Michael Busha Email & Phone Number

Menlo Park, Ca, Us

San Jose, California, Us

Securiti is the pioneer of the Data Command Center, a centralized platform that enables the safe use of data and GenAI. It provides unified data intelligence, controls and orchestration across hybrid multicloud environments. Large global enterprises rely on Securiti's Data Command Center for data security, privacy, governance, and compliance.Securiti has been recognized with numerous industry and analyst awards, including "Most Innovative Startup" by RSA, "Top 25 Machine Learning Startups" by Forbes, "Most Innovative AI Companies" by CB Insights, "Cool Vendor in Data Security" by Gartner, and "Privacy Management Wave Leader" by Forrester.For more information, please visit securiti.ai and follow us on LinkedIn.

Mountain View, California, Us

Elastica is the leader in Data Science Powered™ Cloud Application Security. Its CloudSOC™ platform empowers the companies to confidently leverage Cloud and SaaS applications while staying safe, secure and compliant.Following Elastica Apps on the extensible CloudSOC™ platform enable full life cycle of visibility & security for the Cloud and SaaS applications being used in your company:- Audit: Uncovers Shadow IT and highlights vulnerabilities in cloud apps being used by employees- Detect: Uses machine learning to detect intrusions and threats based on account activity in cloud apps- Protect: Provides defense against intrusions and compliance violations in cloud apps being used- Investigate: Enables forensic investigations on historic account activity on cloud apps being usedFor more info: http://www.elastica.net

Stanford, Ca, Us

• Developed a C++, Python, and IDL pipeline to validate and refine galaxy formation algorithms by comparing statistical properties (structure, 2-point functions, etc) of simulated and observed data.• Continued development of Statistical and Machine Learning algorithms for modeling galaxy properties.

Zurich, Ch

• Devised a C++ and IDL multiple regression method to model the distribution of 2 billion galaxies(Using 100TB of storage, 500k CPU hours), an order of magnitude more than contemporary methods.• Developed artificial neural network and statistical classifiers to model the characteristics (colors and multiband photometry) of galaxies in our universe using C++, Python, and IDL. This is the only such code capable of working on the billions of simulated objects probed by contemporary experiments.• Enabled accurate cleaning of observational data by creating a “transfer function” to add realistic errors and contaminants (instrumentation errors, spurious detections, etc) to simulated data.• Led a team across multiple institutions to write a pipeline that produces the most realistic synthetic galaxy surveys (in terms of data volume and simulated properties) using Python, IDL, and bash.

Stanford, Ca, Us

• Developed a Bayseian Inference framework in Python and IDL to compare simulated models of dark matter to observations of galaxies, reducing systematic uncertainties in the mass of our galaxy by 25%.• Combined observational data from large-volume surveys with massive simulation data to predict the distribution of dim objects surrounding bright galaxies. These predictions went three orders ofmagnitude dimmer than previous studies and were subsequently validated by observations.• Constrained the ionization history of the universe using a Monte Carlo exploration of analytic models for the interaction between photoionizing radiation and galaxy formation as well as observations.

Ann Arbor, Michigan, Us

• Made the first predictions for the long-term structure of dark matter halos in our universe using largeparallel N-body simulations run in C using MPI.• Built and administered a Beowulf cluster.

Doctor Of Philosophy (Ph.D.), Physics

Bachelor Of Arts (B.A.), Physics