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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>1st Workshop on Machine Learning and Combinatorial Optimization (ML&CO 2026)</title>
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<a href="https://ai4co.org/workshop" target="_blank">ML&CO @ICML'26</a>
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<span>Workshop</span>
</div>
<div class="hero-title">🥥CoCOPT: The Coevolution of Machine Learning and Combinatorial Optimization</div>
<div class="hero-details"><i>July 10th, 2026, Seoul, South Korea </i></div>
</div>
</section>
<section class="content-section">
<h2>Overview</h2>
<p class="tagline">
<strong>Bridging Machine Learning (ML) \& Combinatorial Optimization (CO) -- from ML for CO to CO for ML. Advancing foundations, scalability, and real-world AI-driven decision making. </strong>
</p>
<p>
Machine learning (ML) and combinatorial optimization (CO) are two foundational paradigms for solving large-scale real-world problems. ML maps data into high-dimensional continuous parameter spaces and optimizes models through gradient-based, data-driven learning, enabling breakthroughs in image recognition, language processing, and robotics. In contrast, CO focuses on complex discrete decision-making, leveraging advanced search, sampling, and branch-and-cut algorithms to address challenges in scheduling, planning, routing, hardware/chip design, drug discovery, etc.
</p>
<p>
In recent years, the synergy between ML and CO has expanded exponentially. ML has increasingly accelerated CO solvers through automated heuristic discovery, learned search guidance, especially for nonconvex, uncertain, and large-scale problems that are evasive to off-the-shelf human solvers. Reciprocally, CO provides the fundamental reasoning and constrained search frameworks integral to modern ML systems, enhancing their expressiveness, efficiency, and reliability from training to inference. Looking ahead, CO serves a pivotal domain distinguished by different inductive biases (say, from vision and language), such as decision-making landscape representation and reasoning, motivating next-generation AI systems for countless applications. Together, these trends signal a deep coevolution of ML and CO that is reshaping the future of AI-driven decision-making.
</p>
<p>
The workshop will feature invited speakers across two complementary tracks: <em>(a) ML for CO Track</em>,, focusing on ML-driven methods for CO, and <em>(b) CO for ML Track</em>, showcasing how combinatorial techniques, particularly discrete searching/sampling/reasoning, can enhance ML. By bringing together these perspectives, the workshop aims to capture this emerging direction, foster cross-community dialogue, and position the ML-CO interface as a timely research agenda for the ICML community.
</p>
</section>
<section class="content-section">
<h2>Scope and Goals</h2>
<p>
This workshop explores the growing intersection of machine learning (ML) and combinatorial optimization (CO), with a focus on both ML4CO and CO4ML. We welcome contributions across a wide range of topics within <strong>ML4CO</strong>, including but not limited to <em>Differentiable CO solver</em>, <em>Neural CO solver/Generative CO solver</em>, and <em>Automatic CO Algorithm Discovery</em>. On the <strong>CO4ML</strong> side, we invite works that leverage combinatorial frameworks to address the inherent discrete challenges within structured machine learning tasks, such as <em>Combinatorial Matching&Structural Alignment</em> and <em>Decoding/Search Algorithm for LLMs</em>.
</p>
<p>
By bringing together these communities, we aim to foster new collaborations and inspire innovative research directions at the intersection of ML and CO. By fostering shared understanding and encouraging contributions to common infrastructure—such as benchmarks, scalable tools, and system-level integrations—we hope to accelerate mutual progress and strengthen the foundation for future breakthroughs.
</p>
</section>
<section class="content-section" id="speakers">
<h2>Speakers & Panelists</h2>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/organizers/cathy-wu.jpg" alt="Cathy Wu">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://www.wucathy.com/">Cathy Wu</a></div>
<div class="speaker-affiliation">MIT</div>
<div class="speaker-bio">
Cathy Wu is an associate professor at MIT Department of Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, & Society (IDSS). Her research focuses on using machine learning to tackle the challenging optimization and control problems that are prevalent in transportation systems.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> Hybridizing machine learning model-based methods for optimization.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/changhyun-kwon.jpg" alt="Changhyun Kwon">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://www.chkwon.net/">Changhyun Kwon</a></div>
<div class="speaker-affiliation">KAIST</div>
<div class="speaker-bio">
Changhyun Kwon is Associate Professor in Industrial and Systems Engineering at KAIST. His current focus is to improve the efficiency of heuristic and exact algorithms using machine-learning approaches to solve large-scale vehicle routing problems and mobility service operations problems.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> Learning-Based Separation Algorithms for Cutting Planes in CO.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/alexander-novikov.jpg" alt="Alexander Novikov">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://www.linkedin.com/in/alexander-novikov-b0a968a6/">Alexander Novikov</a></div>
<div class="speaker-affiliation">Google DeepMind</div>
<div class="speaker-bio">
Alexander Novikov is a Research Scientist at Google DeepMind. His work focuses on foundational and applied problems in machine learning, with a particular emphasis on algorithmic discovery. Recently, he has contributed to advances in automated algorithm design for challenging mathematical problems using large language models.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/junchi-yan.jpg" alt="Junchi Yan">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://thinklab.sjtu.edu.cn">Junchi Yan</a></div>
<div class="speaker-affiliation">Shanghai Jiao Tong University</div>
<div class="speaker-bio">
Junchi Yan is a Full Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University. His research focuses on machine learning for real-world decision-making and intelligent systems. He previously spent 10 years in industry research and advising roles, including at IBM Research and Amazon.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> Structure-Aware Learning and Optimization Landscapes in Foundation Models.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/cunxi-yu.jpg" alt="Cunxi Yu">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://research.nvidia.com/person/cunxi-yu">Cunxi Yu</a></div>
<div class="speaker-affiliation">NVIDIA</div>
<div class="speaker-bio">
Cunxi Yu is a Senior Research Scientist at NVIDIA Research. She develops AI solutions for decision-making, focusing on combining machine learning and deep reinforcement learning with data-driven optimization and socially aware algorithms.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> Application of GNNs and LLMs in Solving Real-World CO Problems.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/maximilian-schiffer.jpg" alt="Maximilian Schiffer">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://www.ot.mgt.tum.de/osm/team/maximilian-schiffer/">Maximilian Schiffer</a></div>
<div class="speaker-affiliation">Technical University of Munich</div>
<div class="speaker-bio">
Maximilian Schiffer is Associate Professor of Business Analytics & Intelligent Systems at TUM School of Management, Technical University of Munich (TUM). His research focuses on developing operations research and prescriptive analytics methods to solve central societal problems, especially in the field of mobility and transportation.
</div>
<div class="speaker-presentation">
<strong>Topic:</strong> Combinatorial Optimization Augmented Machine Learning.
</div>
</div>
</div>
<h3 style="margin-top: 3rem; margin-bottom: 2rem;">Panelists</h3>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/organizers/jinkyoo-park.jpg" alt="Jinkyoo Park">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://pure.kaist.ac.kr/en/persons/jinkyoo-park/">Jinkyoo Park</a></div>
<div class="speaker-affiliation">KAIST</div>
<div class="speaker-bio">
Jinkyoo Park is an Associate Professor in Industrial and Systems Engineering at KAIST, and also the founder and CEO of Omelet, a startup developing AI-based CO solvers. His research focuses on AI-based CO, reinforcement learning, and ML for industrial applications, as well as data-driven decision-making.
</div>
</div>
</div>
<div class="speaker">
<div class="speaker-image">
<img src="assets/photos/speakers/hyeonah-kim.jpg" alt="Hyeonah Kim">
</div>
<div class="speaker-content">
<div class="speaker-name"><a target="_blank" href="https://hyeonahkimm.github.io/">Hyeonah Kim</a></div>
<div class="speaker-affiliation">Mila & Université de Montréal</div>
<div class="speaker-bio">
Hyeonah Kim is a postdoctoral researcher at Mila and Université de Montréal. Her research interests lie in scientific discovery with deep learning, with a particular interest in GFlowNets, active learning, and sample-efficient training.
</div>
</div>
</div>
</section>
<section class="content-section" id="call-for-papers">
<h2>Call For Papers</h2>
<div class="tldr-section">
<h3>TL;DR:</h3>
<ul>
<li><strong>Length:</strong> 4-8 pages (not including references and appendix)</li>
<li><strong>Formatting:</strong> <a href="https://icml.cc/Conferences/2026/AuthorInstructions">ICML latex style</a>. Please change the footnote to reflect the name of the workshop.</li>
<li><strong>Dual Submission Policy:</strong> We accept submissions of ongoing unpublished work and work that is currently under submission.</li>
<li><strong>No Proceedings:</strong> The workshop is non-archival, which means you can submit the same or extended work as a publication to other venues after the workshop.</li>
<li><strong>Submission Deadline:</strong> April 15, 2026</li>
<li><strong>Notification Deadline:</strong> May 15, 2026</li>
<li><strong>Camera-Ready Deadline:</strong> June 1, 2026</li>
<li><strong>Submission Portal:</strong> <a href="#">OpenReview</a></li>
</ul>
</div>
<!-- Countdown Timers -->
<div class="countdown-container">
<div class="countdown-item">
<h4>Submission Deadline</h4>
<div class="countdown-timer" id="countdown-submission"></div>
</div>
<div class="countdown-item">
<h4>Notification</h4>
<div class="countdown-timer" id="countdown-notification"></div>
</div>
<div class="countdown-item">
<h4>Camera-Ready</h4>
<div class="countdown-timer" id="countdown-camera-ready"></div>
</div>
</div>
<p class="cfp-intro">For detailed submission guidelines and more information, please refer to the <a href="workshop/call-for-papers.html" class="cfp-link">call for papers</a>.</p>
</section>
<section class="content-section" id="schedule">
<h2>Workshop Schedule</h2>
<table class="schedule-table">
<thead>
<tr>
<th>Time (KST)</th>
<th>Event</th>
</tr>
</thead>
<tbody>
<tr>
<td class="time-cell">08:20 - 08:30</td>
<td class="event-cell">
<div class="event-type">Opening</div>
<div class="event-title">Opening Remarks</div>
</td>
</tr>
<tr>
<td class="time-cell">08:30 - 09:00</td>
<td class="event-cell">
<div class="event-type">Morning Keynote</div>
<div class="event-title">Hybridizing Machine Learning Model-Based Methods for Optimization (Speaker: <span class="event-speaker">Cathy Wu</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">09:00 - 09:30</td>
<td class="event-cell">
<div class="event-type">Morning Keynote</div>
<div class="event-title">Learning-Based Separation Algorithms for Cutting Planes in CO (Speaker: <span class="event-speaker">Changhyun Kwon</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">09:30 - 10:00</td>
<td class="event-cell">
<div class="event-type">Morning Keynote</div>
<div class="event-title">AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery (Speaker: <span class="event-speaker">Alexander Novikov</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">10:00 - 11:00</td>
<td class="event-cell">
<div class="event-type">Session</div>
<div class="event-title">Poster Session / Break</div>
</td>
</tr>
<tr>
<td class="time-cell">11:00 - 11:40</td>
<td class="event-cell">
<div class="event-type">Session</div>
<div class="event-title">Oral Presentations (8 min each, 5 selected workshop papers)</div>
</td>
</tr>
<tr>
<td class="time-cell">11:40 - 13:00</td>
<td class="event-cell">
<div class="event-type">Break</div>
<div class="event-title">Lunch Break</div>
</td>
</tr>
<tr>
<td class="time-cell">13:00 - 13:30</td>
<td class="event-cell">
<div class="event-type">Afternoon Keynote</div>
<div class="event-title">Structure-Aware Learning and Optimization Landscapes in Foundation Models (Speaker: <span class="event-speaker">Junchi Yan</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">13:30 - 14:00</td>
<td class="event-cell">
<div class="event-type">Afternoon Keynote</div>
<div class="event-title">Application of GNNs and LLMs in Solving Real-World CO Problems (Speaker: <span class="event-speaker">Cunxi Yu</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">14:00 - 14:30</td>
<td class="event-cell">
<div class="event-type">Afternoon Keynote</div>
<div class="event-title">Combinatorial Optimization Augmented Machine Learning (Speaker: <span class="event-speaker">Maximilian Schiffer</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">14:30 - 15:30</td>
<td class="event-cell">
<div class="event-type">Session</div>
<div class="event-title">Poster Session / Break</div>
</td>
</tr>
<tr>
<td class="time-cell">15:30 - 16:15</td>
<td class="event-cell">
<div class="event-type">Session</div>
<div class="event-title">Breakout Roundtable: The Future of <i>ML for CO</i> and <i>CO for ML</i></div>
</td>
</tr>
<tr>
<td class="time-cell">16:15 - 16:45</td>
<td class="event-cell">
<div class="event-type">Panel Discussion</div>
<div class="event-title">"<i>Mind Map of CO-ML Coevolution</i>" (Panel Leads: <span class="event-speaker">Jinkyoo Park</span> and <span class="event-speaker">Hyeonah Kim</span>)</div>
</td>
</tr>
<tr>
<td class="time-cell">16:45 - 17:00</td>
<td class="event-cell">
<div class="event-type">Closing</div>
<div class="event-title">Closing Remarks & Social Events</div>
</td>
</tr>
</tbody>
</table>
</section>
<section class="content-section" id="organizers">
<h2>Organizers</h2>
<div class="organizers-grid">
<div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/sirui-li.jpg" alt="Sirui Li">
</div>
<div class="organizer-name"><a href="https://siruil.github.io/" target="_blank">Sirui Li</a></div>
<div class="organizer-affiliation">Senior Researcher, Microsoft Research</div>
</div>
<!-- <div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/federico-berto.jpg" alt="Federico Berto">
</div>
<div class="organizer-name">Federico Berto</div>
<div class="organizer-affiliation">PhD Student, KAIST</div>
</div> -->
<div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/yining-ma.jpg" alt="Yining Ma">
</div>
<div class="organizer-name"><a href="https://yining043.github.io/" target="_blank">Yining Ma</a></div>
<div class="organizer-affiliation">Postdoctoral Associate, MIT</div>
</div>
<div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/zhiguang-cao.jpg" alt="Zhiguang Cao">
</div>
<div class="organizer-name"><a href="https://zhiguangcaosg.github.io/" target="_blank">Zhiguang Cao</a></div>
<div class="organizer-affiliation">Assistant Professor, SMU</div>
</div>
<!-- <div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/ido-greenberg.jpg" alt="Ido Greenberg">
</div>
<div class="organizer-name">Ido Greenberg</div>
<div class="organizer-affiliation">Senior Research Scientist, NVIDIA</div>
</div> -->
<div class="organizer">
<div class="organizer-image">
<img src="assets/photos/organizers/eli-meirom.jpg" alt="Eli Meirom">
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<div class="organizer-name"><a href="https://research.nvidia.com/person/eli-meirom" target="_blank">Eli Meirom</a></div>
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<div class="organizer-name"><a href="https://shengyu-feng.github.io/" target="_blank">Shengyu Feng</a></div>
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<div class="organizer-name"><a href="https://syj5268.github.io/" target="_blank">Yoonju Sim</a></div>
<div class="organizer-affiliation">PhD Student, KAIST</div>
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</section>
<section class="content-section" id="community-building">
<h2>Community Building</h2>
<p>
Collaboration across machine learning, operations research, and diverse ML applications is essential for the success of ML and CO. This workshop will serve as a platform to spark dialogue, inspire novel solutions, and drive future advances. We aim to build lasting connections among researchers, practitioners, and industry professionals. Designed as the start of an ongoing workshop series, we will foster a growing, supportive community with long-term engagement.
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We have launched the <strong>AI4CO Community</strong> which includes an active Slack channel that already hosts 400+ members and is open to all interested in ongoing collaboration. Join our community to stay connected with the latest developments in the intersection of AI and combinatorial optimization.
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