2026 International Conference on Artificial Intelligence and Computational Modeling (AICM 2026)
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About this event
2026 International Conference on Artificial Intelligence and Computational Modeling (AICM 2026)
The 2026 International Conference on Artificial Intelligence and Computational Modeling (AICM 2026) is a premier interdisciplinary forum for researchers, engineers, and industry professionals globally. It is sponsored by Zhejiang Sci-Tech University and will be held in Hangzhou, China from November 27 to 29, 2026.
Event Overview
This conference aims to present cutting-edge advances, exchange innovative ideas, and foster collaborations in the rapidly evolving fields of artificial intelligence and computational modeling. Participants will have the opportunity to engage with plenary keynote speeches, invited sessions, oral and poster presentations, and panel discussions highlighting emerging trends, theoretical breakthroughs, and real-world applications.
Conference Highlights
- Dates: November 27-29, 2026
- Venue: Hangzhou, China
- Scope: Artificial Intelligence, Computational Modeling, and related interdisciplinary areas.
- Target Audience: Researchers, Engineers, Industry Professionals, Scholars, Students
Participation Formats
- Manuscript Submission: Both full papers and abstracts are welcome.
- Invited Speeches: Opportunities to deliver invited speeches at the conference.
- Special Sessions: Participants can organize special sessions for in-depth discussions on focused topics.
Call for Papers
Submissions are invited on, but not limited to, the following themes:
- AI & Computational Modeling:
- Statistical & Computational Learning
- Optimization for Large-Scale Models
- Causal Inference & Representation Learning
- Neural Network Dynamics
- Uncertainty Quantification & Probabilistic Modeling
- Distributed & Federated Learning
- Low-Data & Self-Supervised Learning
- Non-Transformer Architectures
- World Models & Physical Consistency
- Computational Complexity of AI Models
- Trustworthy AI & Robustness Guarantees
- Advanced AI Models & Computational Paradigms:
- Large Model Pre-training & Efficient Tuning
- Generative AI & Multimodal Modeling
- Graph Neural Networks & Geometric Learning
- Model Lightweighting
- MoE & Dynamic Inference Systems
- Brain-Inspired Computing & Neuromorphic AI
- Digital Twins & Real-Time Simulation Modeling
- Agentic AI & Autonomous Decision-Making
- Edge-Cloud Collaborative AI Computing
- Quantum Machine Learning Models
- Trustworthy, Secure & Sustainable AI:
- Explainable AI & Interpretability
- Adversarial Robustness & Attack-Defense
- Privacy-Preserving AI
- Model Fairness & Bias Mitigation
- AI Safety Alignment & Value Learning
- Green AI & Low-Carbon Computing
- Lifelong Learning & Anti-Forgetting
- Model Watermarking & IP Protection
- AI Reliability Assessment in Critical Scenarios
- AI Resilience Modeling in Open Environments
- AI & Computational Modeling Applications:
- Smart Healthcare & Precision Medicine
- Autonomous Systems & Robotic Vision
- NLP & Speech-Language Modeling
- Intelligent Manufacturing & Industry 4.0
- Intelligent Transportation & Autonomous Driving
- Smart City & Urban Computing
- Climate & Environmental Sustainability
- Smart Energy & Grid Optimization
- Computational Finance & Risk Prediction
- AI for Aerospace Engineering Simulation
- Cybersecurity & Threat Intelligence
- Smart Agriculture
Important Dates
- Submission Due: July 30, 2026
- Notification Date: August 30, 2026
- Registration Due: September 15, 2026
- Camera Ready: October 30, 2026
Contact Information
Secretary: Maggie Xu
Email: aicm_conf@vip.163.com
Website: https://aicm.net/
Data sheet
2026 International Conference on Artificial Intelligence and Computational Modeling (AICM 2026) – Event Attendee & Buyer Profile Analysis
Event date: November 27–29, 2026
Location: Hangzhou, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 International Conference on Artificial Intelligence and Computational Modeling (AICM 2026) |
| Event Date | November 27–29, 2026 |
| Event Status | Upcoming |
| Venue | Hangzhou, China; specific venue not publicly confirmed in the provided official website text |
| City | Hangzhou |
| State / Region | Zhejiang Province |
| Country | China |
| Organizer | Sponsor confirmed: Zhejiang Sci-Tech University |
| Official Event Website | aicm.net |
| Event Type | International academic and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Industrial Engineering |
| Audience Reach | Global, with strong China and Asia-Pacific relevance |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for attendance volume; high for event identity, dates, city, country, themes, and sponsor based on official website content |
| Main Purpose of Event | To convene researchers, engineers, scholars, students, and industry professionals to present advances, exchange ideas, publish papers, and build collaborations in artificial intelligence and computational modeling. |
About the Event
AICM 2026 is positioned as an international conference focused on artificial intelligence and computational modeling. According to the official event website, it is sponsored by Zhejiang Sci-Tech University and will take place in Hangzhou, China on November 27–29, 2026. The program format includes keynote speeches, invited sessions, oral presentations, poster presentations, panel discussions, special sessions, and paper submissions.
From a commercial and lead-generation perspective, this is a high-relevance knowledge and partnership event rather than a pure trade exhibition. It matters most for organizations selling AI software, compute infrastructure, research platforms, simulation tools, academic partnerships, technical services, industrial AI applications, and innovation collaborations. The audience is likely to include decision-influencers from universities, research institutes, engineering teams, innovation groups, and applied AI buyers, although a current-year attendee list has not been publicly disclosed in the provided official material.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University research leaders | Universities, labs, academic AI centers | Influence software, compute, datasets, research tools, and collaboration decisions | High for research technology vendors and partnership-driven outreach |
| Applied AI engineers and technical teams | AI startups, enterprise R&D teams, engineering organizations | Evaluate model development platforms, infrastructure, APIs, MLOps, simulation tools | Very relevant for product trials, demos, and technical solution selling |
| Industry innovation and digital transformation leaders | Manufacturing, transportation, healthcare, smart city, energy organizations | Influence adoption of AI applications and computational modeling use cases | High for vendors with applied AI, optimization, simulation, or analytics offerings |
| Research scholars and doctoral candidates | Academic departments and graduate programs | Strong technical influence; limited direct purchasing authority | Useful for pipeline building, trials, publications, and advocacy |
| Conference speakers and session organizers | Senior academics, research leaders, invited experts | Shape technology evaluation priorities and collaboration networks | Relevant for account-based outreach and credibility-led engagement |
| Industry professionals from AI application domains | Smart manufacturing, autonomous systems, cybersecurity, finance, agriculture, healthcare | Evaluate fit-for-purpose AI solutions for domain-specific operations | High where supplier offerings match application-focused conference tracks |
| Publishing and conference proceedings contributors | Authors, reviewers, paper presenters | Primarily academic influence rather than direct procurement | Moderate relevance for reputation, thought leadership, and ecosystem building |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Hangzhou | Local universities, research groups, AI companies, and technical professionals | High | Hangzhou is a major China technology hub and a practical draw for academic and industry participation |
| Host province: Zhejiang | Regional academic institutions, industrial innovation teams, engineering organizations | High | Strong regional relevance due to sponsor location and local innovation ecosystem |
| Nearby business hubs | Shanghai, Suzhou, Nanjing, Ningbo, Shenzhen, Beijing | Medium to High | Likely source of enterprise innovation teams, applied AI vendors, and research collaborators |
| National reach: China | Researchers, engineers, students, and industry professionals across China | High | Official website positions the event as a national and international academic forum |
| International reach | Global researchers, engineers, and industry professionals | Medium | Confirmed by official positioning as an international conference, but no country-level attendee breakdown is publicly disclosed |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The official website describes AICM 2026 as an international conference for researchers, engineers, and industry professionals from around the globe. |
| National | Secondary practical reach | China-based attendance is likely to be materially important due to the host location, language accessibility, and local academic sponsorship. |
| Regional | Tactical concentration | Zhejiang and East China are likely to deliver a concentrated share of in-person participation. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Zhejiang Sci-Tech University | Academic sponsor / host-side organization | Officially named on the event website as the sponsoring institution; highly relevant for academic partnerships, AI research collaboration, and conference-linked solution outreach | zstu.edu.cn | Dean, Professor, Research Director, Lab Director, AI Program Lead, Procurement Office, IT Director | Confirmed Sponsor / Exhibitor |
| Current-year speaker organizations | Academic / industry expert organizations | Speaker page exists on the official website, but organization names were not included in the provided source text | Professor, Principal Scientist, CTO, Research Director | Not publicly extractable from provided source text | |
| Current-year attendee / buyer organizations | Research, enterprise, and university participants | The official website confirms audience types but does not publish a current-year attendee organization list in the provided content | AI Lead, Research Director, Engineering Manager, Innovation Director | Attendance list not publicly confirmed |
Buyer-organization transparency is limited in the provided official source text. This event is suitable for targeted B2B attendee profiling by role and organization type, but not currently suitable for a fully verified attendee-list build based solely on published current-year organization names.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Academic | Director / Executive / Senior | Often drives research direction, partnerships, grants, and technical platform adoption |
| 2 | Research Director | R&D | Director | Owns research tooling, compute priorities, and institutional collaborations |
| 3 | AI Scientist / Principal Scientist | AI / Data Science | Senior / Manager | Influences platform selection for model development, experimentation, and benchmarking |
| 4 | CTO | Technology | C-Level | Relevant for startups, applied AI firms, and enterprise innovation teams evaluating strategic technologies |
| 5 | Head of AI / AI Program Lead | AI / Innovation | Director / VP | Core buyer/influencer for AI frameworks, application pilots, and deployment tools |
| 6 | Engineering Manager | Engineering | Manager | Useful for evaluation of developer tools, APIs, compute environments, and testing workflows |
| 7 | Innovation Director | Innovation / Strategy | Director | Relevant where AI is being adopted across industrial or commercial functions |
| 8 | IT Director | IT / Infrastructure | Director | Important for hosting, integration, security, and platform procurement |
| 9 | Lab Director | Research / Laboratory | Director | High-value target for instrumentation, software, compute, and collaboration services |
| 10 | Procurement Office / Purchasing Manager | Procurement | Manager | Relevant mainly for university and institutional technology purchasing follow-through |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for AI platforms, services, integration, and deployment | AI software, MLOps, data platforms, systems integration |
| 2 | Computer Software | Strong alignment with model development and AI application builders | Developer tools, applied AI products, enterprise applications |
| 3 | Research | Direct fit with academic and scientific conference participation | Institutional research collaboration and technical partnerships |
| 4 | Higher Education | Conference sponsor and major audience segment are university-linked | Research labs, academic AI centers, university IT and procurement |
| 5 | Industrial Automation | Fits intelligent manufacturing and Industry 4.0 application themes | Simulation, predictive systems, smart manufacturing AI |
| 6 | Computer Hardware | Relevant for compute, accelerators, and AI infrastructure discussions | GPU/edge devices, embedded AI infrastructure |
| 7 | Electrical/Electronic Manufacturing | Relevant where AI is applied to manufacturing, sensors, and embedded systems | Operational AI, digital twins, quality optimization |
| 8 | Automotive | Aligned with intelligent transportation and autonomous driving themes | Perception systems, simulation, autonomous decision support |
| 9 | Hospital & Health Care | Relevant due to smart healthcare and precision medicine topics | Clinical AI, modeling, decision support |
| 10 | Financial Services | Relevant via computational finance and risk prediction track themes | Predictive analytics, risk modeling, AI automation |
| 11 | Telecommunications | Relevant for edge-cloud AI computing and large-scale systems | Network AI, edge deployment, distributed learning |
| 12 | Environmental Services | Relevant through climate and sustainability modeling themes | Environmental analytics, optimization, monitoring |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website text provided | No attendee number stated |
| Exhibitor count | Not applicable / not publicly disclosed | Unconfirmed | Official website text provided | This appears to be a conference rather than an expo-led exhibition |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website text provided | Buyer-side participation is inferred from audience categories, not quantified |
| Speaker count | Not publicly confirmed in the provided source text | Unconfirmed | Official website text provided | Speakers section exists, but count not included in source text |
| Sponsor count | 1 publicly named sponsor | Confirmed | Official website text provided | Zhejiang Sci-Tech University named as sponsor |
| Historical attendance | No prior-year attendance data provided | Unavailable | Provided official source text only | Cannot estimate responsibly without verified historical figures |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Advanced AI models | Model development, training efficiency, tuning workflows | Technical demos, benchmarking sessions, research partnerships | AI software platforms, model tooling, compute optimization |
| Computational modeling | Simulation accuracy, optimization, large-scale modeling | Application workshops and use-case collaboration | Simulation software, numerical computing, digital twin platforms |
| Trustworthy and secure AI | Explainability, robustness, privacy, governance | Thought leadership, compliance framing, enterprise risk reduction | AI governance, privacy-preserving AI, security testing |
| Generative and multimodal AI | Content generation, multimodal reasoning, efficient deployment | Pilot discussions and solution architecture conversations | LLM tools, multimodal platforms, inference optimization |
| Edge-cloud collaborative AI | Scalable deployment, latency control, device-to-cloud orchestration | Architectural consultations with IT and engineering teams | Cloud infrastructure, edge platforms, networking solutions |
| Intelligent manufacturing and Industry 4.0 | Predictive optimization, automation, quality analytics | Industry case studies and operations-led solution mapping | Industrial AI, vision systems, digital twins, automation software |
| Smart healthcare | Clinical decision support, data modeling, precision workflows | Research-to-clinic collaboration outreach | Healthcare AI, analytics, data platforms |
| Cybersecurity and threat intelligence | Threat detection, resilience, trustworthy model operation | Security-led discussions with enterprise and research teams | AI security, monitoring, adversarial testing |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong fit for AI, research, software, compute, and innovation-oriented sellers |
| Decision-maker availability | Medium | Many attendees are likely to be technical influencers rather than pure procurement signatories |
| Data collection potential | Medium | Useful for role-based capture, but currently limited by lack of publicly disclosed attendee organization data |
| Apollo targeting potential | High | Clear job-title, industry, and use-case mapping for AI and research ecosystem targeting |
| Geographic targeting potential | High | Can target Hangzhou, Zhejiang, broader China, and Asia-linked AI ecosystems |
| Best outreach approach | High | Thought leadership, technical relevance, research collaboration framing, and use-case-led messaging work best |
| Overall lead quality | High | Especially strong for technical B2B sellers and institutional partnership outreach |
| Best use case | High | ABM prospecting, AI ecosystem mapping, academic-industry partnership development, and warm conference outreach |
| Limitations / risks | Medium | Public attendee transparency is limited; this reduces certainty for direct attendee-list building and named-account verification |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Industrial Automation; Computer Hardware; Electrical/Electronic Manufacturing; Automotive; Hospital & Health Care; Financial Services; Telecommunications; Environmental Services | Align account lists with the event’s AI and applied modeling themes |
| Departments | Engineering; Information Technology; Research; Product; Operations; Innovation; Education | Surface technical influencers and program owners |
| Seniority | C-Level; VP; Director; Head; Manager; Senior | Prioritize strategic and technical decision-makers |
| Job titles | CTO; Head of AI; Director of Research; Professor; Principal Investigator; Lab Director; AI Scientist; Principal Scientist; Engineering Manager; Innovation Director; IT Director; Dean | Match the conference’s likely high-value attendee roles |
| Geography | Hangzhou; Zhejiang; Shanghai; Beijing; Shenzhen; China; East Asia; Asia-Pacific | Concentrate on the most likely physical and regional participation zones |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Capture startups, scale-ups, enterprises, and major institutions |
| Keywords | artificial intelligence; computational modeling; machine learning; generative AI; multimodal; digital twin; simulation; trustworthy AI; autonomous systems; edge AI; smart manufacturing; computer vision | Refine discovery toward event-aligned research and deployment topics |
| Technologies, if relevant | Cloud infrastructure; GPU computing; MLOps; AI frameworks; simulation platforms | Identify technically mature accounts |
| Revenue range, if relevant | Use open range; narrow only if the client’s price point requires it | Avoid excluding universities and research-led entities with nonstandard revenue profiles |
| Company type | Private; Public; Educational; Research Institution | Support both commercial and institutional prospecting |
Suggested Apollo Search Logic: ("artificial intelligence" OR "AI" OR "machine learning" OR "computational modeling" OR simulation OR "digital twin" OR "trustworthy AI" OR "generative AI") AND (CTO OR "Head of AI" OR "Research Director" OR Professor OR "Principal Investigator" OR "Lab Director" OR "Innovation Director") AND (China OR Hangzhou OR Zhejiang OR Shanghai).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| AICM 2026 Official Website | Official event website | Event name, official dates, city, country, sponsor, conference purpose, audience types, participation formats, topics, awards, and important deadlines | High |
| Provided official website text excerpt | Primary-source content supplied by user | Resolved date discrepancy by confirming the official website states November 27–29, 2026 in Hangzhou, China | High |
| Verification note | Research assessment | Specific venue name, attendance figure, attendee organization list, speaker count, and named buyer-company participation were not publicly confirmed in the provided official source text | High confidence in the limitation statement |
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