Jorge Guerra, '14
Jorge Guerra was born in Maracaibo, Venezuela and moved to St. Petersburg, Florida at the age of twelve. He is pursuing an undergraduate degree in Computer Engineering with a minor in Mathematics. In addition to being a Mcnair Scholar, Jorge is also an iCubed and Excel scholar. He has held various leadership positions in The Society of Hispanic Professional Engineers (SHPE), serving as the organization's president during his sophomore year. He was also a mentor for the Internet Science and Technology Fair (ISFT) program. Jorge has participated in different industrial internships. He has worked at Intel Co. as a Probe Product Engineer as well as a Product Development Engineer. Jorge has also worked for Boeing Co. as a Knowledge Based System Engineer. He conducted research during the summer of 2014 at University of California-Berkeley in the SUPERB-ITS program. Jorge currently works in the Intelligent System Lab (ISL) at the University of Central Florida as an undergraduate researcher. His research interests are in the areas of multi-agent systems and robotics applications.
Ph.D. in Computer Science
A First-Order Open-Universe Partially Observable Markov Decision Process BLOG Model Conducted at the University of California-Berkeley for SUPERB-ITS. Mentor: Siddharth Srivastava, Ph.D., Electrical Engineering and Computer Science, College of Engineering, University of California-Berkeley Abstract: Through the used of an extended Probabilistic Programming Languages (PPL), we were able to come up with a probabilistic model to represent non-trivial descriptions of agent's ability for observation and action using the First Order Open University Partially Observable Markov Decision Process algorithm . This model can be use to motive the question of implementing Open-Universe POMDP decision planning process as a robot's main choice for sequential action decisions. We developed a probabilistic model that guided a robot to accomplish his goal of picking up an specific object. Using a Probabilistic programming languages (PPLs) such as Bayesian Logic (BLOG) provided us with a very useful method of designing and applying sequential planning models that deal with a lot of uncertainty in the world; more specifically such programming language was able to handle open-universe problems – the problems where exists uncertainty about the number of objects in the world and uncertainty about the identities of those objects – very efficiently. Also, implementing the POMDP algorithm, a decision-theoretic planning model for an agent, provided us with the tools needed for the agent to deal with a partially observable environment through stochastic actions. After a model was created, we ran a derived Open University Point-Based Value Iteration (OU PBVI) engine in order to process the model and produce the corresponding sequence of actions.
Summer Research Institution
University of California Berkeley
Computer Engineering | Computer Sciences
Guerra, Jorge, "Jorge Guerra, '14" (2017). McNair Scholars. 64.