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Maira A. de C. Gatti, PhD

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MairaMarac.jpg

Dr. Maira A. de C. Gatti is currently a GenAI Tech Lead at YDUQS, Brazil's largest university holding company. As a former Research Staff Member, she co-founded IBM Research Brazil, the Brazilian Lab of IBM T.J. Watson Jr. Research Center. She successfully technically led and coordinated significant R&D projects in the Artificial Intelligence area for over 15 years. She pioneered the establishment of the Conversational Systems area in Brazil before ChatGPT. Many of the cloud-based AI solutions she led resulted in international and national publications in top AI conferences like AAAI and IJCAI, patents, and business outcomes.

She holds a Ph.D. (2009) and M.Sc. (2006) in Computer Science from PUC-Rio, and a Bachelor's degree in Computer Science from UERJ. Her doctoral thesis, titled Engineering of Self-Organizing Emergent Multi-Agent Systems: A Design Method and Architecture, presents an innovative design method implemented using Monte-Carlo Simulation, a Planner, and Reinforcement Learning. Her master's work, Dependability of Law-Governed Open Multi-Agent Systems, developed a novel method to ensure availability and security in open systems.

Maira has over 70 published scientific papers and 30 US Patents in AI.

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CarEer path
2000-2004
2001-2004
2005-2006
2006-2009
2005-2011
2011-2022
2022- 
2023- 
Bachelor's Degree in Informatics
Institute of Mathematics and Statistics, UERJ
Software Engineer
@Internet Ventures
MSc in Informatics
PUC-Rio, in collaboration with Un. Pierre et Marie Curié
PhD in Informatics
PUC-Rio, University of Waterloo, King's College London
AI Software Engineer and Professor
PUC-Rio, Software Engineering Lab (LES), Computer Graphics Lab (Tecgraph)
Research Staff Member. AI and Cloud.
IBM Research Brazil
Post-Graduated Program of Informatics
Collaborator Professor, Federal University of State of RJ
Gen AI Tech Lead
Yduqs, Rio de Janeiro
AGENDA
MAPA
PALESTRANTES
Patent PORTFOLIO
Patent US20220028366A1
Embodied Negotiation Agent and Platform
Patent US10070050B2
Device, system and method for cognitive image capture
Patent US9485319B1
Simulation to find user behavior impact in social media network
Patent US20170154111A1
Managing item life-cycle at home with internet of things
Patent US20130086462
Method and System for Retrieving Legal Data for User Interface Form Generation by Merging Syntactic and Semantic Constraints
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Publications
April 2020
July 2019
Feb 2018
HUMAINE: Human Multi-Agent Immersive Negotiation Competition
2020 CHI Conference on Human Factors in Computing Systems, Tokyo, Japan.
Embodied Conversational AI Agents in a Multi-modal Multi-agent Competitive Dialogue
In the Proc. of the 28th International Joint Conference on Artificial, IJCAI 2019, Macau, China.
Specifying and Implementing Multi-Party Conversation Rules with Finite-State-Automata
32nd AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, USA.
 2017-2018
Ravel: a MAS Orchestration Platform for Human-Chatbots Conversations
Engineering Multi-Agent Systems (AAMAS 2018), Stockolm, Sweden. Integrated to IBM Watson Development Cloud.
 2016-2017
Tutorial: Programming Conversational Agents with Akka Actors
The 18th European Agent Systems Summer School 2016

In this tutorial I present how conversational agents can be implemented as Akka Actors using a mixed-initiative dialog strategy (Gatti de Bayser et. al, 2017), i.e., by combining both the system- and user-initiative in a question and answer scenario. Akka is a toolkit and runtime that implements the Actor Model on the JVM and was inspired by Erlang in many ways. Akka can be used to build highly concurrent, distributed, and resilient message-driven applications. A large ecosystem of more than 250 Github projects has grown on Akka, in programming languages like Scala, Java, Groovy, and JRuby. The tutorial covers topics from agent pro-activeness, natural language processing, fault-tolerance, and finally an exemplar integration with a mobile app to demonstrate the Cognitive Investment Advisor Application, including the system and code.

Gatti de Bayser et. al, 2017: A Hybrid Architecture for Multi-Party Conversational Systems.
A scalable architecture for real-time analysis of microblogging data. IBM Journal of Research and Development (Print).
IBM Journal of Research and Development (Print)
Jul 2015
Handling Big Data on Agent-based Modeling of Online Social Networks with Mapreduce.
Winter Simulation Conference, Washington, USA.
Dec 2014
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts.
Int. Conf. on Computational Linguistics, COLING, Dublin, Ireland.
Aug 2014
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Dec 2013
A Simulation-based Approach to Analyze the Information Diffusion in Microblogging Online Social Network.
Winter Simulation Conference, Berlin, Germany
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© 2022           Maira A. de C. Gatti, PhD                   Rio de Janeiro, RJ, Brazil

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