LatinX in AI (LXAI) Research at ICML 2022

This is an official workshop of the LatinX in AI (LXAI) organization at ICML, which will be held in Baltimore Convention Center, Ballroom 3 & 4, Baltimore, Maryland USA July 18, 2022.

REGISTRATION

Call for Participation

The workshop is a one-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, research scientists, and engineers for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in artificial intelligence and machine learning. While all presenters will identify primarily as latinx, all are invited to attend.

If you have any questions feel free to contact the workshop chairs at lxai-icml-chairs-2022@latinxinai.org.

 

Important Dates

 

Submission Instructions

We strongly encourage students, postdocs, and researchers who primarily identify as LatinX in all areas of Machine Learning to submit extended abstracts describing new or work-in-progress research. We welcome submissions in theory, methodology, and applications. Submissions will be peer-reviewed and abstracts which are selected to present will be invited to submit a camera-ready version of their full paper to be included in the Journal of LatinX in AI Research (JLXAIR). 

Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give oral presentations. Authors accepted to present will be offered presentation coaching. 

While the presenting author need not to be the first author of the work, authors are asked to highlight the contribution of LatinX individuals — particularly the presenting author. This information should be included in the end of the extended abstract (after references) as an unnumbered section. Furthermore, although the event focuses primarily on researchers who identify as LatinX, everyone is invited to attend. Authors are also encouraged to sign up to review for LXAI.

Submission Requirements

Submissions will be double-blind peer-reviewed and should be submitted as a PDF file through Microsoft CMT. All submissions must be in English and strictly follow the guidelines provided by the ICML 2022 Paper Writing Best Practices to avoid the risk of being rejected without consideration of their merits. Each extended abstract should have up to four pages (including figures, and tables). 

At the authors' discretion, supplementary material can also be uploaded (as a separate PDF document) to CMT. However, the extended abstract must be self-contained, clearly stating the research problem, motivation, and technical contribution, including experimental results when applicable. Please, keep in mind that supplementary materials are not mandatory and may not be considered by reviewers. In other words, simply delegating central elements of the research to the supplementary material will not be tolerated. 

The second round of submission deadline has been extended until June 3rd, 2022 AoE. Please note that no extensions will be offered for submissions.

Early Submissions

Submission deadline: May 2nd, 2022

Author Notification: May 23rd, 2022

Camera Ready: June 20th, 2022

Regular Submissions

Submission deadline: June 3, 2022

Author Notification: June 20th, 2022

Camera Ready: July 10th, 2022


Call for Volunteers

LatinX in AI is in need of key support for all the activities that LXAI brings to our community. Volunteers are scheduled as needed for specific roles including registration table, timekeeper, social media liaison, MC, or on-call for 2-4 hour time periods on the day of the workshop. We will require volunteers to help in the organization in the days before the workshop in roles such as advertisement design. We will give priority to registration grants for our volunteers, so we strongly recommend that you apply using the link below.

Financial Assistance

LatinX in AI is committed to supporting LatinX & hispanic individuals from all around the world. This year since the workshop and ICML conference will be virtual, we want to ensure that our workshop is accessible to everyone, no matter where they live. As such, we are happy to provide support for registration fees and internet data grants to help those that have financial needs. If you believe you need financial assistance to cover your internet/data expenses or to virtually register to ICML, please make sure to fill out these applications as accurately and truthfully as possible.

Mentoring Program

LatinX in AI is hosting a mentoring program alongside our official workshops. The LatinX in AI Mentoring Program requires mentors and mentees to meet once a month. On the day of the workshop, some mentees will be asked to share their experience and the learnings they obtained from the program.

Mentoring Program Notification: April 26, 2022

Mentoring Program End Date: July 23, 2022

Following the completion of this program, you'll be asked to complete a quick survey about your experience and opportunities for our improvement. Mentors who complete the program and survey are eligible to receive a $25 voucher as a token of appreciation on behalf of LatinX in AI.


WORKSHOP SPONSORS

PLATINUM

GOLD

 
 
 
 
 

SILVER

 
 
 

BRONZE

 
 
 
 
 
 
 
 

 KEYNOTE SPEAKER

 

Maria Jose Escobar is an Electronic Civil Engineer and had her Masters in Electronic Engineering from the Federico Santa María Technical University (USM), Valparaíso, Chile. In addition, she completed a Ph.D. in Signal and Image Processing at the INRIA Sophia Antipolis, France. Between 2004 and 2019, she served as a Faculty Member of the Department of Electronic Engineering of the USM and Principal Investigator of the Advanced Center for Electrical and Electronic Engineering AC3E. Her main research lines are biological vision, computational neuroscience, artificial intelligence, and cognitive robotics. Between December 2019 and March 2022, she has served as Regional Ministerial Secretary for Science, Technology, Knowledge, and Innovation, Macrozona Centro. In March 2022, she returned to Academia as an Associate Professor at the USM

 
 

Moacir Antonelli Ponti is in search of fair and explainable ways to apply machine learning and computer vision to improve people's lives. He is Data Science Expert at Mercado Libre, responsible for the scientific and technological decisions in cooperation with the Data Science team for Fraud Prevention in the marketplace, while he is also Associate Professor at the Universidade de São Paulo (USP), Brazil. He has published more than 50 peer-reviewed papers in journals and conferences, and also holds a CNPq fellowship due to outstanding scientific production since 2018. He served on the program committee and/or as an area chair of several conferences, e.g. BMVC, CVPR, EECV, WACV, SIBGRAPI, and is currently Associate Editor at IEEE Signal Processing Letters. With 11+ years of experience in teaching, was the principal investigator of many research projects with public funding and industry partners. Dr. Ponti received a Google Latin America Research Award in 2017-2018 and co-authored the book "Machine Learning: a practical approach on the statistical learning theory", published by Springer.

 
 

Olga Lucía Quintero Montoya is a Control Engineer from Minas School of National University of Colombia-Medellin, obtained her Ph.D. in Control Engineering Systems from the Institute of Automatica at Universidad Nacional de San Juan en Argentina in 2008. From 2008 to 2011 she was a consultant for oil and gas companies and developed several works on telecommunications markets in Latin America. Also was a Professor at USFQ in Quito.

In 2011 she joined the Mathematical Sciences Department at Universidad EAFIT and currently is the Academic Director of the Mathematical Engineering Ph.D. Program and Mathematical Modeling Research Group at Universidad EAFIT.

She is a Senior Researcher of COLCIENCIAS (Colombian National Science Foundation) and Visiting Professor at the Technical University of Delft TUDelft (The Netherlands) and Cornell University.

She is also Alumni of the International Visitor Leadership Program of the U.S. State Department and serves as a national and international referee of several journals and institutions, including the Colombian Congress.

Her research interests are not only Control Systems but also Bayesian Filtering, Data Assimilation, Multidimensional Signal Analysis, Artificial Intelligence, and Machine Learning.

 
 

Maria De-Arteaga is an Assistant Professor at the Information, Risk and Operation Management (IROM) Department at the University of Texas at Austin, where she is also a core faculty member in the Machine Learning Laboratory and an affiliated faculty of Good Systems. She holds a joint Ph.D. in Machine Learning and Public Policy and an M.Sc. in Machine Learning, both from Carnegie Mellon University, and a. B.Sc. in Mathematics from Universidad Nacional de Colombia. Her research focuses on the risks and opportunities of using machine learning to support experts’ decisions in high-stakes settings, with a particular interest in algorithmic fairness and human-AI collaboration. Her work has been featured by UN Women and Global Pulse, and has received best paper awards at WITS’21, NAACL’19, and Data for Policy’16, and research awards from Google and Microsoft Research.

 
 

ACCEPTED PAPERS

Title Presenting Author Affiliation Presentation Type
An Introduction to Quantum Natural Language Processing and a Study Case Javier Orduz Baylor University Poster
Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations Pablo Rivas Baylor University Poster
AI For Preservation of Cultural Heritage: Classifier Guided Diffusion for Image Inpainting. Applications to Fine Art Lucia Cipolina-Kun University of Bristol Poster
A Model-Based Filter to Improve Local Differential Privacy Juan M. Gutierrez Quantil Poster
A Study on the Predictability of Sample Learning Consistency Alain Raymond-Sáes Pontificia Universidad Católica de Chile Poster
Link Prediction from Heterogeneous Opinion Mining Networks with Multi-Domain Applications Bernardo M. Costa University of São Paulo Poster
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis Guilherme L. Toledo, Ricardo M. Marcacini University of São Paulo Poster
Interpretable Process Mining Opportunities and Challenges Joe Huamani University of São Paulo Poster
 

SPOTLIGHT TALK

CJ Barberan received his Ph.D. at Rice University in April 2022 working with Richard G. Baraniuk. His research work delved into interpretability/explainability with deep learning. He has been awarded the NSF GRFP, DoD NDSEG, GEM, and Ford Foundation Pre-Doctoral Fellowships. At Rice University he was a fellowship coach starting from 2019 until 2021 where he has aided several others in being awarded several fellowships.

Ramesh Doddaiah is a Ph.D. Student in Worcester Polytechnic Institute and Senior Principal Engineer in Dell TechnologiesOver 17 years of experience working in the US and India data storage industry for companies spanning start-ups to Top5 in Cloud Storage. Holding 11 patents with 21 more pending. Significant expertise in designing embedded software for open systems block storage, inline data de-duplication, and data encryption. Specializing in applying DL, ML, and RL in red hot IO Path.

 ORGANIZERS

 

Workshop Chairs

Operations & Logistics - Fanny Nina Paravecino (Microsoft), Juan Miguel Vidal (Quantil)

Presentation - CJ Barberan (RICE University), Ramesh Doddaiah (DELL)

Mentorship - Wayner Jose Barrios Quiroga (Darmouth)

Program Committee - Laura Montoya (LXAI, Accel.AI), Jose Manuel Saavedra Rondo (Universidad de los Andes, Chile)

Public Relations & Website - Javier Orduz (Baylor University, Texas), Maria Luisa Santiago (Accel.AI)

Sponsor & Finance - Abraham Ramos (Accel.AI)

Volunteer - Julio Hurtado (Pontificia Universidad Catolica de Chile)

Visa - Walter Perez Casas (UNICAMP), Andres Marquez (RIVIAN)

 

Program Committee

Arnob Ghosh

Asra Aslam

CJ Barberan

Dennis Núñez-Fernández

Fanny Nina Paravecino

Jesús García Ramírez

Jose Delpiano

Julio Hurtado

Laura Galindez Olascoaga

Mansi Ranjit Mane

Pablo Rivas

Ramesh Doddaiah

Ramya Tekumalla

Rodrigo Bonini

Satheesh Perepu

Sirnam Swetha

Violeta Chang

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