Zhuohang (Alice) Feng

PhD Candidate at University of Toronto | MoGen GSA Co-President | DNA Damage and Repair

Canada

About

I am a Ph.D. student specializing in Molecular Genetics, with a strong foundation in advanced laboratory techniques. My expertise includes genome-wide CRISPR screening, fluorometric imaging, PCR, western blotting, high-throughput, high-content drug screening, and embryonic stem cell culturing. Through my research, I continually strive to contribute to cutting-edge scientific discoveries and innovations in the field.

Experience

  • University of Toronto (2 yrs 6 mos)
    • MoGen GSA - Co-President
      Feb 2026 - Present · 6 mos

    • MoGen GSA - VP Student Life
      Feb 2025 - Feb 2026 · 1 yr 1 mo

      Vice President of Student Life, Molecular Genetics Graduate Student Association Executive Committee

    • MoGen GSA - International Student Liaison
      Feb 2024 - Jan 2025 · 1 yr

      Organizing events led by and with a focus on international students and advocating for international students at the University of Toronto, including financial, social, and cultural issues.

  • Doctoral Student at Department of Molecular Genetics - University of Toronto
    Aug 2023 - Present · 3 yrs

    Applying genome-wide CRISPR screens and in vivo mouse models to uncover novel endogeneous sources of DNA double-strand breaks that contribute to neurodegenerative disorders.

  • MSc Student (Biochemistry and Biomedical Sciences) at McMaster University
    Sep 2021 - Aug 2023 · 2 yrs

    Performed high-content phenotypic screens of synthetic compounds and used chemical genomics to uncover novel mechanisms of cancer stem cell biology. I also applied single-cell RNA-sequencing analysis along with clinical efficacy data of 101 current oncology drugs to identify the correlation between drugs' cancer stem cell selectivity and clinical efficacy.

  • Graduate Student Startup Competition at StemImage Technologies (CEO)
    Feb 2022 - Feb 2023 · 1 yr 1 mo

    StemImage aims to address challenges commonly faced by research labs conducting phenotypic screening in drug discovery. Traditional methods are often costly and time-intensive, with labs relying on expensive antibodies for cell staining or lineage tracking and labor-intensive screening experiments. To overcome these obstacles, StemImage focuses on developing a computational solution that offers a more efficient and cost-effective alternative to antibody-based staining for image classification in drug discovery.

  • Research Assistant (Bhatia Lab) at McMaster University
    Apr 2020 - Mar 2021 · 1 yr