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My research/work experience, and skills- I currently specialize in diffusion models, computer vision and diffusion geometry, but am interested in various problems with complex data, where stochastic systems and optimization come into play.

General Information

Full Name Jonathan Patsenker
Date of Birth 10th March 1997

Education

  • 2026 (expected)
    PhD, Applied Mathematics
    Yale University, New Haven, CT
    • Advised by Ronald Coifman and Yuval Kluger
    • Selected Courses: Sampling Algorithms in Machine Learning, Applied Data Mining & Machine Learning, Theory of Deep Learning, Topics in Numerical Computation, Harmonic Analysis on Graphs, Topics in Sparse Analysis, Numerical Methods for PDEs
  • 2018
    BS, Computer Science and Mathematics (Concentration on Operations Research)
    Rensselaer Polytechnic Institute, Troy, NY
    • Advised by Malik Magdon-Ismail
    • Selected Courses: Machine Learning from Data, Computational Optimization, Data Mining, Intro to Data Mathematics, Randomized Algorithms, Computer Algorithms, Math in Medicine and Biology, Math Models of Operations Research

Professional Experience

  • 2023
    Technical Development Intern
    Moderna TX., Norwood, MA
    • Designed a novel technique, leveraging deep transfer learning methods with approaches based in diffusion geometry to automate analysis of CryoEM image data containing lipid nanoparticles
    • Implemented a custom pipeline and codebase to run the novel technique on CryoEM data in an industrial research environment to handle high throughput data
    • Designed and implemented a novel method to model the kinetics of relevant reactions for high throughput industrial processing

Research Experience

  • 2019-Present
    PhD Researcher
    Kluger Lab & Applied Mathematics Program, Yale University
    • Developing theoretical frameworks for distilling and mining features from large generative models
    • Developed, tested, and analyzed a novel method for solving inverse problems by leveraging information generative models with strong theoretical guarantees
    • Designed novel deep learning based models and manifold learning models for analyzing high resolution multi-channel images
    • Developed large language model-based approaches for robust feature embeddings of protein sequence data
    • Developed novel framework for modeling AI model training with physical simulation
    • Collaborated with pathologists to design data-driven solutions to drug survivability, cancer identification problems
    • Developed and implemented novel tensor imputation methods by leveraging higher-order information efficiently
    • Contributed to developing theoretical framework for the analysis of the popular embedding method, word2vec
    • Published peer-reviewed publications at top level conferences and journals
  • 2017-2018
    Undergraduate Researcher
    Computer Science Department, Rensselaer Polytechnic Institute
    • Developed, implemented and tested a statistically informed method for network routing on large graphs
  • 2015-2017
    Research Intern
    Systems Biology, Harvard Medical School
    • Developed, and implemented a statistical method for assessing the quality of genomes and proteomes
    • Deployed a tool for proteome research for annotating and cleaning proteomes

Technical Skills

  • Programming languages and libraries: Python, pytorch, tensorflow, numpy, scipy, R, Matlab, C, C++, Julia, Java
  • Areas of Expertise: deep learning, machine learning, generative modeling, computer vision, language modeling, large-scale data analysis, optimization, stochastic processes, numerical optimization, general statistical methods, inverse problems

Leadership Experience

  • 2021-2025
    Teaching Fellow
    Mathematics Department, Yale University
    • Collaborated with faculty to design curriculum in the Discrete Mathematics (MATH 244) course.
    • Tutoring undergraduate students in both the Discrete Mathematics (MATH 244) and Structure of Networks (AMTH 160) courses
    • Designing, building and maintaining an automated homework server for the Structure of Networks (AMTH 160) course
  • 2022
    Yale Biotech Club Datathon Mentor
    • Assisted undergraduate, graduate, and professional students in datathon in partnership with Boehringer Ingelheim and Code Ocean for tackling problems in healthcare informatics
  • 2014-2018
    Project Leader and External Mentor
    RCOS (Rensselaer Center for Open Source)
    • Founded and led a student-based research group in computational music generation and analysis
    • Collaborated with students and professors to build style-specific rhythm generators, and chord progression generators
    • Worked on feature based and deep learning based approaches for music generation