personal photo of Sudikshaa Vijayakumar

Sudikshaa Vijayakumar

Tagline:Predoctoral Fellow IIT Bombay | Postgraduate in Human Genetics | Publications on Cancer Drug Resistance and 3D Tumor Models

Chennai, Tamil Nadu, India

About Me

Hey, this is Sudikshaa Vijayakumar, from Chennai. A passionate researcher with a bachelor's in bioinformatics and a master's in Human Genetics. I recently completed my 1 year fellowship at IIT Bombay. Skilled in genomic data analysis and interpretation, machine learning tools, mammalian cell culture and PCR techniques, I thrive in collaborative environments where I can contribute to innovative research and projects.
Besides academics, I have a keen interest in various artworks. I like to sing as well.

Education

  • Master of Science

    from: 2022, until: 2024

    Field of study:Human GeneticsSchool:Sri Ramachandra InstituteLocation:Chennai, India

  • Bachelor of Science

    from: 2019, until: 2022

    Field of study:BioinformaticsSchool:Sri Ramachandra InstituteLocation:Chennai, India

Publications

  • “3D Aggregates of SiHa Cells”– Pathology- Research and Practice

    Journal ArticlePublisher:Pathology - Research and PracticeDate:2025
    Authors:
    Sabari Krishnan B. B.Sudikshaa VijayakumarRaveena DhakshanamoorthyAkshaya BaskaranRavi Maddaly
    Description:

    This study aims to investigate the morphology and morphometrics of agarose hydrogel-induced 3D aggregates of SiHa cell line.

  • Drug resistance in human cancers - Mechanisms and implications

    ReviewPublisher:Life SciencesDate:2024
    Authors:
    Sudikshaa VijayakumarRaveena DhakshanamoorthyAkshaya BaskaranB. Sabari KrishnanRavi Maddaly
    Description:

    We present here comprehensively the drug resistance in cancers along with their mechanisms. Also, apart from resistance to regularly used chemotherapeutic drugs, we present resistance induction to new generation therapeutic agents such as monoclonal antibodies. Finally, we have discussed the experimental approaches to understand the mechanisms underlying induction of drug resistance and potential ways to mitigate induced drug resistance.

Skills

  • Molecular Biology: PCR, Western Blot, Plasmid/DNA/RNA Isolation, Immunofluorescence, 3D Cell Culture

  • Cytogenetics: Karyotyping, FISH, PBLC, CVS, Amniotic Fluid Culture, Genetic Counseling

  • Observership Bioinformatics: Sequence Alignment (BLAST, CLUSTALW), UNIPROT, SWISSPROT, AutoDock Programming: R (statistical analysis, ML), HTML

  • Soft Skills: Scientific Writing, Communication, Team Collaboration

Languages Known: English, Tamil, Hindi

Work Experiences

  • Project Associate, IIT Bombay

    from: 2024, until: 2025

    Organization:IIT BombayLocation:Mumbai, India

    Description:
    • Project: Role of hybrid cancer stem cells in invasion
    • Analyzed fluorescence intensity, motility, and nuclear/cytoplasmic ratio of MDA ACTN4 and OVOL2 cells using
      ImageJ.
    • Assisted in generation of ACTN4-NLS and OVOL2-sh2 lines via LVP transfection and luciferase-tagged cell lines.
    • Performed FACS sorting, Western blotting, and RT-PCR for gene/protein expression profiling.

Achievements

  • Predoctoral Fellow – NPTEL (Ministry of Education), 2024–2025

  • Subject Topper – 98% in CBSE English (Class XII, 2019)

Projects

  • Master’s Thesis (Mar – Jul 2024)

    date: 2024

    Organization:Sri Ramachandra Institute

    Description:
    • Investigated how low initial priming doses (LIPDs) of cisplatin and 5-FU influence cytotoxicity in HCT-116 colon cancer cells.
    • Studied differential responses in 2D vs. 3D agarose-based cultures using the liquid overlay technique.
    • Determined IC50 values and explored chemotherapy resistance in ex vivo tumor-like environments.
  • Bachelor’s Thesis (Mar – Jul 2022)

    date: 2022

    Organization:Sri Ramachandra Institute

    Description:
    • Built predictive models for hepatocellular carcinoma using gene expression data from the GEO database.
    • Applied Lasso, Support Vector Machine, Multi-Layer Perceptron, and Random Forest algorithms in R.
    • Compare model performances and interpreted gene significance in cancer prediction.