I am a mathematician from Venezuela. I will talk about my trajectory, how I became a mathematician, why I am living now in Brazil. I hope my story motivates young women to study math.

Abstract

The Fourth Industrial Revolution (IR 4.0) represents a new chapter in mathematics, the benefits of digital transformation as well as the challenges of University 4.0.  IR 4.0 involve many elements of problem solving and systemic thinking that can be assisted by computers, implementation of coding, ICT competence skills such as collaboration, digital literacy, critical thinking, and problem-solving ability to solve everyday problems especially in complex mathematical tasks. Developing problem solving skills and higher order thinking skills  represents a fundamental change in the way we live, work and  the ability to collaborate one another. Discussion-based approaches are the development platform of critical thinking skills, problem solving ability to be more creative, fluid, and perceptive to identify the implicit premises. Early thinking around IR 5.0 era provide technology challenges based on the recycle complete automation of problem solving in  analytical capabilities, big data, blockchain, IoT and smartcity. As a conclusion, effective problem-solving requires a combination of creative thinking, sound analytical skills, enable traceability and searchability  for open innovation in problem-solving processes.

Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach 29 a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still difficult to determine the execution order of the constraint-based approach to reducing the total cost. Plan optimization is crucial in composing the order of bilingual dictionaries creation with the consideration of the methods and their costs. We formalize the plan optimiza- tion for creating bilingual dictionaries by utilizing Markov Decision Process (MDP) with the goal to get a more accurate estimation of the most feasible optimal plan with the least total cost before fully implementing the constraint-based bilingual lexicon induction. We model a prior beta distribution of bilingual lexicon in- duction precision with language similarity and polysemy of the topology as α and β parameters. It is further used to model cost function and state transition probability. We estimated the cost of all investment plans as a baseline for evaluating the proposed MDP-based approach with total cost as an evaluation metric. After utilizing the posterior beta distribution in the first batch of experiments to construct the prior beta distribution in the second batch of experiments, the result shows 61.5% of cost reduction compared to the estimated all investment plans and 39.4% of cost reduction compared to the estimated MDP optimal plan. The MDP-based proposal outperformed the baseline on the total cost.

Abstract:

Much has been said about few enrolments of female postgraduate students in mathematics. This scenario has led to a decreased number of female mathematicians, thus reduce the number of women in mathematics for doctoral degree. This, inadvertently contribute to a decline of female professors in mathematics. This phenomenon has also increased the gap between male and female professors in mathematics in developing countries. Thus, this talk aims to shed lights into trials and tribulations of woman mathematics professor. Some challenges and achievements will be presented throughout my career as a female mathematics professor

 

Abstract:

Mathematics is a subject which acts as a gateway to science. According to the famous Philosopher Kant, “A Science is exact only in so far as it employs Mathematics”. In other words, Mathematics has provided the foundations for many of the greatest discoveries and innovations of the last century. Its origins date back to ancient times when algebra and geometry were used for commercial purposes, measuring crops and fields, as well as valuing money. Nowadays, Mathematics provide both the theoretical frameworks and applied tools to address problems in commerce, technology and science as “Mathematics is a Science of all Sciences and art of all arts.” Every area of Mathematics has its own unique applications to the different career options. For example, Algebra is very important for computer science, cryptology, networking, study of symmetry in Chemistry and Physics. Calculus (including differential equations) is used in Chemistry, Biology, Physics, Engineering, the motion of water (hydrodynamics), rocket science, molecular structure, option price modelling in Business and Economics models, etc. Women are encouraged to give the necessary time and thought process to build a future they dream of. The career world is competitive. The competition and the opportunities in the career world become a serious problem for women if they do not do well in Mathematics, because then they are excluding themselves from the many career paths that require Mathematics. Women have to study hard, achieve a level of excellence, and take such a fundamental discipline like Mathematics seriously if they think they may go into any of the Physical, Social, Health Sciences, Business, Medicine, or related areas. Aside from being useful to becoming a High School teacher, a degree in Mathematics is quite useful in diverse job areas. You can find more detailed information in the informative book "101 Careers in Mathematics" , by Andrew Starrett (editor), MAA publication, 1996, this book is a helpful tool  to learn more about career choices in the field of Mathematics.
 
 

As the world hurtles further into the Fourth Industrial Revolution , data is everywhere. Almost every sector collects data to solve problems and gain insight into trends and development. However, raw data alone isn’t enough to work with as unorganized and raw data may clutter the businesses’ digital infrastructure and slow down the system. For this reason, an organisation need a competent data team to glean insights and wring actionable answers out of data for a better data driven decision making. A data team is usually made up of data analyst, data scientist and data engineer. Regardless of any of these roles, a data person requires a strong grounding in mathematics. In this talk, I’m aiming to talk about the different roles in data science field and how math graduates can make a headway in a data science career.

 

My main areas of research are:

 

I am also expert in gender issues in science.  I am Vice-Rector for Gender Policies,  and I am currently member of the Equal Opportunities Committee of the Unione Matematica Italiana and of the Standing Committee of European Women in Mathematics (EWM). I am the coordinator of the Editorial Team of EWM. 

 

I will talk about my work, research and education. Interactions with the attendees are welcome.