Recent Publications (→ complete vita):

Shrager, J, Shapiro, M, Hoos, W (2019) Is Cancer Solvable? Towards Efficient and Ethical Biomedical Science. J Law Med and Ethics, 47 (2019): 362-368. DOI: 10.1177/1073110519876164
ELIZA in BASIC; Ch. 4 in Stefan Holtgen and Marianna Baranovska (Eds.) Hello, I'm Eliza.
The Scientist as User; presentation in the User-Focused Beam Line Control and Monitoring for X-ray Science workshop in the 2018 SSRL/LCLS Users' Meeting
Prototyping a precision oncology 3.0 rapid learning platform; Connor Sweetnam, et al. BMC Bioinformatics,2018, 19:341
A TedEd-Style video I explaining GCTA (Global Cumulative Treatment Analysis)
Precision Medicine: Fantasy meets reality (a letter appearing in Science Magazine in 2016)
Molecular Tumor Boards: What they are; What they do; What they need. (A talk I gave at Microsoft Research Feb. 2015)
Rapid Learning for Precision Oncology (Nature Reviews Clinical Oncology, 2014)
Theoretical Issues for Global Cumulative Treatment Analysis (GCTA). (arXiv:1308.106) [In 2016, GCTA was selected among the 10 finalist, from among 157 applicants, in the Harvard Business School's Precision Trials Challenge Competition.]
Cancer: A computational disease that AI can cure (AI Magazine 2011)
On the Privacy Practices of Just Plain Sites (arXiv:1507.00790)
Demandance (arXiv:1507.01882)
→ The journey from child to scientist: Integrating cognitive development and the education sciences (APA Press 2012)
BioBIKE: A Web-based, programmable, integrated biological knowledge base (Nucleic Acids Research 2009)

If you're looking for something not in the above, please see → my complete vita

Brief Bio: Dr. Jeff Shrager is co-founder, CTO, and Director of Engineering and Research of xCures, and an Adjunct Professor in the Symbolic Systems Program at Stanford. He holds degrees in Computer Science (BSE/MSE) and Cognitive and Developmental Psychology (PhD) from The University of Pennsylvania and Carnegie Mellon University, respectively. His work spans Artificial Intelligence and Cognitive Science, including cognitive and developmental neuroscience, formal and informal science and math education, scientific computing, human learning and brain development, artificial intelligence, machine learning, molecular, microbial, and marine biology and genomics, bioinformatics, nonlinear mathematics, and many other areas. Jeff has authored, or co-authored, over one hundred peer-reviewed papers, and three books. He has also co-founded two successful AI-based biomedical companies, one in drug discovery robotics and another in cancer informatics. xCures is his third biomedical startup. Jeff's current work focuses on how science works and how scientists think, and on building intelligent tools, agents, models, and platforms to support and improve scientific reasoning, and other aspects of the scientific process.

Extended research summary: My work focuses primarily on how science works and how scientists think, and on building intelligent tools, agents, models, and infrastructure to support and improve scientific reasoning, and other aspects of the scientific process. Symbolic and sub-symbolic computation must co-operate to support flexible, robust learning and cognition. Symbolic-level "complex" learning and reasoning is often depicted by, for example, "book learning", the pinnacle of which is taken to be philosophical or scientific reasoning and discovery. Meanwhile, sub-symbolic "sensory-motor" or "perceptual" learning is depicted by, for example, learning to walk, cook, or drive. Through computational modeling, as well as laboratory and field research, I study how symbolic and the sub-symbolic/sensory-motor/perceptual computation co-operate in enabling flexible and robust learning and cognition in many areas. Especially interesting is early child development, during which period the brain is becoming organized, and the child is embedded in a rich cognitive and sensory-motor scaffolding. My computational work in this area has produced several influential models of brain self-organization, as well as of how high level reason interacts with, and indeed relies upon, the sub-symbolic cognitive infrastructure, while, at the same time, the organization of the sub-symbolic sensory-motor systems is guided by higher level activity. Three projects stand out in triangulating my contributions in these areas: 1. My model (with David Klahr) of "instructionless learning" based on a process, called "commonsense perception", which combines symbolic and "perceptual" reasoning; 2. My model (with Mark Johnson) of cortical parcellation, which explains how the brain obtains its functional architecture, and which was a precursor to later "deep learning" architectures; and 3. My model (with Bob Sielger) of the development of arithmetic knowledge and strategic skill, which has been widely influential upon a generation of developmental modelers, as well as in educational science, and which my colleagues and I continue to evolve to encompass recent findings in Systems Neuroscience. I also apply my ideas through application to real-world, scientific biocomputing: I co-founded, and served as CTO and engineering lead for, two (slightly) successful scientific biocomputing companies, as well as having envisioned, created, and lead the team who developed BioBike and BioDeducta, decade-long NASA and NSF-funded projects that built the world's first cloud-based "intelligent" scientific computing engine (a far precursor to Wolfram Alpha). I have co-authored nearly a hundred peer-reviewed papers in areas such as machine learning, graph theory, developmental psychology, computational psychology, drug discovery, molecular biology, bioinformatics, privacy and computer security, and even philosophy of science.