A collection of post-mortems
Table of Contents
We’ve just completed the 2026 Summer Institutes in Computational Social Science. The purpose of the Summer Institutes is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science. The Summer Institutes are for both social scientists (broadly conceived) and data scientists (broadly conceived). This summer we had a mixture of in-person institutes and virtual institutes across the world. In addition to various partner locations run by SICSS alumni, we continue to grow our network of computational social science researchers.
These post-mortems describe, for each site, a) how the Institute was run, b) what each site thinks worked well, and c) what each site will do differently next time. We hope that this will be useful to others organizing similar Summer Institutes or future organizers of SICSS sites. If you are interested in hosting a partner location of SICSS 2027 at your university, company, NGO, or governmental organization, please read our information for potential partner locations.
This page includes post-mortem reports from all locations in order to facilitate comparisons, as well as an overview of key themes and takeaways. As you will see, different sites did things differently, and we think that this kind of customization was an important part of how we were successful.
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Adam Mickiewicz University, Poznan — September 7-18, 2026
Universidad del Rosario, Bogota, Colombia — June 4-12, 2026
FGV Communication, Brazil — July 27 - August 6, 2026
National University of San Martin, Buenos Aires — August 10-21, 2026
Chicago State University — July 13-23, 2026
Boca Raton, FL — June 1-5, 2026
Habib University, Karachi, Pakistan — June 8-19, 2026
SICSS-Habib 2026 was the first edition of the Summer Institute in Computational Social Science held in Pakistan. It ran at Habib University in Karachi from June 8 to June 19, 2026, with 17 participants.
The program centered on the societal impacts of technology in the Global South, and that focus ran through the guest talks and every group project. This sat within a broader ambition for the institute spanning causal inference, digital polarization, and civic discourse. For a first edition in a country with no existing Computational Social Science community, we judged breadth more useful than a single tightly defined theme.
Funding
SICSS-Habib 2026 was funded by Tech for Open Minds (TOM), the SICSS initiative supporting research and training on how digital technologies shape open-mindedness, humility, and polarization. The grant covered the institute in full, and no participant paid a fee.
On the participant side, it paid for travel for the seven funded participants, all meals and refreshments for the two-week period, and some merchandise for the institute consisting of t-shirts, notebooks, and pens. On the program side, it covered travel and accommodation for invited speakers, speaker honoraria, and wages for the student teaching assistants who supported the two weeks.
Organizing team
SICSS-Habib 2026 was organized by Dr. Muhammad Qasim Pasta, Associate Professor of Computer Science at Habib University, and Asad Tariq, a Dean’s Fellow in the same department. Asad handled correspondence with guest lecturers and ran day-to-day operations across the two weeks.
Two student teaching assistants, funded through the grant, supported the program. Their role was primarily logistical, including keeping sessions running and absorbing the practical problems that surface hourly across a two-week institute.
However, the support participants needed the most was technical, and for that, in future editions, we wish to employ a TA dedicated solely to the programming side: working alongside participants during the workshops, debugging code, and resolving the technical problems.
1. Outreach and application process
We advertised through LinkedIn, which produced 59 applications from across Pakistan against roughly 25 seats. Dr. Qasim Pasta also circulated the call directly through his own network, which included professors at other universities, and contacts across departments within Habib. For the next edition we would add a targeted mailing list to reach an even larger number of people.
Of the total 25 seats, half of the seats were reserved for women, a deliberate response to regional gender disparities in computational research that we built into the allocation from the start.
Applications comprised of a CV, a statement of interest, a writing sample, and questions about prior exposure to computational social science. We evaluated each on:
Both organizers reviewed every application jointly. Beyond the formal criteria, what we weighed most heavily was ambition and motivation. We looked for applicants who wanted to apply computational methods to socially relevant problems, and who showed an interest in building a computational social science community in Pakistan. For a first edition in a country with no existing SICSS presence, we cared more about finding people likely to carry the field outward afterwards.
We extended 24 offers and 17 attended, an attrition of roughly 29 percent. Some of the seven stopped responding altogether, and others replied but did not appear. We had no means of recovering those places. For the next edition we will maintain a ranked waitlist so that late declines can be backfilled and also follow up actively with anyone who goes quiet after accepting.
Seven of the 17 received travel funding to reach Karachi. Participants came from Karachi, Hyderabad, Sukkur, Islamabad, Multan, Lahore, Balakot, and Gilgit — a reach from Sindh through Punjab and the federal capital up to Khyber Pakhtunkhwa and Gilgit-Baltistan.
Who attended
The cohort was evenly divided by gender: 9 women and 8 men among the 17, bringing the balance to 9 women and 8 men so the reserved-seat commitment held through to the room itself, not merely to the offers we made.
Institutional spread was wide, and the 16 participants came from 14 distinct institutions. Alongside Habib, participants came from IBA, UIT University, and Bahria University in Karachi; Forman Christian College in Lahore; The Women University Multan; Muhammad Ali Jinnah University; National Defence University and the International Islamic University in Islamabad; Quaid-e-Awam University of Engineering, Science and Technology in Nawabshah; and Government Degree College in Balakot. Two were Pakistani nationals who had studied abroad, at the University of Manchester and at Universitas Diponegoro in Indonesia. One came from outside the university sector altogether, from The Citizens Foundation, an education non-profit. By province of origin, we had 10 participants from Sindh, 2 from Punjab, 2 from Gilgit-Baltistan, 1 from Khyber Pakhtunkhwa, and 1 from Islamabad.
The disciplinary backgrounds of these participants were mixed and frequently interdisciplinary, as depicted by the fields named by the participants in the application form. Data science was the most common (5), followed by computer science, linguistics, sociology, and economics (3 each), communication (2), and single participants from computer engineering. Counted by mentions, social science and humanities fields outnumbered computing fields roughly two to one.
The career stages of the participants ran from recent graduates through faculty, with faculty being the largest single group with 6 of the 16 participants indicating that they held a teaching position, in some cases alongside ongoing graduate study. Two were doctoral candidates or recent PhDs, three were master’s students, and the remainder were recent graduates. None of the 16 had attended a SICSS before.
Prior programming experience
Participants who applied rated their own Python and R ability on a 0–10 scale. The distribution explains a good deal of what followed. The mean self-rating was 4.1 for both languages, but the spread was extreme: 8 of 16 rated their Python at 3 or below, 7 of 16 rated R at 3 or below, and 6 rated themselves at 3 or below in both. At the other end, 3 participants rated themselves 7 or above in both Python and R. Several reported an outright zero as well.
We therefore ran a two-week program of hands-on computational workshops for a cohort in which more than a third were close to beginners in both languages those workshops relied on.
2. Pre-arrival and onboarding
We published the schedule and a pre-arrival page ahead of the institute. The materials asked participants to read Matt Salganik’s Bit by Bit: Social Research in the Digital Age, to work through the online SICSS Boot Camp if they needed beginner-level coding skills, and to install R and RStudio and create a GitHub account.
We ran two communication channels: the official SICSS Discord as the formal one, and an informal WhatsApp group for faster logistical announcements. Participants used WhatsApp considerably more, and future editions could plan on that rather than treat it as an overflow channel.
The gap we would close next time is expectation-setting about the sessions themselves. Our materials prepared participants for R, but the program also required NetLogo and Google Colab, neither of which appeared in the preparation. In the future iterations of the institute, we will circulate fuller guidance on what sessions to expect and which tools each will use.
Accommodation was the clearest gap, and by a wide margin the most common subject of pre-arrival questions. Habib University has no on-campus housing, and participants traveling from outside Karachi had to arrange their own lodging. Several said it was the most stressful part of attending. We intend to pursue this issue on two tracks: requesting an allocation for participant accommodation in our next funding application, and, regardless of whether that succeeds, treating accommodation as ours to coordinate, by circulating a vetted list of nearby guesthouses and hostels with indicative prices and travel times to campus, negotiating a block rate with one or two properties, and connecting out-of-town participants with each other early enough that those who want to share a booking can arrange it.
3. The first week
Each day of the first week followed the same shape, including a hands-on workshop, a guest talk, and an afternoon group exercise, built around a daily theme: introduction and research ethics, text as data, agent-based modeling, social network analysis and digital trace data, and AI and large language models.
Workshops were led by Dr. Qasim Pasta (Habib University) on research ethics and, separately, on network analysis in R; Dr. Muhammad Yaseen Khan (FAST National University of Computer and Emerging Sciences, Karachi) on quantitative text analysis; Zain Ahmed Usmani (Habib University) on agent-based modeling using Python’s Mesa library; Asad Tariq (Habib University) on social simulations using NetLogo; and Dr. Tafseer Ahmed Khan (Mohammad Ali Jinnah University, Karachi) on using large language models in Google Colab. Guest talks came from Dr. Umberto Mignozzetti (University of California, San Diego), whose opening keynote made the case for the field; Dr. Muhammad Bilal (FAST NUCES, Islamabad) on social media data and Society 5.0; Dr. Shah Jamal Alam (Habib University) on simulating social complexity; Dr. Ihsan Ayyub Qazi (Lahore University of Management Sciences) on doctor-AI collaboration in diagnostic reasoning; and Dr. Abdul Samad (Habib University) on AI and LLMs for social science.
The week closed with a Friday afternoon “Bouncing Ideas” session, where most of the research speed-dating took place; it carried over into a dedicated group formation session on the Monday.
The skill spread described above made itself across the hands-on sessions. In practice, the less experienced leaned on the more experienced, asking for help whenever they got stuck, and the organizers were on hand to guide anyone who needed it. This worked, though informally rather than by design, and it depended on the more confident programmers being willing to guide others.
Participants told us that the first week was too dense. Each day had roughly six and a half hours of structured content, with little room to absorb one session before the next. Each day started and ended with a social gathering over tea and refreshments, allowing participants to interact and build networks.
4. The weekend
We held a communal breakfast on the Saturday between the two weeks. It was a small thing, but it gave participants unhurried time with each other and with us outside a classroom setting, and the tone of the second week was noticeably easier for it. We would add more of this next time including a city tour, and further social gatherings including dinners.
5. The second week: group projects
The second week shifted from structured instruction to independent group work.
Dr. Rabeea Jaffari (Mehran University of Engineering and Technology) opened the week with a talk, “To Be Or Not To Be Human,” on the dilemmas raised by anthropomorphic large language models.
Participants grouped together into teams of two to four for their projects that they worked on for the rest of the second week. We deliberately asked them to generate their own research questions rather than offering pre-packaged project ideas or datasets, and every group ended up pursuing a question and a data collection strategy of its own. This took longer to get moving than a pre-supplied brief would have, but the resulting engagement was noticeably higher.
Five teams presented on the final day:
Who Speaks About Education? Mapping the Voices: A computational content analysis of four years of education coverage in Dawn, The Express Tribune, and The News, coding every quoted individual by institutional role. State actors dominated, international donors were quoted as often as teachers, parents were absent entirely, and grassroot voices appeared only during strikes.
Beyond Policy on Paper: Using Large Language Models to Detect Anti-Christian Discrimination in Pakistani Social Media Discourse. LLM-based sentiment and discourse analysis of Facebook content about Pakistan’s Christian minority, mapping discriminatory discourse across employment, housing, blasphemy, and social exclusion. The team set this against the PICA Act and minority quota regulations to locate the gap between protection on paper and practice.
Politics, News, and Generative AI: Analyzing how LLMs contain Systematic Bias in Political Content. A comparison of how DeepSeek, ChatGPT, and Claude handle contested issues, including that of Palestine, Iran, the climate, US tariffs, development, using prompts written to mirror journalists across the left–right spectrum in Pakistan, China, and the United States.
The Digital Visibility Index: A Digital Transparency Audit of Sindh’s Provincial Departments. Every Sindh departmental website scored 0–100 on whether it functions, carries accessible contact information, has been updated within twelve months, publishes RTI-mandated disclosures, and offers any interactive channel. The team produced a ranked index, a dashboard, and a policy brief in four days, using a method scalable to other provinces.
Brokering in the Headlines: How different newspapers like Dawn and the Guardian construct Pakistan’s role as mediator in the recent US–Iran conflict, built by turning thousands of articles into a network of who deals with whom. The measure was whose coverage places Pakistan at the center of the story.
The institute closed with a group photograph of participants and organizers.
6. Post-departure
Every participant received a certificate of completion along with school merchandise. Participants remain on the official SICSS Discord, and several groups have stayed in touch with the organizers to continue their projects further, and the cohort remains in occasional contact with one another.
7. Feedback and evaluation
We gathered feedback verbally from all participants on the final day. Participants suggested that a written form would let them respond at greater length, and we will add one for the next edition.
Responses about organization and logistics were positive overall, except for accommodation for out-of-town participants, discussed above. Participants valued the mix of lectures, hands-on tutorials, and project work, and the range of disciplinary perspectives among the speakers.
Two criticisms came through consistently:
Too little social science. Participants across the cohort wanted more dedicated sessions on social science research methods and theory. The program was not devoid of this as the opening day included a two-and-a-half-hour ethics seminar with a group exercise, and several of the guest talks were framed around social questions, but the workshops, which formed the backbone of each day, were weighted toward computational tooling. Given that social science and humanities fields outnumbered computing ones roughly two to one among the cohort, and that the institute exists precisely at the intersection of the two, this is the most substantive piece of feedback we received.
Group projects started too late. Participants wanted group formation and ideation to begin earlier. For this edition, the process began with Friday afternoon’s “Bouncing Ideas” session on day five and concluded with the dedicated speed-dating session on Monday of week two, day six of ten. Teams therefore had four working days between settling on a question and presenting results which they felt was too little time.
8. Plans for the next edition
The organizing team reviewed the feedback in full. Alongside the changes described in place above including rebalancing the two weeks, adding a dedicated technical TA, and coordinating accommodation, we agreed on the following:
Strengthen the social science content: more talks and workshops led by people from social science backgrounds, and a research design workshop covering the range of research methodologies participants might draw on for their projects.
Consider a pre-institute remote bootcamp: we are still weighing a short online bootcamp in the weeks before the institute, to bring participants to a common baseline before the program formally begins.
Ouagadougou, Burkina Faso — August 10-21, 2026
Istanbul, Turkiye — July 6-18, 2026
University of Johannesburg, South Africa — June 28 - July 10, 2026
Nairobi, Kenya — June 15-26, 2026
Melbourne, Australia — June 22 - July 3, 2026
Shanghai, China — June 24-30, 2026
Rotterdam, the Netherlands — June 8-19, 2026
University of Pennsylvania, Philadelphia — June 15-26, 2026
Rochester, NY — May 11-24, 2026
New Brunswick, NJ — June 1-12, 2026
Online — May 25-29, 2026
Singapore — July 13-20, 2026
Stanford University — August 10-21, 2026
University of California, Los Angeles — June 8-12, 2026
University of the District of Columbia, Washington, D.C. — June 14-28, 2026
University of Florida (CJC) — June 15-19, 2026
University of the Republic (Udelar), Montevideo — TBD, 2026
Seattle, WA — July 6-17, 2026
SICSS-UW was organized around two complementary goals: exposing participants to a broad range of approaches to computational social science and giving them substantial time to apply those approaches through collaborative research. The program brought together participants with varied disciplinary and technical backgrounds and connected them with researchers and practitioners from academia, industry, and public-interest technology. Across the two weeks, participants engaged with topics ranging from computational methods and statistical reasoning to responsible AI, multimodal models, accessibility, and community-centered data science, while working in groups on research projects of their own.
A central design decision for SICSS-UW was to give group work a prominent role in the structure of the Institute. Rather than organizing this post-mortem primarily around Week 1 and Week 2, we have organized it around Speakers and Group Projects, reflecting the emphasis we placed on collaborative research. We introduced group projects at the beginning of the first week and gave participants substantial time to begin working immediately. This was intentional: starting the projects as early as possible gave groups several days to identify questions, divide responsibilities, learn unfamiliar tools, and troubleshoot methodological or technical problems before the second week. The goal was for the second week to build on work that was already underway rather than requiring groups to establish their projects from scratch midway through the Institute.
This structure also shaped the role of the speakers. We viewed lectures as opportunities to expose participants to methods, perspectives, and research practices that they could then draw on in their group work. The resulting program combined lectures, interactive sessions, and extended periods of collaborative research, with organizers providing guidance while allowing groups substantial autonomy over their projects.
Overall, participant feedback suggests that this balance was successful. Participants particularly valued the diversity of speakers, hands-on learning opportunities, interdisciplinary collaboration, and flexibility of the program. At the same time, their feedback points to several ways future iterations could make the collaborative and educational components even more intentional, including more structured project formation, additional hands-on workshops, clearer accessibility guidance, and more opportunities for community-building. The sections below describe the planning and outreach process, speaker program, group projects, and participant feedback, with particular attention to lessons that may be useful for future SICSS-UW organizers.
Recruitment for SICSS-UW was relatively simple. The organizers, Adam Visokay and Tyler McCormick, promoted the Institute through LinkedIn and Bluesky. Despite the limited formal outreach, the program received a strong pool of applications. This may reflect the increasing familiarity of the SICSS program among researchers and students, reducing the amount of promotion necessary to attract applicants.
The University of Washington (UW) was also particularly well positioned to recruit participants and speakers because of its connections to Seattle’s broader AI and social science community. Additionally, the seminar hosted by the Center for Statistics & Social Sciences at UW helped facilitate connections with researchers working in the Seattle area, while UW’s strengths in computational science provided a natural network through which to identify potential speakers and participants. More broadly, hosting SICSS at UW gave the program access to a community spanning academic research, industry, and public-interest technology.
Given the strong application pool despite relatively minimal recruitment, we would not necessarily recommend substantially increasing outreach in future years. Instead, organizers could continue using existing professional networks and SICSS channels while being somewhat more intentional about reaching communities that may be underrepresented in the applicant pool. In particular, the Seattle-area AI and social science community represents a valuable recruitment network that could be leveraged more systematically in future iterations.
The collection of speakers was a definite strength of SICSS-UW. We brought together researchers and practitioners working across academia, industry, and public-interest technology, giving participants exposure to a wide range of methodological approaches, substantive research areas, and perspectives on the development and use of computational methods. Participants highlighted the diversity of speakers as a valuable aspect of the program and appreciated the opportunity to encounter multiple perspectives on related questions.
The program included sessions covering computational social science methods, research infrastructure, online communities, statistical reasoning, responsible AI, evaluation of large language models, multimodal AI, industry research, accessibility, and community-centered data science. Our first speakers were Sarah Stone and Scott Henderson, who introduced participants to computational environments. Their talk, titled RigConfig: Tools, Pipelines, and Frameworks, provided a practical perspective on computational research infrastructure and reproducible workflows. Benjamin Mako Hill led sessions on Introduction to Text as Data Methods and The Study of Online Populations, connecting computational approaches to questions about language and online communities. Sasha Johfre’s Power, Privilege and Statistics brought a critical social-scientific perspective to quantitative research and encouraged participants to think carefully about the assumptions embedded in statistical practice.
Several sessions focused on emerging challenges in AI research. Trey Causey led sessions titled Rubrics, Evaluations, and LLMs-as-Judges and Developing an LLM-as-a-Judge for Your Research, giving participants practical frameworks for evaluating AI-generated outputs. Noah Smith discussed OLMo, Molmo, and Open Multimodal AI Infrastructure to Accelerate Science, introducing participants to developments in open language and multimodal AI infrastructure. Riccardo Fogliato provided a perspective grounded in industry research on responsible AI and the evaluation and safety of language models. Jess Holbrook and Jian Yang drew on their industry experience to share how research operates in technology companies.
Other sessions demonstrated how computational methods can be applied to concrete social problems. Jon Froehlich presented Project Sidewalk and led an interactive session around the project, showing how crowdsourcing and AI can be used to address accessibility in the built environment. Madeleine Daepp’s sessions on Data Science with Communities in the Loop and Red Teaming emphasized the importance of incorporating communities and critical perspectives into the development of computational systems.
The format of these sessions was an important part of their success. While participants valued the lectures broadly, they particularly appreciated opportunities to actively engage with the material. Madeleine Daepp’s red-teaming exercise was highlighted in participant feedback as an especially successful session. This suggests that future iterations of SICSS should continue to invite speakers with diverse expertise, while also encouraging them to incorporate interactive components, like exercises, demonstrations, structured discussion, or hands-on activities. The most successful sessions did not simply introduce participants to a new topic; they gave them an opportunity to work through a problem or experience a method themselves.
Rather than presenting computational social science as a single toolkit, the speakers exposed participants to different ways of thinking about computational research: how to build reproducible research infrastructure, study online populations and text, reason about statistical assumptions, evaluate AI systems, conduct industry research, and develop computational tools in partnership with communities. This was particularly useful for a cohort with varied disciplinary and technical backgrounds, as it exposed them to methodologies outside their areas of expertise.
There are also several concrete changes we would make to the speaker program in future years. First, speakers should receive clearer accessibility guidance before the Institute. In particular, we would provide speakers with the University of Washington’s existing accessibility guidelines for presentations, including recommendations around text size and slide design. Providing these expectations in advance would make accessibility a more consistent part of the speaker preparation process rather than something addressed on a session-by-session basis.
Second, we would be more explicit when inviting speakers that interactive engagement is encouraged. Participant feedback suggests that lectures were most valuable when they created opportunities for discussion, participation, or practical application. Rather than prescribing a particular format for every speaker, we would communicate that the goal is to balance expert insights with opportunities for participants to actively engage with the ideas being presented.
The second component of the two weeks was group project work, with substantial blocks of time set aside for participants to apply ideas from the programming to their own research questions. Every participant was part of a group. In total there were five teams: AI Metaphor, Counter-Extremism, Conspiratorial Rhetoric, Candor, and Web-based AI. The first four teams worked with data participants brought from their own research or with web-scraped data. Several participants brought questions or data from their own PhD research and joined or formed these four groups.
At the beginning of the first week, we introduced the Web-based AI project, focused on a question that had come up in planning: what fraction of content on the web is AI-generated? Rather than treating the problem as a straightforward classification task, we wanted participants to think about how to make a statistically principled estimate. The team worked with data from Common Crawl, a large, publicly available dataset of web pages collected through repeated crawls of the internet. Participants divided into three sub-teams of about three people each, working on different parts of the larger problem: sampling, AI detection, and modeling. This allowed participants to choose an area based on the skills they wanted to develop while contributing to a common research question. The sampling sub-team developed a network-based sampling approach based on URLs and domains from this dataset to handle its scale. The detection sub-team used CrowdStrike Falcon AIDR (AI Detection and Response), a commercial AI detection tool, to identify AI-generated content. The modeling sub-team used these outputs to estimate the proportion of AI-generated content on the web from the sample, aiming to make a broader population-level inference.
The AI Metaphor, Counter-Extremism, and Conspiratorial Rhetoric teams examined topics related to how people perceive and are influenced by AI, extremism, and conspiracy theories, respectively. The Candor team examined affective contagion in conversations, using the CANDOR corpus. Across the five projects, there was considerable overlap in interests around AI, ethics, and access.
Participants came in with different levels of programming experience. To reduce the coding burden, UW eScience provided all participants with access to Claude Code through CloudBank, a cloud computing resource that supported the infrastructure needed for their analyses. This allowed participants with different levels of programming experience to contribute to the computational work.
The organizers generally supported the groups without directing the projects. We checked in with groups roughly every other day to hear where they were in the process, work through methodological or logistical questions, and discuss strategies for moving forward when groups were stuck. Groups then decided how to incorporate that feedback and what to work on next.
As the two weeks progressed, participants became more comfortable working with one another. There was collaboration across groups and areas of expertise, both during project work and in discussions following talks. The institute concluded with groups presenting their projects to fellow SICSS participants as well as SICSS organizers, discussing the methods, challenges, and results of their work. Some members of the Web-based AI team expressed interest in continuing the project after the institute, and are planning to work with SICSS organizers toward a joint paper and publication.
Going forward, participants suggested using a pre-program survey to explicitly solicit research ideas ahead of time. Having participants share short project pitches before the program begins would allow participants to identify shared interests earlier and arrive with clearer collaboration possibilities. Once participants arrived, another suggestion was for organizers to provide more support in forming research groups and scoping projects.
Regarding structure, participants suggested making it clearer that group work and collaboration could continue after scheduled programming ended each day. Explicitly communicating that participants were welcome to stay after hours could encourage additional collaboration without making attendance mandatory.
Overall, participants responded very positively to the structure of the program, interactive learning opportunities, food and logistics, and opportunities for interdisciplinary collaboration. Several themes emerged around increasing hands-on learning opportunities, creating more structured community-building opportunities, improving accessibility, and making collaboration more intentional.
Regarding the structure of SICSS-UW, participants appreciated having consistent default hours when everyone was expected to be present, while also maintaining flexibility for meetings, collaborations, and individual work. The 9:30 AM start time was also helpful, as it gave participants time before the program began each morning. Participants appreciated the food and refreshments offered during the program, including the variety of lunch options, snacks, coffee, and sparkling water.
Participants provided insight on how the program might be improved going forward. To begin, speakers should be provided with clearer accessibility guidelines for slides and presentations (e.g., text size). The University of Washington provides such guidelines, which could be provided to speakers ahead of time. Addressing this concern will help make sessions more accessible to all participants.
Several participants also expressed interest in more structured discussion-based learning opportunities. They suggested incorporating guided reading seminars or discussion groups around the required readings assigned before the program began. This could create more opportunities for engagement with the material and peer learning, as well as setting the tone for the two weeks to come.
Relatedly, hands-on workshops were one of the most valued components of the program. Participants suggested prioritizing more practical, applied sessions alongside the lectures. Some ideas here included optional after-hours workshops that focused on specific skills, like navigating GitHub, understanding version control and computational workflows, building familiarity with statistical methods, and working with data in R. These sessions would help participants with different academic backgrounds gain any knowledge needed to make the most of their two weeks with SICSS.
Especially for GitHub, a short introductory session could help participants with less technical experience become more comfortable with the tools necessary to collaborate during the program. Here, it was suggested that a short survey should be sent to participants ahead of the program, to gather information about participants’ technical backgrounds and help organizers develop a set of introductory courses that speak directly to the participants’ needs.
There was also interest in creating more structured opportunities for community-building outside the formal schedule. For instance, a dedicated Discord channel for organizing social activities, pre-planned group outings or field trips (e.g., to local research labs, technology companies, or other computational social science communities), and other organized activities based around local Seattle experiences.
Housing accessibility was also identified as a significant challenge. Participants noted that housing in Seattle is expensive and suggested organizers explore possible partnerships or discounts through the University of Washington. Likewise, to subsidize the overall costs incurred by participants outside of the program, to-go boxes should be provided for lunch leftovers. Finally, participants suggested better labeling of different dietary options (e.g., vegan, gluten-free), and offering additional drink options, such as soda, juice, tea, and hot water.
Uyo, Nigeria — July 20-31, 2026
Johannesburg, South Africa — August 17-21, 2026