2026 11th International Conference on Computational Intelligence and Applications (ICCIA 2026)
🎯 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 11th International Conference on Computational Intelligence and Applications (ICCIA 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
About this event
2026 11th International Conference on Computational Intelligence and Applications (ICCIA 2026)
Dates: August 14–16, 2026
Venue: Qingdao, China
Hosted by: College of Science, China University of Petroleum (East China)
Co-hosted by: School of Computer Science and Technology, Hainan University, China
Venue: Qingdao, China
Hosted by: College of Science, China University of Petroleum (East China)
Co-hosted by: School of Computer Science and Technology, Hainan University, China
Overview
The 11th International Conference on Computational Intelligence and Applications (ICCIA 2026) will convene in Qingdao, China, from August 14 to 16, 2026. Co-sponsored by China University of Petroleum (East China) and hosted by its College of Science in collaboration with Hainan University’s School of Computer Science and Technology, this event continues a legacy of fostering innovation in computational intelligence. Since its inception in 2016, ICCIA has served as a global platform for academia and industry to exchange cutting-edge research and applications in fields such as neural networks, fuzzy systems, evolutionary computation, and hybrid intelligent systems.
Computational Intelligence (CI) encompasses theories, designs, and applications inspired by biological and linguistic principles. While traditionally rooted in neural networks, fuzzy systems, and evolutionary computation, CI has expanded to include ambient intelligence, artificial life, cultural learning, and social reasoning. The conference emphasizes interdisciplinary approaches, encouraging submissions that merge multiple domains—such as neuro-fuzzy or evolutionary-symbolic systems—to address complex challenges.
Key Focus Areas
- Neural Networks and Deep Learning
- Fuzzy Logic and Systems
- Evolutionary Computation
- Hybrid Intelligent Systems
- Pattern Recognition and Machine Learning
- Image/Audio/Video Compression and Retrieval
- Applications in Industry, Healthcare, and Cognitive Systems
Important Dates
| Milestone | Date |
|---|---|
| Submission Opens | January 5, 2026 |
| Submission Deadline | June 30, 2026 |
| Acceptance Notification | July 15, 2026 |
| Final Paper Submission | July 25, 2026 |
| Registration Deadline | July 25, 2026 |
Target Audience
ICCIA 2026 attracts a global audience of researchers, engineers, academics, and industry professionals. Key attendee profiles include:
- University professors and researchers in computer science, engineering, and mathematics
- Ph.D. students and graduate scholars
- R&D managers in AI, machine learning, and data science companies
- Engineers specializing in automation, robotics, or intelligent systems
- Industry leaders seeking innovative solutions for business applications
- Policymakers and educators focused on STEM development
Geographic Reach
While rooted in China, ICCIA has a global footprint. Past events have drawn participants from Asia, Europe, North America, and beyond. Qingdao’s selection as the 2026 venue underscores the conference’s aim to strengthen international collaboration in computational intelligence.
Sample Buyer Profiles
| Priority | Company | Website | Best Title to Target | Why a Good Fit |
|---|---|---|---|---|
| 1 | IBM Research | https://www.research.ibm.com/ | AI Research Manager | Active in neural networks and hybrid systems; seeks cutting-edge research partnerships. |
| 2 | Siemens AG | https://new.siemens.com/ | Industrial AI Solutions Director | Applies CI in automation and smart manufacturing; potential for B2B collaboration. |
| 3 | DeepMind | https://www.deepmind.com/ | Machine Learning Researcher | Leader in deep learning; likely to engage with academic innovations. |
| 4 | NVIDIA | https://www.nvidia.com/ | GPU Technology Director | Provides hardware for CI applications; interested in research partnerships. |
| 5 | Microsoft Research | https://www.microsoft.com/en-us/research/ | Principal Research Engineer | Invests in evolutionary computation and hybrid systems research. |
| 6 | Toshiba Research Europe | https://www.trel.eu/ | Chief Technology Officer | Engaged in fuzzy systems and industrial AI applications. |
| 7 | Google Brain | https://blog.research.google/teams/brain/ | Neural Networks Research Lead | Pioneering work in deep learning; targets academic collaborations. |
| 8 | General Electric (GE) Digital | https://www.ge.com/digital/ | Industrial Software Director | Applies CI in predictive maintenance and IoT; potential software buyers. |
| 9 | Hitachi, Ltd. | https://www.hitachi.com/ | AI Innovation Manager | Invests in hybrid intelligent systems for smart cities and energy. |
| 10 | Accenture AI | https://www.accenture.com/us-en/services/artificial-intelligence | AI Solutions Architect | Seeks CI applications for enterprise clients; potential consulting partnerships. |
| 11 | University of Edinburgh (School of Informatics) | https://www.informatics.ed.ac.uk/ | Professor of Machine Learning | Leading academic institution in CI; potential for research collaborations. |
| 12 | Toyota Technological Institute | https://www.tti.edu/ | Research Scientist | Focuses on evolutionary computation and robotics applications. |
| 13 | SAP AI Research | https://www.sap.com/ | Enterprise AI Director | Integrates CI into business analytics; potential software buyers. |
| 14 | Carnegie Mellon University (Machine Learning Department) | https://www.cs.cmu.edu/ | Department Head | Prominent in neural networks research; seeks academic and industry ties. |
| 15 | Samsung Electronics (AI Center) | https://www.samsung.com/research/ | AI Hardware Development Lead | Develops CI-enabled consumer and industrial electronics; potential for tech partnerships. |
Estimated Attendance
While exact figures for ICCIA 2026 are not provided, past editions have drawn several hundred attendees globally. Given the event’s growth and Qingdao’s prominence as a tech hub, attendance is projected to exceed 500 participants, including researchers, industry professionals, and students.
Engagement Opportunities
ICCIA 2026 offers multiple avenues for buyer engagement:
- Exhibitions: Showcase AI software, hardware, or services to a targeted technical audience.
- Sponsorships: Align your brand with keynote sessions, workshops, or networking events.
- Collaborative Research: Initiate partnerships with academic institutions or industry leaders.
- Talent Acquisition: Recruit top-tier researchers and engineers through direct networking.
Contact
For inquiries, please contact the conference organizers at iccia@zhconf.ac.cn.
Data sheet
2026 11th International Conference on Computational Intelligence and Applications (ICCIA 2026) – Event Attendee & Buyer Profile Analysis
Event date: August 14–16, 2026
Location: Qingdao, Shandong, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 11th International Conference on Computational Intelligence and Applications (ICCIA 2026) |
| Event Date | August 14–16, 2026 |
| Event Status | Upcoming |
| Venue | Qingdao, China; specific venue facility not publicly confirmed in the supplied official website text. |
| City | Qingdao |
| State / Region | Shandong |
| Country | China |
| Organizer | Co-sponsored by China University of Petroleum (East China); hosted by the College of Science, China University of Petroleum (East China); co-hosted by the School of Computer Science and Technology, Hainan University. |
| Official Event Website | iccia.org |
| Event Type | International academic and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global, with strong China-based academic and technical participation expected |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for volume metrics; high for dates, city, country, and organizer structure based on official event website content. |
| Main Purpose of Event | To convene researchers, engineers, and industry participants around computational intelligence theory, applications, deep learning, hybrid intelligent systems, and related AI research and implementation topics. |
About the Event
ICCIA 2026 is the 11th edition of an international conference focused on computational intelligence and its applications. Based on the official event website, the conference will be held in Qingdao, China from August 14 to 16, 2026, and is co-sponsored by China University of Petroleum (East China), hosted by its College of Science, and co-hosted by Hainan University’s School of Computer Science and Technology. The program emphasis includes neural networks, fuzzy systems, evolutionary computation, hybrid intelligent systems, pattern recognition, symbolic machine learning, and deep learning-related research.
From a commercial and lead-generation perspective, this is a high-value niche event for academic technology vendors, AI software providers, research infrastructure suppliers, scientific publishing platforms, GPU/HPC ecosystem players, lab computing providers, and university partnership teams. It matters most for organizations targeting research-led technical buyers, faculty decision-makers, university labs, advanced engineering groups, and industrial R&D functions rather than broad-volume trade show buyers. It is suitable for focused B2B attendee list building, but likely better for precision account targeting than high-volume event list sales.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University faculty and principal investigators | Universities, colleges, research labs | Influence software, compute, data, instrumentation, and collaboration platform purchases | High for research software, AI platforms, academic tools, cloud credits, and HPC vendors |
| Research scientists and postdoctoral teams | Academic institutes, applied AI labs | Technical evaluators and solution recommenders | High for tools requiring hands-on technical validation |
| Industrial R&D engineers | AI teams, engineering centers, applied analytics units | Recommend pilots, evaluate technical fit, influence implementation budgets | High for model development, MLOps, data processing, compute infrastructure, and algorithmic services |
| Department heads and deans | Science, computer science, engineering faculties | Budget owners for programs, labs, partnerships, and strategic collaborations | Strong for institutional partnerships and multi-seat deployments |
| Graduate researchers and doctoral candidates | Universities and research centers | End users and future advocates; lower direct authority | Useful for grassroots adoption, demos, trials, and community building |
| Conference speakers and committee members | Senior academics and technical leaders | High influence over research direction, procurement recommendations, and partnerships | Good targets for thought leadership outreach and strategic sponsorship |
| Academic procurement and IT support teams | University procurement offices, IT departments, lab operations teams | Validate compliance, purchasing process, deployment support | Relevant for enterprise software, hardware, subscriptions, and managed services |
| Industry partnership and business development teams | Universities, research alliances, innovation hubs | Source collaborations, sponsored research, technology transfer relationships | Relevant for organizations selling partnerships, grants support, and innovation programs |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Qingdao | Local university faculty, students, host institution teams, nearby research communities | Medium | Local attendance likely anchored by China University of Petroleum (East China) and nearby academic institutions |
| Shandong Province | Regional universities, engineering schools, industrial research groups | Medium to High | Useful for regional outreach into applied engineering and computer science ecosystems |
| Major China academic hubs | Beijing, Shanghai, Nanjing, Xiamen, Haikou and other cities linked to prior ICCIA editions | High | Official history indicates recurring participation from Chinese universities tied to earlier editions |
| National China market | Researchers, professors, AI engineers, graduate students, organizing institutions | High | China is the strongest likely attendee base due host institutions and event location |
| International academic reach | Overseas researchers and technical authors in computational intelligence | Medium | The event is positioned as international; precise country mix is not publicly confirmed in the supplied source |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The conference is explicitly positioned as an international event and has a multi-year history across several cities and host institutions. |
| National | Strong secondary reach | China-based participation is likely to dominate due host location, host institutions, and prior conference continuity in China. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| China University of Petroleum (East China) | University / research buyer | Co-sponsor of ICCIA 2026 and likely anchor institution for faculty, labs, and conference operations | upc.edu.cn | Dean, Professor, Lab Director, IT Director, Procurement Manager | Confirmed Current-Year Participant |
| College of Science, China University of Petroleum (East China) | Academic department / lab buyer | Official host unit for the 2026 conference | upc.edu.cn | Dean, Department Head, Research Group Leader, Program Coordinator | Confirmed Current-Year Participant |
| Hainan University | University / research buyer | Co-host institution for ICCIA 2026 and named in prior ICCIA history | hainanu.edu.cn | Professor, Dean, Research Director, Partnerships Director, Procurement Manager | Confirmed Current-Year Participant |
| School of Computer Science and Technology, Hainan University | Academic school / technical buyer | Official co-hosting school for the 2026 edition | hainanu.edu.cn | Dean, Department Chair, AI Lab Director, Faculty Lead | Confirmed Current-Year Participant |
| North China University of Technology | University / research buyer | Official prior-year host/sponsor reference in conference history | ncut.edu.cn | Professor, Dean, Lab Director, IT Director | Prior-Year Participation Evidence |
| Nanchang University | University / research buyer | Official prior-year sponsor reference in conference history | ncu.edu.cn | Dean, Research Director, Faculty Lead, Procurement Manager | Prior-Year Participation Evidence |
| Beijing Technology and Business University | University / research buyer | Its School of Computer and Information Engineering is cited as supporting the 2020 virtual ICCIA | btbu.edu.cn | Professor, School Director, IT Director, Research Operations Lead | Prior-Year Participation Evidence |
| School of Computer and Information Engineering, Beijing Technology and Business University | Academic school / technical buyer | Explicitly referenced in the conference history for the 2020 edition | btbu.edu.cn | School Director, Program Head, Faculty Lead, Systems Manager | Prior-Year Participation Evidence |
| Huaqiao University | University / research buyer | Official prior-year sponsor reference in conference history for 2021 | hqu.edu.cn | Dean, Faculty Lead, AI Research Director, Procurement Manager | Prior-Year Participation Evidence |
| Nanjing Tech University | University / research buyer | Official prior-year sponsor reference in conference history for 2022 | njtech.edu.cn | Professor, Research Center Director, IT Director, Dean | Prior-Year Participation Evidence |
Prior-year participation evidence. Not a confirmed attendee list for the current edition.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Faculty | Senior | High influence on technical adoption, lab tools, and collaborative projects |
| 2 | Dean / Associate Dean | Academic Leadership | Executive | Can sponsor partnerships, program investments, and institutional purchases |
| 3 | Lab Director / Research Center Director | Research Operations | Director | Owns or strongly influences infrastructure, software, and experimental tool selection |
| 4 | Department Head / Chair | Computer Science / Engineering / Science | Director | Important for departmental tools, curriculum technology, and faculty-wide adoption |
| 5 | AI Research Scientist | R&D / Research | Mid-Senior | Evaluates technical merit and fit for algorithms, data pipelines, and experimentation |
| 6 | IT Director | Information Technology | Director | Critical for deployment, integration, systems approval, and campus infrastructure |
| 7 | Procurement Manager | Procurement | Manager | Handles purchase process, vendor onboarding, and compliance |
| 8 | Partnerships Director / Industry Liaison | External Relations / Innovation | Director | Useful for sponsored research, pilot programs, and institutional collaboration |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Core event audience includes universities, faculties, and labs | Research software, compute, academic subscriptions, lab systems |
| 2 | Research | Direct match to scientific and applied research institutions | Algorithm development, data platforms, collaboration tools |
| 3 | Information Technology & Services | Relevant for AI implementation and systems integration attendees | Deployment, data engineering, model ops, system integration |
| 4 | Computer Software | Software-driven computational intelligence use cases dominate the theme | AI platforms, analytics, simulation, MLOps, visualization |
| 5 | Computer Hardware | Compute-intensive AI workloads create hardware demand | Servers, accelerators, workstations, lab compute nodes |
| 6 | Semiconductors | Relevant where AI research intersects with compute architecture and acceleration | Chips, edge AI, GPU ecosystem outreach |
| 7 | Industrial Automation | Applied AI and intelligent control often connect to industrial systems | Machine intelligence, optimization, predictive control |
| 8 | Mechanical or Industrial Engineering | Computational intelligence has cross-functional engineering relevance | Optimization, modeling, system design, automation research |
| 9 | Education Management | Useful for university administration and program-level decision-makers | Academic partnerships, digital learning tools, institutional software |
| 10 | Electrical/Electronic Manufacturing | Relevant to organizations applying AI in sensing, control, and embedded systems | Applied R&D outreach and university-industry collaboration |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website text supplied | No attendee number published in the provided official content |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website text supplied | This appears to be a conference format rather than a large exhibition-led expo |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website text supplied | Academic and R&D attendees are expected, but no official buyer count is given |
| Speaker count | Not publicly confirmed in supplied text | Unconfirmed | Official navigation references keynote and invited speakers | Speaker section exists, but no speaker totals were provided in the source text |
| Sponsor count | 2 named institutions in current-year organizer structure | Confirmed | Official website text supplied | China University of Petroleum (East China) and Hainan University are explicitly named in current event structure |
| Historical attendance | Not publicly confirmed in supplied source | Historical data unavailable | Official website history section references prior editions only | No prior-year attendance figures were provided in the supplied text |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence / Deep Learning | Model development, experimentation, training efficiency | Demo technical workflows, benchmark performance, publish case studies | AI platforms, training tools, MLOps, model deployment environments |
| Computational Intelligence Research | Support for neural networks, fuzzy systems, evolutionary computation, hybrid methods | Position around specialized research workflows and algorithm experimentation | Research software, optimization tools, simulation environments |
| Pattern Recognition | Classification, detection, feature analysis, evaluation | Offer benchmark datasets, workflow automation, visualization capabilities | Computer vision tools, analytics platforms, annotation/data solutions |
| Image / Audio / Video Retrieval | Compression, retrieval, search, and multimodal analysis | Show applied media intelligence use cases and technical acceleration | Data storage, search platforms, AI media tooling, GPU infrastructure |
| Hybrid Intelligent Systems | Combining multiple AI techniques for better outcomes | Promote interoperability, modular architectures, and workflow orchestration | Integration platforms, modular software stacks, consulting services |
| Academic-Industry Collaboration | Joint research, sponsored programs, lab partnerships | Target senior faculty and partnership offices with pilot or grant-aligned offers | Research partnerships, grant support, enterprise-academic collaboration packages |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong fit for suppliers targeting universities, AI research teams, and advanced technical evaluators |
| Decision-maker availability | Medium | Senior academics and lab leaders are likely present, but procurement ownership may be distributed |
| Data collection potential | Medium | Better for named account mapping than for mass attendee-file extraction based on current public data |
| Apollo targeting potential | High | Academic, research, software, and IT filters can be tightly aligned to the event theme |
| Geographic targeting potential | High | China-first targeting with optional APAC/global research overlays is practical |
| Best outreach approach | High | Use research-led messaging, technical briefs, collaboration offers, and institution-specific value propositions |
| Overall lead quality | High | High quality for niche B2B technical sales; lower suitability for mass-market product selling |
| Best use case | High | Account-based outreach to research institutions, faculty, AI labs, and university partnership offices |
| Limitations / risks | Medium | Publicly confirmed current-year attendee volume and named attendee lists are limited; event is more academic than procurement-heavy |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Information Technology & Services; Computer Software; Computer Hardware; Semiconductors; Industrial Automation; Mechanical or Industrial Engineering; Education Management; Electrical/Electronic Manufacturing | Build a core audience aligned to AI research and applied technical adoption |
| Departments | Research; Information Technology; Engineering; Education; Operations; Procurement; Business Development | Capture both technical evaluators and institutional decision-makers |
| Seniority | Owner, Partner, CXO, VP, Director, Head, Manager, Senior | Prioritize roles with budget influence or technical authority |
| Job titles | Professor, Principal Investigator, Dean, Associate Dean, Department Chair, Lab Director, Research Director, AI Research Scientist, IT Director, Procurement Manager, Partnerships Director, Innovation Director | Create a high-fit title layer for outreach |
| Geography | China first; Shandong; Qingdao; plus Beijing, Shanghai, Jiangsu, Fujian, Hainan; optional APAC and global university targets | Focus on probable event-related and adjacent research ecosystems |
| Employee size | 201–500; 501–1,000; 1,001–5,000; 5,001+ | Best for universities, research institutes, and technical enterprises with formal budgets |
| Keywords | computational intelligence, deep learning, neural networks, fuzzy systems, evolutionary computation, pattern recognition, machine learning, artificial intelligence, research lab, computer science | Improve relevance inside target industries |
| Technologies | Use only if relevant to the seller’s offer, such as cloud, AI/ML stacks, data platforms, HPC environments | Narrow the list to likely deployment-ready accounts |
| Revenue range | Optional; use cautiously because university profiles may not map cleanly to commercial revenue bands | Avoid excluding good academic accounts |
| Company type | Educational institution; research organization; private company; public organization | Separate academic buyers from commercial AI adopters |
Suggested Apollo Search Logic: Target China-based and APAC/global accounts using combinations such as “Professor” OR “Principal Investigator” OR “Lab Director” OR “Research Director” OR “Dean” AND “artificial intelligence” OR “machine learning” OR “computational intelligence” OR “deep learning” OR “pattern recognition” OR “computer science”. Build separate lists for academic institutions and applied industry R&D teams.
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 |
|---|---|---|---|
| ICCIA 2026 Official Website | Official event website | Event title, dates, city, country, organizer structure, host/co-host details, conference theme, history references, deadlines, and contact email | High |
| Official website supplied text: “Conference Venue: Qingdao, China/中国 青岛” and overview text | Primary-source extracted content | Confirmed August 14–16, 2026 dates; Qingdao, China location; current-year co-sponsor, host, and co-host naming | High |
| Official website history / welcome address text | Primary-source historical evidence | Prior-year institutional participation references for North China University of Technology, Nanchang University, Huaqiao University, Nanjing Tech University, and BTBU support | Medium to High |
| User-supplied known detail: Shandong region | Provided event context | State/region field, consistent with Chinese text indicating Qingdao, Shandong | Medium |
| No public attendance figure found in supplied official content | Verification note | Attendance, exhibitor count, buyer count, and speaker totals remain unconfirmed | High confidence in non-availability within supplied source set |
🎯 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 11th International Conference on Computational Intelligence and Applications (ICCIA 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