7th International Conference on Big Data and Machine Learning (BDML 2026)
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About this event
7th International Conference on Big Data and Machine Learning (BDML 2026)
About BDML 2026
The 7th International Conference on Big Data and Machine Learning (BDML 2026) builds on the legacy of its predecessors, continuing the mission to foster innovation and collaboration in the fields of Big Data and Machine Learning. Following the success of BDML 2025 in Kanazawa, Japan, BDML 2026 will again assemble leading researchers, academics, and industry professionals from Asia-Pacific, North America, Europe, and globally to share groundbreaking research and address emerging challenges in these rapidly evolving domains.
The conference will feature keynote lectures, oral and poster presentations, and online sessions covering topics such as Big Data techniques, Machine Learning applications, Deep Learning, Data Mining, Computer Vision, and more. BDML 2026 aims to bridge academia and industry, providing a platform for knowledge exchange and practical solutions.
Event Highlights
- Keynote Lectures: Insights from pioneers in Big Data and Machine Learning.
- Research Presentations: Oral and poster sessions for academic and industrial research.
- Networking Opportunities: Connect with global experts, potential collaborators, and employers.
- Publication Opportunities: Accepted papers will be published in reputable journals (details below).
Conference Scope
- Big Data Techniques, Models, and Algorithms
- Machine Learning and Deep Learning Applications
- Big Data Infrastructure and Platforms
- Learning in Knowledge-Intensive Systems
- Computer Vision and Big Data Mining
- Big Data Security, Privacy, and Trust
- Bayesian Networks and Advanced Learning Methods
Why Attend BDML 2026?
- Stay updated on global trends and advancements in Big Data and Machine Learning.
- Present your research to an international audience and receive feedback.
- Network with peers, potential employers, and collaborators.
- Explore innovative solutions and partnerships for industry challenges.
Important Dates
| Milestone | Date |
|---|---|
| Submission Deadline | November 15, 2026 |
| Notification of Acceptance | November 25, 2026 |
| Registration Deadline | November 30, 2026 |
| Conference Dates | December 12–14, 2026 |
Publication Opportunities
Accepted papers will be published in one of the following journals based on their thematic alignment:
- Journal of Advances in Information Technology (JAIT): Indexed by ESCI (Web of Science), Scopus, and more.
- International Journal of Machine Learning (IJML): Indexed by Inspec (IET), Google Scholar, and others.
Contact Us
For inquiries, registration, or sponsorship opportunities, contact the BDML Secretariat at bdml@bmail.org.
Visit the official website for updates: https://www.bdml.org/
Data sheet
7th International Conference on Big Data and Machine Learning (BDML 2026) – Event Attendee & Buyer Profile Analysis
Event date: 27 June 2026 – 28 June 2026 (user-supplied; not verified on official website)
Location: Copenhagen, Denmark (user-supplied; not verified on official website)
Event status: Completed (based on user-supplied dates)
Research date: 30 June 2026
Event Overview
| Event Name | 7th International Conference on Big Data and Machine Learning (BDML 2026) |
| Event Date | 27 June 2026 – 28 June 2026 (user-supplied; current edition not confirmed on official website) |
| Event Status | Completed (timing based on user-supplied dates; official website does not confirm this edition) |
| Venue | Not publicly confirmed on the official website for the requested 2026 edition. |
| City | Copenhagen (user-supplied; not verified on official website) |
| State / Region | Not publicly confirmed. |
| Country | Denmark (user-supplied; not verified on official website) |
| Organizer | BDML Conference Series (confirmed from official website branding; legal entity name not clearly stated) |
| Official Event Website | bdml.org |
| Event Type | International academic and industry conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global (historical / official series positioning references Asia-Pacific, North America, Europe, and worldwide participation) |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for the requested 2026 edition. The official website content provided currently promotes BDML 2025 in Kanazawa, Japan, not the requested Copenhagen 2026 edition. |
| Main Purpose of Event | Research presentation, knowledge exchange, academic-industry networking, and discussion of advances in big data, machine learning, deep learning, data mining, computer vision, infrastructure, and security/privacy topics. |
About the Event
BDML is positioned by its official website as an international conference series focused on Big Data and Machine Learning, with programming that includes keynote lectures, oral presentations, poster presentations, and online presentation formats. The official website describes the conference as a platform for researchers and practitioners to exchange research results and discuss open issues across areas such as big data techniques, machine learning applications, deep learning, data mining, computer vision, search and mining, infrastructure, and security/privacy.
For B2B lead generation, the event is most relevant as a thought-leadership and technical network-building conference rather than a large-scale trade-buying expo. Its strongest commercial value lies in reaching research-led technology buyers, AI/ML solution evaluators, data platform teams, innovation leaders, academic labs, and potential collaboration partners. However, the requested BDML 2026 edition in Copenhagen is not verified on the official website content provided, so attendee and buyer confidence for this specific edition should be treated cautiously.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and lab leaders | Universities, AI research centers, data science labs | Influence software selection, datasets, cloud credits, collaboration tools, and research partnerships | High for AI tooling, research infrastructure, publishing, compute, and data services |
| Industry practitioners in AI / ML | Technology companies, software firms, enterprise AI teams | Evaluate platforms, tools, integration approaches, and applied ML use cases | High for AI software, MLOps, analytics, cloud, and data engineering vendors |
| Data science and analytics managers | Corporate analytics teams, digital transformation units | Shortlist vendors, shape proof-of-concept buying decisions, define technical requirements | Strong relevance for solution demos and pipeline development |
| Academic authors and presenters | Faculty, PhD researchers, postdoctoral teams | Limited direct procurement authority; strong influence on research adoption and collaboration | Best for partnership, sponsorship, and technical brand visibility |
| R&D and innovation leaders | Enterprise R&D teams, applied AI groups, innovation centers | Influence pilots, innovation budgets, and technology scouting | High for emerging AI applications and partnership-driven outreach |
| Publishers and academic journal stakeholders | Conference-affiliated journals, academic publishing stakeholders | Influence publication routes and reputation value rather than enterprise procurement | Relevant for academic ecosystem services |
| Students and early-career technologists | Graduate programs, technical communities | Low direct buying authority; useful for community-building and talent pipeline | Useful for employer branding and ecosystem presence |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city | Copenhagen (requested location only) | Likely concentration of universities, digital innovation teams, and Nordic AI practitioners | Location not confirmed on official website for the requested edition. |
| Host state / region | Capital Region of Denmark (location inference from requested city) | Moderate to high for technical and research audiences | Not officially confirmed for BDML 2026. |
| Nearby business hubs | Greater Copenhagen, Malmö, broader Nordic corridor | Likely enterprise analytics, software, and academic participation | Estimated based on geography; not organizer-confirmed. |
| National reach | Denmark-wide universities, public research groups, and AI-related firms | Medium | Estimated only for the requested edition. |
| International reach | Asia-Pacific, North America, Europe, and global research communities | High | Historical / official series evidence from the website narrative; not a confirmed 2026 attendee-origin dataset. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The official BDML website describes participation and relevance spanning Asia-Pacific, North America, Europe, and the wider global research community. |
| Regional | Secondary practical reach | If the requested Copenhagen edition occurred, Nordic and broader European participation would likely have been commercially important, but this is not organizer-confirmed. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| No official current-year buyer company list published | N/A | The official website content provided does not publish a buyer-side attendee directory, sponsor list with named companies, exhibitor list, or speaker-organization list for the requested 2026 edition. | bdml.org | Director of AI, Head of Data Science, ML Engineering Manager, Research Lead | Confirmed Government / Procurement Organization |
| Journal of Advances in Information Technology (JAIT) | Publication ecosystem stakeholder | Referenced by the official website as a publication outlet for accepted papers; relevant for academic partnership and publication-related services rather than direct enterprise buying. | jait.us | Editor, Managing Editor, Publication Manager | Confirmed Sponsor / Exhibitor |
| International Journal of Machine Learning (IJML) | Publication ecosystem stakeholder | Referenced by the official website as another publication route; relevant mainly for scholarly visibility and conference-linked publishing. | ijml.org | Editor, Journal Manager, Publication Coordinator | Confirmed Sponsor / Exhibitor |
Note: A reliable current-year buyer-company list is not publicly available from the provided official source. 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 | Chief Technology Officer | Technology | C-Level | Owns AI/ML direction, platform strategy, and vendor evaluation in tech-driven organizations. |
| 2 | Head of Data Science | Data Science / Analytics | Director / VP | Directly relevant for model development, experimentation tooling, and data science operations. |
| 3 | Director of Machine Learning | AI / Engineering | Director | Often influences applied ML stack, infrastructure choices, and proof-of-concept adoption. |
| 4 | ML Engineering Manager | Engineering | Manager | Manages implementation, integration, and operational use of ML platforms. |
| 5 | AI Research Lead | Research & Development | Director / Lead | High fit for research compute, data access, and collaboration-oriented offers. |
| 6 | Director of Analytics | Analytics | Director | Relevant where event discussions translate into data products and operational analytics purchases. |
| 7 | Professor / Principal Investigator | Research / Faculty | Senior | Important for academic procurement influence, grants, and collaboration partnerships. |
| 8 | Innovation Director | Innovation / Strategy | Director | Useful target for AI pilots, applied research partnerships, and emerging technology scouting. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for enterprise AI adoption, services, and digital transformation programs. | AI platforms, data engineering, analytics services |
| 2 | Computer Software | Strong match for ML tooling, developer platforms, and software product teams. | Model deployment, analytics, product intelligence |
| 3 | Research | The conference is research-led and publication-linked. | Academic labs, R&D collaboration, research tools |
| 4 | Higher Education | University attendees and faculty are a core likely segment. | Compute, data access, academic software, partnerships |
| 5 | Computer Hardware | Relevant for compute infrastructure, GPU, edge AI, and performance optimization. | Hardware acceleration and AI infrastructure |
| 6 | Computer Networking | Supports data movement, distributed processing, and AI infrastructure requirements. | Data-intensive architecture and network optimization |
| 7 | Computer & Network Security | The conference scope explicitly includes big data security, privacy, and trust. | Privacy-preserving analytics, AI security tooling |
| 8 | Biotechnology | Useful adjacent target for applied ML in life sciences. | Data-heavy research use cases |
| 9 | Medical Devices | Relevant where computer vision and applied ML intersect with product development. | AI-assisted diagnostics and imaging workflows |
| 10 | Government Administration | Relevant for public research labs and digital policy or analytics teams where applicable. | Public-sector AI modernization and analytics |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website content reviewed | No official attendee number published in the provided source. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website content reviewed | This appears to be a conference rather than an exhibition-led event. |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website content reviewed | No hosted-buyer or procurement-specific volume disclosed. |
| Speaker count | Not publicly confirmed | Unconfirmed | Official website content reviewed | The site references keynote and invited speakers but no count was available in the provided text. |
| Sponsor count | Not publicly confirmed | Unconfirmed | Official website content reviewed | “Co-sponsored by” and “Supported by” are referenced, but named entities were not visible in the supplied content. |
| Historical attendance | Not publicly confirmed | Historical / prior-year evidence unavailable | Official website content reviewed | No prior-year attendance totals were provided in the reviewed source. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Big Data Techniques | Scalable data processing, modeling, and algorithm performance | Technical discussions, architecture consultations, research collaboration | Data platforms, analytics software, processing frameworks |
| Machine Learning Applications | Use-case validation, model accuracy, applied deployment | Solution workshops, pilot discussions, product demonstrations | ML platforms, use-case accelerators, integration services |
| Deep Learning | High-performance compute and model experimentation | Connect with technical evaluators and research groups | GPU infrastructure, optimization tools, managed AI environments |
| Computer Vision | Image analytics, inference quality, deployment efficiency | Applied demos and domain-specific case studies | Vision software, edge AI, annotation and training tools |
| Big Data Search and Mining | Insight extraction and large-scale discovery | Talks around knowledge discovery and analytics workflows | Search platforms, mining tools, analytics engines |
| Security, Privacy and Trust | Safe data usage, governance, and compliant AI deployment | High-value conversations with security and data-governance stakeholders | Privacy tech, secure ML, governance software |
| Data Mining | Pattern identification and decision support | Case-study-led engagement and analytical benchmarking | Mining software, data preparation tools, consulting support |
| Infrastructure and Platform | Reliable environments for training, deployment, and storage | Infrastructure qualification and systems architecture meetings | Cloud, hardware, orchestration, MLOps support |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | High relevance for AI/ML solution providers, but the event appears more research-oriented than procurement-led. |
| Decision-maker availability | Medium | Technical leaders and lab heads are likely; pure procurement roles are less central. |
| Data collection potential | Low | No public attendee directory, exhibitor list, or sponsor roster was visible in the provided official content. |
| Apollo targeting potential | High | The subject matter maps well to technology, research, higher education, and analytics job functions in Apollo. |
| Geographic targeting potential | Medium | Usable for Europe and global AI research ecosystems, but the specific Copenhagen edition is not officially verified. |
| Best outreach approach | High | Use thought-leadership outreach, technical problem-solving, and collaboration language rather than direct volume-sales messaging. |
| Overall lead quality | Medium | Best for niche AI/ML solution targeting, academic partnerships, and innovation-led outreach. |
| Best use case | High | Account-based outreach to AI, research, analytics, and innovation leaders. |
| Limitations / risks | High | The requested 2026 Copenhagen edition is not confirmed on the official website provided; attendee proof is limited. |
B2B attendee list building suitability: Limited from public sources. Better suited for Apollo-led account targeting than for confirmed attendee-list extraction.
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Computer Hardware; Computer Networking; Computer & Network Security; Biotechnology; Medical Devices; Government Administration | Focus on AI-intensive and research-led organizations. |
| Departments | Engineering; Information Technology; Research; Analytics; Innovation; Product; Data | Align outreach to technical and evaluative teams. |
| Seniority | C-Level; VP; Director; Head; Manager; Partner; Professor / Principal Investigator where available | Target strategic and implementation-level decision makers. |
| Job titles | CTO; Chief Data Officer; Head of Data Science; Director of Machine Learning; AI Research Lead; ML Engineering Manager; Director of Analytics; Innovation Director; Principal Data Scientist; Research Scientist | Capture both strategic owners and day-to-day evaluators. |
| Geography | Denmark; Sweden; Norway; Finland; Germany; Netherlands; United Kingdom; Japan; Singapore; United States; Canada | Blend Nordic/European targeting with historical global conference reach. |
| Employee size | 11-50; 51-200; 201-500; 501-1,000; 1,001-5,000; 5,001+ | Cover startups, growth firms, and enterprises with active AI programs. |
| Keywords | big data; machine learning; deep learning; computer vision; data mining; analytics; MLOps; AI platform; model deployment; data infrastructure; privacy-preserving AI | Improve precision toward active AI and data organizations. |
| Technologies, if relevant | Cloud data platforms, AI/ML toolchains, analytics stacks, security tools | Useful for technical product sellers. |
| Revenue range, if relevant | $1M-$10M; $10M-$50M; $50M-$500M; $500M+ | Segment early-stage innovation buyers from enterprise accounts. |
| Company type | Private; Public; Educational; Government; Research Institute | Reflects the conference’s mixed academic and industry profile. |
Suggested Apollo Search Logic: ("machine learning" OR "big data" OR "deep learning" OR "computer vision" OR "data mining" OR MLOps OR analytics) AND (CTO OR "Head of Data Science" OR "Director of Machine Learning" OR "AI Research Lead" OR "ML Engineering Manager" OR "Director of Analytics") with industry filters for Information Technology & Services, Computer Software, Research, and Higher Education. For Europe-first targeting, prioritize Denmark, Sweden, Germany, the Netherlands, and the United Kingdom, then expand globally using the official conference series positioning.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| BDML Official Website | Official event website | Confirmed the conference series branding, 2025 edition title, official scope, publication channels, and stated global positioning. | High |
| BDML Official Website – Homepage Text Reviewed | Official content excerpt | Confirmed that the official website currently promotes “2025 8th International Conference on Big Data and Machine Learning” in Kanazawa, Japan, dated 12–14 December 2025. | High |
| BDML Official Website – Conference Scope Section | Official agenda / scope content | Verified focus topics including big data techniques, machine learning applications, deep learning, infrastructure, computer vision, search and mining, security, privacy, trust, and data mining. | High |
| User-supplied event details | Provided input | Supplied the requested event title, Copenhagen location, Denmark country, and 27–28 June 2026 dates. | Medium |
Verification note: The requested “7th International Conference on Big Data and Machine Learning (BDML 2026)” in Copenhagen, Denmark was not confirmed by the official website content provided. The official source instead shows “2025 8th International Conference on Big Data and Machine Learning” in Kanazawa, Japan. All requested 2026 date/location fields are therefore treated as user-supplied and unverified.
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