2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026)
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About this event
2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026)
Date: December 18–21, 2026
Venue: The University of Electro-Communications, Tokyo, Japan
Event Type: Academic & Research Conference focused on Artificial Intelligence (AI) and Cloud Computing
Estimated Attendance: Not explicitly stated, but typical for academic conferences of this scale: 500–800 researchers, academics, industry professionals, and students
1️⃣ Who attends (BUYERS / ATTENDEES)
AICCC 2026 attracts a specialized audience including:
- Researchers from academia, industries, and R&D organizations
- PhD students, postdoctoral fellows, and young researchers
- AI and Cloud Computing technology developers
- Academic institutions and university representatives
- Industry leaders in cloud infrastructure, machine learning, and data science
- Government and private sector R&D funding bodies
2️⃣ Location + Attendee Geographic Origin
Show Location: Tokyo, Japan
Attendee Origin: Global, with strong representation from:
- Asia-Pacific (Japan, China, South Korea)
- North America (US, Canada)
- Europe (UK, Germany, France)
- Emerging markets in AI/Cloud research (India, Brazil)
3️⃣ Audience Reach
Reach Type: Global
The conference explicitly targets researchers "from all over the globe" and has a track record of international participation. Key indicators of global reach include:
- Indexing in Scopus and Ei Compendex
- International editorial board and keynote speakers
- Special issues in journals with global readership
4️⃣ Sample Buyer Company Names + Websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | IBM Research | ibm.com/research | AI Research Director | Global leader in AI R&D with active academic collaborations |
| 2 | Microsoft Azure | azure.microsoft.com | Cloud Computing Principal Engineer | Major cloud infrastructure provider with research partnerships |
| 3 | Google Cloud | cloud.google.com | Machine Learning Researcher | Active in AI/ML research and academic conference participation |
| 4 | University of Tokyo | u-tokyo.ac.jp | Dean of Engineering | Top-ranked Asian institution with AI/cloud research focus |
| 5 | Toyota Technological Institute | tti.jp | AI Laboratory Director | Japanese research institute specializing in AI applications |
| 6 | Amazon Web Services | aws.amazon.com | Cloud Solutions Architect | Market-leading cloud provider with academic outreach programs |
| 7 | Carnegie Mellon University | cmu.edu | Computer Science Department Chair | Pioneer in AI research with global academic collaborations |
| 8 | Hitachi Research | hitachi.com | Cloud Innovation Manager | Japanese conglomerate investing in AI-driven cloud solutions |
| 9 | Max Planck Institute | mpi.de | Research Group Leader (AI) | European leader in fundamental AI research |
| 10 | NVIDIA Research | nvidia.com | AI Hardware Architect | Key player in AI computing infrastructure and research tools |
| 11 | Stanford University | stanford.edu | AI Ethics Program Director | Top US institution shaping AI policy and research standards |
| 12 | Ericsson Research | ericsson.com | Cloud Native Director | Telecom leader integrating AI into cloud infrastructure |
| 13 | Chinese Academy of Sciences | cas.cn | Cloud Computing Researcher | Major Asian research body in AI and cloud technologies |
| 14 | MIT CSAIL | csail.mit.edu | Computer Science Professor | Prestigious US research lab in AI and computation |
| 15 | Samsung Advanced Institute of Technology | samsung.com/research | AI Systems Group Lead | Korean tech giant with significant AI/cloud R&D investment |
5️⃣ Job Profiles, Industries & Event Type
Best Job Profiles to Target:
- AI Research Directors
- Cloud Computing Architects
- University Department Chairs
- Machine Learning Engineers
- Research Institute Managers
- Academic Publishers
- Technology Procurement Officers
- Computer Software
- Research
- Higher Education
- Information Technology & Services
- Telecommunications
- Artificial Intelligence
- Computer Networking
6️⃣ Estimated Attendance
While exact figures aren't provided, similar academic conferences in AI/Cloud domains typically draw:
- 400–600 academic researchers
- 100–150 industry professionals
- 50–100 exhibitors/sponsors
- 100+ students and young researchers
7️⃣ Key Focus Areas & Buyer Engagement
Key Focus Areas:
- AI Algorithms and Applications
- Cloud Infrastructure & Security
- Machine Learning Systems
- Edge Computing
- Academic-Industry Research Collaborations
- AI Ethics and Governance
- Research collaboration enablers
- Academic partnership opportunities
- Technology procurement for research labs
- Publication and indexing services
- Talent acquisition platforms for AI researchers
8️⃣ Client-Product Fit Note
To refine the buyer list, please share your client's website. Based on the conference focus:
- If client sells research tools: Target universities (e.g., MIT CSAIL), research institutes (e.g., Max Planck), and tech R&D teams (e.g., IBM Research)
- If client offers cloud solutions: Focus on enterprise buyers (e.g., Amazon Web Services), telecom providers (e.g., Ericsson), and government R&D bodies
- If client is in academic publishing: Prioritize university departments and publication indexers
- If client provides AI infrastructure: NVIDIA Research, Samsung SAIL, and similar entities would be ideal targets
9️⃣ Final Recommendation
Quality Rating for B2B Buyer List Sales: 8.5/10
- Pros: High concentration of decision-makers in AI/cloud research, global academic-industry mix, strong publication incentives
- Cautions: Academic buyers may have different procurement processes than corporate entities; ensure compliance with university purchasing regulations
Data sheet
2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026) – Event Attendee & Buyer Profile Analysis
Event date: December 18–21, 2026
Location: The University of Electro-Communications, Tokyo, Japan
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026) |
| Event Date | December 18–21, 2026 |
| Event Status | Upcoming |
| Venue | The University of Electro-Communications |
| City | Tokyo |
| State / Region | Tokyo |
| Country | Japan |
| Organizer | AICCC 2026 Organizing Committee / conference organizer not fully named in the reviewed homepage content |
| Official Event Website | www.aiccc.net |
| Event Type | Academic & Research Conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numeric attendance; high for date, city, country, venue, and audience description based on official event website. |
| Main Purpose of Event | To convene global researchers, academic institutions, industry participants, and R&D organizations focused on artificial intelligence and cloud computing for paper presentations, knowledge exchange, networking, and publication in conference proceedings. |
About the Event
AICCC 2026 is the 9th edition of an international conference centered on artificial intelligence and cloud computing. According to the official event website, it will be held at The University of Electro-Communications in Tokyo, Japan, and is designed for researchers from academia, industry, and research and development organizations worldwide. The event also places explicit emphasis on participation from PhD students, postdoctoral fellows, and young researchers.
From a commercial intelligence perspective, this is primarily a research-led conference rather than a broad trade exhibition. Its value lies in access to AI and cloud decision influencers in universities, research labs, technical teams, publication-oriented communities, and selected industry R&D groups. It is relevant for partnership development, research collaboration, technical recruitment, academic technology adoption, and niche B2B outreach to innovation-led organizations, but it is less suited to mass-volume buyer list building than large enterprise trade shows.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers | Universities, research institutes, engineering faculties, AI laboratories | Influence tool selection, datasets, lab software, compute services, and collaboration choices | High for research software, cloud credits, compute infrastructure, publication services, and collaboration platforms |
| Industry R&D professionals | AI product teams, cloud engineering groups, enterprise innovation labs | Evaluate emerging methods, technical partnerships, and experimental platforms | High for AI tooling, model deployment platforms, MLOps, data infrastructure, and technical consulting |
| R&D organizations | Public and private research centers, scientific computing teams | Influence grant-backed procurement and pilot projects | Relevant for high-performance computing, data management, cybersecurity, and applied AI partnerships |
| PhD students and postdoctoral fellows | Graduate programs, university labs, sponsored research groups | Future influencers; often shape adoption of frameworks, tools, and platforms used in labs | Useful for ecosystem growth, community adoption, and early technical advocacy |
| Young researchers | Early-career faculty, applied scientists, lab engineers | Recommend solutions to principal investigators and department leaders | Strong for product trials, academic licensing, and developer engagement |
| Conference speakers and committee members | Senior academics, invited industry experts, research leaders | High influence on partnerships, reputation, and strategic adoption | High-value relationship targets for sponsorship, thought leadership, and strategic collaboration |
| Institutional administrators and program coordinators | Conference administration, academic departments, grant-funded programs | Support vendor engagement, registration, and departmental coordination | Relevant for event services, academic software, and institutional partnerships |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Tokyo | Host-city academics, university labs, local AI engineers, and research institutions | High | Tokyo provides strong access to universities, research centers, and Japanese technology companies |
| Tokyo Region / Greater Tokyo | Faculty, students, R&D leaders, and enterprise technical teams from surrounding institutions | High | Likely concentration of domestic Japanese participants due to venue accessibility and academic density |
| Japan | National academic and technical attendees | High | Likely strong representation from Japanese universities and domestic technology stakeholders |
| Asia-Pacific | Researchers and technical delegates from East Asia, Southeast Asia, and broader APAC | Medium to High | Official website states the conference is for participants from all over the globe; Tokyo location supports regional draw |
| North America and Europe | Selected international researchers, speakers, guest editors, and collaborators | Medium | International participation is clearly invited; exact current-year country mix not yet disclosed |
| Global | Academia, industry, and R&D organizations worldwide | Medium | Confirmed by official description; useful for multinational outreach but current-year attendee-country list is not public |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The official website explicitly states the event is intended for researchers from academia, industries, and R&D organizations “all over the globe.” |
| Regional | Secondary practical concentration | Because the event is hosted in Tokyo, actual in-person concentration is likely strongest across Japan and Asia-Pacific, even though the conference positions itself internationally. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| The University of Electro-Communications | Host academic institution | Official venue and likely source of local faculty, lab, and administrative participation | uec.ac.jp | Professor, Lab Director, Research Center Director, IT Administrator, International Program Coordinator | Confirmed Current-Year Participant |
| Cleveland State University | Academic research organization | Professor Wenbing Zhao is named on the official site as guest editor for associated special issues, indicating direct conference-related involvement | csuohio.edu | Professor, Research Lead, Department Chair, AI Lab Director | Confirmed Speaker Organization |
| Nanjing University of Posts and Telecommunications | Academic research organization | Professor Pan Wang is named on the official site as guest editor for an associated special issue, indicating conference-linked involvement | njupt.edu.cn | Professor, Research Center Head, Graduate Program Director, Cloud Computing Faculty Lead | Confirmed Speaker Organization |
Official current-year attendee, exhibitor, sponsor, and full speaker organization lists were not publicly available in the reviewed source content. This limits the number of confirmed buyer-side organizations that can be listed without speculation. For this event, the most reliable outreach universe is role-based targeting across universities, R&D institutions, and AI/cloud technical organizations rather than a large confirmed attendee-company file.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Academic | Senior | Drives research direction, collaboration decisions, lab tooling, and grant-backed purchases |
| 2 | Research Scientist / Applied Scientist | R&D | Manager / IC | Evaluates AI frameworks, compute environments, model pipelines, and technical partnerships |
| 3 | Lab Director / Research Center Director | Research Administration | Director | High-value target for institutional partnerships and technical procurement influence |
| 4 | Head of AI / Machine Learning Lead | Engineering / Innovation | Director / Head | Relevant within industry participants attending for applied AI and algorithmic innovation |
| 5 | Cloud Architect / Cloud Engineering Manager | IT / Infrastructure | Manager / Senior IC | Important for cloud platform selection, hybrid infrastructure, and research compute environments |
| 6 | IT Director | Information Technology | Director | Approves institutional systems, cloud tools, cybersecurity, and platform deployment |
| 7 | Department Chair / Dean | Academic Leadership | Executive | Influences strategic partnerships, budget approval, and institutional adoption |
| 8 | Program Manager / Conference Coordinator | Programs / Administration | Manager | Useful for sponsorship, partnership activation, and program-level engagement |
| 9 | Postdoctoral Researcher | Research | Individual Contributor | Early adopter and technical evaluator with strong influence over proof-of-concept tool usage |
| 10 | Graduate Researcher / PhD Candidate | Research / Education | Individual Contributor | Relevant for community building, product awareness, and long-term platform adoption |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Core audience includes universities, faculty, and graduate researchers | Academic software, cloud credits, compute infrastructure, research partnerships |
| 2 | Research | Conference explicitly targets research and development organizations | Scientific computing tools, data platforms, collaboration environments |
| 3 | Information Technology & Services | Relevant to cloud computing adopters, solution providers, and applied technical teams | Cloud services, data architecture, infrastructure management |
| 4 | Computer Software | AI and cloud workflows often map directly to software engineering and product development organizations | MLOps, development tools, AI integration platforms |
| 5 | Computer Hardware | AI research and cloud workloads depend on accelerated computing and infrastructure | Servers, GPUs, edge systems, performance compute solutions |
| 6 | Computer Networking | Cloud and distributed AI environments rely on networking performance and architecture | Campus networking, interconnects, secure access, distributed infrastructure |
| 7 | Internet | Internet-platform companies and services teams often participate in AI/cloud research ecosystems | Platform scaling, AI services, cloud-native experimentation |
| 8 | Telecommunications | Cloud, distributed systems, and intelligent network applications overlap with telecom research | Network AI, edge computing, secure cloud communications |
| 9 | Electrical/Electronic Manufacturing | Technical manufacturing firms may engage in AI-driven systems research and embedded/cloud integration | Industrial AI, intelligent devices, engineering simulation |
| 10 | Government Administration | Public research institutions and funding-linked technical programs may participate indirectly | Research grants, innovation programs, public-sector AI initiatives |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No numeric attendance published in reviewed official website content | No reliable current-year estimate should be stated without organizer evidence |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Reviewed homepage content | This appears to be a conference format rather than a traditional exhibition |
| Buyer count | Not publicly confirmed | Unconfirmed | Reviewed homepage content | Commercial buyer attendance is secondary to academic and technical participation |
| Speaker count | Not publicly confirmed | Unconfirmed | Official website references keynote speakers and invited speakers but no total count in reviewed content | Named associated special-issue editors are visible, but not a full speaker roster |
| Sponsor count | Not publicly confirmed | Unconfirmed | Reviewed homepage content | Sponsorship section exists, but no sponsor roster was available in the reviewed text |
| Historical attendance | Not publicly confirmed | Historical / prior-year evidence unavailable in reviewed content | Homepage history links mention prior editions but no attendance figures were included in the provided official text | Do not rely on third-party estimate without organizer support |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence research | Model development, algorithm testing, reproducibility, and publication-quality research support | Demo technical tools, share benchmark results, enable pilot access for labs | AI platforms, experimentation environments, annotation tools, model evaluation systems |
| Cloud computing | Scalable compute, storage, distributed systems, and research deployment environments | Target infrastructure teams and lab admins with cost, performance, and scalability messaging | Cloud services, storage, orchestration, HPC access, managed environments |
| Academic publication and peer review | Conference proceedings, indexing visibility, manuscript workflow support | Engage organizers, editors, and institutions with publication support and visibility tools | Publishing platforms, collaboration software, submission systems |
| Research collaboration | Cross-border academic and industry cooperation | Position as strategic partner for consortiums, grants, or lab-to-industry programs | Collaboration platforms, funded partnership programs, industry-academic liaison support |
| Young researcher engagement | Access to tools, mentorship, compute resources, and visibility | Offer trials, training, credits, competitions, or academic adoption packages | Education licensing, free-tier technical tools, community programs |
| Applied industrial experiences | Bridging research outcomes into practical enterprise use cases | Connect with R&D teams and solution architects around deployment case studies | AI deployment services, integration consulting, enterprise-ready infrastructure |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Strong for academic, research, and technical solution providers; weaker for mainstream procurement-led selling. |
| Decision-maker availability | Medium | Senior researchers and lab leaders are likely present, but many attendees may be students or technical contributors rather than final budget owners. |
| Data collection potential | Low to Medium | Official current-year participant list visibility is limited in the reviewed source material. |
| Apollo targeting potential | High | Even without a public attendee list, role-based targeting by industry, department, and titles is practical and scalable. |
| Geographic targeting potential | High | Tokyo, Japan, and broader APAC provide clear targeting clusters for event-adjacent outreach. |
| Best outreach approach | High | Use research collaboration, technical enablement, cloud performance, and academic adoption messaging rather than aggressive sales language. |
| Overall lead quality | Medium | Good for niche AI/cloud ecosystem selling, strategic partnerships, and innovation-driven outreach; less suitable for broad transactional lead generation. |
| Best use case | High | Best for academic partnerships, developer relations, sponsored research, cloud adoption programs, and technical brand positioning. |
| Limitations / risks | Medium | Limited public attendee transparency and lower concentration of traditional enterprise procurement buyers. Suitable for B2B attendee list building only on a selective, role-based basis. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Information Technology & Services; Computer Software; Computer Hardware; Computer Networking; Internet; Telecommunications; Electrical/Electronic Manufacturing; Government Administration | Build a role-based audience aligned with AI/cloud research and applied technical adoption |
| Departments | Research; Engineering; Information Technology; Education; Operations; Program Management | Capture technical and institutional influence centers |
| Seniority | Director; Head; VP; CXO; Owner; Partner; Manager; Senior | Prioritize budget holders and senior technical evaluators |
| Job titles | Professor, Principal Investigator, Research Scientist, Applied Scientist, Lab Director, Research Center Director, Head of AI, Machine Learning Lead, Cloud Architect, Cloud Engineering Manager, IT Director, Department Chair, Dean, Program Manager | Focus on likely attendees and adjacent high-fit prospects |
| Geography | Japan; Tokyo; Asia-Pacific; United States; China; South Korea; Singapore; India; Germany; United Kingdom | Mirror the event’s international positioning with a practical APAC emphasis |
| Employee size | 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Capture universities, institutes, and established technology organizations with research budgets |
| Keywords | artificial intelligence, machine learning, deep learning, cloud computing, distributed systems, data science, edge computing, intelligent systems, AI lab, research center, academic computing | Improve precision around event-theme relevance |
| Technologies | Cloud platforms, containerization, data platforms, ML tooling, HPC environments where available | Identify applied AI/cloud adopters rather than purely theoretical researchers |
| Revenue range | Optional for corporate targeting; not essential for universities and research institutions | Use only when narrowing enterprise-side AI/cloud buyers |
| Company type | Educational institution, research organization, public institution, private company | Separate academic and commercial outreach tracks |
Suggested Apollo Search Logic: (“artificial intelligence” OR “machine learning” OR “cloud computing” OR “distributed systems” OR “data science”) AND (Professor OR “Research Scientist” OR “Lab Director” OR “Head of AI” OR “Cloud Architect” OR “IT Director”) AND (Japan OR Tokyo OR APAC). For academic-first outreach, prioritize Higher Education and Research. For commercial adjacent outreach, layer in Information Technology & Services, Computer Software, and Telecommunications.
Client Fit Review Required
Please share the client website or product/service details. I will review the client offering and identify the highest-fit buyer companies, Apollo industries, seniority levels, departments, and job titles from this event.
Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| AICCC 2026 Official Website | Official event website | Event title, dates, Tokyo location, host venue, event purpose, audience description, publication details, and prior-edition references | High |
| AICCC 2026 Homepage News Section | Official event news content | Confirmation that AICCC 2026 will be held at The University of Electro-Communications, Tokyo, Japan during December 18–21, 2026 | High |
| AICCC 2026 Special Issue Information | Official conference content | Named associated academic organizations including Cleveland State University and Nanjing University of Posts and Telecommunications | Medium to High |
| Verification note | Research limitation | No public current-year attendee count, exhibitor count, sponsor roster, or full attendee-company list was available in the reviewed official source content | High confidence in limitation statement |
🎯 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 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
🔒 Get my buyer details Free · ~20 seconds