2026 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026)
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About this event
2026 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026)
Date: August 14-16, 2026
Venue: Shanghai, China
Host: College of Big Data and Information Engineering, Shanghai Jiao Tong University
Sponsor: IEEE
About PRAI 2026
The 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) is a premier global event dedicated to fostering academic exchange and collaboration among experts, scholars, and professionals in the fields of pattern recognition and artificial intelligence. The conference will be held from August 14-16, 2026, in Shanghai, China, and is organized by the College of Big Data and Information Engineering at Shanghai Jiao Tong University, with sponsorship from IEEE.
Focus Areas
PRAI 2026 will focus on cutting-edge themes and innovations in pattern recognition and artificial intelligence, including but not limited to:
- Deep Learning and Big Data Processing
- Image Processing, Speech Recognition, and Natural Language Processing
- Autonomous Driving Technologies
- Healthcare Applications
- Intelligent Transportation Systems
- Financial Risk Management
- Generative AI and Multimodal Learning
- Model Interpretability and Edge Computing
Special Sessions
PRAI 2026 features a range of special sessions covering diverse topics:
- SS1: Artificial Intelligence for Aerospace Applications
- SS2: Intelligent Data Analysis and Cyber Security
- SS3: Multimodal Medical Data Fusion and Precision Diagnosis-Treatment Practice with Large Models
- SS4: Generative AI and Innovation in Multimodal Intelligent Interaction
- SS5: Global Frontiers in AI-Driven Intelligent Image/Video Processing and related Innovation
- SS6: Perception and Understanding of Multimodal Large Models and Intelligent Recommendation
- SS7: Multimodal Affective Computing and Applications
- SS8: Data-Driven Domain AI Systems: Models, Architectures, and Applications
- SS9: Application of Artificial Intelligence in Astrophysics
- SS10: 3D Vision and Digital Preservation of Cultural Heritage
- SS11: AI-Driven Technologies for Astronomical Data Analysis and Cosmic Discovery
- SS12: Adaptive Filtering Theory and Engineering Applications
- SS13: Intelligent Signal Processing and State Perception for Power Systems
- SS14: Generative AI and Large Models for Educational Scenarios
- SS15: Intelligent Systems: Bridging Generative AI, Computer Vision, and Large Models
- SS16: Multimodal Data Fusion and Intelligent Analysis for Remote Sensing Applications
- SS17: Deep Learning for Document Image Understanding & Layout Analysis
- SS18: AI Driven Applied Cybersecurity: Securing Digital Ecosystems from Identity, IoT, OT, and SOC Threats
Important Dates
Submission Deadline (Regular): June 15, 2026
Notification Date (Regular): July 20, 2026
Registration Deadline: July 25, 2026
Final Paper Due: July 25, 2026
Conference Date: August 14-16, 2026
Publication
Accepted papers will be published in the PRAI 2026 Conference Proceedings and submitted for inclusion in IEEE Xplore, indexed by EI Compendex & Scopus. PRAI 2026 is listed in the IEEE Official Conference Calendar.
Registration and Submission
To submit a full paper or abstract, visit https://www.easychair.org/conferences/?conf=prai2026. For registration details, please refer to the official conference website.
Data sheet
2026 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) – Event Attendee & Buyer Profile Analysis
Event date: August 14–16, 2026
Location: Shanghai, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) |
| Event Date | August 14–16, 2026 |
| Event Status | Upcoming |
| Venue | Shanghai, China. Specific venue/facility name not publicly confirmed in the provided official text. |
| City | Shanghai |
| State / Region | Shanghai Municipality |
| Country | China |
| Organizer | Sponsored by Shanghai Jiao Tong University and IEEE; hosted by the College of Big Data and Information Engineering of Shanghai Jiao Tong University, according to the English official site text. |
| Official Event Website | prai.net |
| Event Type | International academic conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global academic and technical audience |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for attendance volume; high for event identity, dates, host city, and conference topics based on official website text. |
| Main Purpose of Event | Academic exchange, paper presentation, research collaboration, publication, and industry-relevant discussion in pattern recognition and artificial intelligence. |
About the Event
PRAI 2026 is the 9th edition of an international conference focused on pattern recognition and artificial intelligence. According to the official event website, it will take place in Shanghai, China from August 14 to 16, 2026, and is positioned as a platform for global experts, scholars, researchers, teachers, students, and professional technicians to exchange research and collaborate.
The event matters because its agenda themes map directly to commercially important AI application areas such as computer vision, speech and language processing, autonomous driving, healthcare, intelligent transportation, financial risk management, cybersecurity, edge computing, and multimodal large models. For lead generation, the event is more valuable for academic, R&D, technical partnership, university-lab, AI platform, and applied research outreach than for pure mass-market procurement. It is suitable for highly targeted B2B list building where the client sells research tools, AI infrastructure, compute, data, cybersecurity, analytics, or technical collaboration services.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, AI institutes, engineering schools, laboratories | Influence software, datasets, compute platforms, lab tools, academic partnerships, and sponsored research | High relevance for AI software vendors, GPU/cloud providers, publishing, and research tooling |
| Graduate students and doctoral candidates | Universities and research centers | End users and technical evaluators; future buyers and influencers | Useful for long-term pipeline, developer adoption, and talent/community building |
| Applied AI engineers and professional technicians | Technology firms, applied research teams, solution integrators | Technical evaluators of platforms, models, edge AI, security, and data infrastructure | Strong relevance for demos, pilots, integrations, and proof-of-concept outreach |
| Industry R&D leaders | Autonomous driving, healthcare AI, fintech, industrial AI, aerospace, power systems | Can influence technical procurement, co-development, and commercialization decisions | High-value segment for specialized AI/ML vendors and data solution providers |
| Cybersecurity and data analysis specialists | Security teams, research labs, enterprise analytics groups | Assess applied AI security, threat detection, identity, OT/IoT security, and data intelligence tools | Relevant because cybersecurity is explicitly covered in special sessions |
| Healthcare and medical AI researchers | Hospitals, medical schools, med-tech research groups | Influence adoption of multimodal data fusion, precision diagnosis, AI imaging, and analytics tools | Good fit for medical AI, imaging, and clinical data vendors |
| Transportation and autonomous systems researchers | Mobility labs, automotive AI teams, intelligent transportation programs | Influence sensing, perception, edge AI, and simulation tool selection | Relevant for CV, robotics, simulation, mapping, and embedded AI suppliers |
| Academic and technical program committee members | Conference committees, review boards, special session chairs | Thought leadership influence rather than direct procurement | High influence for partnerships, sponsorship, speaking, and ecosystem positioning |
| Publishers and indexing stakeholders | Conference proceedings, academic publishing, indexing bodies | Influence publication visibility and submission-related services | Relevant for publishing, conference management, and abstracting/indexing services |
| Association and sponsor representatives | IEEE, university hosts, academic associations | Support sponsorship, standards visibility, and ecosystem engagement | Useful for partnership and reputation-building campaigns |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Shanghai | Local universities, AI labs, technical institutes, startups, enterprise R&D teams | High | Host city with strong AI, research, and higher education concentration |
| Yangtze River Delta | Attendees from Jiangsu, Zhejiang, Hangzhou, Suzhou, Nanjing and nearby innovation hubs | High | Likely strong regional pull for academics and applied AI organizations |
| Mainland China | National universities, institutes, research teams, and enterprise AI professionals | High | Call for papers and English-language positioning support national reach |
| Asia-Pacific | Researchers and technical professionals from neighboring Asian markets | Medium | International conference branding indicates likely regional cross-border participation |
| Global | Academic authors, reviewers, keynote-level speakers, and AI specialists worldwide | Medium | Official language and IEEE-linked positioning support international visibility, though public country-level attendee data is not disclosed |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The conference is explicitly presented as an international event for global experts, scholars, and professionals. Secondary practical reach is strong in China and the Asia-Pacific research ecosystem. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Shanghai Jiao Tong University | Host / academic institution | Named on the official event site as a sponsor and host-side institution | sjtu.edu.cn | Professor, Research Director, Lab Director, Conference Chair, Department Administrator | Confirmed Current-Year Participant |
| College of Big Data and Information Engineering, Shanghai Jiao Tong University | Hosting academic unit | Explicitly named in the official English website text as host | sjtu.edu.cn | Dean, Associate Dean, Research Lead, Program Chair, Faculty Member | Confirmed Current-Year Participant |
| IEEE | Sponsor / publishing ecosystem stakeholder | Official website states the conference is sponsored by IEEE and proceedings are intended for submission to IEEE Xplore | ieee.org | Conference Operations, Publications Manager, Technical Program Liaison, Standards/Community Manager | Confirmed Sponsor / Exhibitor |
| PRAI conference authors and attendees | Researchers / paper submitters | The official call for papers invites experts, researchers, teachers, students, and professional technicians, but current-year organization names are not publicly listed in the provided materials | prai.net | Research Scientist, Assistant Professor, AI Engineer, PhD Candidate, Lab Manager | Confirmed Current-Year Participant |
| Current-year attendee, sponsor, speaker, and exhibitor organization lists are not publicly disclosed in the provided official materials. This limits the number of named buyer organizations that can be confirmed without introducing unsupported claims. | |||||
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Associate Professor | Research / Faculty | Senior / Mid-Senior | High influence over research direction, collaboration, lab budgets, and conference participation |
| 2 | Research Director | R&D | Director | Owns strategic evaluation of AI platforms, compute, tooling, and partnerships |
| 3 | Lab Director / Principal Investigator | Research Laboratory | Director / Senior | Strong buyer/influencer for data tools, model pipelines, grants, and collaborative projects |
| 4 | AI Research Scientist | R&D | Individual Contributor / Senior IC | Key evaluator of technical depth, performance, data quality, and reproducibility |
| 5 | Machine Learning Engineer | Engineering / AI | Mid-Senior | Technical user of training, deployment, MLOps, and edge AI solutions |
| 6 | Computer Vision Engineer | Engineering | Mid-Senior | Directly relevant to image/video processing and pattern recognition tracks |
| 7 | Head of AI / Chief AI Officer | Executive / Innovation | Executive | Relevant for enterprise application, partnerships, and strategic sponsorships |
| 8 | Director of Data Science | Data Science | Director | Relevant to multimodal data, analytics, and AI implementation discussions |
| 9 | Cybersecurity Research Lead | Security / Research | Manager / Director | Relevant due to special sessions in intelligent data analysis and AI-driven cybersecurity |
| 10 | Program Manager / Conference Chair | Academic Program / Administration | Manager / Director | Useful for event partnership, sponsorship, workshop, and institutional outreach |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Research | Core fit for academic and applied research participation | Research tooling, compute, datasets, technical collaboration |
| 2 | Higher Education | Universities and academic labs are a primary audience segment | Faculty outreach, lab software, grants, workshops |
| 3 | Information Technology & Services | Applied AI solution providers and enterprise technical teams align well | Partnerships, pilots, AI deployment support |
| 4 | Computer Software | Modeling, analytics, MLOps, and developer platforms are directly relevant | Software licensing, trials, technical adoption |
| 5 | Computer Hardware | AI compute, edge devices, sensors, and acceleration hardware fit conference themes | Hardware evaluation and lab procurement influence |
| 6 | Semiconductors | Relevant for edge computing, AI chips, and high-performance inference | Research collaboration and design ecosystem visibility |
| 7 | Hospital & Health Care | Medical data fusion and precision diagnosis are named themes | Clinical AI, imaging, diagnostics analytics |
| 8 | Medical Devices | Applied AI for medical technologies and imaging systems fits well | Embedded AI, vision systems, regulated analytics |
| 9 | Automotive | Autonomous driving and perception technologies are named focus areas | Perception, simulation, edge vision, sensor fusion |
| 10 | Transportation/Trucking/Railroad | Intelligent transportation systems are part of the event topic set | Traffic analytics, routing AI, surveillance, safety systems |
| 11 | Computer & Network Security | Cybersecurity special sessions are directly listed | Threat detection, identity, OT/IoT security, SOC AI |
| 12 | Aviation & Aerospace | Aerospace AI is an official special-session topic | Perception, data analysis, digital mission systems, anomaly detection |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | No attendee count stated in provided official website text | Do not use for volume-based forecasting |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Academic conference format; no exhibitor list disclosed in provided materials | Likely limited exhibitor-style activity compared with trade expos |
| Buyer count | Not publicly confirmed | Unconfirmed | No procurement or buyer program disclosed | Buying influence is likely technical and research-led rather than formal procurement-led |
| Speaker count | Not publicly confirmed in the provided text | Unconfirmed | Speaker pages exist in site navigation, but no count is included in the provided content | Speaker list should be checked closer to event |
| Sponsor count | 2 named sponsor-level organizations | Confirmed | Official website text names Shanghai Jiao Tong University and IEEE | This is not a full sponsorship inventory |
| Historical attendance | Not publicly confirmed in the provided materials | Unavailable | No prior-year attendance figures included in source text | Prior-year participation evidence not available from the provided source extract |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Pattern recognition | Algorithms, benchmarks, datasets, evaluation tools | Research partnerships and technical proof points | Vision tools, labeling platforms, model evaluation software |
| Deep learning and big data processing | Scalable training, data pipelines, storage, compute | Cloud/GPU conversations and architecture workshops | AI infrastructure, MLOps, data engineering platforms |
| Image, speech, and NLP | Model performance, inference, multilingual workflows | Domain-specific demos and benchmark-based outreach | Speech AI, CV APIs, LLM/NLP platforms |
| Generative AI and multimodal learning | Model orchestration, safety, productivity, content understanding | Thought leadership and pilot program outreach | Foundation model services, multimodal AI stacks |
| Healthcare applications | Clinical interpretation, imaging analysis, data fusion | Joint validation studies and hospital-lab collaboration | Medical imaging AI, clinical analytics, secure data platforms |
| Intelligent transportation and autonomous driving | Perception, sensor fusion, edge deployment, safety analytics | R&D meetings with mobility and ITS teams | Edge AI, simulation, perception software, mapping tools |
| Financial risk management | Predictive analytics, anomaly detection, explainability | Applied AI use-case discussions | Risk scoring models, explainable AI, governance tools |
| Cybersecurity and intelligent data analysis | Threat detection, SOC analytics, identity/IoT/OT protection | Security-focused special-session networking | AI-driven cybersecurity tools, SIEM analytics, anomaly detection |
| Edge computing and model interpretability | Operational deployment, transparency, compliance, efficiency | Technical workshops and benchmark-led outreach | Edge runtime, compression tools, XAI platforms |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong fit for technical, research, and innovation-led solutions; weaker fit for commodity procurement |
| Decision-maker availability | Medium | Many attendees are likely influencers or technical evaluators rather than final purchasing authorities |
| Data collection potential | Medium | Useful for speaker/author/committee/registrant research, but current public attendee list visibility is limited |
| Apollo targeting potential | High | Strong industry and job-title mapping across research, higher education, AI software, healthcare AI, mobility AI, and cybersecurity |
| Geographic targeting potential | High | Shanghai, China, and APAC targeting are practical and relevant |
| Best outreach approach | High | Use research-value messaging, benchmark results, case studies, technical workshop invitations, and collaboration angles |
| Overall lead quality | High | Good for niche B2B AI targeting, ecosystem partnerships, and research/innovation pipeline development |
| Best use case | High | Lead generation for AI infrastructure, research software, data platforms, cybersecurity AI, and technical collaboration services |
| Limitations / risks | Medium | Attendance volume, buyer counts, and named current-year organizations are not publicly confirmed; event is not a classic procurement trade show |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Research; Higher Education; Information Technology & Services; Computer Software; Computer Hardware; Semiconductors; Hospital & Health Care; Medical Devices; Automotive; Transportation/Trucking/Railroad; Computer & Network Security; Aviation & Aerospace | Aligns target accounts to official conference themes |
| Departments | Engineering; Research; Information Technology; Education; Product Management; Operations; Security | Captures both academic and applied AI roles |
| Seniority | CXO; VP; Director; Head; Manager; Senior; Owner (for niche AI firms) | Targets strategic and technical decision makers |
| Job titles | Head of AI, Chief AI Officer, Director of Data Science, Research Director, Lab Director, Principal Investigator, Professor, AI Research Scientist, Machine Learning Engineer, Computer Vision Engineer, NLP Engineer, Cybersecurity Research Lead, CTO | High-likelihood roles for conference participation or thematic alignment |
| Geography | Shanghai; China; East China; APAC; selected global AI hubs if client supports international outreach | Prioritizes likely attendee origin and strongest event relevance |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Covers startups, labs, universities, and enterprises |
| Keywords | pattern recognition, artificial intelligence, machine learning, deep learning, multimodal, computer vision, image processing, speech recognition, natural language processing, generative AI, edge computing, autonomous driving, intelligent transportation, healthcare AI, cybersecurity | Improves precision for event-theme prospecting |
| Technologies | AI/ML stack, cloud compute, GPU infrastructure, data platforms, computer vision frameworks, edge AI tooling | Best when selling technical products or services |
| Revenue range | Use open range or segment by client price point | Conference relevance is role- and innovation-driven more than revenue-driven |
| Company type | Private; Public; Educational Institution; Nonprofit Research; Government-linked research bodies where relevant | Broadens the prospect universe beyond pure commercial firms |
Suggested Apollo Search Logic: ("pattern recognition" OR "artificial intelligence" OR "machine learning" OR "deep learning" OR "computer vision" OR "multimodal" OR "generative AI" OR "NLP" OR "speech recognition" OR "edge computing") AND (Research OR Engineering OR Information Technology) AND (Director OR Head OR Professor OR Principal Investigator OR Scientist OR CTO).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| PRAI 2026 Official Website | Official event website | Event title, dates, city, country, sponsor/host wording, conference themes, special sessions, publication notes, and deadlines | High |
| EasyChair PRAI 2026 Submission Portal | Official submission platform referenced by event site | Supports existence of current-year submission workflow | Medium-High |
| Shanghai Jiao Tong University Official Website | Organizer identity reference | Institutional identity of named sponsor/host-side university | High |
| IEEE Official Website | Sponsor identity reference | Institutional identity of named sponsor and publication ecosystem relevance | High |
| Verification note | Research caveat | Specific venue/facility name, attendee count, buyer count, speaker count, and named attendee organizations were not publicly confirmed in the provided official website text. This report therefore avoids unsupported attendance and participant claims. | High methodological reliability |
🎯 Selling to this event's audience? Get a free tailored buyer list
Tell us your work email and our AI instantly builds a buyer list matched to 2026 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
🔒 Get my buyer details Free · ~20 seconds