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Mark D Hannel

from Brooklyn, NY
Age ~35
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Related to:
Terry C Hannel, 58Dawn Johanna Thompson, 57Tara Hannel, 58Paul Cook, 54Dustin C Hannel, 33Mirdza Enins
Connected to:
Barbara J Hannel, 74Bradley J Hannel, 41Clifford L Hannel, 62Lars M Hannell, 70Alan B Hannemann, 59Albert J Hannemann, 54Beth N Hannemann, 47Carolyn E Hanneman, 86Charles A Hannema, 68Chris E Hanneman, 50

Mark Hannel Phones & Addresses

  • Brooklyn, NY
  • 6429 Mooresville Rd, Indianapolis, IN 46221

Work

Company: Insight data science Jan 2019 to Apr 2019 Position: Data science fellow

Education

Degree: Doctorates, Doctor of Philosophy School / High School: New York University 2011 to 2016 Specialities: Physics, Philosophy

Skills

Academic Tutoring • Physics • Mathematics • Condensed Matter Physics • Programming • Latex • Digital Image Processing • Experimentation • Computational Physics • Linux • Ubuntu • Python • C • Matlab • Mathematica • Data Analysis • Mathematical Modeling • Idl • Sql • Machine Learning • Data Science

Emails

j***[email protected]

Industries

Higher Education

Resumes

Resumes

Mark Hannel Photo 1

Data Science Engineer

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Location:
New York, NY
Industry:
Higher Education
Work:
Insight Data Science Jan 2019 - Apr 2019
Data Science Fellow
Neuberger Berman Jan 2019 - Apr 2019
Data Science Engineer
New York University Aug 2011 - Sep 2018
Phd Candidate, Physics
Endeavor Tutoring May 2013 - Mar 2018
Academic Tutor
New York University Aug 2012 - May 2013
Part-Time Adjunct Instructor
Education:
New York University 2011 - 2016
Doctorates, Doctor of Philosophy, Physics, Philosophy Purdue University 2007 - 2011
Bachelors, Bachelor of Science, Physics
Skills:
Academic Tutoring
Physics
Mathematics
Condensed Matter Physics
Programming
Latex
Digital Image Processing
Experimentation
Computational Physics
Linux
Ubuntu
Python
C
Matlab
Mathematica
Data Analysis
Mathematical Modeling
Idl
Sql
Machine Learning
Data Science

Publications

Us Patents

Colloidal Fingerprints For Soft Materials Using Total Holographic Characterization

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US Patent:
20170307497, Oct 26, 2017
Filed:
Nov 11, 2015
Appl. No.:
15/526287
Inventors:
- New York NY, US
David B. RUFFNER - New York NY, US
Aaron YEVICK - New York NY, US
Mark HANNEL - New York NY, US
International Classification:
G01N 15/02
G06K 9/00
G03H 1/00
G03H 1/00
G01N 15/00
G01N 15/02
Abstract:
Systems and methods for uniquely identifying fluid-phase products by endowing them with fingerprints composed of dispersed colloidal particles, and by reading out those fingerprints on demand using Total Holographic Characterization. A library of chemically inert colloidal particles is developed that can be dispersed into soft materials, the stoichiometry of the mixture encoding user-specified information, including information about the host material. Encoded information then can be recovered by high-speed analysis of holographic microscopy images of the dispersed particles. Specifically, holograms of individual colloidal spheres are analyzed with predictions of the theory of light scattering to measure each sphere's radius and refractive index, thereby building up the distribution of particle properties one particle at a time. A complete analysis of a colloidal fingerprint requires several thousand single-particle holograms and can be completed in ten minutes.

Machine-Learning Approach To Holographic Particle Characterization

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US Patent:
20170241891, Aug 24, 2017
Filed:
Oct 12, 2015
Appl. No.:
15/518739
Inventors:
- New York NY, US
Aaron YEVICK - New York NY, US
Mark HANNEL - New York NY, US
International Classification:
G01N 15/14
G01N 15/02
Abstract:
Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computation-ally intensive, and thus slow. Machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers. The resulting stream of precise particle-resolved tracking and characterization data provides unparalleled insights into the composition and dynamics of colloidal dispersions and enables applications ranging from basic research to process control and quality assurance.

Fast Feature Identification For Holographic Tracking And Characterization Of Colloidal Particles

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US Patent:
20170059468, Mar 2, 2017
Filed:
Feb 12, 2015
Appl. No.:
15/118785
Inventors:
- New York NY, US
Mark Hannel - New York NY, US
David G. Grier - New York NY, US
Bhaskar Jyoti Krishnatreya - New York NY, US
Assignee:
New York University - New York NY
International Classification:
G01N 15/14
G06F 17/11
G06N 99/00
G01N 21/41
G03H 1/00
Abstract:
A method and system for identification of holographic tracking and identification of features of an object. A holograph is created from scattering off the object, intensity gradients are established for a plurality of pixels in the holograms, the direction of the intensity gradient is determined and those directions analyzed to identify features of the object and enables tracking of the object. Machine learning devices can be trained to estimate particle properties from holographic information.
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Mark D Hannel from Brooklyn, NY, age ~35 Get Report