
Hirsh
PhD candidate
Expertise
Technology Policy, Web Development, Cyberdefense/Cybersecurity, Election Security

Polygence mentors are selected based on their exceptional academic background, teaching experience, and unique ability to inspire the next generation of innovative thinkers and industry leaders.

Technology Policy, Web Development, Cyberdefense/Cybersecurity, Election Security

Molecular Biology, AI genetics, chemistry, developmental biology, Coding,

computer science, data science, machine learning, blockchain, cloud computing, web applications, Internet of Things

social psychology, developmental psychology, moral psychology, artificial intelligence

vehicle security, vehicle reliability, cybersecurity, applied machine learning, Computer Science, Operating System Security

Statistical Analysis, including regression analysis, machine learning, and use of R and Python. Statistical analysis in topics such as politics, physics, chemistry, astronomy, public health, and medicine. Biostatistical Analysis, including longitudinal studies and survival analysis

Artificial Intelligence, Mechanical Engineering, Aerospace Engineering, Robotics, Computer Science

Chemical Engineering, Electrical Engineering, Mechanical Engineering, Optimization. Operations Research, Environmental Engineering, Applied Math

Renewable energy (solar), heat transfer, computational materials science

Electrical and Computer Engineering/Computer Science/Scientific Simulation/Chip Design/Software Engineering

business, business analytics, data, data visualization, data analysis, marketing, power BI, Business, Management Science, Engineering, Food science & Food Processing Engineering, Google Analytics, cloud, cloud computing, enterprise tech infrastructure, Artificial intelligence (AI)/ Machine Learning (ML) application for business

Cognitive neuroscience, psychology, biomedical engineering, electical & computer engineering, machine learning

method development to better understand the disease causes, applications of machine learning/deep learning in biology

Physics: semiconductor, quantum, astrophysics, electromagnetism, nuclear. Mathematics: algebra, calculus, data set manipulation with code. Computer science: AI model training and testing, ML applications and integration. Mechanical engineering: Heat transfer, fluid mechanics, designing practical experiements/tools

Statistical analysis, including regression, longitudinal analysis, and machine learning using R. Statistical analysis in topics such as politics, education, public health, and economics. Research design, including experimental design and survey design. Comparability studies, including propensity score matching and measurement invariance.

physics, astronomy, computational astrophysics, fusion, quantum computing, Reviewing Dark Matter Models, AI/Machine Learning Applied to the Characterization of Exoplanets Observed by JWST, Fermi Analysis of the Drake Equation, cybersecurity, quantum-resistant encryption