sasha chernenkoff sasha.chernenkoff@ucalgary.ca   ·   github   ·   linkedin education master of science, specialization in bioinformatics   ·   cumming school of medicine, university of calgary, canada   ·   in progress
GPA: 4.0/4.0
thesis: transfer learning for tissue-specific prediction of protein abundance from DNA sequence
areas of interest: computational genomics, representation learning, gene and protein expression, statistical genetics bachelor of science, honours in chemistry   ·   dietrich school of arts and sciences, university of pittsburgh, usa
GPA: 4.0/4.0
thesis: selective inhibition of histone lysine demethylases using sterically modified inhibitors experience research associate   ·   department of biochemistry and molecular biology, cumming school of medicine, university of calgary, canada
september 2023 - present
applying transfer learning to pretrained genomic foundation models to predict tissue-specific protein abundance and personalized gene expression directly from DNA sequence r&d scientist   ·   perkinelmer genomics
june 2020 - august 2023
developed second-tier biochemical diagnostic tests for newborn screening programs: LC-MS/MS biomarker panel for MPS I & II; Western blot CRIM-status assay for Pompe disease research associate   ·   department of chemistry, dietrich school of arts and sciences, university of pittsburgh, usa
september 2018 - june 2020
synthesized small-molecule inhibitors of enzymes involved in transcriptional regulation and used mutagenesis to generate enzyme variants for evaluating inhibitor selectivity and binding mechanisms publications Chernenkoff, S., Enoma, D., Weeraman, J. & Long, Q. Tissue-specific prediction of protein abundance via transfer learning. In preparation (2026). Kemogne, A., Weeraman, J., Wang, D., Enoma, D., Li, C., Sridharan, G., Chernenkoff, S., Chekouo, T., Zhang, Q. & Long, Q. Genomic variational autoencoder enables stable representation learning in high-dimensional genomic data with moderate sample sizes. Under review (2026). Kuwik, J., Hinkelman, K., Waldman, M., Stepler, K. E., Wagner, S., Arora, S., Chernenkoff, S., Cabalteja, C., Sidoli, S., Robinson, R. A. S. & Islam, K. Activity guided azide-methyllysine photo-trapping for substrate profiling of lysine demethylases. J. Am. Chem. Soc. 145, 21066-21076 (2023). abstracts Donti, T., Smith, S., Chernenkoff, S., Ellgass, M. & Hegde, M. P012: Measuring non-reducing terminal glycosaminoglycan fragments increases specificity and differentiates mucopolysaccharidosis type I (MPS I) from mucopolysaccharidosis type II (MPS II). Genet. Med. Open 1, 100022 (2023). poster presentations Chernenkoff, S., Enoma, D., Weeraman, J. & Long, Q. TL-Prot: Tissue-specific prediction of protein abundance via transfer learning. ASHG Annual Meeting, Montreal, QC (2026). Chernenkoff, S., Zhang, Q. & Long, Q. Variant annotation for low-prevalent disorders empowered by transfer-learning. ACHRI Research Retreat, Banff, AB (2023). Smith, S. E., Gelb, M. W., Herbst, Z. M., Dellagatta, J. N., Chernenkoff, S., Donti, T. & Khaledi, H. Measurement of non-reducing terminal glycosaminoglycan fragment increases specificity of second-tier testing for mucopolysaccharidosis type I (MPS I). APHL Newborn Screening Symposium, Tacoma, WA (2022). awards & scholarships ACHRI Graduate Scholarship   ·   september 2026 Canada Graduate Scholarships - Master's Program (CIHR)   ·   april 2024 Phillips Medal   ·   february 2020 American Chemical Society Undergraduate Award in Organic Chemistry   ·   october 2019 skills dry lab: deep learning   ·   transfer learning   ·   genomic representation models   ·   statistical genetics   ·   sequence alignment   ·   variant calling (GATK)   ·   variant annotation   ·   high-performance computing (SLURM)   ·   git   ·   python   ·   java wet lab: LC-MS/MS   ·   Western blotting   ·   organic synthesis   ·   mutagenesis   ·   protein expression and purification