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Efficient markets through system-wide optimization

What if we could nudge markets to behave ideally according to economic theory? 

We have a domain agnostic patent pending system that draws on techniques  from operations research  to make near optimal matches among transacting actors. We can  optimize for various objectives at the individual and system  level given constraints, learned/explicit preferences, and other factors.  



Hyper-efficient defi

We are building a flexible and optimal decentralized cryptocurrency exchange powered by MarketX's core ideas and algorithms. 

This would make intentionally optimized trading matches coupled with Nasdaq like execution rebates  that maximize both the health of the system and trader utility. These would be determined from user preferences expressed as buy or sell trading strategies along with various system parameters. 

Job Matcing

Optimal job matching

The job market is hindered by new economic, social, and regulatory realities. We are building a system that combines AI, traditional mathematical optimization techniques, and the power of Blockchain to create efficient and optimal matching of job seekers and employers.


Michael Karlin
Founder and Managing Director

Cofounder of multiple frontier tech startups and an inventor of novel technologies in these spaces: the MarketX Patents, potential Cancer & Malaria Therapy, Artificial Intelligence, and software. Co-CEO of Ibex Biosciences, a Biotech startup focusing on the development of monoclonal antibodies and gene therapies to treat cancer and other diseases. Previous experience includes work at a large Manhattan based law firm, a global accounting firm, and various financial funds. 

Accomplished and ranked athlete in Tower running, Masters Track, and Masters Road Running. Michael holds a JD degree from the University of Pennsylvania and a MBA from the George Washington University, and is a Certified Public Accountant (inactive) and Certified Financial Planner.

Dan Zanger

Dr. Zanger holds a Ph.D. in Mathematics from the Massachusetts Institute of Technology (MIT).  Prior to MarketX, Dan served as a Visiting Assistant Professor at the Department of Statistics and Applied Probability, University of California, Santa Barbara. He has has close to 20 years of  experience as a federal contractor as well,  working as a research engineer, mathematician, and principal investigator on a range of projects involving applications of machine learning, natural language processing, statistical data analysis, quantum computational science, information security and data privacy, and related areas.


He has authored  a number of research publications in such peer-reviewed technical journals and/or conference proceedings as Mathematics of Operations Research, Mathematical Finance, and Finance and Stochastics. In 2001, Dr. Zanger collaborated with Tim Berners-Lee in the DARPA Agent Markup Language project.



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