# Mathematics

## The KPZ fixed point

## The Topology of Azumaya Algebras

## Symmetry, bifurcation, and multi-agent decision-making

## Integers in many-body quantum physics

## Birational geometry and algebraic cycles

## Using mathematics to fight cancer

What can mathematics tell us about the treatment of cancer? In this talk I will present some of work that I have done in the modeling of tumor growth and treatment over the last fifteen years.

Cancer is a myriad of individual diseases, with the common feature that an individual's own cells have become malignant. Thus, the treatment of cancer poses great challenges, since an attack must be mounted against cells that are nearly identical to normal cells. Mathematical models that describe tumor growth in tissue, the immune response, and the administration of different therapies can suggest treatment strategies that optimize treatment efficacy and minimize negative side-effects.

However, the inherent complexity of the immune system and the spatial heterogeneity of human tissue gives rise to mathematical models that pose unique challenges for the mathematician. In this talk I will give a few examples of how doctors, immunologists, and mathematicians can work together to understand the development of the disease and to design effective treatments.

This talk is part of the PIMS Diversity in Mathematics Summer School and is intended for a general audience: no knowledge of biology or advanced mathematics will be assumed.

### Biography

A California native, Professor Radunskaya received her Ph.D. in Mathematics from Stanford University. She has been a faculty member in the Math Department Pomona College since 1994.

In her research, she specializes in ergodic theory, dynamical systems, and applications to various "real-world" problems. Some current research projects involve mathematical models of cancer immunotherapy, developing strategies for targeted drug delivery to the brain, and studying stochastic perturbations of dynamical systems.

Prior to her academic career, Professor Radunskaya worked extensively as a cellist and composer. Her music, described as "techno-clectic", combines traditional forms with improvisation, acoustic sounds with electronic, computer-generated, and found sounds, and abstract structures with narrative visual and sonic elements.

Contrary to popular belief, Professor Radunskaya thinks that anyone can succeed in mathematics, and she has committed herself to increasing the participation of women and underrepresented groups in the mathematical sciences.

She is currently the President of the Association for Women in Mathematics, and co-directs the EDGE (Enhancing Diversity in Graduate Education) program, which won a "Mathematics Program that Makes a Difference" award from the American Mathematics Society in 2007, and a Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM) in 2015.

Professor Radunskaya was recently been elected as a Fellow of the American Math Society, and she is the recipient of several awards, including a WIG teaching award in 2012, and the 2017 AAAS Mentor award.

## Class Numbers of Certain Quadratic Fields

## BCData 2018 Career Panel

### Moderated Questions

- What was the first job you had after graduation and how did you get it?
- What do you like most/least about your current work?
- If you could go back in time and change one thing about your career choices what would you do?
- What advice do you have for the students in the audience looking for their first job?

### Speaker Bios

**Bernard Chan**is currently a data scientist at BuildDirect.com (BD), an e-commerce platform in flooring, tiles and other home improvement products. At BD, Bernard is part of the analytics team and he specializes in logistics related data problems such as freight rate and route planning. Prior to working at BD, Bernard was a applied mathematics researcher in dynamical systems and bifurcation theory.

**Soyean Kim**is a professional statistician (P.STAT) who is passionate about ethical use of data and algorithms to contribute to the betterment of society. She currently leads a team of data scientists at Technical Safety BC, a safety regulator in Canada. Her key contribution includes advancing ethics roadmap in predictive system and deployment of AI and machine learning to help safety inspection process. Her previous leadership roles include her tenure at PricewaterhouseCoopers and Fortis as a rate design manager. She is an advocate for “Data for Good” and a speaker on the topic of real world applications of AI. Her latest speaking engagement includes PAPIs in London, UK which is a series of international AI conferences, and BC Tech Summit in Vancouver.

**Michael Reid**received a Bachelor’s in Mathematics from UMBC before starting work as junior web developer for a US federal government consulting agency. After moving to Vancouver, he’s worked in software engineering at companies ranging from small consulting firms to Amazon Web Services. He recently co-founded Nautilus Technologies, a machine learning and data privacy startup in Vancouver.

**Parin Shah**is a Data Scientist at KPMG focused on solving machine learning and data engineering problems in the space of mining, gaming, insurance and social media. Previously, he spent 2.5 years helping develop the digital analytics practice for an e-commerce firm, Natural Wellbeing, where he worked on setting up data infrastructure, building consumer analytics models and website experimentation. Parin was a fellow at a UBC machine learning workshop and has an undergraduate degree from the University of British Columbia (UBC) with a coursework concentrated in economics with statistics and computer science electives.

**Dr. Aanchan Mohan**is a machine learning scientist and software engineer at Synaptitude Brain Health. He is currently working on software and machine learning methods to encourage circadian regulation with the goal of improving an individual’s brain health. His current research interests include problems in natural language processing. Dr. Mohan has worked on Bayesian and deep learning methods applied to time series signals across multiple domains. He holds a PhD from McGill University where he focused on transfer learning and parameter sharing in acoustic models for speech recognition. He supervises students and actively publishes in the area of speech processing. He is a named co-inventor on two issued patents in the area of speech processing, and one filed patent in the area of wearable devices. He is a co-organizer of the AI in Production, and Natural Language Processing meetups in Vancouver.