In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with existing models. To what extent the metrics with which such improvements were measured were accurately capturing the intended goal, whether the numerical differences in the resulting values were significant, or whether uncertainty played a role in this study and if it should have been taken into account, was of secondary importance. Whereas probability theory, be it frequentist or Bayesian, used to be the gold standard in science before the advent of the supercomputer, it was quickly replaced in favor of black box models and sheer computing power because of their ability to handle large data sets. This evolution sadly happened at the expense of interpretability and trustworthiness. However, while people are still trying to improve the predictive power of their models, the community is starting to realize that for many applications it is not so much the exact prediction that is of importance, but rather the variability or uncertainty. The work in this dissertation tries to further the quest for a world where everyone is aware of uncertainty, of how important it is and how to embrace it instead of fearing it. A specific, though general, framework that allows anyone to obtain accurate uncertainty estimates is singled out and analysed. Certain aspects and applications of the framework -- dubbed `conformal prediction' -- are studied in detail. Whereas many approaches to uncertainty quantification make strong assumptions about the data, conformal prediction is, at the time of writing, the only framework that deserves the title `distribution-free'. No parametric assumptions have to be made and the nonparametric results also hold without having to resort to the law of large numbers in the asymptotic regime.

The author has recently introduced abstract algebraic frameworks of analogical proportions and similarity within the general setting of universal algebra. The purpose of this paper is to build a bridge from similarity to analogical proportions by formulating the latter in terms of the former. The benefit of this similarity-based approach is that the connection between proportions and similarity is built into the framework and therefore evident which is appealing since proportions and similarity are both at the center of analogy; moreover, future results on similarity can directly be applied to analogical proportions.

Recently, I rediscovered my passion for mathematics and artificial intelligence, which I used to hate while I was getting my degree in Computer Sciences. Lately, I've been focused on software design, automated testing, microservices, and functional programming. I also love learning new programming languages, so I've been wondering whether to go deeper into Golang, Scala, or Python. But in the end, all of them are tools -- tools for building what kind of things? In my case, my day-to-day job consists in building and maintaining microservices, as a full-stack developer.

Ontario is increasing support for students in the science, technology, engineering and mathematics (STEM) disciplines, including artificial intelligence, to continue to build a highly skilled workforce and support job creation and economic growth. Leading businesses from around the world choose Ontario because of its talented workforce, strong public education system and commitment to universal health care. These same qualities help to support an ecosystem that enables locally owned companies to succeed and grow. To bolster provincial competitiveness, the government plans to increase the number of postsecondary students graduating in the STEM disciplines by 25 per cent over the next five years. This initiative will boost the number of STEM graduates from 40,000 to 50,000 per year and position Ontario as the number one producer of postsecondary STEM graduates per capita in North America.

Her contributions in this endeavor include serving as PI of the SAIC Evaluation and Knowledge Infrastructure Team for the DARPA Machine Reading Program, which focused on developing methods to extract formal knowledge from free text; and PI of TAILCM, an IARPA sponsored seedling that has investigated the feasibility of translating regulatory text written in natural language to rules in formal logic programming languages. Before joining SAIC, Dr. Morgensten was Research Staff Member at the IBM T. J. Watson Research Center in Hawthorne, NY where she developed leading technologies in the areas of decision support, knowledge management, customer relationship management, business rules, and the semantic web. Dr. Morgenstern has served as technical lead for developing and deploying applications for Fortune-500 companies in a diversity of industries, including medical insurance, banking, insurance, telephony, software sales, and business continuity. Her patents have won several IBM awards due to their value to industry. Dr. Morgenstern has been on the editorial boards of Journal of Artificial Intelligence Research, Annals of Mathematics and Artificial Intelligence, and Electronic Transactions of Artificial Intelligence.

Negation as failure and incomplete information in logic programs have been studied by many researchers, mainly because of their role in the foundations of declarative reading of logic programming. This paper gives a review of some of the definitions of the concepts related to of the declarative reading of logic programming. Then, the paper provides a framework to overcome misleads and to solve a misleading case study. The paper begins with reviewing the relevant work of contributions to logic programming emphasizing many concepts such as negation as failure, closed world assumption, incomplete information, and their consequences (Section 2). Then we comment on the standard definitions of the relevant logic programming concepts such as: compound terms, substitution, common instance, facts, rules, reduction, variables quantification, unifier, Most General Unifier (MGU), computation, and structured data (Section 3). Then we briefly discuss the semantics of logic programming. A logic program can have many semantics according the point of view. The common semantics are operational, denotational, and declarative (Section 4).