About me

Arman Karshenas, Ph.D.

I am Dr. Arman Karshenas, a researcher, data scientist, and entrepreneur with a passion for leveraging machine learning to solve complex problems in biophysics, genomics, and life sciences. With a diverse academic background and practical experience, I have contributed to advancements in computational biology, data-driven insights for drug development, and applications of artificial intelligence in biological research. For a full list of my professional, and academic accomplishments and publications, please refer to my Resume.

Industry Experience

I have had the opportunity to work in both academia and industry, bridging the gap between scientific research and real-world applications.

  • Data Scientist / ML Engineer Intern — Pivotal Bioventures (Jan 2024 – Aug 2024)
    • Applied machine learning and data science methods to drug development and clinical trials. My work focused on building predictive models to improve decision-making processes for biotech investments, analyzing large datasets to derive actionable insights, and optimizing the tools used for due diligence in mergers and acquisitions (M&A).
  • Fellow — Pear VC Persian Founder Circle (Aug 2024 – Nov 2024)
    • Participated in a fellowship designed to support Persian founders with entrepreneurship insights.

Research Experience

My research spans several areas of biophysics, machine learning, and computational biology, with an emphasis on applying AI models to biological datasets.

  • Graduate Student Researcher — UC Berkeley (May 2022 – Dec 2024)
    • Researching genome regulation from the sequence space of enhancers in Prof. Garcia’s lab, combining computational and experimental methods.
  • PhD Rotation Projects — UC Berkeley (Aug 2021 - May 2022)
    • Developed assays for viral detection of influenza strains and genome re-assortment in Fletcher’s lab.
    • Created computational pipelines for protein folding studies in the Marqusee lab.
    • Studied transcription variability between sister chromatin in developing D. melanogaster embryos in Garcia lab.
  • MPhil Project — University of Cambridge (Oct 2020 – Jul 2021)
    • Developed a novel deep learning pipeline to extract morphological measurements from micro-CT scans and conducted genome-wide association tests on cichlid fish samples in Prof. Richard Durbin’s group.

Education

  • Ph.D. in Biophysics — University of California, Berkeley (Aug 2021 – Dec 2024)
    • Thesis: On the Computational & Experimental Dissection of Developmental Enhancers’ Functionality in Gene Expression Regulation.
  • MPhil in Biological Sciences (First Class Honours, GPA 4.0) — University of Cambridge (Sep 2020 – Aug 2021)
    • Thesis: Quantitative Analysis of Cichlid Head Morphology from Micro-CT Data for Genetic Studies.
  • BA in Electrical Engineering (First Class Honours, GPA 4.0) — University of Oxford (Oct 2017 – Jul 2020)
    • Graduated top of the class with 167 students.

Skills

  • Programming Languages: Python, R, SQL, MATLAB, C++
  • Machine Learning: Deep Learning, CNNs, Random Forests, LLMs, MLOPS
  • Data Analysis & Visualization: Pandas, NumPy, SciPy, Matplotlib, ggplot2, Tableau
  • Database Management: MySQL, PostgreSQL, MongoDB
  • Tools: Git, Airflow, Docker, TensorFlow, PyTorch, AWS

Awards and Achievements

Talks and Conferences

I have actively participated in various academic and professional events, where I have presented my research and contributed to discussions in the fields of biophysics and machine learning.

  • 2024: St. Jude Hospital National Graduate Student Symposium (NGSS), St. Jude Hospital, Memphis, TN, USA (Mar 26-28) –– Talk & Poster Presentation
  • 2024: Systems Biology: Global Regulation of Gene Expression, Cold Spring Harbor Laboratory, Long Island, NY, USA (Mar 13-16) –– Poster Presentation
  • 2023: Biophysics Retreat, University of California, Berkeley, CA, USA (Oct 27-29) –– Talk & Poster Presentation
  • 2023: Machine Learning Workshop, Innovative Genomics Institute (IGI), University of California, Berkeley, CA, USA (Jun 29) –– Talk Presentation
  • 2023: Biophysics Symposium, University of California San Francisco, San Francisco, CA, USA (Jun 21) –– Poster Presentation
  • 2020: The 6th International Conference of Quantitative Genetics (ICQG), Virtual (Nov 2-12) –– Participation
  • 2020: International Federation of Automatic Control (IFAC) World Congress, Berlin, Germany (Jul 11-17) –– Talk Presentation
  • 2019: International Workshop on Control Engineering and Synthetic Biology, University of Oxford, Oxford, UK (Sep 9-11) –– Poster Presentation
  • 2018: Synthetic Biology Leadership Council (SBLC), Royal Academy of Engineering, London, UK (Nov 21) –– Talk Presentation
  • 2018: IGEM 2018, Boston, US (Oct 24-28) –– Talk Presentation

Teaching Experience

  • Fall 2023 Quantitative Biology Bootcamp – Instructor & Teaching Assistant — UC Berkeley
  • Fall 2022 Quantitative Biology Bootcamp – Instructor & Teaching Assistant — UC Berkeley
  • Spring 2022 Physics 8B – Instructor & Teaching Assistant — UC Berkeley
  • Michaelmas/Lent/Easter 2020-2021 Mathematical Biology – Instructor & Supervisor — Wolfson College, Trinity Hall, Hughes Hall, University of Cambridge
  • Lent 2021 Computational Genomics – Instructor & Teaching Assistant — Isaac Newton Mathematics Institute, University of Cambridge

Volunteer Work

  • Volunteer Math Teacher at San Quentin Prison (May 2024 - Aug 2024)
  • President, Iranian Student Association ISAA at UC Berkeley (Aug 2021 – Mar 2024)
    Organizing academic, social, and professional events for Iranian graduate students.