision-making capabilities
Paul Courtney — Applications of Machine Learning in Clinical Data Management

Rashan Jibowu — Driving Strategic Decisions with Machine Learning

Leila Pirhaji, Ph.D. — AI-Driven Drug Discovery via Metabolomics
  • Metabolomics, or studying small molecules, are essential for discovering the right therapeutics for the right patients
  • How AI can contribute to characterizing a large number of metabolites, or small molecules, inexpensively and fast
Wayne R. Danter, MD — DeepNEU: Cellular Reprograming Comes of Age–a Machine Learning Platform with Application to Rare Diseases Research
  • Stem cell research will inevitably be transformed by computer technologies. The results of the initial DeepNEU project indicate that currently available stem cell data, computer software and hardware are sufficient to generate basic artificially induced pluripotent stem cells (aiPSC)
  • The application of this computer technology to generate disease specific aiPSCs has the potential to improve 1. disease modeling, 2. rapid prototyping of wet lab experiments, 3. grant application writing and 4. specific biomarker identification in a highly cost-effective manner



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