To join via Zoom: To join this seminar, please request Zoom connection details from headsec [at] stat.ubc.ca.
Presentation 1
Time: 4pm – 4:30pm
Speaker: Jingyiran Li, UBC Statistics MSc Co-op Student
Title: From biostatistics to AI algorithmic trading: A validator's perspective
Abstract: Sophia kicked off her industrial experience with a Research Analyst position on a prospective cohort study with Health Canada–MIREC Group. Afterwards, she switched to the insurance industry by working as a Data Engineer who dealt extensively with big data platforms and tools such as HDFS, Spark, Jenkins, and various databases such as Hive, Drill, and IBMDB. Finally, Sophia landed an AI Scientist position with RBC Capital Markets where she mainly focused on validating high frequency trading algorithms through traditional benchmarking, predictive uncertainty quantification, and simulating optimal liquidation strategy using deep reinforcement learning. Sophia will share her work experiences in three different roles from three different industries and provide relevant insights to those who are interested in pursuing a career in those industries.
Presentation 2
Time: 4:30pm – 5pm
Speaker: Harper Cheng, UBC Statistics MSc Co-op Student
Title: My co-op experience at the BC Cancer Research Centre: aGCT RNA-seq sample analysis
Abstract: Adult granulosa cell tumours or aGCT are characterized by their slow growth and usually occur in peri- or post-menopausal women with a median age of diagnosis of 50 to 54 years. The majority of aGCTs are diagnosed at an early stage with an indolent prognosis. However, one-third of patients relapse, typically 4–7 years after initial diagnosis leading to death in 50% of these patients with advanced-stage tumours. Surgery is the foundation of treatment for both primary and recurrent disease, but there is a subset of patients with relapsed tumours where surgery is not an option.
Using whole-transcriptome paired-end RNA sequencing technology, Shah et al. (4) identified a single somatic missense mutation in FOXL2 (402C→G) in four GCT with the predicted consequence to be the substitution of a tryptophan residue for a highly conserved cysteine residue at amino acid position 134 (C134W).
Although we have translated this FOXL2 mutation discovery into a biomarker that can be used for differential diagnosis, we still do not understand how this FOXL2 mutation promotes tumourigenesis. So that leads to the overarching question of: During the last decade, our understanding of the molecular pathogenesis of aGCTs has significantly improved, whereas the developments of chemotherapeutic regimens and especially targeted therapies have remained modest. We analyzed bulk RNA seq data in the hope of elucidating how FOXL2 mutation promotes tumourigenesis.