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- Salomé Aguilar
- Shrestha Mohanty
- Shakira
Experience
- 2018 - present Ph.D. Candidate - CCC - MIT.
- 2021 Summer - Research Intern - Google Translate Montreal.
- 2017 - 2018 Adjunct Professor - P. Catholic University of Chile (PUC).
- 2016 - 2018 Data Scientist - Falabella.com.
- 2016 - 2017 Research Assistant - IACS, Harvard University.
- 2016 - 2017 M.Sc. Machine Learning - PUC.
- 2012 - 2017 B.S. Engineering and CS - PUC.
Conferences & Events
- 2024 July - Poster @ IC2S2. Philadelphia, USA.
- 2023 Sept - Workshop lecturer @ Ars Electronica, Linz, Austria.
- 2023 April - Speaker @ TEDx Bentley U. Does AI hold our same values?
- 2023 Jan - Keynote Talk & Lecture @ MIT–Chile Research Workshop.
- 2022 May - Full paper (video) @ HumEval, ACL 2022, Dublin, Ireland.
- 2021 Oct - Attendee, CSCW MOSafely: Building An Open-Source HCAI Community To Make The Internet A Safer Place For Youth.
- 2020 Nov - Extended abstract (paper) @ CODI 2020, EMNLP.
- 2020 Aug - Panelist, Mitigating Bias in Analytics @ WIA and Tableau.
- 2020 July - Full paper accepted (video) @ NUSE ACL.
- 2020 July - Leader WiML breakout session @ ICML.
- 2020 July - Poster and video presentation @ WiML at ICML.
- 2019 Dec - Poster @ WiML at NeurIPS, Vancouver, Canada
- 2019 Nov - Paper @ ACM CSCW, Austin, Texas, USA
- 2019 Sept - Full paper @ ACII, Cambridge, United Kingdom
- 2019 May - Full paper oral presentation @ SIAM SDM, Calgary, Canada
- 2018 - Panelist @ WIDS, Santiago, Chile
- 2017 - Speaker, Graduation Speech @ DCC UC, Santiago, Chile
News and service
- 2023 - Awardee, MIT Women of Excellence Award
- 2023 - General Chair @ MIT–Chile Research Workshops
- 2023 - Program Committee FAcct2023
- 2022 - Board @ Women in Machine Learning (WiML)
- 2022 - Volunteer @ MIT Path of Professorship
- 2022 - Program Committee @ Data-driven Humanitarian Mapping, KDD
- 2021-2023 MIT Teaching Development Fellow
- 2022 Winter - Teaching Workshop Facilitator at MIT
- 2020 - Logistics Co-Chair @ WiML at NeurIPS (Core responsibilities: Gather.town design and implementation & Funding Program)
- 2020-21 - Reviewer for CHI21, CSCW21
- 2020 Fall - TA for Understanding Public Thought, MIT & UW-Madison
- 2020 - Super-volunteer @ WiML at ICML
- 2020 - Reviewer for the ICML Deploy and Monitor ML workshop
- 2020-21 - Hall Council Chair, MIT Sidney Pacific Grad Community
- 2019 - Reviewer for the 14th NeurIPS WiML workshop
- 2019 - Travel Grants, MIT Media Lab (3 x $2,000) and WiML (1 x $550)
- 2019 Fall - Grad TA for machine learning class at EECS, MIT
Main Printed Media
- 2024 - Featured in exhibition. Museum für Kommunikation, Berlin, Germany.
- 2023 - Diario Financiero, DF MAS, Chile
- 2022 - El Mercurio main section, Chile, 2022 (link, pdf)
- 2020 - El Mercurio cover and innovation section, Chile, 2020 (pdf, La Segunda
- 2019 - MIT Media Lab en español, La Razón, España
- 2019 - Emociones en Twitter, El País, España
Other invited lectures & presentations
- 2024 May – Guest Lecture, LLMs and us, KAIST G-SCHOOL @ MIT, hosted by Prof. Hyeon Yeo.
- 2023 October – Guest lecture, LLMs, responsible sentiment analysis, and content moderation in social media @ Advances in Affective Computing, MIT, hosted by Prof. Roz Picard.
- 2023 October – Guest presentation, Thesis @ Intro to MAS at MIT, research highlight.
- 2023 May – Lecture and Workshop, Intro to ML & LLMs, MIT GSL and MIT MISTI.
- 2023 March – Invited Research Presentation, Community-aligned Content Moderation with Rationale Generation @ Generative AI for Constructive Communication, MIT.
- 2022 Oct – Invited Lecture, Responsible sentiment analysis, affect manipulation and social media @ Advances in Affective Computing, MIT, hosted by Prof. Roz Picard.
- 2022 August – Talk, ML in Industry @ Agrosuper, Chile.
- 2022 June – Lecture and Workshop, Intro to ML & ML in Industry, MIT GSL and MIT MISTI.
- 2022 April – Lecture, Expectation Maximization and Mixture of Gaussians, Data Mining @ PUC CS.
- 2021, 2019 Spring – Guest presentation @ Affective Computing class by Prof. Roz Picard, MIT
- 2020 Oct – Talk, Innovation and Entrepreneurship @ U. La Sabana, Colombia
- 2020 Oct – Talk @ Social Computing & Visualization Group, PUC
- 2020 Oct – Research @ PUC CS/DCC, PhD Seminar
- 2020 Aug – Talk, Data Science in Industry @ U. de Concepción, Chile
- 2019 – Various research presentations @ PUC Chile
- 2019 - AI Demos, Panasonic Corporation x 3 @ MIT Media Lab
- Ars Electronica Large Language Models (LLMs) and Us
Led by CCC PhD Candidate Belén Saldías,Research Scientist Jad Kabbara, and Professor Deb RoyThis workshop gave participants the opportunity to explore and challenge the capabilities of Large Language Models (LLMs). Addressing questions such as: What can LLMs do and how can they mislead us? How would you probe the AI’s values to see if its values sync with yours? ars.electronica.art | ccc/pressmedia | JKU
TEDx
2023 – As unbiased as they may seem, AI chatbots can harbor outdated values. In her TEDxBentleyU talk, Belén Saldías explores this issue and reveals what's happening behind the scenes of the AI technology we see so much of today. In her talk, Belén will walk us through some stunning AI capabilities and show the impact of hidden challenges.
Acknowledgements and Awards
Acknowledgment for High Teaching Quality
2018 Data Mining course, 100% satisfaction & 100% recommendation level, supported by students. Engineering School of the Pontifical Catholic University of Chile.
Best Computer Science Thesis Award
2017 Awarded by the Engineering School of the Pontifical Catholic University of Chile.
Master Degree Excellence Scholarship
2016-2017 Engineering School of the Pontifical Catholic University of Chile gave me a scholarship for pursuing my entire master research.
Acknowledgment for Great Quality of Support to Teaching
2015 Acknowledgment for Great Quality of Support to Teaching (Teacher Assistant), 2015. Courses: Advanced Programming and Stochastic Models. Pontifical Catholic University of Chile.
Honor Enrollment - University Admission 2012
2012 Honor enrollment - Pontifical Catholic University of Chile.
National Math top score - University Admission 2012
2012 National Math top score - University Admission 2012. Ministry of Education of Chile
TA @ PONTIFICAL CATHOLIC UNIVERSITY OF CHILE
Advanced Programming
Approximate Bayesian Inference
Approximate Bayesian Inference
- Teacher’s Chief Assistant (2013-2015)
- Course Coordinator (2015-2016)
- C# (2013-2014)
- Python (2015-2016)
It was a flip classroom environment. I was in charge of creating evaluations and weekly practical activities for more than a hundred students.
In addition, I oversaw a 25-member team. Together we gave feedback and graded students' assestments.
Finally, I supported this class' professors on developing and publishing the book "Advanced Computer Programming in Python", which includes the developed material.
I also served as TA for some introductory courses related to computer programming.
Approximate Bayesian Inference
Approximate Bayesian Inference
Approximate Bayesian Inference
2017 - TA
We studied machine learning from a probabilistic perspective.
Bayesian learning: the beta-binomial and the Dirichlet-multinomial model.
Monte Carlo Inference: I also learned and developed customized implementations of rejection sampling, importance sampling.
MCMC inference: we solved problems implementing Gibbs sampling, metropolis hastings, annealing methods.
Variational inference: KL divergence, the mean field method, and Variational Bayes EM.
Data Structures and Algorithms
Approximate Bayesian Inference
Business Intelligence Technologies
2015 - 2016 - TA
This course teaches the fundamental data structures and their main algorithms. We evaluated complexity on memory and time. We also studied the main techniques for solving discrete optimizations.
We implemented and evaluated: linked lists, queues, heaps, hash tables, trees (binary, red-black, B), graphs (BFS, DFS), dictionaries, prioritized queues, disjoint sets, greedy algorithms (Dijkstra, minimum cost coverage), divide to conquer, dynamic programming, and sorting algorithms.
Business Intelligence Technologies
Business Intelligence Technologies
Business Intelligence Technologies
2017 - TA
This course teaches widely used tools and applications for automatic analysis and data mining processes. Starting by presenting strategies for building warehouse systems, the lessons go through applications for visualizing and querying data by non-experts.
This subject gave me a big picture of every piece of software and model I developed during my time as a data scientist. Now I know how they fit together.
Stochastic Models
Business Intelligence Technologies
Stochastic Models
2015 - 2016 - Chief TA
The course introduces the basis of stochastic modeling systems. This course presents basic techniques and concepts under the most widely used analytics models in operations research for representing probabilistic systems.
We studied Poisson processes, discrete-time Markov chains, and continuous-time Markov chains. We also learned process simulation.
Data Mining
Business Intelligence Technologies
Stochastic Models
2016 - Chief TA
4th- and 6th-year university students. Analysis and implementation of basic techniques and algorithms in data mining. We study data warehouses, ETL, data preprocessing, data visualization, association rules, linear regression, classification algorithms (logistic regression, decision trees, random forest, KNN), clustering methods (k-means, hierarchical, and gaussian mixtures with EM).
Ars Electronica
2023 – Large Language Models (LLMs) and Us – Lecture led by MIT PhD Candidate Belén Saldías.
What can LLMs do and how can they mislead us? How would you probe the AI’s values to see if its values sync with yours?
PhD dissertation @ MIT CCC
Shaping decentralized & community-centered content curation with LLMs
- How can we we take advantage of LLMs to assist with bridging language– and in consequence values–barriers among distinct online communities?
- Can increasing content control through distributed moderation be a pathway to opportunities for bridging communities?
To empirically address these research questions, I've worked in implementing Odessa – Odessa, a DEcentralized Social Systems App!
Dissertation committee: Deb Roy, Jonathan Zittrain, Rosalind Picard
General exams committee: Deb Roy, Sasha Rush, Veronica Barassi
A life style
Basically, I didn't ask for any money; I only said to the manager that I would work for them as hard and smart as I could if they gave me a meet&greet with Shakira when she came to Chile. I started these conversations around March 2016, and Shakira went to Chile in Dec 2016, and the deal worked out - I might as well write the (very) long version of the story later.
I'd like to share with you ...
Belén Saldías – belen [at] mit [dot] edu
Incoming Assistant Teaching Professor at the Information School at the University of Washington, starting in Summer 2025.
Recently received my Ph.D. from MIT, working at the MIT's Center for Constructive Communication.
In the next few weeks catch me in person: Oxford Internet Institute, MIT Media Lab, and UW iSchool.
Stay tuned or reach out for more updates!
Autum 2025
IMT 598 Generative AI Ethics (4)
INFO 474 Interactive Visualization (5) A&H, RSN
INFO 474 Interactive Visualization (5) A&H, RSN
This special topic course will delve into the opportunities and challenges associated with generative AI systems. Generative AI systems have been rapidly proliferating and are widely used by laypeople, students, researchers, and developers. While they offer practical human-AI interaction settings, their impact on society remains poorly understood. We will explore how to responsibly use, develop, and deploy generative AI systems while also raising awareness about the numerous open questions that are critically important to the sciences and society.
Special topics for iSchool undergrad major and minor, MSIM, MLIS, and PhD.
Course website: TBA
INFO 474 Interactive Visualization (5) A&H, RSN
INFO 474 Interactive Visualization (5) A&H, RSN
INFO 474 Interactive Visualization (5) A&H, RSN
Introduces techniques and theory for designing, developing, and evaluating interactive visualizations of structured data such as numbers, text, and relationships. Emphasizes a human-centered approach grounded in cognitive science, perceptual psychology, and visual analytics. Students gain practical experience building web-based visualizations, with attention to usability, accessibility, and empirical evaluation. Topics include visual perception, visual encoding, prototyping, and interaction design.
Prerequisite: INFO 340 or CSE 154; CSE 123, CSE 143, or CSE 163; and either QMETH 201, Q SCI 381, STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 290, STAT 311, or STAT 390.
Course website: TBA
Winter 2025
INFO 330 Databases and Data Modeling (5) RSN
INFO 330 Databases and Data Modeling (5) RSN
INFO 330 Databases and Data Modeling (5) RSN
Introduction to database systems, focused on relational models, languages, and systems and the application of conceptual, logical, and physical database design. Key topics include the relational model, SQL, entity-relationship modeling, three-tier architectures, implementation of database applications, and non-relational databases. Links data modeling decisions to social justice outcomes.
Course website: TBA
INFO 330 Databases and Data Modeling (5) RSN
INFO 330 Databases and Data Modeling (5) RSN
Spring 2026
INFO 354 Data Justice (4) SSc, DIV
IMT 561 Data Visualization: Design and Dev (4)
IMT 561 Data Visualization: Design and Dev (4)
Introduction to data justice. Addresses historical and contemporary problems of injustice and inequality linked to data, statistics, and computation. Situates topics within broader theories of justice, in particular gender, racial, and socioeconomic justice. Covers core issues, including: data discrimination and violence; statistics, norms, and governmentality; surveillance and data capitalism.
Course website: TBA
IMT 561 Data Visualization: Design and Dev (4)
IMT 561 Data Visualization: Design and Dev (4)
IMT 561 Data Visualization: Design and Dev (4)
Introduction to human-centered design, development, and evaluation of data visualizations. Coursework focuses on techniques for designing and coding visualizations of structured data, including evaluating usability and accessibility. Topics include principles of visual perception; fundamentals of visual analytics; technical dimensions of visual encoding; prototyping; and usability testing.
Course website: TBA
Want me to learn about your work?
Share your work OR COURSES with my students
I realize there is so much I'm unaware of, and that knowledge advances faster than I could ever keep up with. I'm constantly searching for related content, but I'm sure I'm missing out on some amazing reports, findings, reflections, and visualizations that we could learn from.
If you have published work, taught courses, or deployed projects related to any of the courses I'm teaching, please share it with me if you'd want me to learn from you and share your knowledge with my classes. Should that happen, I'll make sure to cite you properly and let you know that my students are enjoying learning from you!
Nothing is too small or too big.
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