Reflections from LatinX in AI (LXAI) at ICLR 2021
The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics, and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics. Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers to entrepreneurs and engineers, to graduate students and postdocs.
LatinX in AI (LXAI) was excited to host our third official social at ICLR! LatinX in AI (LXAI) socials are aimed at LatinX individuals working on or interested in AI with a goal to increase the visibility of researchers of LatinX origin. Those already working in AI have the opportunity to connect with fellow LatinX members and make their own work known, while those new to the field benefit from the scientific exchange, guidance, and advice of researchers with the same background. Participants are able to engage in discussions about AI (formal and informal) and to share their thoughts on how to increase the presence of LatinX in AI.
During this social, we featured invited talks from prominent LatinX in AI community members and hosted roundtable discussions facilitating conversations among attendees.
On Day 1 of our social, our keynote speakers included…
Soledad Villar, Assistant Professor, Johns Hopkins University. Department of Applied Mathematics and Statistics. Mathematical Institute for Data Science.
Topic: Perspectives on Graph Neural Networks
Soledad Villar is an Assistant Professor in Applied Mathematics and Statistics at Johns Hopkins University. Her research focuses on mathematical data science. In particular, she is interested in optimization algorithms arising from data applications and machine learning. She received a PhD in Mathematics from the University of Texas at Austin in 2017 and after that she was a postdoc at the Simons Institute in UC Berkeley, and the Center for Data Science at NYU. Her research is sponsored by NSF, The Simons Foundation, and EOARD. Soledad is originally from Uruguay.
Alvaro Soto, Associate Professor, Catholic University Chile, Head of the Artificial Intelligence Lab
Topic: Beyond Memory, Coaching Our AI Models
Alvaro Soto received his Ph.D. in Computer Science from Carnegie Mellon University in 2002, with a specialization in cognitive robotics. Afterward, he joined the Catholic University in Chile (UC) where he is currently an Associate Professor. At UC, he leads the Artificial Intelligence Lab (ialab), a leading research group in Latin America with more than 50 members. Also, he is one of the co-founders of Zippedi, an AI-based company dedicated to creating robots for the retail industry that currently operates in several countries. His main research focuses on studying different aspects behind the creation of cognitive machines.
Day 2 of our social featured keynotes by…
Martina G. Vilas, Neuroscience Ph.D. Student, Department of Neuroscience, Max-Planck-Institute AE, Frankfurt, Germany
Topic: Evaluating the Reproducibility of Deep Learning Research in Cognitive Computational Neuroscience
Martina Vilas is currently working at the Max-Planck-Institute AE, doing cognitive neuroscience research using computational modeling techniques. Her work focuses on understanding how the brain represents abstract knowledge, and how it uses this type of information to make predictions about future events. More broadly, she is interested in the development of computational methods that probe the format and structure of neural representations. Martina is also an advocate of open and reproducible science (particularly computational reproducibility). She is a core contributor and maintainer of The Turing Way open-source project, and a past mentor and current expert in the Open Life Science program.
Pedro Magalhães Braga, Ph.D. Student, Associate Professor, Federal University of Pernambuco
Topic: Towards Learning Interpretable Representations for Perception
Pedro is a Ph.D. student in Computer Science at the Center of Informatics — Federal University of Pernambuco (CIn-UFPE), Brazil. He received MSc and BSc degrees in Computer Science from CIn-UFPE, and the Catholic University of Pernambuco (UNICAP) with University Honors, respectively. In 2020, the Brazilian Computer Society pointed his MSc thesis as one of the best thesis on AI in Brazil. Moreover, he is currently acting as a Fixed-term Professor at CIn-UFPE. Also, he has been organizing the LatinX in AI Research Workshops since 2019. His research interests rely on Unsupervised Learning, Semi-Supervised Learning, and Representation Learning.
During our roundtable discussions, we featured three areas of interest to discuss with our members: Careers, Internships, and Research. Our organizers and senior members of our community were invited to facilitate these discussions with our attendees.
The careers roundtable was facilitated by Javier Turek and had a lot of attention from the attendees. This table started with a presentation of Javier’s experience, who also provided important tips to grow professionally. One great tip he shared was “to be one step ahead of the next step in our career”, being aware of where you want to be next. After that, the participants introduced themselves and raised some of their questions, which were debated among all the participants. One very common question was about the different types of AI jobs in industry and the differences among them. The answers covered positions such as machine learning engineer, data scientist, applied machine learning scientist, and research scientist among others. A follow-up question touched on strategies to landing one of these jobs in the industry.
Everyone agreed that a well-written resume is a good starting point. However, getting to an interview stage is challenging, in particular for those residing in Latin America. Some advice included: reaching out to friends and colleagues, professional social networks (i.e., LinkedIn), and university recruitment events. An interesting observation was made for students seeking full-time jobs: it may take several months, so it is better to start early. This depends on factors such as the interviewing process that may take a month, obtaining work visas, or the relocation time. Topics on entrepreneurship were briefly discussed as well. Lastly, the benefits of having a mentor as well as being a mentee were brought up as an enlightening aspect to always keep in mind while navigating our career paths.
The internship roundtable was facilitated by Dennis Núñez Fernandez and had a shy attendance compared to the other roundtables. Nevertheless, several important links about summer schools, internships, and residency programs were shared. All of these resources were well received by the participants, who will definitely benefit substantially from this internship table.
The research roundtable was facilitated by Sara Garcia, along with Dr. Matias Valdenegro, who is a researcher at the German Research Center for Artificial Intelligence, and Dr. Alexandre de Siqueira, who is a postdoctoral researcher at the Berkeley Institute for Data Science at the University of California Berkeley. They provided advice on doing research in AI as a self-identified Latinx professional. The senior researchers shared their different views on a number of topics around conducting research. First, they touched on how to get started in research as an undergraduate student, which included tips on how to approach a professor or researcher for a collaboration. They also discussed the benefits of doing research as an undergrad. Also, they discussed the framing of research projects as well as methods for keeping up-to-date with the advancements in AI. Many agreed that it was a great starting point for finding an interesting research topic. Suggestions went from signing up for AI news feeds, to searching for keywords in arxiv. Lastly, they addressed the different types of papers and publishing sites to best submit your research paper.
A key takeaway from all the discussions was that LatinX in AI is a great community and offers a platform full of opportunities to find collaborators, access resources, and connect with mentors who can guide you at any step in your career. It’s up to each one of us to connect and get the most out of it.
Research Mentorship Pitch Session
For the first time ever, LatinX in AI hosted a research mentoring pitch session, where members of our community and members of Google Brain pitched research ideas for potential collaborations. Each pitch was 3 minutes long and we had fruitful research discussions afterward. A follow-up form was shared with participants so people could express interest in collaborations. The matching is currently underway and there are already a number of collaborations that are beginning as a consequence of this event! Finally, some of the collaborations with Google Brain mentors may result in Google cloud compute credits assigned for the collaboration.
LatinX in AI wants to share great appreciation to our organizers who met weekly for several months and planned two great sessions for our members at the ICLR conference. Thank you!
Sara Iris Garcia is an adjunct lecturer at Mariano Galvez University in Guatemala and an advocate for increasing the participation of women in stem careers. Sara is a member of the LatinX in AI community since 2019.
Dennis Núñez Fernández is a research assistant at Universidad Peruana Cayetano Heredia (UPCH), Peru. There, he develops Artificial Intelligence algorithms for early and low-cost medical diagnosis. Dennis is member of the LatinX in AI community since 2019.
Javier Turek is a research scientist in machine learning at Intel Labs working on the intersection between ML, NLP, and Neuroscience. Javier has been an active member of the LatinX in AI community since 2019.
Pablo Samuel Castro is a staff research software developer with Google Research, Brain team, working mostly on fundamental reinforcement learning as well as machine learning applied to creativity. He is also an active musician.
Maria Luisa Santiago is the Operations and Logistics Coordinator for Accel.AI and LXAI. She has been with the organization since August of 2020. Maria Luisa or Lois’ Academic Background is in Psychology but recently has been taking short courses on Artificial Intelligence and Data Science.
This year, LatinX in AI is hosting our first official workshop at CVPR known as LatinX in Computer Vision (LXCV) which will be hosted virtually on Saturday, June 19th, 2021.
The LXCV 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 computer vision. While all presenters will identify primarily as LatinX, all are invited to attend.
Learn More: https://www.latinxinai.org/cvpr-2021
Do you identify as Latinx and are working in artificial intelligence or know someone who is Latinx and is working in artificial intelligence?
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