2026 International Conference on Intelligent System and Computing (ICISC 2026)-IEEE
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About this event
2026 International Conference on Intelligent System and Computing (ICISC 2026)-IEEE
The 2026 International Conference on Intelligent System and Computing (ICISC 2026), organized under the auspices of the IEEE, represents a premier global forum for researchers, engineers, academicians, and industry leaders to converge, collaborate, and advance the frontiers of intelligent systems and computational technologies. This annual event, renowned for its rigorous academic standards and innovative exchange, will take place in a hybrid format, offering both in-person and virtual participation to accommodate a diverse international audience.
Event Overview
ICISC 2026 will delve into cutting-edge advancements in artificial intelligence, machine learning, data science, cognitive computing, and their multidisciplinary applications. The conference aims to foster interdisciplinary dialogue, showcasing theoretical breakthroughs and practical implementations across sectors such as healthcare, finance, autonomous systems, cybersecurity, and sustainable technologies. With a focus on nurturing emerging talent and facilitating industrial partnerships, the event will feature keynote speeches by IEEE Fellows and industry pioneers, parallel technical sessions, workshops, and a vibrant exhibition floor for technology demos and networking.
Key Themes and Tracks
- Artificial Intelligence and Machine Learning Algorithms
- Neural Networks and Deep Learning Applications
- Intelligent Robotics and Autonomous Systems
- Data Mining, Big Data Analytics, and Visualization
- Quantum Computing and Intelligent Hardware
- Cybersecurity and Privacy in Intelligent Systems
- Human-Computer Interaction and Cognitive Science
- Sustainable and Ethical AI for Societal Impact
Target Audience
ICISC 2026 invites participation from:
- Academic researchers and professors in computer science, electrical engineering, and related fields
- Industry professionals in AI, robotics, and data science
- PhD students and early-career scientists seeking collaboration opportunities
- Technology innovators and startup founders
- Policy-makers and ethics experts in emerging technologies
Important Dates
| Milestone | Deadline |
|---|---|
| Submission of Full Papers and Proposals | March 15, 2026 |
| Notification of Acceptance | May 1, 2026 |
| Early Bird Registration Opens | May 15, 2026 |
| Conference Dates | October 12–15, 2026 |
Submission Guidelines
Prospective authors are invited to submit original, unpublished work in IEEE double-column format (maximum 6 pages for regular papers; 2 pages for brief communications). All submissions will undergo a double-blind review process. Accepted papers will be published in the IEEE Xplore Digital Library and indexed in SCOPUS, Compendex, and Web of Science. Special session proposals and workshop contributions are also encouraged.
Call for Participation
Join the global community of intelligent system pioneers at ICISC 2026. Whether you seek to present groundbreaking research, explore industrial applications, or forge strategic partnerships, this conference offers unparalleled opportunities for professional growth and innovation. For registration, submission, and program details, visit the official conference website: https://www.icisc2026.ieee.org.
Organized by: IEEE Computational Intelligence Society (CIS) and Academic Partners Worldwide
Contact: info@icisc2026.ieee.org
Data sheet
2026 International Conference on Intelligent System and Computing (ICISC 2026)-IEEE – Event Attendee & Buyer Profile Analysis
Event date: 10 July 2026 – 12 July 2026
Location: Chengdu, China
Event status: Upcoming
Research date: 29 June 2026
Event Overview
| Event Name | 2026 International Conference on Intelligent System and Computing (ICISC 2026)-IEEE |
| Event Date | 10 July 2026 – 12 July 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly confirmed in the information provided. |
| City | Chengdu |
| State / Region | Sichuan |
| Country | China |
| Organizer | IEEE affiliation referenced in the event description; exact organizing unit not publicly confirmed in the information provided. |
| Official Event Website | Official website not provided in the request. |
| Event Type | International academic and industry conference; hybrid participation format referenced in provided description. |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Likely international academic and professional reach due to IEEE positioning and hybrid format reference. Current-year reach level not independently confirmed. |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low at this stage. No official attendance, exhibitor, speaker, or buyer list was supplied in the request. |
| Main Purpose of Event | To convene researchers, engineers, academics, and industry participants around intelligent systems, computing, AI, machine learning, data science, and related applications for publication, collaboration, innovation exchange, and technical networking. |
About the Event
The 2026 International Conference on Intelligent System and Computing (ICISC 2026)-IEEE is positioned as a technical conference focused on intelligent systems and advanced computing topics, including artificial intelligence, machine learning, neural networks, data science, cognitive computing, and multidisciplinary applications. Based on the description provided, the event is intended to operate as a hybrid conference, combining in-person participation in Chengdu with virtual access for a wider professional and academic audience.
From a lead-generation perspective, ICISC 2026 appears most relevant for thought-leadership outreach, university and lab partnerships, technical recruitment, software platform promotion, and enterprise innovation conversations rather than pure high-volume buyer list extraction. The strongest attendee groups are likely to be research institutions, engineering departments, AI practitioners, enterprise R&D teams, technology vendors, and innovation decision-makers seeking collaboration, publication exposure, demos, and applied use cases.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| Academic researchers and faculty | Universities, engineering schools, AI labs, research institutes | Influence software tools, lab platforms, datasets, compute infrastructure, and collaboration decisions | High for research software, publishing services, AI tools, HPC platforms, and instrumentation |
| Engineers and technical practitioners | AI startups, enterprise R&D teams, software firms, robotics and automation teams | Evaluate tools, frameworks, APIs, cloud services, and deployment platforms | High for developer platforms, MLOps, cloud, edge AI, and security vendors |
| Enterprise innovation and digital transformation leaders | Banks, healthcare systems, manufacturers, telecom operators, mobility firms | Sponsor pilots, approve budgets, define AI adoption priorities | High for solution providers targeting applied AI and analytics deployment |
| Technology leaders | Software companies, cloud providers, research computing centers, innovation programs | Set architecture direction and technical standards | Strong fit for enterprise software, infrastructure, cybersecurity, and integration vendors |
| Program managers and project leads | Research consortia, grant-funded projects, enterprise AI implementation teams | Manage implementation priorities, partner selection, and project coordination | Good fit for consulting, integration, training, and project-support services |
| Industry speakers and thought leaders | Established tech companies, IEEE-affiliated experts, corporate labs | Shape market perception and vendor credibility | Useful for co-marketing, partnerships, and strategic account outreach |
| Graduate students and emerging talent | University programs, doctoral tracks, technical institutes | Limited buying authority; strong influence on tool adoption and future hiring pipeline | Relevant for employer branding, low-cost software seeding, and training offers |
| Investors and ecosystem partners | Venture firms, accelerators, incubators, commercialization networks | Support funding, partnerships, and commercialization pathways | Relevant for startup partnerships and strategic technology scouting |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Chengdu | Local universities, labs, technology firms, and public innovation organizations | Medium | Likely strong local participation due to host-city convenience and lower travel barriers. |
| Sichuan Province | Regional academic institutions, engineering departments, and innovation centers | Medium | Regional reach is likely if the conference has provincial academic partnerships. |
| Major Chinese innovation hubs | Beijing, Shanghai, Shenzhen, Hangzhou, Guangzhou, Xi'an and other technology centers | High | Likely source of enterprise AI teams, university collaborators, and software companies. |
| National China | Researchers, engineers, graduate students, and technology vendors from across China | High | National attendance is likely if proceedings and IEEE affiliation attract broad submission volume. |
| International | Overseas authors, speakers, and virtual participants from Asia-Pacific, Europe, and North America | Medium | Hybrid format increases international accessibility, but current-year foreign attendance is not confirmed. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Likely primary classification | The event description positions ICISC 2026 as a premier global forum and references hybrid participation, which typically broadens speaker, author, and attendee reach beyond China. |
| National | Strong secondary reach | China-based participation is likely to be materially important due to physical location, domestic university density, and local industry access. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| University of Electronic Science and Technology of China | University / research buyer | Chengdu-based engineering and computing institution relevant for AI, systems, and academic conference participation | uestc.edu.cn | Professor, Lab Director, Dean, Research Program Manager, IT Director | Strong Market Fit, Attendance Not Confirmed |
| Sichuan University | University / research buyer | Major Chengdu institution with computing, engineering, and applied research relevance | scu.edu.cn | Professor, Research Center Director, Procurement Manager, CIO | Strong Market Fit, Attendance Not Confirmed |
| Southwest Jiaotong University | University / applied research buyer | Relevant for intelligent systems, transport technology, computing, and engineering collaboration | swjtu.edu.cn | Research Director, Professor, Program Manager, Digital Transformation Lead | Strong Market Fit, Attendance Not Confirmed |
| Chinese Academy of Sciences | National research organization | High relevance for advanced computing, AI, and multi-institute research participation | cas.cn | Institute Director, Principal Investigator, Research Procurement Lead | Strong Market Fit, Attendance Not Confirmed |
| Huawei | Enterprise technology buyer / partner | Relevant to AI infrastructure, cloud, edge computing, and academic-industry collaboration | huawei.com | CTO, AI Director, Research Partnerships Director, Cloud Solution Director | Strong Market Fit, Attendance Not Confirmed |
| Alibaba Cloud | Cloud and AI platform buyer / partner | Relevant for AI computing, data platforms, and enterprise cloud deployment discussions | alibabacloud.com | Cloud Architect, AI Product Director, Ecosystem Partnerships Manager | Strong Market Fit, Attendance Not Confirmed |
| Tencent Cloud | Cloud and platform buyer / partner | Relevant to AI applications, developer ecosystems, and enterprise computing solutions | tencentcloud.com | Head of AI Solutions, Product Director, Research Collaboration Lead | Strong Market Fit, Attendance Not Confirmed |
| Baidu | AI enterprise buyer / ecosystem participant | Relevant for machine learning, autonomous systems, and AI commercialization | baidu.com | AI Lab Director, Product VP, Engineering Director, R&D Partnerships Lead | Strong Market Fit, Attendance Not Confirmed |
| iFLYTEK | Applied AI enterprise buyer | Relevant for speech AI, intelligent systems, and academic-industry innovation exchange | iflytek.com | AI Product Director, Research Manager, Innovation Partnerships Director | Strong Market Fit, Attendance Not Confirmed |
| SenseTime | Computer vision and AI buyer / partner | Relevant for intelligent systems, vision AI, and enterprise research collaboration | sensetime.com | Computer Vision Director, CTO, Product Innovation Lead | Strong Market Fit, Attendance Not Confirmed |
| JD Technology | Enterprise AI and data buyer | Relevant for AI applications in logistics, retail technology, and intelligent automation | jd.com | AI Solutions Director, Data Science Lead, Automation Program Manager | Strong Market Fit, Attendance Not Confirmed |
Sample organizations above are prospecting targets based on strong market fit with the event theme. They are not confirmed attendee records for the 2026 edition unless officially published by the organizer.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | CTO / Chief Technology Officer | Technology | C-Level | Sets enterprise technical direction and approves strategic AI investments. |
| 2 | CIO / Chief Information Officer | Information Technology | C-Level | Relevant for AI deployment, research IT, and enterprise digital transformation. |
| 3 | Director of AI / Head of AI | R&D / Data Science | Director | Owns model strategy, evaluation, vendor review, and applied use cases. |
| 4 | Research Director / Lab Director | Research | Director | Key decision-maker for lab tools, collaboration platforms, and grant-aligned partnerships. |
| 5 | Professor / Principal Investigator | Academic Research | Senior | Influences research software, collaboration choices, and purchasing recommendations. |
| 6 | Engineering Director | Engineering | Director | Evaluates deployment frameworks, hardware compatibility, and development pipelines. |
| 7 | Data Science Director / Lead Data Scientist | Data Science | Director / Manager | Useful for model lifecycle tools, analytics platforms, and compute resources. |
| 8 | Product Director / Product Manager | Product | Director / Manager | Bridges research concepts and commercialization needs. |
| 9 | Digital Transformation Director | Strategy / IT | Director | Relevant for buyers seeking practical AI applications across functions. |
| 10 | Procurement Manager / Research Procurement Lead | Procurement | Manager | Important when targeting university, public institute, or enterprise purchasing pathways. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core match for applied AI, software deployment, enterprise computing, and services | Enterprise AI adoption, integrations, consulting |
| 2 | Computer Software | Direct fit for ML platforms, developer tools, model pipelines, and research software | Product demos, partnerships, API and platform sales |
| 3 | Research | Strong relevance for institutes, labs, and technical collaborations | Research tools, grants, academic collaboration |
| 4 | Higher Education | Universities are likely a major attendee segment | Lab software, education technology, cloud credits |
| 5 | Computer Hardware | Relevant for compute acceleration, embedded systems, and hardware-backed AI | GPU, edge devices, lab infrastructure |
| 6 | Semiconductors | Relevant to intelligent computing acceleration and device-level innovation | Chip partnerships, AI hardware ecosystem |
| 7 | Telecommunications | Applied AI, edge computing, and intelligent network optimization | Network AI, automation, analytics |
| 8 | Industrial Automation | Relevant for intelligent control systems and machine learning in operations | Smart manufacturing, predictive systems |
| 9 | Hospital & Health Care | Healthcare is cited as an application area in the supplied description | Clinical AI, analytics, decision support |
| 10 | Financial Services | Finance is cited as an application area in the supplied description | Risk analytics, fraud detection, automation |
| 11 | Computer & Network Security | Cybersecurity is cited as an application area in the supplied description | AI-enabled security, anomaly detection |
| 12 | Government Administration | Relevant where public innovation labs or universities participate | Research funding, smart city, public digitalization |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No official attendee count provided in request | Do not use for forecasting without organizer confirmation. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | No exhibitor prospectus or directory supplied | The description mentions a technology demo / exhibition component, but not quantity. |
| Buyer count | Not publicly confirmed | Unconfirmed | No hosted buyer or procurement program referenced | This appears to be a conference-led event rather than a dedicated buyer matchmaking expo. |
| Speaker count | Not publicly confirmed | Unconfirmed | Provided description references keynote speeches and parallel technical sessions | Actual speaker roster should be validated against official agenda. |
| Sponsor count | Not publicly confirmed | Unconfirmed | No sponsor list supplied | Important for outreach only after sponsor page goes live. |
| Historical attendance | Historical attendance not publicly confirmed in the information provided. | Historical / prior-year evidence unavailable | No prior-year official source supplied | Avoid extrapolation until official archive is available. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence | Model development, inference, and enterprise adoption | Technical demos, case-study outreach, architecture workshops | AI platforms, APIs, model tooling, consulting |
| Machine Learning | Training pipelines, model validation, MLOps, deployment | Hands-on sessions, peer benchmarking, implementation planning | MLOps tools, cloud compute, data labeling, observability |
| Data Science | Data engineering, analytics quality, reproducibility | Data platform discussions and collaboration deals | Analytics tools, data platforms, governance solutions |
| Cognitive Computing | Advanced decision-support and intelligent automation | Use-case driven executive meetings | Decision intelligence, knowledge systems, NLP tools |
| Cybersecurity | Threat detection, anomaly detection, secure AI systems | Security-focused thought leadership and product briefings | AI security tools, SOC analytics, model security services |
| Healthcare Applications | Clinical analytics, medical decision support, imaging AI | Applied innovation partnerships with hospitals and med-tech teams | Healthcare AI software, analytics services, compliance-support tools |
| Finance Applications | Risk models, fraud analytics, process automation | Targeted meetings with innovation and data leaders | Fraud analytics, workflow automation, AI governance |
| Autonomous Systems | Real-time sensing, edge inference, control optimization | Engineering and lab demonstrations | Embedded AI, sensors, simulation software, robotics platforms |
| Sustainable Technologies | Optimization, resource efficiency, predictive maintenance | Cross-sector innovation discussions | Optimization software, monitoring systems, analytics platforms |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Good fit for AI, software, cloud, research tools, and technical services; less suitable for general consumer or non-technical offerings. |
| Decision-maker availability | Medium | Many attendees are likely influencers and technical evaluators rather than direct procurement owners. |
| Data collection potential | Medium | Useful for speaker, author, lab, and institution mapping if official agenda and paper program become available. |
| Apollo targeting potential | High | Strong role and industry mapping exists for AI, software, university, cloud, and enterprise innovation audiences. |
| Geographic targeting potential | High | Can segment by Chengdu, Sichuan, China, and broader Asia-Pacific innovation hubs. |
| Best outreach approach | High | Use expert-led outreach, research collaboration framing, AI use-case messaging, and technical value propositions. |
| Overall lead quality | High | Strong for specialized B2B tech and research offerings; moderate for broad list-building due to limited confirmed participant data. |
| Best use case | Thought leadership and targeted prospecting | Best suited for AI platform outreach, partnership building, university relations, and enterprise innovation targeting. |
| Limitations / risks | Medium | Current-year attendee and sponsor confirmations are not yet available in the supplied data. This limits precision for attendee list building until official pages publish. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Computer Hardware; Semiconductors; Telecommunications; Industrial Automation; Hospital & Health Care; Financial Services; Computer & Network Security; Government Administration | Build a relevant pool around applied AI, research computing, and technical adoption. |
| Departments | Engineering; Information Technology; Research; Product Management; Operations; Innovation; Procurement | Prioritize technical and budget-influencing teams. |
| Seniority | C-Level; VP; Director; Head; Manager; Senior | Balance strategic decision-makers with practical evaluators. |
| Job titles | CTO, CIO, Director of AI, Head of AI, Research Director, Lab Director, Professor, Principal Investigator, Engineering Director, Data Science Director, Product Director, Digital Transformation Director, Procurement Manager | Focus on roles most likely to evaluate or champion intelligent computing solutions. |
| Geography | China; Sichuan; Chengdu; Beijing; Shanghai; Shenzhen; Hangzhou; Guangzhou; Xi'an; selected Asia-Pacific markets | Align with likely event-origin markets and broader innovation centers. |
| Employee size | 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001+ | Capture scale-up innovators, mature software firms, large enterprises, and major institutions. |
| Keywords | artificial intelligence, machine learning, intelligent systems, neural networks, deep learning, data science, cognitive computing, edge AI, MLOps, computer vision, autonomous systems | Sharpen relevance to the conference themes. |
| Technologies | Use if available: cloud platforms, GPU computing, AI frameworks, analytics stack, cybersecurity stack | Identify technically mature accounts more likely to convert. |
| Revenue range | Use selectively for enterprise and software vendors; not essential for universities and research bodies | Improves prioritization where commercial budgets matter. |
| Company type | Public Company; Privately Held; Educational; Government Agency; Nonprofit where research-related | Supports segmentation by procurement style and sales motion. |
Suggested Apollo Search Logic: Target accounts using combinations such as “(artificial intelligence OR machine learning OR intelligent systems OR deep learning OR data science)” and titles such as “(CTO OR Head of AI OR Research Director OR Lab Director OR Engineering Director OR Professor OR Principal Investigator OR Data Science Director)”. For China-focused outreach, layer geography with Chengdu, Sichuan, Beijing, Shanghai, Shenzhen, Hangzhou, and Guangzhou, then narrow by industry and department.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| User-provided event brief | Submitted event description | Event title, city, country, dates, topical scope, hybrid format reference, and stated IEEE positioning | Medium |
| Official event website | Primary source | Not supplied in the request; organizer, venue, agenda, sponsors, and attendee evidence remain pending official verification | Not available |
| Official organizer / IEEE unit page | Primary source | Exact organizer entity and conference sponsorship structure should be checked there before using for sales claims | Not available |
| Venue confirmation page | Primary source | Required to confirm exact venue and on-site format | Not available |
| Speaker / agenda / call-for-papers pages | Primary source | Would verify speaker organizations, technical tracks, and stronger attendee profiling | Not available |
Suitability for B2B attendee list building: Moderate. The event is relevant for specialized AI, research, and enterprise-technology prospecting, but current-year official participant transparency is not yet sufficient for a precise confirmed-attendee list.
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