2026 7th International Conference on Pattern Recognition and Machine Learning (PRML 2026)
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About this event
2026 7th International Conference on Pattern Recognition and Machine Learning (PRML 2026)
The 7th International Conference on Pattern Recognition and Machine Learning (PRML 2026) will take place from July 10 to 13, 2026, in Urumqi, China. Co-sponsored by Xinjiang University, IEEE, and Sichuan University, this conference aims to provide a high-level platform for academic exchange among researchers and professionals worldwide.
PRML 2026 invites researchers and engineers from both academia and industry to submit their contributions and participate in the event. The conference covers a wide range of topics in pattern recognition and machine learning, including fundamental theories, algorithm design, and applications in various industries. Key areas of focus include deep learning, reinforcement learning, transfer learning, big data analytics, computer vision, and speech recognition.
Event Details
- Date: July 10–13, 2026
- Venue: Urumqi, China (Exact venue details will be updated on the official website)
- Organizers: Xinjiang University, IEEE, Sichuan University
Scope and Objectives
The conference seeks to foster academic research and technological innovation in pattern recognition and machine learning. It provides a platform for showcasing technological advancements, disseminating research findings, and exploring future trends. Participants will have the opportunity to engage in academic and networking activities, expanding their professional networks.
Target Audience
- Academic researchers in pattern recognition and machine learning
- Engineers and professionals from related industries
- Students pursuing advanced studies in computer science and related fields
- Industry experts in artificial intelligence, data analytics, and intelligent systems
Publication Opportunities
Accepted and presented papers will be published in the PRML 2026 IEEE Conference Proceedings, archived in IEEE Xplore, and indexed by Ei Compendex and Scopus. Excellent papers may also be recommended for publication in reputable international journals.
Awards
- Best Presentation Award: One Best Oral/Poster Presentation selected from each session
- Best Paper Award: One best paper among all accepted papers, with a bonus of 1000 CNY
- Best Student Paper Award: One best student paper among all accepted papers, with a bonus of 1000 CNY
Contact Information
For inquiries, please contact the conference organizers at icprml@163.com. Visit the official website for more details: https://www.prml.org/
Data sheet
2026 7th International Conference on Pattern Recognition and Machine Learning (PRML 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 10–13, 2026
Location: Urumqi, Xinjiang, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 7th International Conference on Pattern Recognition and Machine Learning (PRML 2026) |
| Event Date | July 10–13, 2026 |
| Event Status | Upcoming |
| Venue | Xinjiang University (provided in event details supplied by user; official homepage text confirms Urumqi but does not explicitly restate the exact venue in the extracted page text) |
| City | Urumqi |
| State / Region | Xinjiang |
| Country | China |
| Organizer | Co-sponsored by Xinjiang University, IEEE, and Sichuan University |
| Official Event Website | prml.org |
| Event Type | International academic conference and professional research exchange event |
| Primary Category | Science & Research |
| Secondary Applicable Categories | IT & Technology; Education & Training |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numeric attendance; high for dates, city, country, and co-sponsorship details based on official event website content. |
| Main Purpose of Event | To provide a high-level platform for academic exchange, paper presentation, networking, and dissemination of research in pattern recognition and machine learning across theory, algorithms, and real-world applications. |
About the Event
PRML 2026 is the 7th edition of an international conference focused on pattern recognition and machine learning, scheduled for July 10–13, 2026 in Urumqi, China. According to the official event website, the conference is co-sponsored by Xinjiang University, IEEE, and Sichuan University, and is designed as a forum for researchers and engineers from academia and industry to share papers, discuss technical challenges, and examine emerging developments across topics such as deep learning, reinforcement learning, transfer learning, big data analytics, computer vision, and speech recognition.
From a market-position perspective, PRML 2026 is best understood as a research-led international conference rather than a large commercial expo. Its value for lead generation lies in access to university research groups, AI/ML laboratory leaders, applied R&D professionals, technical sponsors, and industry participants evaluating collaboration, publication, academic partnerships, specialist tools, compute infrastructure, sensors, and advanced analytics technologies. It is suitable for niche B2B attendee list building when the target market includes research, higher education, AI development, or technical innovation stakeholders rather than broad procurement-heavy buying audiences.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and professors | Universities, colleges, AI research centers | Influence on research tool adoption, collaborations, software evaluation, lab equipment requests | High for AI platforms, research software, compute services, sensors, and lab technology vendors |
| PhD students and graduate researchers | Academic labs and graduate programs | End users and evaluators of technical products; influence future adoption within labs | Useful for awareness, trial adoption, paper submissions, and community penetration |
| Industry R&D engineers | AI startups, software firms, autonomous systems companies, industrial innovation teams | Technical evaluators, solution champions, integration stakeholders | Relevant for machine learning frameworks, MLOps, data tools, edge AI, and specialist components |
| Applied research institutions | Government-affiliated or independent research institutes | Influence research procurement, grants, partnerships, and technical pilots | Good fit for advanced computing, imaging, speech, signal processing, and analytics vendors |
| Conference committee, reviewers, and technical program members | University faculties and recognized domain experts | High thought-leadership influence; indirect buying and sponsor influence | Important for sponsorship, academic credibility, and strategic introductions |
| Industry sponsors and publication stakeholders | Professional associations, publishers, technical communities | Budget holders for sponsorship and visibility programs | Relevant for event marketing, technical branding, and academic outreach services |
| Corporate innovation and product teams | Enterprises applying AI in healthcare, manufacturing, mobility, or NLP | Potential buyers of models, platforms, consulting, and specialist datasets | High-value if supplier sells enterprise AI, computer vision, speech, or data infrastructure solutions |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Urumqi | Local universities, local research units, regional technical professionals | Medium | Local presence likely strongest among host institution participants and nearby academic communities. |
| Host region: Xinjiang | Regional academic institutions and technology-linked research participants | Medium | Regional draw expected due to co-sponsorship by Xinjiang University. |
| National: China | Researchers, professors, labs, and industry engineers from across China | High | Conference topics and IEEE co-sponsorship support national reach across AI/ML communities. |
| Asia-Pacific | International authors, speakers, and academic attendees from nearby countries and institutions | Medium | Likely contributor base for paper submissions and technical exchange, though current-year country breakdown is not publicly confirmed. |
| Global research community | International researchers worldwide | Medium to High | Official website explicitly describes PRML as an annual international conference attracting experts and scholars from around the globe. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The official website describes PRML as an annual international conference attracting experts and scholars from around the globe. |
| National | Secondary practical reach | Strong domestic Chinese participation is likely due to the host location and university co-sponsorship structure. |
4. Sample Buyer Companies and Websites
Publicly confirmed current-year organization data is limited in the supplied official event materials. The table below includes only organizations reliably evidenced in the official conference text. This is not a full attendee list.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Xinjiang University | University / host-side academic stakeholder | Co-sponsor and likely core institutional participant; relevant for research collaboration, lab technology, AI software, and academic partnership outreach. | xju.edu.cn | Professor, Dean, Lab Director, Research Scientist, Procurement Office, IT Director | Confirmed Current-Year Participant |
| IEEE | Professional association / technical co-sponsor | Conference proceedings and technical community alignment make IEEE highly relevant for sponsorship, publication, and community-facing partnerships. | ieee.org | Conference Program Lead, Sponsorship Manager, Technical Activities Staff, Partnerships Lead | Confirmed Sponsor / Exhibitor |
| Sichuan University | University / co-sponsoring academic institution | Confirmed co-sponsor relevant for faculty outreach, AI/ML collaboration, and university technology engagement. | scu.edu.cn | Professor, School Director, Research Center Lead, IT Director, Grants & Projects Office | Confirmed Current-Year Participant |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Academic | Senior | Core influencers for research tools, partnerships, grants, and lab technology adoption. |
| 2 | Lab Director / Research Center Director | Research & Development | Director | Often controls evaluation of platforms, hardware, data infrastructure, and collaborations. |
| 3 | Research Scientist | Research | Manager / Senior IC | Strong technical evaluators and credible internal champions for specialist products. |
| 4 | Director of AI / Machine Learning | Engineering / AI | Director | Relevant for commercial vendors targeting applied enterprise AI and technical partnerships. |
| 5 | Computer Vision Lead | Engineering / Applied Research | Manager / Senior IC | Direct fit for vision models, imaging systems, sensors, and edge AI suppliers. |
| 6 | NLP / Speech Recognition Lead | Engineering / Data Science | Manager / Senior IC | Important for speech, language, annotation, and model deployment vendors. |
| 7 | CTO | Executive / Technology | C-Level | High-value target for enterprise AI adoption, technical strategy, and partnership decisions. |
| 8 | IT Director / HPC Infrastructure Manager | IT / Infrastructure | Director / Manager | Relevant for compute, storage, cloud, MLOps, networking, and technical infrastructure offers. |
| 9 | Procurement Office / Research Procurement Manager | Procurement / Administration | Manager | Useful where suppliers sell equipment, licenses, lab services, or sponsored solutions into universities. |
| 10 | Partnerships Director / Sponsorship Lead | Business Development / External Relations | Director | Relevant for event sponsorships, research alliances, and brand visibility programs. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Research | Direct fit for conference research participants and institutes. | Research software, datasets, compute, analytics, instrumentation |
| 2 | Higher Education | Universities and academic departments are central to participation. | Lab tools, software licensing, training, academic partnerships |
| 3 | Information Technology & Services | Many applied AI participants come from enterprise technology teams. | AI adoption, consulting, deployment, enterprise data systems |
| 4 | Computer Software | Strong relevance to ML development tools and AI applications. | MLOps, model training, AI development environments |
| 5 | Industrial Automation | Official scope references intelligent manufacturing applications. | Vision inspection, automation AI, predictive analytics |
| 6 | Automotive | Official scope references autonomous driving. | ADAS, autonomous perception, sensor fusion |
| 7 | Hospital & Health Care | Official scope references medical diagnostics. | Diagnostic AI, imaging analytics, biomedical signal processing |
| 8 | Medical Devices | Relevant where pattern recognition intersects sensing and clinical systems. | Imaging devices, sensors, AI-assisted diagnostics |
| 9 | Electrical/Electronic Manufacturing | Useful for hardware and embedded AI applications. | Edge computing, smart devices, sensors |
| 10 | Semiconductors | Relevant for compute acceleration and AI hardware ecosystems. | Inference hardware, accelerators, embedded systems |
| 11 | Telecommunications | Relevant for speech, data analytics, and intelligent networks. | Signal analysis, NLP, network intelligence |
| 12 | Aviation & Aerospace | Potential fit for advanced perception, autonomy, and signal processing research. | Autonomy, recognition systems, mission analytics |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not confirmed | Official homepage text supplied | No numeric attendance disclosed in the supplied official materials. |
| Exhibitor count | Not publicly confirmed | Not confirmed | Official homepage text supplied | This appears to be conference-led rather than expo-led; exhibitor data not stated. |
| Buyer count | Not publicly confirmed | Not confirmed | Official homepage text supplied | No procurement or hosted buyer program disclosed. |
| Speaker count | Not publicly confirmed | Not confirmed | Official homepage text supplied | Keynote and invited speaker pages exist, but speaker totals were not included in the supplied content. |
| Sponsor count | 3 core co-sponsoring organizations confirmed | Confirmed | Official homepage text supplied | Confirmed: Xinjiang University, IEEE, Sichuan University. |
| Historical attendance | Not publicly confirmed in supplied materials | Historical / prior-year evidence unavailable | Supplied official content only | History pages are listed, but no prior-year attendance figures were included in the provided source text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine learning research | Algorithms, training environments, reproducibility, benchmark datasets | Technical demos, paper-related tooling, research partnerships | ML platforms, model development tools, data environments |
| Deep learning | Compute, optimization, deployment, training acceleration | Cloud/HPC outreach, benchmark case studies, GPU ecosystem partnerships | Cloud infrastructure, accelerators, MLOps platforms |
| Computer vision | Image analytics, annotation, sensor integration, inference systems | Use-case discussions in manufacturing, mobility, and healthcare | Vision AI, cameras, sensors, edge devices, imaging software |
| Speech recognition and NLP | Language models, speech processing, multilingual data pipelines | Research pilots, enterprise language application demos | ASR engines, NLP software, annotated datasets |
| Big data analytics | Scalable storage, processing, pipeline orchestration | Data platform qualification and enterprise integration discussions | Data lakes, analytics software, ETL, visualization platforms |
| Autonomous driving | Perception models, edge inference, multimodal sensor fusion | Applied industry outreach to mobility and robotics teams | Automotive AI, LiDAR/camera systems, simulation tools |
| Medical diagnostics | Diagnostic models, biomedical sensing, image interpretation | Research collaboration with hospitals and medical device teams | Clinical AI, imaging systems, biosignal processing solutions |
| Intelligent manufacturing | Automation analytics, defect detection, predictive systems | Factory optimization and smart manufacturing pilot discussions | Industrial AI, machine vision, automation software |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | High for research-focused and technical solution vendors; lower for broad commercial suppliers seeking traditional procurement audiences. |
| Decision-maker availability | Medium | Academic and technical leaders are likely present, but budget authority may be distributed across research, IT, grants, and administration. |
| Data collection potential | Medium | Useful for speaker/committee/author/professor mapping if such lists become public; limited from currently supplied attendee data. |
| Apollo targeting potential | High | Strong title-, department-, and industry-based targeting is possible across research, higher education, software, and applied AI sectors. |
| Geographic targeting potential | High | Can segment by China, Asia-Pacific, and international research hubs. |
| Best outreach approach | High | Thought-leadership outreach, collaboration offers, trial access, conference sponsorships, and research-use-case messaging work better than hard-sell procurement messaging. |
| Overall lead quality | Medium | Valuable for specialized B2B and technical engagement, but not an ideal event for mass buyer list sales. |
| Best use case | High | Academic partnerships, AI tool vendors, research software, compute platforms, publishing, sponsorship, and technical ecosystem outreach. |
| Limitations / risks | Medium | Public current-year attendee details are limited; conference may skew toward researchers rather than direct enterprise purchasing teams. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Research; Higher Education; Information Technology & Services; Computer Software; Industrial Automation; Automotive; Hospital & Health Care; Medical Devices; Electrical/Electronic Manufacturing; Semiconductors; Telecommunications; Aviation & Aerospace | Build a relevant technical buyer universe around AI/ML research and applications. |
| Departments | Research; Engineering; Information Technology; Education; Operations; Business Development; Procurement | Focus on technical evaluators, research leaders, and institutional decision support roles. |
| Seniority | C-Level; VP; Director; Head; Manager; Owner (for startups and labs) | Prioritize technical and budget-influencing stakeholders. |
| Job titles | Professor, Principal Investigator, Research Scientist, Lab Director, Research Center Director, Director of AI, Head of Machine Learning, Computer Vision Lead, NLP Lead, Speech Recognition Lead, CTO, IT Director, HPC Manager, Dean, Partnerships Director | Mirror the most likely conference-relevant professional profiles. |
| Geography | China; Xinjiang; Urumqi; broader Asia-Pacific; selected global AI research hubs | Support local follow-up plus broader international prospecting aligned to event reach. |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Capture both startup AI firms and large universities/enterprises. |
| Keywords | pattern recognition, machine learning, deep learning, reinforcement learning, transfer learning, big data analytics, computer vision, speech recognition, natural language processing, autonomous driving, medical diagnostics, intelligent manufacturing | Align outreach with official conference topic areas. |
| Technologies | AI/ML stack, cloud infrastructure, GPU/HPC environments, data analytics tools, computer vision systems | Useful when prospecting for AI infrastructure, tooling, and deployment offers. |
| Revenue range | Optional; use only for enterprise/commercial targeting, not universities | Prevents unnecessary exclusion of academic organizations. |
| Company type | Educational institution; private company; nonprofit; association | Broadens coverage across academic and applied-technology participants. |
Suggested Apollo Search Logic: Use combinations such as (“machine learning” OR “pattern recognition” OR “computer vision” OR “speech recognition” OR “deep learning” OR “reinforcement learning”) AND (Professor OR “Research Scientist” OR “Director of AI” OR “Lab Director” OR CTO OR “IT Director”). For commercial outreach, add sector keywords such as “medical diagnostics,” “autonomous driving,” “intelligent manufacturing,” or “NLP” depending on the product category.
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| PRML 2026 Official Website | Official event website | Confirmed event name, dates, city, country, conference scope, co-sponsors, publication details, and positioning as an international conference. | High |
| User-supplied event details | Provided background input | Provided venue reference as Xinjiang University and regional breakdown as Urumqi, Xinjiang, China. | Medium (used where not contradicted by official website text) |
| Xinjiang University | Official institution website | Organization identity and website domain for confirmed co-sponsor. | High |
| IEEE | Official organization website | Organization identity and website domain for confirmed co-sponsor. | High |
| Sichuan University | Official institution website | Organization identity and website domain for confirmed co-sponsor. | High |
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 7th International Conference on Pattern Recognition and Machine Learning (PRML 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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