2026 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026)
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About this event
2026 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026)
Official Website: https://www.bdai.net/
Event Overview
The 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026) is a premier academic and industry gathering focused on advancing research, innovation, and applications in big data technologies and artificial intelligence. The conference aims to foster collaboration among scholars, engineers, and professionals to address emerging challenges and opportunities in these fields.
Key Highlights
- Committees: Organizing Committees, Technical Committees
- Speakers: Keynote Speakers, Invited Speakers
- Research Areas: Big Data Analytics, Machine Learning, AI Applications, Data Science, Intelligent Systems
- Participation: Open to researchers, industry experts, educators, and students
Participation Information
Author's Guide: Prospective authors are invited to submit original research papers aligned with the conference themes. Details on formatting and submission procedures are available on the official website.
Registration: Participants may register as Authors or Delegates. Registration details, including deadlines and fees, will be updated on the official website.
Venue & Logistics
Note: Specific details regarding the conference dates, venue, city, and country for BDAI 2026 are not explicitly stated in the provided website content. Historical conferences (e.g., BDAI 2025 in Taicang, BDAI 2024 in Beijing) suggest a pattern of hosting in Chinese cities, but official confirmation for 2026 should be obtained from the official website.
Target Audience
- Academic Researchers
- AI and Big Data Engineers
- Industry Professionals
- Policy Makers
- Students and Educators
Event Type
Academic and Industrial Conference with Technical Sessions, Keynote Speeches, Workshops, and Networking Opportunities
Contact
For inquiries, visit the official website and use the provided contact information.
Data sheet
2026 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026) – Event Attendee & Buyer Profile Analysis
Event date: 03 Jul 2026 - 05 Jul 2026
Location: Chongqing University of Science and Technology, Chongqing, China
Event status: Upcoming
Research date: 29 Jun 2026
Event Overview
| Event Name | 2026 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026) |
| Event Date | 03 Jul 2026 - 05 Jul 2026 |
| Event Status | Upcoming |
| Venue | Chongqing University of Science and Technology |
| City | Chongqing |
| State / Region | Chongqing Municipality |
| Country | China |
| Organizer | Not publicly named in the copied official website text; conference organizing and technical committees are referenced. |
| Official Event Website | bdai.net |
| Event Type | International academic and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global, with strong China-centered academic and technical participation likely |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numeric attendance; official website confirms conference structure and prior editions, but not public attendee totals in the supplied official text. |
| Main Purpose of Event | Research presentation, technical exchange, keynote learning, paper submission, networking, and collaboration around big data and artificial intelligence. |
About the Event
BDAI 2026 is positioned as the 9th edition of an international conference focused on big data and artificial intelligence. The official event website publicly references organizing committees, technical committees, keynote speakers, invited speakers, paper submission, registration for authors and delegates, visa guidance, venue information, and scheduling, indicating a structured research-led conference intended for both academic and applied technology participants.
From a commercial intelligence perspective, the event matters most for organizations selling AI infrastructure, data platforms, research software, cloud services, developer tools, semiconductors, model deployment platforms, and university or lab partnerships. It is more valuable for thought-leadership, strategic relationship building, and technical buyer discovery than for classic trade-show procurement, because attendee mix is likely to include researchers, faculty, engineers, graduate students, and R&D-led enterprise teams rather than pure purchasing departments.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, institutes, AI labs, data science departments | Influence technical evaluation, lab tooling choices, research software adoption, and collaboration decisions | High relevance for compute, datasets, software licenses, AI platforms, and academic partnerships |
| Corporate AI and data science teams | Enterprise innovation teams, software firms, industrial R&D groups, applied AI divisions | Evaluate applied AI methods, data engineering stacks, MLOps tools, and pilot partnerships | High relevance for enterprise AI vendors and technical solution providers |
| PhD candidates and graduate students | Research programs, academic labs, innovation centers | Low direct budget authority; strong technical influence and future-user value | Useful for awareness, talent engagement, community building, and product trials |
| Keynote and invited speaker organizations | Senior academics, research institutes, enterprise technologists | Thought-leadership influence, standards influence, partnership signaling | Strong for sponsorship, executive networking, and strategic partnerships |
| Data platform and infrastructure decision-makers | Cloud teams, HPC teams, enterprise architecture groups, labs | Can influence procurement of compute, storage, GPU, analytics, and deployment tools | High for vendors in AI infrastructure and analytics ecosystems |
| Industry engineers and solution architects | Technology vendors, integrators, software developers, applied engineering teams | Shape product selection, technical fit, and integration pathway | Very relevant for demos, APIs, proof-of-concepts, and developer adoption |
| Research collaboration and grant-seeking participants | Universities, public research bodies, consortia | Influence consortium tools, co-development agreements, and funded project choices | Relevant for long-cycle partnership selling rather than short-cycle transactional selling |
| Enterprise innovation leaders | Large corporates exploring AI adoption across products and operations | Can sponsor pilots, evaluations, and internal AI adoption programs | Relevant for high-value enterprise outreach, especially if solution is technical and strategic |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Chongqing | Local university participants, faculty, students, and technology professionals | Moderate | Host-city attendance likely strongest for nearby academic and institutional communities |
| Host region: Chongqing Municipality / Western China | Regional universities, labs, industrial research teams, and technology firms | Moderate to High | Western China location may attract strong inland research and applied industry participation |
| Nearby business and research hubs | Chengdu-Chongqing economic zone, major universities, AI startups, software firms | High | Good potential for data science, smart manufacturing, and applied AI attendees |
| National China reach | Researchers and technical delegates from leading universities and enterprises across China | High | Prior editions in Beijing, Guangzhou, Jiaxing, and Taicang indicate national rotation and broader domestic recognition |
| International reach | Authors, delegates, and speakers from overseas institutions and companies | Moderate | International positioning is supported by the event title and official visa page, though country-level attendee mix is not publicly listed in the supplied source text |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is branded as an international conference and includes a visa section on the official site, indicating non-domestic delegate relevance. Practical attendance is likely strongest from China and the Asia-Pacific academic/technology ecosystem. |
| National | Secondary reach description | Historical editions across multiple Chinese cities suggest broad domestic recognition and recurring China-wide participation. |
4. Sample Buyer Companies and Websites
No current-year attendee, sponsor, exhibitor, or speaker organization list was visible in the supplied official website text. To avoid unsupported claims, the table below reflects only organization-level entities directly evidenced by the official event structure or venue information. This is not a confirmed buyer list for the current edition.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Chongqing University of Science and Technology | Host venue / academic institution | Venue-linked institution relevant for research collaboration, campus AI tooling, and academic partnerships | Not verified from supplied source text | Dean, Research Director, Head of AI Lab, IT Director | Confirmed Current-Year Participant |
| BDAI 2026 Organizing Committees | Conference leadership body | Relevant for sponsorship, strategic partnerships, and senior introductions | bdai.net | Conference Chair, Organizing Committee Member, Partnerships Lead | Confirmed Current-Year Participant |
| BDAI 2026 Technical Committees | Technical evaluation body | Likely to include senior researchers and technical leaders who influence solution credibility | bdai.net | Professor, Principal Scientist, Technical Committee Member | Confirmed Current-Year Participant |
| BDAI 2026 Keynote Speaker Organizations | Speaker-side institutions | High-value relationship targets once speaker affiliations are published | bdai.net | Chief Scientist, Professor, Director AI, Head of Research | Confirmed Current-Year Participant |
| BDAI 2026 Invited Speaker Organizations | Speaker-side institutions | Potential source of senior academic and industry contacts once affiliations are published | bdai.net | Director of Data Science, AI Research Lead, Professor | Confirmed Current-Year Participant |
| Author-side submitting institutions | Universities and research bodies | Submitting authors represent the most probable technical attendee base | bdai.net | Professor, Associate Professor, Research Scientist, Lab Manager | Confirmed Current-Year Participant |
| Delegate-side attending institutions | Academic and technical organizations | The official site provides delegate registration, confirming attendee participation even though names are not public | bdai.net | Delegate, Research Engineer, Data Scientist, Lecturer | Confirmed Current-Year Participant |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Head of AI / AI Director | Engineering / Innovation | Director | Owns AI program direction and vendor evaluation in enterprise and research settings |
| 2 | Chief Data Officer | Data / Executive Office | C-Level | High-value target for data governance, analytics, and AI platform decisions |
| 3 | Director of Data Science | Data Science / Analytics | Director | Directly relevant for model development, experimentation, and tooling |
| 4 | Machine Learning Engineering Manager | Engineering | Manager | Strong evaluator of deployment tools, infrastructure, and model lifecycle platforms |
| 5 | Research Director | R&D | Director | Relevant for academic labs, institutes, and publicly funded research organizations |
| 6 | Professor / Principal Investigator | Academic Research | Senior | Influences lab software, datasets, collaboration tools, and funded research adoption |
| 7 | Chief Technology Officer | Technology | C-Level | Relevant when applied AI firms attend for solution benchmarking and partnerships |
| 8 | IT Director | IT / Infrastructure | Director | Useful target for infrastructure, cloud, storage, security, and university platforms |
| 9 | Data Platform Architect | Architecture / Data Engineering | Senior / Manager | Shapes technical fit for data pipelines, governance, and compute frameworks |
| 10 | Partnerships Director | Business Development / Alliances | Director | Important for ecosystem building, sponsored research, and commercial collaboration |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Direct match for AI, analytics, and enterprise data solution providers | Enterprise AI tools, data platforms, consulting, implementation |
| 2 | Computer Software | Core fit for model development, MLOps, analytics, and developer tooling | Software evaluation and partnership outreach |
| 3 | Higher Education | Conference format strongly aligns with universities and academic labs | Research software, labs, academic partnerships, campus infrastructure |
| 4 | Research | High relevance for institutes, labs, and innovation centers | Collaboration, grants, compute, data access, technical tooling |
| 5 | Internet | Internet-native companies commonly invest in AI and data engineering | Applied AI, recommendation systems, platform intelligence |
| 6 | Computer Hardware | Relevant for AI compute, GPU systems, servers, and edge platforms | Infrastructure procurement and solution benchmarking |
| 7 | Semiconductors | AI acceleration and compute optimization align with technical conference themes | Chip ecosystem partnerships, research demos, inference hardware |
| 8 | Telecommunications | Applied AI for networks, data processing, and edge intelligence | Network analytics, optimization, edge AI |
| 9 | Industrial Automation | Strong fit where AI is applied to manufacturing, robotics, and process optimization | Applied analytics, predictive systems, machine intelligence |
| 10 | Electrical/Electronic Manufacturing | Useful for AI-enabled design, testing, and smart production environments | Manufacturing AI and data-driven operations |
| 11 | Government Administration | Relevant only where public universities and state-backed research ecosystems participate | Research funding, digital transformation, public-sector AI initiatives |
| 12 | Computer Hardware | Especially relevant for high-performance computing needs in AI research | GPU clusters, workstations, storage, inference appliances |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No public numeric attendance visible in supplied official website text | No reliable current-year figure should be claimed |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Conference website structure does not indicate an expo hall or exhibitor directory in the supplied text | This appears to be conference-led rather than exhibition-led |
| Buyer count | Not publicly confirmed | Unconfirmed | No official attendee segmentation totals published in supplied source text | Most attendees are likely technical and research oriented rather than formal procurement buyers |
| Speaker count | Not publicly confirmed | Partially confirmed structure | Official site confirms keynote speakers and invited speakers sections exist | Names and counts were not visible in the supplied copied text |
| Sponsor count | Not publicly confirmed | Unconfirmed | No sponsor listings were visible in supplied official text | Sponsorship may exist but is not evidenced here |
| Historical attendance | Not publicly confirmed | Historical / prior-year evidence unavailable | Official site lists prior-year host cities only | No verified historical totals available from supplied source text |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Artificial Intelligence | Model development, evaluation, deployment, and applied research | Technical demos, research partnership discussions, keynote networking | AI platforms, model tooling, MLOps, inference services |
| Big Data Analytics | Scalable analytics, data pipeline efficiency, data governance | Architect-to-architect discussions and lab or enterprise proof-of-concepts | Data platforms, ETL/ELT tools, lakehouse solutions, governance software |
| Machine Learning | Experimentation, feature engineering, reproducibility, optimization | Workshop-style selling, benchmarking conversations, academic evaluations | ML frameworks, experiment tracking, training tools, notebooks |
| Data Science | Collaboration, data visualization, access control, model lifecycle | Target directors, researchers, and data leads with use-case messaging | Analytics tools, notebooks, collaboration platforms, observability |
| Intelligent Systems | Applied AI in devices, robotics, and autonomous or industrial systems | Cross-sector conversations with applied engineering teams | Edge AI, embedded compute, sensors, automation software |
| Academic-Industry Collaboration | Co-authorship, funded research, talent pipeline, lab validation | Sponsorship, grants, trial access, alliance building | Research credits, pilot programs, strategic partnerships |
| Cloud and Compute | Scalable training environments, storage, and GPU capacity | Infrastructure comparison and budget-justification discussions | Cloud credits, HPC, GPUs, storage, orchestration |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Strong for technical and research buyers; weaker for pure procurement-led selling |
| Decision-maker availability | Medium | Likely access to researchers, faculty, engineering leads, and selected senior speakers, but not necessarily budget owners in volume |
| Data collection potential | Low to Medium | Public organization-level attendee data is limited based on supplied official source material |
| Apollo targeting potential | High | Conference themes map well to Apollo targeting across AI, software, research, higher education, and data leadership roles |
| Geographic targeting potential | High | Useful for Chongqing, Western China, national China, and broader APAC technical audiences |
| Best outreach approach | High | Use thought-leadership, technical use cases, co-research language, trial access, and workshop-led outreach rather than generic sales messaging |
| Overall lead quality | Medium | Best for complex AI/data solutions, infrastructure, or research partnerships; less ideal for broad non-technical products |
| Best use case | High | Lead generation for AI/data vendors, speaker targeting, academic collaboration, and targeted outbound into AI decision-makers |
| Limitations / risks | Medium | Public attendee-name visibility is limited; conversion may require longer technical nurture cycles. Suitable for B2B attendee list building only if role-based or organizer-approved data sources are available. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Higher Education; Research; Internet; Computer Hardware; Semiconductors; Telecommunications; Industrial Automation; Electrical/Electronic Manufacturing | Concentrates on the most relevant AI and big-data buyer environments |
| Departments | Engineering; Information Technology; Research; Education; Operations; Business Development | Captures technical evaluators and collaboration stakeholders |
| Seniority | C-Level; VP; Director; Head; Manager; Owner; Partner | Prioritizes authority and technical influence |
| Job titles | Chief Data Officer; Chief Technology Officer; Head of AI; Director of Data Science; Director of Machine Learning; Research Director; Professor; Principal Investigator; IT Director; Data Platform Architect; Machine Learning Engineering Manager; Partnerships Director | High-fit titles for AI, data, and research-led engagement |
| Geography | China; Chongqing; Sichuan; Beijing; Shanghai; Guangdong; Jiangsu; Zhejiang; APAC research hubs | Focuses on likely attendee-origin regions and adjacent technical hubs |
| Employee size | 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001-10,000; 10,001+ | Suitable for enterprise buyers, universities, institutes, and established tech firms |
| Keywords | artificial intelligence; big data; machine learning; data science; deep learning; analytics; MLOps; computer vision; NLP; intelligent systems; AI lab; research center | Improves precision toward event-theme alignment |
| Technologies | Cloud GPU; Hadoop; Spark; Kubernetes; TensorFlow; PyTorch; Databricks; Snowflake | Useful when selling adjacent technical infrastructure or software |
| Revenue range | Use broad range or leave open for universities and institutes; apply mid-market to enterprise filters for commercial accounts | Avoid over-restricting academic organizations that may not map cleanly by revenue |
| Company type | Public Company; Privately Held; Educational; Government; Nonprofit where research-led outreach is relevant | Supports mixed academic and industry targeting |
Suggested Apollo Search Logic: ("artificial intelligence" OR "big data" OR "machine learning" OR "data science" OR "intelligent systems" OR MLOps OR analytics) AND (CTO OR "Chief Data Officer" OR "Head of AI" OR "Director of Data Science" OR Professor OR "Research Director" OR "Machine Learning Manager") AND (China OR Chongqing OR Sichuan OR Beijing OR Shanghai OR Guangdong OR Jiangsu).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| BDAI Official Website | Official event website | Confirmed BDAI 2026 branding, conference structure, committees, keynote and invited speaker sections, author and delegate registration, venue page, visa page, schedule page, and prior-year host-city history | High |
| User-supplied event brief | Provided event input | Used for start date, end date, city, country, and venue because these fields were not visible in the copied official website text provided | Medium |
| Official website text extract supplied in prompt | Primary-source excerpt | Showed navigation and historical edition cities: BDAI 2025 Taicang, BDAI 2024 Beijing, BDAI 2023 Jiaxing, BDAI 2022 Virtual, BDAI 2021 Virtual, BDAI 2020 Virtual, BDAI 2019 Guangzhou, BDAI 2018 Beijing | High |
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 9th International Conference on Big Data and Artificial Intelligence (BDAI 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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