Mathematics Colloquium

Dr. Gülden Karakök from University of Northern Colorado

Abstract

Numerous reports, policy and standards documents, and research studies emphasize the importance of creativity. For example, the recent report from the World Economic Forum noted that creativity at work is one of the top-three demanded skills, and that it "has jumped from 10th place to third place in just five years" (Schöning & Witcomb, 2017, para. 12). Within the domain of mathematics, similar emphases are made by mathematicians, mathematics education researchers and policy/standards makers. For example, the Mathematical Association of America’s (MAA) CUPM Curriculum Guidelines (Schumacher & Siegel, 2015) for majors in the mathematical sciences states that "a successful major offers a program of courses to gradually and intentionally lead students from basic to advanced levels of critical and analytical thinking, while encouraging creativity and excitement about mathematics" (p. 9). In this talk, I will briefly summarize some of the research on mathematical creativity at the K-16 levels and introduce the work of the Creativity Research Group focusing on undergraduate mathematics courses. Our research group aims to explore ways in which undergraduate students' mathematical creativity can be fostered and explicitly valued in mathematics courses that include proof-construction and/or problem solving activities. I will introduce the Creativity-in-Progress Reflections (CPR) on Proving and Problem Solving tools that we designed. These formative assessment tools were created to enhance mathematical creativity (of users) while facilitating proof-construction and problem-solving heuristics as well as fostering metacognition. With two categories, Making Connections and Taking Risks, these formative assessment tools aim to develop mathematical discourse centered around aspects of creativity related to fluency, elaboration, flexibility, and originality. I will provide some examples of how one might implement these tools in various mathematics courses as well as discuss some illustrative empirical examples from our research studies.

Prof. Janet Duncan from FCAS, FSA, MAAA

Abstract

Insurance is a contractual promise to reimburse policyholders for future losses. Consumers often comment that they don't understand their insurance rates – it all seems very mysterious to them. But in reality, creating insurance rates is very logical when broken down into its component parts. The fundamentals of insurance ratemaking are very similar to the pricing of many other products, i.e., understanding cost and determining a target profit load. The major difference is that for many products, the cost is easily determined from the manufacturing process. However, for insurance, the cost involves significant uncertainty about the future. This presentation will introduce the audience to fundamental insurance principles and the mystery behind insurance ratemaking.

About the Presenter

Professor Janet Duncan has over 30 years of property/casualty financial analysis experience, including commercial and personal lines reserving and pricing, financial and capital modeling, planning, and management reporting. Janet's work experience includes six years as CNA's senior vice president and signing actuary, responsible for $17 billion of property/casualty reserves, including standard commercial lines, specialty lines, and discontinued operations. Prior to CNA, Janet worked at XL Capital, serving in roles of increasing responsibility including executive vice president and chief finance officer of XL Insurance Europe and Asia. She also worked with PricewaterhouseCoopers LLP (consulting and audit support), and served in various actuarial roles with Aetna Life & Casualty where she began her insurance career. Janet has a bachelor's degree in Math/Actuarial Science from the University of Connecticut. She has served on many actuarial committees including the CAS Committee on Professionalism Education, the CAS Committee on Reserves, the AAA IFRS Task Force, the AAA Opinion Seminar Committee, and the SOA Strategic Planning Task Force. She is now working as a lecturer at the Department of Applied Probability and Statistics at UC, Santa Barbara.

Dr. Junyuan Joanne Lin from Loyola Marymount University

Abstract

This research aims to predict proteins' functions from protein-protein interaction (PPI) networks. The PPI networks we study include physical and genetic interactions between labeled and unlabeled proteins. This allows us to predict proteins' unknown functions based on the function labels of closely interacted "neighbors". In this presentation, I will present our award-winning graph-based algorithms that achieve the best prediction accuracy worldwide in the 2016 Disease Module Identification DREAM Data Mining Challenge. We define the diffusion state distance (DSD) metric, which sets appropriate distances to measure proteins' proximity on PPI networks as well as many other close-knit networks including social and energy networks. Fast algorithms, such as the unsmoothed aggregation algebraic multigrid method with random projections, are adopted to compute the DSD efficiently. Based on random walks combining with random projections, we propose graph-based methods to construct k-nearest-neighbor (kNN) graphs under the DSD metric for function prediction. We test our proposed algorithms on different networks to demonstrate that the computational cost of the algorithms is nearly optimal.