2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026)
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About this event
2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026)
Dates: October 26–28, 2026
Venue: Bangkok, Thailand
Event Type: Academic and Research Symposium
About the Event: The 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026) is a premier interdisciplinary forum dedicated to advancing the integration of machine learning and social computing research. This event brings together researchers, practitioners, and innovators to exchange cutting-edge ideas, explore interdisciplinary collaborations, and address the impact of machine learning on social sciences and real-world social systems. Amid the rapid evolution of AI and data-driven technologies, MLSC 2026 aims to bridge theoretical advancements with practical applications, focusing on ethical norms, policy frameworks, and human-centric AI aligned with social needs.
Key Objectives:
- Foster in-depth discussions on ethical norms and policy frameworks in AI.
- Promote the development of human-centric AI that aligns with social needs.
- Provide a platform for researchers and practitioners to share innovative research and applications.
- Encourage interdisciplinary collaborations between machine learning and social computing fields.
Target Audience:
- Researchers in machine learning and social computing.
- Practitioners in AI and data-driven technologies.
- Innovators and developers in related fields.
- Academics and educators in computer science, social sciences, and related disciplines.
- Policy makers and industry leaders interested in AI ethics and applications.
Geographic Reach: Global, with a focus on attracting international researchers and practitioners. The event is held in Bangkok, Thailand, a vibrant city that blends cultural heritage with global innovation, providing an exceptional backdrop for this convergence of minds.
Estimated Attendance: The event is expected to draw a diverse group of attendees, including researchers, practitioners, academics, and industry professionals from around the world. While specific numbers are not provided, the symposium's interdisciplinary nature and global reach suggest a substantial and varied audience.
Key Focus Areas:
- Machine Learning Theory and Methods
- Social Computing Theory and Methods
- Digital Society
- Cross-Integration Technologies and Systems
- Intelligent Transportation and Urban Management
- Application Practices in various sectors
Buyer Engagement Opportunities:
- Sponsorship and exhibition opportunities for companies in AI, machine learning, and social computing.
- Networking sessions for academics and industry professionals to explore collaborations.
- Workshops and special sessions for in-depth discussions on specific topics.
- Keynote speeches and panel discussions with renowned experts in the field.
Sample Buyer Company Names and Websites (Based on Industry Relevance):
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | International Business Machines Corporation (IBM) | https://www.ibm.com/ | AI Research Director | IBM is a leader in AI research and development, making them a prime target for machine learning and social computing innovations. |
| 2 | Google LLC | https://www.google.com/ | Machine Learning Engineer | Google is at the forefront of machine learning applications, making their engineers and researchers key attendees. |
| 3 | Microsoft Corporation | https://www.microsoft.com/ | AI Ethics and Policy Lead | Microsoft is actively involved in AI ethics and policy, aligning with the symposium's focus on ethical norms and policy frameworks. |
| 4 | Facebook (Meta) Platforms, Inc. | https://www.facebook.com/ | Social Computing Researcher | Meta is heavily invested in social computing and platforms, making their researchers a natural fit for the symposium. |
| 5 | Amazon.com, Inc. | https://www.amazon.com/ | Principal Scientist, Machine Learning | Amazon utilizes machine learning extensively, making their scientists and engineers valuable attendees. |
| 6 | Intel Corporation | https://www.intel.com/ | AI Hardware Architect | Intel's focus on AI hardware makes them a relevant buyer for innovations in machine learning technologies. |
| 7 | NVIDIA Corporation | https://www.nvidia.com/ | Director, AI Research | NVIDIA is a leader in AI computing hardware and software, making them a key player in machine learning advancements. |
| 8 | Accenture plc | https://www.accenture.com/ | Managing Director, AI and Data | Accenture's consulting services in AI and data make them a relevant buyer for insights from the symposium. |
| 9 | SAP SE | https://www.sap.com/ | Chief Data Officer | SAP's focus on enterprise data solutions aligns with the symposium's themes of digital society and intelligent applications. |
| 10 | Siemens AG | https://www.siemens.com/ | Head of AI and Automation | Siemens' work in automation and digitalization makes them a relevant buyer for machine learning and social computing innovations. |
Job Profiles, Industries, and Event Type:
- Job Profiles:
- AI Researchers
- Machine Learning Engineers
- Social Computing Analysts
- Data Scientists
- AI Ethics and Policy Experts
- Academics and Educators in Computer Science and Social Sciences
- Industry Practitioners in AI, Machine Learning, and Social Computing
- Industries:
- Information Technology
- Artificial Intelligence
- Machine Learning and Data Science
- Social Computing and Platforms
- Educational Institutions
- Consulting and Professional Services
- Hardware and Software Development
- Event Type: Academic and Research Symposium focused on interdisciplinary research and applications in machine learning and social computing.
Estimated Attendance: While the exact number is not specified, the symposium is expected to attract a substantial and diverse audience of researchers, practitioners, academics, and industry professionals from around the world, given its global reach and interdisciplinary focus.
Key Focus Areas & Buyer Engagement:
- Machine Learning Theory and Methods
- Social Computing Theory and Methods
- Digital Society and Intelligent Applications
- Cross-Integration Technologies and Systems
- Intelligent Transportation and Urban Management
- Application Practices in various sectors including healthcare, education, and public services.
Buyer engagement opportunities include sponsorship and exhibition spaces, networking sessions, workshops, and keynote speeches, providing multiple avenues for companies to engage with attendees and showcase their innovations.
Client Product Fit Note: To provide a tailored buyer list, please share your client's website. This will allow us to align the sample buyer list with your client's specific product or service offerings, ensuring the most relevant and effective targeting.
Final Recommendation: The 2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026) is a highly relevant event for companies and organizations involved in AI, machine learning, social computing, and related fields. The symposium offers a unique opportunity for buyer engagement, networking, and showcasing innovations, making it a valuable event for attendance and participation.
Data sheet
2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026) – Event Attendee & Buyer Profile Analysis
Event date: October 26–28, 2026
Location: Bangkok, Thailand
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026) |
| Event Date | October 26–28, 2026 |
| Event Status | Upcoming |
| Venue | Specific venue not publicly confirmed on the official website content provided. |
| City | Bangkok |
| State / Region | Bangkok Metropolitan Region |
| Country | Thailand |
| Organizer | Organizer name not clearly disclosed in the official website content provided. |
| Official Event Website | ic-mlsc.org |
| Event Type | Academic and Research Symposium |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | International academic and professional research audience. Confirmed by official welcome text referencing peers from around the world. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for volume metrics; high for dates, city, country, event positioning, and thematic scope based on official website content. |
| Main Purpose of Event | To convene researchers, practitioners, and innovators working at the intersection of machine learning and social computing for paper presentations, publication, collaboration, and discussion of ethics, policy, digital society, intelligent systems, and applied AI. |
About the Event
MLSC 2026 is the second edition of an international symposium focused on the integration of machine learning and social computing. According to the official event website, the symposium will take place in Bangkok, Thailand from October 26 to 28, 2026, and is positioned as an interdisciplinary forum for research exchange across AI, social systems, digital society, intelligent transportation, urban management, and application-focused innovation.
From a commercial intelligence perspective, this is primarily a research-led event rather than a broad trade exhibition. Its relevance is strongest for academic institutions, R&D groups, AI solution providers, data science teams, public-sector digital transformation stakeholders, research publishers, and organizations interested in human-centric AI, policy frameworks, smart cities, and applied machine learning collaborations. It is suitable for expert-network building and selective B2B outreach, but less suitable for large-scale attendee list building because official current-year attendee and exhibitor rosters are not publicly disclosed in the source material reviewed.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers | Universities, graduate schools, AI research labs | Influence research tool adoption, conference partnerships, publication choices, and collaborative projects | High relevance for AI software, cloud compute, datasets, academic publishing, and lab collaboration services |
| Applied AI practitioners | Corporate AI teams, data science groups, product innovation units | Assess applied research, evaluate methods, shape pilot projects and partnerships | Relevant for ML platforms, MLOps, analytics tools, data infrastructure, and consulting services |
| Public-sector digital innovation stakeholders | Smart city units, transport agencies, digital policy groups, public service innovation teams | Influence pilot procurement, policy adoption, research partnerships, and public-interest AI evaluation | Relevant for civic AI, urban analytics, mobility systems, and responsible AI frameworks |
| Technology leaders and solution architects | Software firms, platform providers, systems integrators | Influence solution selection, integration design, and R&D commercialization | Relevant for technical demos, APIs, cloud services, and AI deployment services |
| Research program managers | Universities, institutes, funded research programs | Coordinate submissions, partnerships, grants, and institutional representation | Relevant for consortium building, sponsorships, and project development support |
| Policy, ethics, and social impact specialists | Think tanks, research centers, government advisory units, academic departments | Shape policy frameworks, governance standards, and responsible AI adoption | Relevant for governance, audit, compliance, and explainable AI solutions |
| Graduate students and early-career researchers | Universities and training programs | Lower direct buying authority but strong adoption influence for tools and publications | Useful for long-term pipeline, education products, and community building |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Bangkok | Local academics, Thai universities, local technology professionals, public-sector innovation participants | Moderate | Host city with strong accessibility and conference infrastructure |
| Bangkok Metropolitan Region | Regional universities, R&D centers, corporate technology teams | Moderate to High | Likely source of nearby institutional and enterprise attendees |
| Thailand | National academic, research, digital policy, and AI application communities | High | National reach is likely due to the symposium format and subject matter |
| Southeast Asia | Researchers and practitioners from ASEAN academic and innovation hubs | Moderate | Bangkok is a practical regional meeting point for cross-border participation |
| Asia-Pacific | AI, computing, smart city, and social systems researchers | Moderate | International research themes broaden regional draw |
| Global | Selected international paper authors, keynote participants, and research collaborators | Selective | Official website explicitly encourages connections with peers from around the world |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The symposium is presented as an international event and the official copy references peers from around the world. |
| Regional | Secondary practical concentration | In operational terms, attendee concentration is likely to be strongest across Thailand and wider Asia-Pacific research networks. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Official current-year attendee, speaker-organization, sponsor, exhibitor, and buyer-side organization lists were not publicly disclosed in the official website content reviewed. As a result, a verified current-year sample buyer-company table cannot be populated without risking unsupported claims. This limits the event’s usefulness for confirmed attendee list building. | |||||
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Director of AI Research | Research & Development | Director | Shapes research tooling, institutional collaborations, and advanced technical evaluation |
| 2 | Head of Data Science | Data / Analytics | Head / Director | Relevant for applied machine learning deployment, model evaluation, and team capability building |
| 3 | Professor / Principal Investigator | Academic Research | Senior Individual Contributor / Department Leadership | Key influencer for grants, collaborations, publications, and lab technology choices |
| 4 | Research Program Manager | Research Administration | Manager | Coordinates project partnerships, submissions, and institutional participation |
| 5 | Chief Technology Officer | Technology | C-Level | Relevant where firms attend to evaluate AI methods, commercial applications, or partnerships |
| 6 | Machine Learning Engineer Lead | Engineering | Manager / Lead | Practical evaluator of tools, frameworks, APIs, and data pipelines |
| 7 | Director of Digital Transformation | Strategy / Transformation | Director | Important for public-sector and enterprise adoption of AI-enabled social and operational systems |
| 8 | Smart City Program Manager | Public Innovation / Urban Systems | Manager | Fits official topics covering intelligent transportation, urban management, and public services |
| 9 | Policy Research Director | Policy / Governance | Director | Relevant to ethical norms, governance, and policy framework discussions highlighted by the event |
| 10 | Business Development Director | Partnerships / Commercial | Director | Useful for sponsorship, academic partnerships, platform licensing, and co-innovation opportunities |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Research | Core fit for academic and applied research institutions | Research partnerships, AI tools, datasets, and publication support |
| 2 | Higher Education | Universities are a primary attendee segment | Lab software, academic collaboration, training, and event sponsorship |
| 3 | Information Technology & Services | Broad fit for applied AI vendors and service partners | Solution deployment, integration, and analytics services |
| 4 | Computer Software | Strong fit for ML platforms, analytics tools, and model management vendors | Software evaluation, proof-of-concept, and API adoption |
| 5 | Computer Hardware | Relevant for AI infrastructure and compute-intensive research environments | GPU infrastructure, edge devices, and lab hardware |
| 6 | Government Administration | Relevant to public services, digital society, and urban management themes | Public innovation pilots and digital governance initiatives |
| 7 | Public Policy | Direct fit with ethics and policy framework discussions | AI governance, regulatory analysis, and responsible AI frameworks |
| 8 | Think Tanks | Relevant for social impact, policy, and interdisciplinary analysis | Joint studies, expert panels, and policy collaboration |
| 9 | Education Management | Fits institutions implementing intelligent educational systems | EdTech, adaptive learning, and institutional AI adoption |
| 10 | Management Consulting | Consultants may participate around transformation, AI strategy, and public systems | Advisory partnerships and implementation services |
| 11 | Transportation/Trucking/Railroad | Relevant to intelligent transportation systems and urban traffic topics | Mobility analytics, routing optimization, and smart transport solutions |
| 12 | Hospital & Health Care | Supported by official topic coverage of social analysis in healthcare | Healthcare analytics and socially informed AI applications |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not Publicly Confirmed | Official website content reviewed | No attendee count disclosed |
| Exhibitor count | Not applicable / not disclosed | Not Publicly Confirmed | Official website content reviewed | This appears to be a symposium rather than a trade expo |
| Buyer count | Buyer figure not publicly confirmed by the organizer. | Not Publicly Confirmed | Official website content reviewed | No buyer or delegate segmentation published |
| Speaker count | Not publicly confirmed | Not Publicly Confirmed | Official website content reviewed | Keynote speakers are referenced, but count not provided |
| Sponsor count | Not publicly confirmed | Not Publicly Confirmed | Official website content reviewed | Support/organizer details are incomplete in source content |
| Historical attendance | Not available in the source content reviewed | Historical / prior-year evidence unavailable | Official website content reviewed | History link exists, but no reliable attendance figure was included in the provided text |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine learning theory and methods | Model development, experimentation, reproducibility, compute access | Research collaborations, software trials, workshop participation | ML platforms, notebooks, data tooling, compute infrastructure |
| Social computing | Social network analysis, behavioral modeling, sentiment and user analytics | Methodology demos, collaborative research, applied pilots | Analytics tools, graph databases, social data platforms |
| Ethics and policy frameworks in AI | Responsible AI governance, explainability, policy alignment | Thought leadership, advisory services, framework adoption | Governance consulting, audit tools, compliance frameworks |
| Digital society | Operational transformation across digital economy, management, and services | Public-private pilots, institutional strategy dialogue | Digital transformation services, AI advisory, systems modernization |
| Cross-integration technologies and systems | Data fusion, multimodal learning, adaptive systems | Technical partnerships and integration discussions | Integration platforms, middleware, AI orchestration tools |
| Intelligent transportation and urban management | Mobility optimization, traffic analytics, smart city coordination | Government engagement and urban innovation pilots | Smart mobility platforms, urban data solutions, transport AI |
| Application practices | Real-world use cases in smart cities, healthcare, and education | Case-study driven outreach and partnership development | Vertical AI applications, decision-support systems, analytics services |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Strong for research, AI software, academic services, and policy-oriented solutions; weaker for general product sourcing. |
| Decision-maker availability | Medium | Likely presence of professors, lab leads, directors, and technical leads, but current-year participant names are not publicly listed. |
| Data collection potential | Low | No official current-year attendee directory, exhibitor list, or sponsor roster was identified in the reviewed source content. |
| Apollo targeting potential | High | Thematic targeting is practical across AI, research, higher education, consulting, and public innovation accounts. |
| Geographic targeting potential | High | Clear focus on Bangkok, Thailand, Southeast Asia, and broader international research communities. |
| Best outreach approach | High | Topic-led outreach works best: research collaboration, responsible AI, smart city applications, publication support, and technical tooling. |
| Overall lead quality | Medium | Quality is specialized and high-intent for niche AI and research solutions, but limited for scalable verified attendee acquisition. |
| Best use case | High | Best for precision outreach to universities, AI labs, public innovation programs, and research-aligned technology buyers. |
| Limitations / risks | High | Organizer has not published participant volume, named buyer organizations, or a venue-specific logistics profile in the source reviewed. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Research; Higher Education; Information Technology & Services; Computer Software; Government Administration; Public Policy; Think Tanks; Education Management; Management Consulting; Transportation/Trucking/Railroad; Hospital & Health Care | Captures the most likely institutional and applied AI participant base |
| Departments | Engineering; Information Technology; Research; Education; Operations; Strategy; Product Management; Business Development | Targets both technical and program ownership roles |
| Seniority | C-Level; VP; Director; Head; Manager; Partner; Owner | Focuses on decision-makers and budget influencers |
| Job titles | Director of AI Research; Head of Data Science; Professor; Principal Investigator; Research Program Manager; CTO; Director of Digital Transformation; Smart City Program Manager; Policy Research Director; Machine Learning Lead; AI Product Director; Dean of Engineering | Aligns to likely symposium attendees and influencers |
| Geography | Thailand; Bangkok; Singapore; Malaysia; Vietnam; Indonesia; Philippines; Japan; South Korea; Australia; India | Prioritizes likely Asia-Pacific participation while preserving broader international reach |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Covers labs, startups, universities, large institutions, and public-sector bodies |
| Keywords | machine learning, social computing, responsible AI, human-centric AI, smart city, intelligent transportation, digital society, multimodal data, social network analysis, AI ethics, urban analytics, educational AI | Builds relevance around the official conference themes |
| Technologies | Use if relevant: Python, TensorFlow, PyTorch, cloud AI, MLOps, data analytics stacks | Refines targeting toward technically active ML organizations |
| Revenue range | Use selectively; not essential for university and public-sector targeting | Better used for corporate AI vendors and consulting firms |
| Company type | Private; Public; Nonprofit; Educational Institution; Government Agency | Reflects the interdisciplinary and institution-heavy audience mix |
Suggested Apollo Search Logic: ("machine learning" OR "social computing" OR "responsible AI" OR "AI ethics" OR "smart city" OR "intelligent transportation" OR "social network analysis" OR "digital society") AND (research OR lab OR institute OR innovation OR "data science" OR "AI program") with geography centered on Thailand and Asia-Pacific, then narrowed by director-level research, data science, transformation, and policy roles.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| MLSC 2026 Official Website | Official event website | Event title, dates, city, country, topic scope, publication statement, submission deadlines, and international positioning | High for core event facts |
| Official website content provided in the request | Primary-source extract | Confirmed that the event will be held in Bangkok, Thailand during October 26–28, 2026 and described the symposium focus areas | High |
| Verification note | Research limitation | Specific venue, organizer name, attendance figures, speaker roster, sponsor roster, exhibitor count, and current-year attendee organizations were not clearly disclosed in the reviewed source content | N/A |
🎯 Selling to this event's audience? Get a free tailored buyer list
Tell us your work email and our AI instantly builds a buyer list matched to 2026 2nd International Symposium on Machine Learning and Social Computing (MLSC 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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