Adam Stasiw

Machine Learning for Cancer Research

At Flatiron Health, I build regulatory-grade datasets that help our clients answer targeted cancer research questions.

Outside of my daily work, I’ve had the great privilege of contributing to scientific research through our Hackathons. Currently, two of my abstracts have been published in peer-reviewed journals - one that I co-authored, and one for which I was the lead author:

• Stasiw A, Falk S, Garapati S, Sridharma S, Mendelsohn D, Lakhtakia S, Rech A, Oldridge D, Adamson BJ, Chen R.
  ”Generalizable Machine Learning Framework for Predictive Modeling of Patient Outcomes Using Oncology Electronic Health Records”. Value in Health 23, S74. https://doi.org/10.1016/j.jval.2020.04.1752

• Chen R, Garapati S, Wu D, Ko S, Falk S, Dierov D, Stasiw A, Opong AS, & Carson KR. “Machine Learning Based Predictive Model of 5-Year Survival in Multiple Myeloma Autologous Transplant Patients” Blood 2019; 134 (Supplement_1): 2156. https://doi.org/10.1182/blood-2019-129432

Algorithmic Arts (“Deep Learning for Dance Performance”)

Artistic richness abounds at the intersection of technology and performance. I explored this theme in my original production, "Superhighway", presented at Dixon Place on 10/6/16. You can also read more in my original research paper, "Software for Choreography: Real-Time Analysis of Expressiveness in Dance Performance", which I wrote as an undergraduate in the Computer Science department at Princeton. My work was also written up in the Princeton Engineering newsletter.

Using my work, I taught "Introduction to Digital Dance" to students with Gibney Dance's Digital Technology Initiative; I also taught “Computers as Collaborators”, to introduce the fundamentals of using deep-learning and other algorithmic techniques to enhance on-stage performances.