Student Affinity Groups
Are you a Stanford student interested in creating meaningful interdisciplinary connections within the Stanford community? Do you have ideas on advancing AI to improve the human condition?
If so, you’re invited to apply to have HAI sponsor your affinity group. HAI Student Affinity Groups are small teams of interdisciplinary students (Stanford undergraduates, graduates, and postdocs) who have a shared interest in a topic related to the development or study of human-centered AI. Affinity Groups provide a space for students to share ideas, develop intellectually and strengthen the community of future leaders dedicated to building AI that benefits all of humanity.
The affinity groups have been selected for the 2023-2024 school year.
Steps to Get Started
- Identify a topic of focus and gather an interdisciplinary group of students who share interest in that topic. If you have a topic idea but are looking for others to join your group, fill out this Interest Form. Responses can be found here.
- Identify two students from different disciplines to serve as the leads.
- Identify a faculty mentor; no formal time commitment is required of faculty. If you need support in reaching out to faculty, please contact HAI Research Associate Christine Raval.
- Submit the application form detailing the goals for your group and steps you’ll take to achieve those objectives.
Benefits of Joining HAI Student Affinity Groups
- Funding of up to $1,000 for the academic year to spend on small quarterly or biweekly gatherings, such as: workshopping lunches, student-hosted speakers, book discussions or discussion series.
- Space (physical and intellectual) to share knowledge across disciplines and create collaborations.
- Access to a community of researchers, faculty, and staff committed to promoting human-centered uses of AI, and ensuring that humanity benefits from the technology and that the benefits are broadly shared.
- Inside scoop on HAI events, research, publications, and volunteer opportunities.
Guidelines
- Applications due August 25, 2023.
- Student Affinity Groups will be announced in late September and will run during the fall, winter, and spring quarters.
- Each group must have students from two or more disciplines.
- Each group must have one faculty mentor; no formal time commitment is required of faculty.
- At the end of the Spring quarter, groups must submit a report summarizing the outcomes. If members elect to continue, they must reapply.
FAQs
- How can funds be used? Possible expenses include food expenses, creating marketing materials, or to purchase other materials needed for the program (books, software, printing, etc).
- Can people join mid-program? Yes, new members can join at any point during the academic year.
- Is more funding available if larger project ideas are developed through the affinity groups? Promising research ideas that develop through the affinity group could make a great proposal for the HAI seed grant program.
2023-24 Academic Year
Oftentimes making technology accessible to people with disabilities is a game of catch-up because tools and techniques are not born accessible. This affinity group will be a space where people with disabilities at Stanford can have intentional conversations and develop strategic plans to ensure that emerging technologies, policies, and procedures around generative AI include the interests of people with disabilities. Specific sub-topics will include advocating for fair disability representation in data, articulating research directions for advancements in AI that are grounded in the experiences of people with disabilities, and how people with disabilities can make an impact on the Stanford community and AI community at large through navigating careers/advocacy efforts in AI.
Name Role School Department Sean Follmer Faculty Sponsor School of Engineering Mechanical Engineering Gene Kim Student Co-Lead School of Humanities & Sciences Symbolic systems Aya Mouallem Student Co-Lead School of Engineering Electrical Engineering Trisha Kulkarni Graduate Student School of Engineering Computer Science The "AI for Climate Resilience: Bridging Technology and Humanity" group seeks to harness AI to advance climate resilience. By synergizing expertise from computer science, economics, ethics, design, and policy, our initiative aims to contextualize current challenges to drive climate solutions that resonate across cultural contexts, bolster community resilience, and uphold human-centered governance. We firmly believe that collaborative cross-sector engagement and knowledge exchange are critical to innovating AI-enabled climate solutions grounded in equity, collaboration, and sustainable impact.
Name Role School Department Mykel J. Kochenderfer Faculty Sponsor School of Engineering Aeronautics and Astronautics (Computer Science, by courtesy) Serena Lee Student Co-Lead School of Humanities & Sciences Data Science and Social Systems Bhu Kongtaveelert Student Co-Lead School of Engineering Computer Science & Art Practice Gabrielle Tan Graduate Student School of Sustainability Sustainability Science and Practice MS Derick Guan Undegraduate Student School of Humanities & Sciences Mathematical and Computational Science Griffin Clark Undergraduate Student School of Engineering Chemical Engineering At all levels, the US healthcare system is both complex and opaque; it is a web of intertwined and conflicting incentives, outdated technology, and lack of transparency. AI has the potential to improve healthcare accessibility and equity while reducing cost and improving outcomes, however has been demonstrably difficult to implement in the healthcare system. This affinity group unravels the landscape of healthcare challenges and ideates novel ways to use AI to address key healthcare challenges. How can AI augment the capabilities of clinicians to deliver care efficiently? Can models and algorithms help patients navigate the convoluted landscape of health insurance, providers, and employers? We frame our sessions around expert-led discussion sessions, design sprints, and case studies, each of which will focus on a specific area of interest within healthcare.
Name Role School Department Sophia Wang Faculty Sponsor School of Medicine Ophthalmology Akash Chaurasia Student Co-Lead School of Engineering Computer Science Priyanka Shrestha Student Co-Lead School of Engineering Computer Science Isaac Bernstein Graduate Student School of Medicine Medicine Ank Agarwal Graduate Student School of Medicine Medicine Mahdi Honarmand Graduate Student School of Engineering Mechanical Engineering Aditya Narayan Graduate Student School of Medicine Medicine Shobha Dasari Graduate Student School of Engineering Computer Science We’re interested in interfaces, agents, and tools for audiovisual performance. Recent advances in foundation models have garnered popular interest in applications of AI in artistic domains, however, with this progress comes crucial ambiguities in how humans can and should relate to AI-augmented creative practices. Our group invites students working in and adjacent to music, theater, audio signal processing, computer graphics, virtual reality, generative models, and HCI to study how new forms of computation can shape their work. The organizing goals of our group are (1) to further an understanding of how we as humans ought to relate to machine collaborators, and, in turn, how models ought to be designed to learn from behavior and adapt to users’ needs, (2) to promote artists pushing the boundaries of generative tools and shaping the frontiers of human-computer interaction, (3) to explore how new computational tools give new perspective to the nature of intention, identity, and authenticity in artistic practice, and (4) to connect scholars and artists across disciplines to work together on new creative projects.
Name Role School Department Julius Orion Smith Faculty Sponsor School of Humanities and Sciences Music Nic Becker Student Co-Lead School of Engineering Computer Science Alex Han Student Co-Lead School of Humanities and Sciences Music Miranda Li Student Co-Lead School of Engineering Computer Science Eito Murakami Student Co-Lead School of Humanities and Sciences Music We are interested in understanding how AI can be a gamechanger for employee productivity. Employees globally are plagued by “information overload” at the workplace, in part caused by the SaaS revolution of the 2000s and proliferation of tools. Evidence shows: (i) An average employee spends 2.5 – 5 hours daily on just different communication platforms; (ii) 1 out of 2 workers feel navigating across platforms is more annoying than losing weight! and (iii) 68% feel they don’t have uninterrupted focus time. Research conducted by one of the Co-leads points to extreme employee quotes such as “digital communication fatigue is the biggest bane of my life and is making me an ineffective leader.” As a result, employees suffer longer working hours, mental exhaustion, and a loss of personal time. This is also a silent killer of business output with 2 out of 3 business leaders already seeing a slowdown in strategic thinking and innovation among teams. This issue has become more pressing after the shift to hybrid / remote working. AI promises a new frontier for human productivity. We want to explore how AI can be leveraged to empower employees, cut through the noise and busy work, and “maximize output per unit of human effort”.
Name Role School Department Dan Iancu Faculty Sponsor Graduate School of Business Operations, Information and Technology Saloni Goel Student Co-Lead Graduate School of Business Graduate School of Business Siya Goel Student Co-Lead School of Engineering Computer Science Sanjit Neelam Graduate Student School of Engineering Computational and Mathematical Engineering Teddy Ganea Graduate Student School of Engineering Math and Symbolic Systems Roger Liang Graduate Student Graduate School of Business Graduate School of Business Thai Tong Graduate Student Graduate School of Business Graduate School of Business In this affinity group, we will investigate the human-centered governance of AI. Governance is crucial in shaping the direction of AI research, the manifestation of its beneficial impacts, and mitigation of its harms. While discussions on what ethical and responsible AI entails have become increasingly popular, there is also a pressing need for deliberation on how governance itself should be structured and implemented in order to be effective, proactive, and inclusive.
Specifically, we will study and engage with the different stakeholders involved in AI governance (e.g. international governing leaders, tech entrepreneurs, engineers, ethics nonprofits, users, domain specialists, and educators). We will also seek to understand the parts of a governance toolkit (e.g. private and public regulations, funding, policies, laws, human rights doctrines, economic incentives, technical risk assessment measures, and enterprise software for governance).
Through discussions, speaker events, and outreach, we will merge disciplines such as computer science, management, international relations, and social science. We will understand the technical challenges AI poses for governance, as well as compare and evaluate existing governance frameworks. Valuing diverse perspectives, we aim to conduct panels with speakers across institutions, geographical regions worldwide, and applications of AI. Lastly, we hope to create opportunities for Stanford students and Bay Area residents to explore the intersection of novel innovations in AI governance with their career aspirations and the public sector.
Name Role School Department Taylor Madigan Faculty Sponsor School of Humanities and Sciences Philosophy Priti Rangnekar Student Co-Lead School of Engineering Computer Science John Lee Student Co-Lead Graduate School of Business Graduate School of Business Javokhir Arifov Undergraduate Student School of Engineering Computer Science Larissa Lauer Undergraduate Student School of Humanities and Sciences Data Science & Social Systems Ayush Agarwal Undergraduate Student School of Humanities and Sciences Symbolic Systems Emily Tianshi Undergraduate Student School of Humanities and Social Science International Relations and Data Science Kenneth Bui Undergraduate Student School of Engineering Computer Science The computational journalism HAI affinity group focuses on cultivating diverse perspectives in understanding how machine learning and artificial intelligence can be used responsibly to produce stories that serve the public. From algorithmic accountability journalism that aims to inspect and hold code itself accountable, to emerging research on computational tools and software produced by journalists to tell better stories with data, we want to use this space to convene conversations on how AI is and can be used in newsrooms across journalists, technologists in media, and other practitioners. Computational journalism spans many fields at Stanford and we hope that this affinity group can cultivate a more diverse space to discuss these issues, spanning technical and non-technical researchers, as all are impacted by the news and should have a say in its production.
Name Role School Department Maneesh Agrawala Faculty Sponsor School of Engineering Computer Science Dana Chiueh Student Co-Lead School of Engineering Computer Science Tianyu Fang Student Co-Lead School of Humanities & Sciences Political Science Elias Aceves Graduate Student School of Humanities & Sciences Latin American Studies Michelle Cai Undergraduate Student School of Humanities & Sciences History Chih-Yi Chen Graduate Student School of Engineering Materials Science & Engineering The Ethical and Effective Applications of AI in Education affinity group explores the central question: “Who are we prioritizing in human-centered AI, and which ‘humans’ are included in the loop?” with a deep focus on education. Our group includes student perspectives from computer science, education, law, psychology, and more. Together, we’ll facilitate discussions with guest speakers, Stanford affiliates (current students, faculty, alumni) who are grappling with current challenges related to education and AI and can share case studies from their experience. These discussions will be recorded to share with the broader Stanford-HAI community. Group members will engage with readings and materials recommended by each guest speaker, before entering these cross-disciplinary conversations. Through our sessions, we will be:
- Deepening Awareness: Investigate the systemic inequalities present in education systems, questioning who benefits and who might be left behind as AI systems are rapidly integrated.
- Learning through Collaboration: Make space for robust peer-to-peer exchanges to elevate diverse perspectives and synthesize interdisciplinary insights.
- Engaging in Critical Dialogue: Engage both intellectually and personally, as we bring each of our unique holistic human experiences into the dialogue.
- Taking Action: Develop clear objectives and possible next steps that each of us can take in our respective disciplines to address issues of inequality in education.
- Meeting Current Challenges: Engage with and evaluate contemporary case studies, readings and research.
- Bridging Academic-Industry Gaps: Build bridges between academic research on AI and education, and industry-wide implications for EdTech product development.
Name Role School Department Dora Demszky Faculty Sponsor Graduate School of Education Learning Sciences and Technology Design Samin Khan Student Co-Lead Graduate School of Education Education Data Science Regina Ta
Student Co-Lead School of Engineering Computer Science Radhika Kapoor
Graduate Student Graduate School of Education Developmental and Psychological Sciences Khaulat Abdulhakeem Graduate Student Graduate School of Education Education Data Science Carl Shen Graduate Student School of Engineering Computer Science Our methodology centers around “Bias Limitation and Cultural Knowledge”. We aim to forge pathways for transformative solutions to facilitate the creation of inclusive and equitable AI — systems vigilant against bias, informed by best practices, sensitive to cultural nuances, and dedicated to fair representation and treatment. We explore technical, social, and political considerations to illuminate the necessity of incorporating diverse cultural perspectives, experiences, norms, and knowledge into the algorithm design, development, deployment, and analysis processes. We are committed to amplifying marginalized voices, especially within our Black & African Diaspora communities. We welcome anyone seeking to foster a more inclusive and equitable technological landscape to join us in critical dialogue, discourse, and discovery.
Name Role School Department Mehran Sahami Faculty Sponsor School of Engineering Computer Science Asha Johnson Student Co-Lead School of Engineering Management Science & Engineering (Master's), Computer Science (Undergraduate) Saron Samuel Student Co-Lead School of Engineering Computer Science Justin Hall Undergraduate Student School of Engineering Computer Science Gabrielle Polite Undergraduate Student School of Humanities and Sciences Symbolic Systems Andrew Bempong Undergraduate Student School of Engineering Computer Science Saba Weatherspoon Undergraduate Student Global Studies International Relations Abel Dagne Undergraduate Student School of Engineering Computer Science Eban Ebssa Undergraduate Student School of Humanities and Sciences Symbolic Systems The Social NLP affinity group will focus on topics at the intersection of social sciences and AI, with an emphasis on foundation models and NLP. We will tackle (1) Significant innovations in AI methods, models, or design paradigms applied to social problems, and (2) New theories and concepts from social sciences and new ways to study them using AI. Concrete examples of such topics include: simulating human behaviors with foundation models, AI-driven persuasion, or human information seeking in the foundation models era. Our group is inherently interdisciplinary, including students from Computer Science, Psychology, and Linguistics departments who are well-positioned to address these complex issues.
Name Role School Department Diyi Yang Faculty Sponsor School of Engineering Computer Science Kristina Gligoric Student Co-Lead School of Engineering Computer Science Maggie Perry Student Co-Lead School of Humanities and Sciences Psychology Weiyan Shi Postdoctoral Scholar School of Engineering Computer Science Omar Shaikh Graduate Student School of Engineering Computer Science Cinoo Lee Postdoctoral Scholar School of Humanities and Sciences Psychology Myra Cheng Graduate Student School of Engineering Computer Science Yiwei Luo Graduate Student School of Humanities and Sciences Linguistics Tiziano Piccardi Postdoctoral Scholar School of Engineering Computer Science In a world where automation is becoming increasingly dominant, it is vital to discuss the future of human-machine interaction once AI becomes software and data beyond the screen. We will focus on bringing together a community of students from all schools with a common interest in responsibly furthering the human condition with AI-enabled hardware systems. We will host guest speakers from a variety of backgrounds, from robotics researchers to lawyers, government and industry. After each event, we will explore key questions including the technical, ethical, legal, and moral questions of AI-enabled machines, especially as they enter our workplaces and homes. At the end, we hope to publish an artifact of our research into the future of AI-enabled machines, and recommend avenues for researchers and practitioners.
Name Role School Department Mark Cutkosky Faculty Sponsor School of Engineering Mechanical Engineering Julia Di Student Co-Lead School of Engineering Mechanical Engineering Jeremy Topp Student Co-Lead Graduate School of Business Graduate School of Business Jorge Andres Quiroga Graduate Student Graduate School of Business Graduate School of Business Ali Kight Graduate Student School of Engineering Bioengineering Hojung Choi Graduate Student School of Engineering Mechanical Engineering Rachel Thomasson Graduate Student School of Engineering Mechanical Engineering Nikil Ravi Graduate Student School of Engineering Computer Science Cem Gokmen Graduate Student School of Engineering Computer Science WellLabeled is an affinity group aimed at addressing the ethical challenges related to data annotation in AI development, particularly focusing on toxic and harmful content. The group's goal is to investigate welfare-maximizing annotation approaches by synthesizing ideas from human-centered design, economics, and machine learning. To achieve this, WellLabeled aims to host discussions and speaker seminars. In particular we aim to focus our attention on how to regulate annotators' exposure to distressing content, establish fair compensation mechanisms based on measured harm, and investigate validation methods through human-subject studies.
Name Role School Department Sanmi Koyejo Faculty Sponsor School of Engineering Computer Science Mohammadmahdi Honarmand Student Co-Lead School of Engineering Mechanical Engineering Zachary Robertson Student Co-Lead School of Engineering Computer Science Jon Qian Graduate Student Graduate School of Business Graduate School of Business Nava Haghighi Graduate Student School of Engineering Computer Science
2022-23 Academic Year
Our affinity group is focused on employing AI for solving problems linked to climate and environmental issues. Climate change is one of the biggest challenges faced in the 21st century and is a complex issue that requires diverse perspectives. Discussions will cover the science behind greenhouse gas, disastrous effects of climate change (wildfire, flooding, etc.), humanity’s role in mitigating this issue, and human-centered AI developments that can solve climate-related issues. Discussion topics will be moderated by affinity group leaders Wai Tong Chung, a PhD student and HAI Grad Fellow, and Greyson Assa, a Master's student at the Doerr School of Sustainability.
Name Role School Department Wai Tong Chung Graduate Student Co-Lead School of Engineering Mechanical Engineering Greyson Assa Graduate Student Co-Lead School of Sustainability Sustainability Science and Practice David Wu Graduate Student School of Engineering Aeronautics and Astronautics James Hansen Graduate Student School of Engineering Aeronautics and Astronautics Khaled Younes Graduate Student School of Engineering Mechanical Engineering Seth Liyanage Graduate Student School of Engineering Mechanical Engineering Allison Cong Graduate Student School of Engineering Mechanical Engineering Matthias Ihme Faculty Sponsor School of Engineering Mechanical Engineering/SLAC We are interested in the conditions under which human AI collaboration leads to better decision-making. Algorithms are increasingly being used in high-stake settings, such as in medical diagnosis and refugee settlement. However, algorithmic recommendation in the setting of human AI collaboration can lead to perverse effects. For example, doctors may not put in as much effort when recommendations from algorithms are readily available. Similarly, the introduction of algorithmic recommendation can cause moral hazard, leading to worse decision-making. Our affinity group would like to explore the conditions and incentives affecting human AI collaboration, integrating theories from political science, communication, and HCI.
Name Role School Department Eddie Yang Graduate Student Co-Lead School of Humanities & Sciences Center on Democracy, Development and the Rule of Law Yingdan Lu Graduate Student Co-Lead School of Humanities & Sciences Communication Matt DeButts Graduate Student School of Humanities & Sciences Communication Yiqin Fu Graduate Student School of Humanities & Sciences Political Science Yiqing Xu Faculty Sponsor School of Humanities & Sciences Political Science Recent developments in foundation models like Stable Diffusion and GPT-3 have enabled AI to create in ways that were previously only possible by humans—marking an evolution of AI from a problem-solving machine to a generative machine. Simultaneously, we are seeing these models moving from research to industry. The productization of AI for creative purposes (writing, image generation, etc.) is just beginning to emerge, but will accelerate in the coming years, impacting the media industry and creatives of all kinds (filmmakers, photographers, writers, professional artists, etc.).
While hype around these new tools for creativity is exploding in the media, we have yet to find a student community at Stanford dedicated to exploring the future of creative generative AI. We are interested in understanding the technical capabilities of generative AI models, current product innovations in industry, the impact of generative AI on the future of art creation, and the social and cultural implications of new creative tools. As our team comes from a range of backgrounds (Computer Science, Symbolic Systems, Political Science, and English), our breadth of expertise will enable us to engage in cross-disciplinary conversations.
Name Role School Department Isabelle Levent Undergraduate Student Co-Lead School of Humanities & Sciences Symbolic Systems Lila Shroff Undergraduate Student Co-Lead School of Humanities & Sciences English Regina Ta Graduate Student School of Humanities & Sciences Symbolic Systems Millie Lin Graduate Student School of Engineering Computer Science Sandra Luksic Research Assistant School of Humanities & Sciences Research Assistant, Ethics in Society Mina Lee Graduate Student School of Engineering Computer Science Michelle Bao Graduate Student School of Engineering Computer Science Rob Reich Faculty Sponsor School of Humanities & Sciences Political Science HAI graduate fellows are planning to host a panel with three AI experts from Academic, Government, and Industry moderated by a comedian as an effort to lower the barrier of entry into the AI conversation. This event will join HAI’s effort to raise awareness and inform the general public regarding AI limitations and how AI can empower human capabilities. Our goals are to solidify the HAI graduate fellow community, connect HAI graduate fellows with the general public and Stanford community, start a fun and entertaining conversation about AI limitations, and engage with AI experts in academia, government and industry in an informal setting.
Name Role School Department Alberto Tono Graduate Student Co-Lead School of Engineering Civil and Environmental Engineering Martino Banchio Graduate Student Co-Lead Graduate School of Business Graduate School of Business Yingdan Lu Graduate Student School of Humanities & Sciences Communication Surin Ahn Graduate Student School of Engineering Electrical Engineering Betty Xiong Graduate Student School of Medicine Biomedical Informatics Martin Fischer Faculty Sponsor School of Engineering Civil and Environmental Engineering Our group will develop tools that will improve the next-generation of foundation models. We plan on doing research at different stages of foundation model development. Our research specifically focuses on training foundation models and large language models on newer modalities such as structural biology and joint image-text pairings, and evaluating methods with meta-learning downstream task performance-in-the-loop. Most foundation model research has been in text or image data, and we plan on focusing our work in new multimodal and biological data types as well as evaluating downstream fine-tuned task performance using in-the-loop meta-learning techniques.
Name Role School Department Rohan Koodli Graduate Student Co-Lead School of Medicine Biomedical Data Science Gautam Mittal Graduate Student Co-Lead School of Engineering Computer Science Rajan Vivek Graduate Student School of Engineering Computer Science Douwe Kiela Faculty Sponsor School of Humanities & Sciences Symbolic Systems We propose to develop an affinity group with the topic and purpose of advancing theoretical understandings of human interaction and trust with AI-based systems and technologies. For AI to augment human intelligence while having humans in charge, we must understand how humans interact with AI technologies and build up trust with such systems. Understanding trust in human-AI interaction is critical to develop AI systems that are ethical, safe, authentic, and trustworthy. Despite the importance of this relationship, little attention in the literature has been devoted to advancing theoretical and practical knowledge of human-computer interaction with AI systems. Particularly, there is a gap on this issue that can be addressed by a multidisciplinary approach, such as leveraging knowledge across cognitive psychology, computer science, and user design fields. We also believe that such understanding with an ultimate theoretical framework developed by the end of our affinity group meetings and discussions will be beneficial for researchers and practitioners across disciplines to advance applications of AI systems in real-world problems of human lives.
Name Role School Department Alaa Youssef Postdoc Co-Lead School of Medicine Radiology Xi Jia Zhou Graduate Student Co-Lead Graduate School of Education Graduate School of Education Michael Bernstein Faculty Sponsor School of Engineering Computer Science
Contact Us
For more information, contact HAI Research Associate Christine Raval.