Social
Alex Gu · Zhaoyu Li
[ Conference GHJ ]
Abstract
AI for Mathematics and Theorem Proving is an emerging area as LLMs are becoming strong reasoners, and its promise in advancing the future of mathematics is evident. We hope that through this social, we are able to bring together both ML researchers and mathematicians in order to come together and discuss emerging ideas in the field. We will provide a welcoming environment for everyone to share their unique expertise and wish to foster exciting collaboration opportunities in both fields. We will potentially have discussion groups to facilitate the social process and potentially have mentorship for more junior attendees who are interested in the field.
Social
Thomas F Burns · Lukas Balles
[ Opal 103-104 ]
Abstract
Birds of a Feather is a gathering to bring together researchers, practitioners, and anyone interested in tokenizer-free, end-to-end architectures topics.
We'll have a short panel discussion with researchers from industry and academia, followed by Q&A and free time for people to discuss in smaller groups.
We want to foster innovative ideas and connections among peers, provide a platform for emerging research that might not get the spotlight in the main conference tracks and bridge theoretical discussions with practical implementations. We will also invite young researchers working on similar underrepresented topics who want to share their poster in a smaller setup.
Our target audience is anyone interested in the topic, but we believe it will be interesting for researchers and practitioners working on tokenizer-free approaches, students interested in pursuing work in this area, and industry professionals looking to understand cutting-edge developments.
Social
VIOLETA
[ Opal 101-102 ]
Abstract
Qualified with a Master's degree in Marketing Management (Sofia/Technical University),
I conduct management training seminars (discussions)
and offer individual coaching for modern companies
to help them make informed decisions about their activities.
Social
Gabriel Chua · Shaun Khoo
[ Peridot 201 ]
Abstract
The proposed ICLR social, "LLMs in the Public Sector," organized by GovTech Singapore, will commence with an insightful presentation highlighting key advancements and applications of Large Language Models (LLMs) across essential public sector domains, including Education, Healthcare, and Labour Markets. Following the presentation, participants will engage in a networking session designed to facilitate structured discussions on innovative use-cases, practical implementation experiences, and prevailing challenges. Emphasis will be placed on public sector use-cases, challenges in development and deployment, Responsible AI.
Social
Si Chen ·
[ Opal 101-102 ]
Abstract
The future of AI is global—but how do we ensure it serves everyone? This social session invites ICRL participants from different cultural and linguistic backgrounds to share insights on how AI development and deployment vary across diverse geographical regions and cultural contexts. The session will showcase multilingual and localization case studies across different AI use cases. Explore strategies around data representation, linguistic challenges, and value alignment across cultures, and broaden your AI perspective.
Social
Heather Switzer · Sanjeet Kumar
[ Opal 103-104 ]
Abstract
Have a chance to met top AI Science professionals from Oracle, Dan Roth Chief AI Scientist and Sujith Ravi, VP of AI Science.
Social
Mahdi Ghaznavi · Arash Marioriyad
[ Conference GHJ ]
Abstract
Artificial Intelligence (AI) influences every facet of human life, from professional environments to daily routines and interpersonal relationships. While technology aims to fulfill desires and enable new capabilities, questions are raised about whether human agency is truly enhanced in a world shaped by these technologies across social, economic, and political dimensions In this social event, we will engage in a discourse on the impact of autonomous AI on human agency and explore solutions to potential challenges. Our discussion will focus on how to socio-technically prioritize human agency as a central criterion in AI development, aiming for positive outcomes in AI alignment and safety.
Social
Ehsaneddin Asgari · Ahmed Youssef
[ Peridot 201 ]
Abstract
The Muslims in ML Social is a community gathering aimed at fostering connections among Muslim researchers, engineers, and professionals in machine learning and artificial intelligence. This informal event offers a welcoming space to meet peers, share experiences, and build lasting professional relationships. Attendees will have the opportunity to network, engage in conversations about career paths and research, and connect across regions and backgrounds. While centered on the Muslim community, the event is open to all ICLR participants who are interested in inclusive dialogue and community-building in ML.
Webpage: https://www.musiml.org/
Next events: ICML 2025, AISTATS 2025
Social
Valerie Pang · Miro Plueckebaum
[ Opal 101-102 ]
Abstract
This social is for people who are interested in or working on AI safety to gather together to discuss various topics around AI safety.
Topics include (not exhaustive):
AI alignment: Doing technical and conceptual research focused on getting AI systems to do what we want them to do.
AI policy and governance: Setting up institutions and mechanisms that cause major actors (such as AI companies and national governments) to implement good AI safety practices.
AI strategy and forecasting: Building models of how AI will develop and which actions can make it go better.
Social
Melis Ilayda Bal · Vasiliki Tassopoulou · Erin Grant · Claire Vernade · Reyhane Askari Hemmat
[ Hall 1 Apex ]
Abstract
**WiML is at ICLR\!** 🌟
➡️ [Luma registration](https://lu.ma/ftdnjnf9) (not required)
[Women in Machine Learning (WiML)](https://www.wiml.org/) hosts its signature social at ICLR 2025 \- grab lunch, meet fellow researchers, and hear perspectives on navigating academia vs. industry.
**🍽️ Lunch provided\!**
**SCHEDULE:**
**12:30 \- 12:35 PM:** Opening Remarks
* [Melis Ilayda Bal](https://melisilaydabal.github.io/) (MPI-IS), [Vasiliki Tassopoulou](https://vatass.github.io/) (U Penn)
**12:35 \- 1:20 PM:** Networking & Lunch
* Icebreaker game (12:35 – 12:55 PM)
* **Lunch is served** (up to capacity; \~1 PM)
* Roundtable discussions (12:55 – 1:20 PM)
**1:20 \- 2:00 PM:** Panel Discussion
"Papers, patents, or products? Making the right career call across academia & industry"
The panel explores key career decisions in today's ML landscape: choosing between research publications and product development, weighing academic freedom against industry resources.
**Panelists:**
* [Reyhane Askari](https://reyhaneaskari.github.io/) (FAIR)
* [Katherine Driscoll](https://graphtx.com/) (Graph Therapeutics)
* [Nouha Dziri](https://nouhadziri.github.io/) (AI2)
* [Claire Vernade](https://www.cvernade.com/) (University of Tübingen)
**Moderator:** [Erin Grant](https://eringrant.github.io/) (WiML; UCL)
Social
Si Chen ·
[ Peridot 201 ]
Abstract
As AI systems become more complex and widely deployed, ensuring their safety is more critical than ever. This social session invites ICRL participants to explore practical approaches to AI safety. Through case studies and interactive discussions, we will delve into methodologies such as red teaming, adversarial testing, guardrails, and human alignment. These examples will also explore how cultural and linguistic diversity influences model evaluations and safety considerations. Whether you’re an AI researcher, engineer, or simply passionate about responsible AI, this session offers a chance to connect, exchange ideas, and help shape the future of safer AI systems.
Social
Nikhil Muralidhar · Bharat Srikishan
[ Opal 103-104 ]
Abstract
Data-driven learning techniques like deep learning (DL) are becoming ubiquitous in various scientific disciplines like computational fluid dynamics, materials science, biological sciences, cyber-physical systems and other science and engineering disciplines. Most often DL techniques, (due to their ability to capture highly non-linear relationships) are employed as `cheap' surrogates to expensive computational simulations or real-world experiments. However, certain characteristic behaviors of DL models like their data-hungry nature, spectral bias, rollout error and lack of explainable decision-making often limit their effectiveness in scientific disciplines. This social will serve as a forum to highlight these technical challenges while also discussing a few potential solutions to better leverage data-driven techniques to further accelerate scientific discovery.
Social
Jennifer Ding · Shayne Longpre
[ Topaz Concourse ]
Abstract
To sign up for this social, please register on this Luma page: https://lu.ma/rmyoy2vw
Join folks from teams like Cohere for AI, Data Provenance Initiative and Encord working on data-specific AI problems for a Data-Centric AI social!
Data quality has been one of the biggest drivers of AI advancements to date, from large scale data collection efforts such as ImageNet and Common Crawl, to innovations in targeted data curation such as human feedback and preference gathering for RLHF. A number of data-specific AI challenges have also gained visibility in the past year, in part due to to new opportunities for multimodal data, data mixtures and other data-driven research directions, as well as in response to public backlash surrounding to the reuse of online data for model pretraining, which has raised global discourse around the provenance, governance, and copyright considerations.
This social convenes researchers and practitioners focused on a broad range of data topics, spotlighting key organizations working in this space such as the Data Provenance Initiative and Cohere for AI. Following brief opening remarks, there will be a participant-driven unconference, and finally a mixer. The purpose of this social is to discuss shared challenges and opportunities for further dialogue and collaboration …
Social
Nicholas Lane · Javier Fernandez-Marques
[ Opal 103-104 ]
Abstract
This social/hall would aim to bring people together to discuss the interesting open problems in the domain of decentralized AI (and related fields like private ML, federatd learning etc.) and to look for more opportunities to work together. We would aim to faciliate informal sharing of (1) recent work and results, and (2) proposal of collective action; the overal focus would be on how we can better mobilize to lift the quality and quantity of research in this area. Additional topics of interest include: improved benchmarks, realistic datasets and advancing the common understanding of what are the most important settings for decentralized AI to tackle? The primary hosts would be Flower which is the most popular open-source framework for federated and decentralized AI and academics from the University of Cambridge. If accepted we would also attempt to invite other interested parties to attend to add more life to the proceedings.
Social
Abraham Ramos · Laura Montoya
[ Conference GHJ ]
Abstract
The LatinX in AI (LXAI) Social is a community-driven gathering aimed at fostering connections among LatinX and Hispanic researchers, engineers, and professionals in AI and ML. This event provides a welcoming space for attendees to network, exchange ideas, and discuss key topics related to AI research, industry trends, and diversity in the field.
While this social will not include paper presentations, we plan to feature a few invited speakers to spark meaningful conversations on the challenges and opportunities for LatinX professionals in AI. Our goal is to strengthen the global LXAI community, providing attendees with the opportunity to build professional relationships and collaborate on future initiatives.
Social
Kunal Singh · Pradeep Moturi
[ Peridot 201 ]
Abstract
Test time scaling for LLMs is an emerging frontier that examines how large language models adapt and evolve their responses though reasoning abilities during inference. In recent years, LLMs have demonstrated significant progress in mimicking human-like reasoning—from basic pattern recognition to advanced problem-solving in mathematical contexts. In our social, titled “Test Time Scaling for LLMs,” we delve into the critical role of reasoning and cognitive thinking in LLMs, charting their evolution from earlier foundational works to the cutting-edge models of today. We invite researchers, practitioners, and enthusiasts to share insights and challenges, fostering a collaborative dialogue geared towards understanding the trade-offs between computational cost and reasoning quality. Moreover, we extend our conversation beyond pure mathematics to consider applications in other domains like medical diagnostics, where improved reasoning can lead to safer and more accurate outcomes. This session aims to inspire new ideas for leveraging test time scaling to drive the next wave of advancements in artificial intelligence.
Social
Lekha Patel · Kuris Shuler
[ Opal 103-104 ]
Abstract
The emerging field of digital twins, virtual replicas of physical systems, represents a significant frontier for machine learning research with broad applications across industries. This social will bring together researchers interested in the unique challenges of leveraging ML to create, improve, and deploy digital twins.
This will be an interactive discussion focusing on key questions broadly related to the fidelity, scientific accuracy and limitations of ML for digital twins.
The session will feature brief introductions from participants working in this area, followed by open discussion and potential collaboration opportunities. We welcome researchers from diverse ML backgrounds including reinforcement learning, generative modeling, time-series forecasting, and domain experts from industries leveraging digital twins.
Join us to explore this rapidly evolving intersection of ML theory and practical applications transforming industries including manufacturing, healthcare, and climate science.
Social
Natia Kukhilava · Irakli Butskhrikidze
[ Opal 101-102 ]
Abstract
This social aims to explore the journey of AI multi-agent systems from academic research to deployment in enterprise settings. Discussions will focus on the challenges and successes encountered during this transition, including integration strategies, scalability, and the impact on business operations.
Social
Rishub Tamirisa · Bhrugu Bharathi
[ Peridot 201 ]
Abstract
As AI systems become increasingly capable and widely deployed, ensuring their safety and reliability is more important than ever. Researchers in the ML Safety community are working on various challenges, including interpretability, adversarial robustness, and alignment, which have become more complex with advances in multi-modal and agentic systems. This rapidly evolving field spans industry labs and academic groups, united by the need to address emerging risks.
We want to host a semi-structured meet-up for researchers who are currently working on or interested in safety-related topics to foster discussion and collaboration. We expect at least 150 people to attend. We previously hosted similar events at NeurIPS, ICML, and ICLR in 2023 and 2024, which were very well attended (150-300 people).
The event will open with a 30-minute panel discussion on the state of ML safety research, followed by a brief Q&A session. The rest of the event will consist of informal discussion and mingling among attendees. We will provide drinks and snacks.
Social
Egor Bogomolov · Rauf Kurbanov
[ Conference GHJ ]
Abstract
Discuss ongoing work, upcoming trends, challenges, and job opportunities related to applications of ML in software engineering tools and processes.
Social
Tatia Tsmindashvili · Rapael .
[ Peridot 206 ]
Abstract
LLM agents are transforming automation, research, and real-world applications. With their increasing adoption, understanding the full landscape - from foundational frameworks to deployment trade-offs - is more critical than ever. OpenAI has just released the Agents SDK, and MCP from Anthropic is also available.
This social event at ICLR 2025 will provide a comprehensive view of the evolution of LLM agents, exploring when and where they provide the most value, their strengths and limitations, and the critical factors in building reliable, scalable systems. It will also cover the future of AI agents, including protocols, simulations, and emerging trends.
The event’s agenda now includes four short expert talks and a fireside chat with AI leaders from OpenAI, Meta, LangChain, and other leading AI companies working on AI agents. Attendees will gain valuable insights into different frameworks, system architectures, and simulation approaches, helping them make informed decisions about using LLM agents in their own work. They will also have the opportunity to exchange ideas with top researchers and practitioners, explore collaborative opportunities, and network with others interested in AI agents.
Social
Claas Voelcker · Yanan Long
[ Conference GHJ ]
Abstract
This is a meetup for queer researchers and practitioners working in AI. We have hosted many such meetups over the years at conferences such as ICLR, ICML, NeurIPS, NACCL, IROS, etc. Participants have found them a valuable source of community in an environment that, while generally well-intentioned, can feel alienating to those who do not match the overwhelming norm in aspects of personal identity.
Queer in AI’s mission is to raise awareness of queer issues in AI/ML, foster a community of queer researchers and celebrate the work of queer scientists. We use “queer” as an umbrella term for people with diverse non-normative sexual orientations, romantic orientations, and/or genders, corresponding to acronyms like LGBTQIA2S+. We also explicitly include those questioning their identities.
Queer in AI’s demographic survey reveals that most queer scientists in our community do not feel completely welcome in conferences or other work environments, with the main reasons being a lack of queer community and role models. While there has been progress on these issues in recent years, issues remain, particularly for those who are transgender/non-binary and/or BIPOC. One of many steps towards improving that situation is to provide queer-focused spaces in work contexts such as this social.
Social
Linyi Yang · Minjun Zhu
[ Peridot 201 ]
Abstract
Join us at the AI Co-scientist Discussion social at ICLR 2025! This gathering brings together researchers and practitioners interested in collaboratively building AI agents capable of scientific discovery. Our focus will be on sharing insights, discussing practical approaches, and thoughtfully addressing ethical considerations to responsibly advance AI as co-researchers. Connect with peers passionate about ethical AI development, exchange ideas, and explore new collaborations. We warmly invite anyone committed to shaping the future of AI-assisted scientific research through careful ethical reflection and innovative thinking.
Socials
[ Conference GHJ ]
Abstract
[ Opal 103-104 ]
Abstract
[ Opal 101-102 ]
Abstract
[ Peridot 201 ]
Abstract
[ Opal 101-102 ]
Abstract
[ Opal 103-104 ]
Abstract
[ Conference GHJ ]
Abstract
[ Peridot 201 ]
Abstract
[ Opal 101-102 ]
Abstract
[ Hall 1 Apex ]
Abstract
[ Peridot 201 ]
Abstract
[ Opal 103-104 ]
Abstract
[ Topaz Concourse ]
Abstract
[ Opal 103-104 ]
Abstract
[ Conference GHJ ]
Abstract
[ Peridot 201 ]
Abstract
[ Opal 103-104 ]
Abstract
[ Opal 101-102 ]
Abstract
[ Peridot 201 ]
Abstract
[ Conference GHJ ]
Abstract
[ Peridot 206 ]
Abstract
[ Conference GHJ ]
Abstract
[ Peridot 201 ]
Abstract