Iris Seaman Email and Phone Number

Some of my recent projects: • Designed and developed a scalable Image Copyright Infringement identifier program across Walmart and Amazon listings using computer vision algorithms such as SIFT and hash imaging.• Designed and developed in-house stealth software called Nighthawk which protects hundreds of our customized marketplace accounts. It allows employees stealth access to each account (such as Amazon) via personalized proxy IP-Address, cookies, and browser histories.• Designed and developed AI bots that bypassing bot-detection barriers and auto-create Gmail, Amazon, and Walmart accounts• Designed and created an in-house tool for auto proxy connection monitoring.• Created a chat program using OpenAI for gmail to gmail account routine chatting which minimizes bot detection and gmail account deactivations.Working for Gray Falkon, LLC, means I wear many hats. I am my own project manager, software engineer, and QA. I have independently worked on projects dealing with internet security, computer vision, machine learning, and artificial intelligence. Most of my machine learning skills have been developed and practiced in academia, but my goal is to develop the skills to build end-to-end scalable models that will positively impact the company, and I hope to accomplish that one day.

Became a full-time (+ grave-yard shift, + overtime) Domestic Engineer, AKA, mom! I had my first girl April 2020, and then identical twin girls December 2021. Absolutely beautiful!

Some of Projects/Accomplishments:• Designed and implemented generative models for high-uncertainty scenarios with adversarial agents using Rapidly-exploring random trees.• Published mathematical Nested-Importance Sampling algorithm to express theory of mind models•Designed, implemented, and ran experiments on probabilistic generative models to produce collaborative and adversarial agent behavior data visualized by Pygame to determine if such behavior could be easily identifiable by humans•Trained theory of mind (ToM) agents using reinforcement learning and featurized data to begin training ToM deep learning models.•ICML 2019 Workshop Papers: Generative Modeling and Model-Based Reasoning for Robotics and AI (2 min Spotlight) https://arxiv.org/abs/1812.01569 & Imitation, Intent, and Interaction (oral presentation) https://slideslive.com/38917635/nested-reasoning-about-autonomous-agents-using-probabilistic-programsPhD Student (for 2 years), worked with Jan-Willem Van de Meent. Interests centered around Probabilsic Programming, Nested Inference, Theory of Mind, Cognitive Modeling, and Inferring Intent.Ever since I was introduced to the power of probabilistic programming, I have wanted to use these tools to design autonomous decision-making models which agents can use to reason about the mental states of other agents, including those agents’ beliefs, desires and goals. These capabilities will improve human lives by allowing autonomous agents to interact with and aid humans, infer their needs from limited observations, and reason in conditions of high uncertainty. Most importantly, when applied to high risk situations such as search and rescue, this technology could potentially save lives. To this end I have focused my research interests in the intersection of computer science, Bayesian statistics, and social cognition for artificial intelligence.

Projects & Accomplishments• Designed and implemented generative probabilistic models that used complex primitives (path planners & saliency maps) for decision making• Designed a flexible lightweight embedded probabilistic programming language capable of performing nested inference using MCMC methods• Thesis: Probabilistic Programming for Theory of Mind for Autonomous Decision Making http://scholarsarchive.byu.edu/etd/6826Recognitions:• Presented our research using probabilistic programming and variational inference at a C-UAS (Center for Unmanned Aerial Systems) conference at Georgia Tech. • Selected as Brigham Young University’s representative for the annual CRA-W (Computing Research Association – Women) conference to present my research in Washington D.C. • Invited by Noah Goodman to present my work to the Stanford Computation and Cognition Lab. • Nominated and featured in BYU’s Frontier Magazine as one of the college’s top students currently researching significant areas in STEM.My research centered on giving autonomous agents the ability to simulate theory of mind to reason about decision making, and is founded in two important concepts. First, there is a natural Bayesian formulation to reasoning about the uncertainty inherent in our estimate of another agent’s mental state, and second, probabilistic programming is a natural way to describe models that involve one agent reasoning about another agent.

Projects & Accomplishments:• Project Lead of a goal inference project. Having set up a simple environment consisting of a table and a number of objects laying on it, our objective was to use Gen to simulate models and conduct inference to determine which object agents were reaching for in their early stages of movement. https://github.com/probcomp/Gen.jl• Independently developed WebGL tools to view 3D traces of the generative models while running inference over them. • Designed robust generative models that described realistic reaching data gathered from a Kinect. By applying nested SMC and MCMC methods, I provided an improved, efficient, and probabilistic approach to infer likely goals for agents in noisy realistic scenarios.I worked under Josh Tenenbaum and Vikash Mansinghka as a visiting research assistant in the Brain and Cognitive Science Department at MIT. There, I was critical in the development, testing, and application of a new featherweight embedded probabilistic programming language and compositional inference programming library known as Gen. I helped develop the first goal inference notebook and furthered experiments that demonstrated the efficiency of writing custom proposals as probabilistic programs.This work was essential to my growth and understanding of Bayesian inference and probabilistic programming languages. At Dr. Vikash Mansinghka’s request, I had the privilege of attending the 2017 O’Reilly Artificial Intelligence Conference in New York to assist in tutorial sections of his workshops.

• Developed information retrieval algorithms for video recommendations, specifically religious YouTube clips• Worked with neural networks, LDA models, collaborative and content filtering algorithms, and sentiment analysis to help recommend more relevant videos to users. Applied similar work to movie recommendations, and later to table-top games.• Proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'17), pp. 396-403, Boston, Massachusetts, November 6-8, 2017

• Implemented one of their first iterations of machine learning algorithms on text to free-response questions in our student applications which motivated the company to pursue machine learning approaches for other applications• Collaborated with a large team to develop specialized literacy learning games using Unity• As part of the pipeline of content creation, I was one of the first developers to create prototypes for the design team and collaborated with them to improve the user experience• Contributed to REST APIs for reporting

Software Engineer Internship with the legal department, under LAWS team.• Implemented Features in a Web Application called LAWS in Java • Created Queries with SQL in Oracle• Worked with JQuery, HTML, and Javascript to Implement Features• Created Junit Tests for Every New Feature• Created Automated Test Cases with SAHI• Presented New Features to Software Directors

• Collected twitter feeds for months at a time, and mined for patterns on drug use, abuse, and misuse• Mapped areas in the United States of popular drug use and transaction and explored different drug behaviors along the east coast due to concentration of drug-related tweets • Seaman, I. and Giraud-Carrier, C. (2016). Prevalence and Attitudes About Illicit and Prescription Drugs on Twitter. In Proceedings of the IEEE International Conference on Healthcare Informatics, 14-17.

• Lead a Team of 20 Teaching Assistants • Created Super TA, a web application for Teaching Assistants • Conduct Weekly Meetings• Write and Test Programming Midterms and Finals• Control Unique Student Situations

• Conducted 50 Minute Help Sessions of Programming Programs • Assisted in Debugging Code• Guided in Algorithm Creation• Graded Programming Midterms and Finals• Graded Programming Assignments