2026 8th Asia Conference on Machine Learning and Computing (ACMLC 2026)
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About this event
2026 8th Asia Conference on Machine Learning and Computing (ACMLC 2026)
Date: July 10–12, 2026
Venue: Beijing, China
Event Type: Academic and Research Conference
Estimated Attendance: Not specified, but typically includes researchers, practitioners, professionals from academia, industry, and government.
1. Who Attends (Buyers / Attendees)
ACMLC 2026 attracts a specialized audience of:
- Researchers in machine learning and computing
- Academic faculty and students from universities and research institutions
- Industry professionals from tech companies, startups, and R&D departments
- Government representatives involved in science and technology policy
- Publisher and journal representatives
- Technology vendors and service providers targeting the academic and research sectors
Best Buyer Profiles: Academic institutions, research labs, AI/ML-focused companies, cloud computing providers, educational software vendors, and technology recruiters.
2. Location and Geographic Origin
Event Location: Beijing, China
Attendee Origin: Primarily Asia-Pacific (China, Japan, South Korea, Singapore), with significant representation from North America and Europe due to the conference's academic and research focus.
Geographic Targeting: Asian universities and tech companies, international research collaborations, global AI/ML vendors seeking academic partnerships.
3. Audience Reach
Reach Type: Global, with a strong emphasis on the Asia-Pacific region. The conference's publication in IEEE and indexing by Ei Compendex and Scopus attract international participants.
Regional Focus: Asia-Pacific (primary), North America, Europe.
4. Sample Buyer Company Names and Websites
| Priority | Company | Website | Best Title to Target | Why This is a Good Buyer Fit |
|---|---|---|---|---|
| 1 | Tencent AI Lab | https://ai.qq.com/ | Research Director, AI/ML | Active in machine learning research and development, likely interested in academic collaborations. |
| 2 | Google AI (China Office) | https://www.google.com/ | Head of Research Partnerships | Global leader in AI/ML, frequently engages with academic conferences for talent and research. |
| 3 | Microsoft Research Asia | https://www.microsoft.com/en-us/research/ | Academic Program Manager | Prominent research institute with a strong focus on machine learning and academic collaborations. |
| 4 | Tsinghua University - Department of Computer Science | https://www.cs.tsinghua.edu.cn/ | Dean of Research, Professor of Machine Learning | Top-ranked university in China with active ML research programs and participation in conferences. |
| 5 | IBM Research China | https://www.ibm.com/ibm-research/ | Chief Data Scientist | Engaged in advanced computing and machine learning research, seeks academic and industry partnerships. |
| 6 | Huawei AI Lab | https://www.huawei.com/ | Director of AI Research | Leading Chinese tech company investing heavily in AI/ML for various applications. |
| 7 | Stanford University - Computer Science Department | https://cs.stanford.edu/ | Professor of Machine Learning | International academic leader in ML research, often participates in global conferences. |
| 8 | DeepMind (China Partnerships) | https://www.deepmind.com/ | Head of Academic Collaborations | Global leader in AI research, interested in academic and research partnerships worldwide. |
| 9 | University of Tokyo - Institute for AI | https://ai.u-tokyo.ac.jp/ | Director of Research Collaborations | Prominent Asian research institution with strong ML focus and international collaborations. |
| 10 | NVIDIA China - AI and Deep Learning | https://www.nvidia.com/ | Director of AI Ecosystem Development | Technology provider for ML infrastructure, interested in academic and research partnerships. |
5. Job Profiles, Industries, and Event Type
Best Job Profiles to Target:
- Professor / Lecturer in Machine Learning or Computer Science
- Research Scientist (AI/ML)
- Head of Research and Development
- Academic Program Manager
- Chief Data Scientist
- AI/ML Engineer
- Director of AI Partnerships
- Technology Transfer Manager
- PhD Student in Computer Science or Related Fields
- Postdoctoral Researcher
Industries: Information Technology, Computer Software, Artificial Intelligence, Education Management, Research Institutions, Telecommunications, Financial Services (for AI applications), Healthcare (for ML applications), and Government Research Bodies.
Event Type: Academic and Research Conference focused on Machine Learning and Computing.
6. Estimated Attendance
Estimated Total Footfall: Not specified on the official website, but academic conferences of this caliber typically attract between 500 to 1,500 attendees, including researchers, presenters, and industry participants.
Breakdown: The event includes keynote speeches, parallel sessions, and technical visits, indicating a structured academic environment with a mix of attendees from various sectors.
7. Key Focus Areas and Buyer Engagement
Key Focus Areas:
- Machine Learning Algorithms and Applications
- Computing Systems and Architectures
- Artificial Intelligence Research and Development
- Academic and Industry Collaborations
- Talent Development and Education in AI/ML
- Publication and Dissemination of Research
Buyer Engagement Angle: Position your offering as a solution for academic research, talent acquisition, or industry collaboration. Highlight benefits for research institutions, tech companies, and educational software vendors. Tailor your pitch to attract attendees looking for partnerships, recruitment opportunities, or technology solutions for ML research and education.
8. Client-Product Fit Note
Please share your client's website to enable a detailed analysis of the best buyer fit for ACMLC 2026. Depending on the client's product or service, the target audience may vary significantly:
- Educational Software or LMS: Target academic institutions, professors, and educational administrators.
- AI/ML Research Tools or Infrastructure: Engage with research scientists, heads of R&D, and technology vendors.
- Recruitment Services for Tech Talent: Focus on industry professionals, HR managers, and academic recruiters.
- Technology Solutions for AI Applications: Target industry leaders, CTOs, and directors of innovation in sectors like finance, healthcare, or telecommunications.
Without specific client information, the above table and analysis provide a general overview of potential buyers at ACMLC 2026.
9. Final Recommendation
ACMLC 2026 is a valuable event for companies and institutions seeking to engage with the academic and research community in machine learning and computing, particularly in the Asia-Pacific region. The conference offers opportunities for brand visibility, talent recruitment, research collaborations, and B2B networking. For attendee list sales, focus on academic and industry decision-makers rather than student attendees for higher-value engagement.
Quality Rating for B2B Attendee-List Sales: 7.5/10 (Strong academic and research focus, but may have limited corporate procurement representation compared to industry trade shows.)
Data sheet
2026 8th Asia Conference on Machine Learning and Computing (ACMLC 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 10–12, 2026
Location: Renmin University of China, Beijing, China
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 8th Asia Conference on Machine Learning and Computing (ACMLC 2026) |
| Event Date | July 10–12, 2026 |
| Event Status | Upcoming |
| Venue | Renmin University of China |
| City | Beijing |
| State / Region | Beijing Municipality |
| Country | China |
| Organizer | Organizer name not clearly stated in the supplied official website text. Conference contact email uses the iacsitp.com domain, indicating organizer/secretariat affiliation, but the organizer name should be treated as not fully confirmed from the supplied source set. |
| Official Event Website | acmlc.org |
| Event Type | Academic and Research Conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global, with strong Asia-Pacific concentration |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for numeric attendance; high for event purpose, timing, topic focus, submission deadlines, and publication pathway based on official website content. |
| Main Purpose of Event | To provide a forum for researchers, practitioners, and professionals from industry, academia, and government working in machine learning and computing to discuss research, development, and professional practice, and to present accepted papers and abstracts. |
About the Event
ACMLC 2026 is the 8th edition of the Asia Conference on Machine Learning and Computing, scheduled for July 10–12, 2026 in Beijing, China. According to the official event website, the conference is designed as a forum for researchers, practitioners, and professionals from industry, academia, and government working in machine learning and computing. The program structure includes keynote and invited speeches, parallel session discussions, technical activities, paper presentations, and publication opportunities through IEEE conference proceedings submission and indexing pathways.
From a commercial intelligence perspective, this event matters less as a broad expo and more as a high-value specialist gathering for research-led networking, academic partnerships, applied AI collaboration, recruitment, cloud and compute engagement, and innovation visibility. The strongest participant groups are likely to include university faculty, doctoral researchers, applied R&D teams, public-sector science stakeholders, and technology companies seeking visibility in research communities rather than high-volume consumer or general enterprise purchasing.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University faculty and principal investigators | Universities, AI laboratories, computing departments | Influence software, data platform, cloud, compute, and lab collaboration decisions | High relevance for research tools, compute infrastructure, AI software, and publication services |
| PhD candidates and postgraduate researchers | Academic institutions, research groups | End-user evaluators and future technical champions | Useful for product trials, academic adoption, and early-stage advocacy |
| Applied AI / ML R&D teams | Technology companies, corporate innovation labs, startups | Evaluate frameworks, infrastructure, data tooling, model deployment, and partnerships | High-value audience for technical demos, developer engagement, and co-research opportunities |
| Government science and technology representatives | Science ministries, public research agencies, innovation programs | Policy, grant, public research, and institutional collaboration influence | Relevant for public-sector research partnerships, AI strategy engagement, and funded programs |
| Academic and technical program committee members | Conference committee, peer reviewers, subject experts | Strong opinion leadership; moderate direct buying authority | Important for reputation building, partnerships, and speaker-led visibility |
| Cloud, platform, and developer-relations teams | Cloud providers, AI tooling firms, platform vendors | Seek adoption, collaborations, technical trials, and ecosystem growth | High relevance for B2B outreach, sponsorship, and research ecosystem penetration |
| Publishers and indexing-related stakeholders | Technical publishers, proceedings and indexing ecosystem participants | Support publication visibility and dissemination | Relevant for scholarly communications and conference publication services |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city: Beijing | Local university researchers, Beijing-based labs, public institutions, and technology teams | High | Beijing is a major research, policy, and AI ecosystem center in China. |
| Host region: Beijing Municipality | Regional academic and government-linked attendees | High | Strong concentration of universities, research centers, and national innovation bodies. |
| Nearby business and research hubs | Shanghai, Shenzhen, Hangzhou, Tianjin, Nanjing, Xi'an, and other Chinese innovation corridors | Medium to High | Likely draw for applied AI, enterprise R&D, and university collaboration audiences. |
| National reach: China | Researchers, authors, graduate students, and technical professionals from across China | High | Submission and publication-driven conferences typically attract national paper presenters. |
| Asia-Pacific reach | Japan, South Korea, Singapore, India, Hong Kong, Taiwan, Malaysia, Thailand, and other APAC research communities | Medium | The conference branding and topic area indicate a strong Asia-oriented footprint. |
| International reach beyond APAC | Selected authors, speakers, and scholars from Europe and North America | Medium | Likely driven by publication visibility and international academic participation rather than mass commercial attendance. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The conference has international positioning, English-language academic structure, IEEE proceedings publication pathway, and likely cross-border paper submissions. |
| Asia-Pacific | Strong secondary reach | The event brand, location, and conference identity suggest the strongest concentration of attendance from APAC universities and AI research communities. |
4. Sample Buyer Companies and Websites
Official current-year attendee, buyer, sponsor, and exhibitor organization lists were not publicly available in the supplied official source text. The table below therefore lists strong market-fit prospecting targets relevant to the conference theme and host-city ecosystem. These are not confirmed attendee records for ACMLC 2026.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Renmin University of China | University / host ecosystem | Host venue and strong fit for academic computing, research infrastructure, and collaboration outreach | ruc.edu.cn | Professor, Dean, Lab Director, Research Administrator, IT Director | Strong Market Fit, Attendance Not Confirmed |
| Tsinghua University | University / research buyer | Major AI and computing research institution with likely interest in ML platforms and collaboration | tsinghua.edu.cn | Professor, Director of Research, AI Lab Head, HPC Manager | Strong Market Fit, Attendance Not Confirmed |
| Peking University | University / research buyer | Strong relevance for machine learning, data science, and academic computing tools | pku.edu.cn | Professor, Research Center Director, CIO, Department Chair | Strong Market Fit, Attendance Not Confirmed |
| Chinese Academy of Sciences | National research organization | Core public research buyer for AI, compute, scientific software, and data-intensive workflows | cas.cn | Institute Director, Research Scientist, Procurement Lead, IT Infrastructure Manager | Strong Market Fit, Attendance Not Confirmed |
| Institute of Automation, Chinese Academy of Sciences | AI research institute | Highly aligned with machine learning, intelligent systems, and technical collaboration opportunities | ia.cas.cn | Lab Director, Senior Researcher, AI Program Lead | Strong Market Fit, Attendance Not Confirmed |
| Beihang University | University / engineering research buyer | Relevant for AI, computing, aerospace applications, and technical research procurement | buaa.edu.cn | Professor, School Director, Research Program Manager | Strong Market Fit, Attendance Not Confirmed |
| Beijing Institute of Technology | University / engineering and AI research buyer | Relevant for computational research, labs, and AI applications | bit.edu.cn | Dean, Research Director, IT Director, Procurement Manager | Strong Market Fit, Attendance Not Confirmed |
| ByteDance | Corporate AI / R&D buyer | Large-scale machine learning user with research collaboration and talent sourcing relevance | bytedance.com | Research Scientist, ML Engineering Director, University Relations Lead | Strong Market Fit, Attendance Not Confirmed |
| Baidu | Corporate AI / platform buyer | Strong fit for AI frameworks, research collaboration, and infrastructure procurement | baidu.com | AI Lab Director, Research Engineer Lead, Partnerships Director | Strong Market Fit, Attendance Not Confirmed |
| Alibaba Cloud | Cloud and AI infrastructure provider | Relevant for research compute, data platforms, AI training environments, and academic partnerships | alibabacloud.com | Cloud Solutions Director, Research Partnerships Manager, Education Sales Director | Strong Market Fit, Attendance Not Confirmed |
| Huawei Cloud | Cloud and computing platform buyer / partner | Relevant for AI acceleration, cloud infrastructure, and university R&D outreach | huaweicloud.com | CTO Office, Research Partnerships Manager, Industry Solutions Director | Strong Market Fit, Attendance Not Confirmed |
| Tencent | Corporate AI / research collaboration target | Active in AI, computing, cloud, and university innovation ecosystems | tencent.com | AI Product Director, Research Partnerships Lead, Technical Program Manager | Strong Market Fit, Attendance Not Confirmed |
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 / Director | Drives research direction, technology adoption, collaboration, and lab purchasing influence. |
| 2 | Research Director / Lab Director | R&D / Research Infrastructure | Director / Head | Key target for institutional partnerships, compute needs, and software evaluation. |
| 3 | Dean / Department Chair | Academic Leadership | Executive / Senior | Influences departmental investment, partnerships, and curriculum-linked technology choices. |
| 4 | Chief Information Officer / IT Director | IT / Infrastructure | Director / Executive | Relevant for campus compute, data storage, cloud adoption, and security governance. |
| 5 | Research Scientist / Senior Researcher | R&D | Manager / Senior IC | Technical evaluator and strong champion for model, dataset, and platform adoption. |
| 6 | Machine Learning Director / AI Engineering Director | Engineering / AI | Director | Strong target in corporate research and applied AI teams attending for technical learning or recruiting. |
| 7 | Procurement Manager / Research Procurement Lead | Procurement / Administration | Manager | Relevant where research software, hardware, subscriptions, and services require institutional buying process. |
| 8 | Partnerships Director / University Relations Manager | Business Development / Partnerships | Manager / Director | Important for joint research, sponsored projects, talent pipelines, and ecosystem access. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Core attendee base for authors, faculty, labs, and institutional leaders | Research software, compute, academic partnerships, curriculum tools |
| 2 | Research | Direct fit for public and private research institutes in machine learning and computing | Scientific collaboration, platforms, datasets, model infrastructure |
| 3 | Information Technology & Services | Relevant for service firms, systems integrators, and AI solution providers | Partnerships, implementation support, enterprise AI services |
| 4 | Computer Software | Strong fit for AI/ML tool vendors, developer platforms, and analytics providers | Licensing, pilots, integration, technical adoption |
| 5 | Internet | Large internet platforms often maintain active ML research and applied AI teams | Research collaboration, AI tooling, recruitment outreach |
| 6 | Government Administration | Official website cites government among the target participant groups | Public research programs, science policy engagement, funded initiatives |
| 7 | Computer Hardware | Relevant for GPU, server, and workstation vendors serving AI research users | HPC infrastructure, AI acceleration, lab hardware |
| 8 | Semiconductors | Relevant for AI compute and processor ecosystem players | Research collaborations, hardware enablement, model optimization |
| 9 | Telecommunications | Telecom firms increasingly deploy ML in networks, analytics, and automation | Applied AI use cases, edge compute, data infrastructure |
| 10 | E-Learning | Useful for edtech and academic software companies targeting universities | AI learning tools, assessment platforms, digital education solutions |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not Confirmed | Official website text supplied by user | No numeric attendance disclosed in the supplied source text. |
| Exhibitor count | Not publicly confirmed | Not Confirmed | Official website text supplied by user | Conference appears paper- and session-led rather than exhibitor-led. |
| Buyer count | Not publicly confirmed | Not Confirmed | Official website text supplied by user | No dedicated hosted-buyer or procurement program disclosed. |
| Speaker count | Not publicly confirmed in supplied text | Partially Confirmed Structure | Official website confirms keynote and invited speeches | Program format is confirmed, but numeric speaker volume is not stated in the supplied text. |
| Sponsor count | Not publicly confirmed | Not Confirmed | Official website text supplied by user | No sponsor roster provided in the supplied material. |
| Historical attendance | Not available from supplied official text | Historical / prior-year evidence unavailable | User-supplied official website excerpt | Past editions are listed in site navigation, but no attendance figures were provided in the supplied text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Machine learning research | Algorithms, experimentation environments, datasets, and reproducible workflows | Research demos, papers, workshops, lab collaborations | ML platforms, notebooks, experiment tracking, compute access |
| Computing infrastructure | Training capacity, storage, GPUs, cloud elasticity | Technical consultations with faculty, labs, and IT leaders | Cloud, HPC, servers, storage, AI accelerators |
| Academic publication and dissemination | Proceedings visibility, indexing, presentation opportunities | Author support and publication ecosystem engagement | Publication services, editorial tools, research dissemination solutions |
| University-industry collaboration | Joint projects, sponsored research, internships, technology transfer | Partnership meetings and technical networking | Research partnerships, grants support, co-development programs |
| AI talent and recruitment | Access to emerging researchers, postgraduates, and specialized experts | Brand-building with authors and graduate attendees | Recruitment marketing, employer branding, fellowship programs |
| Government and public research engagement | Innovation policy alignment, research funding, institutional collaboration | Science-policy networking and public program engagement | Advisory services, public-sector AI solutions, consortium support |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong for academic, research, AI platform, cloud, and scientific technology offers; weaker for broad non-technical B2B products. |
| Decision-maker availability | Medium | Senior professors, research leaders, and technical evaluators are likely present, but direct procurement executives may be less concentrated than at commercial trade shows. |
| Data collection potential | Medium | Useful for speaker, author, committee, and academic networking intelligence, but current-year public attendee lists are limited. |
| Apollo targeting potential | High | Well suited to targeting universities, research institutes, AI labs, cloud vendors, and enterprise R&D groups by department and title. |
| Geographic targeting potential | High | Beijing, broader China, and APAC university and AI ecosystems are clear target clusters. |
| Best outreach approach | High | Use research-value messaging: collaboration, compute performance, reproducibility, grants, lab efficiency, publication support, and student enablement. |
| Overall lead quality | High | High-quality niche event for specialized AI, research, cloud, and academic solution providers. |
| Best use case | High | Ideal for targeted B2B outreach, academic partnership development, speaker/author prospecting, and AI ecosystem mapping. |
| Limitations / risks | Medium | Not ideal for high-volume attendee list building from confirmed current-year participant rosters because public attendee and buyer data appears limited. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Computer Software; Information Technology & Services; Internet; Government Administration; Computer Hardware; Semiconductors; Telecommunications; E-Learning | Capture academic, research, and applied AI decision environments. |
| Departments | Research; Engineering; Information Technology; Education; Operations; Procurement; Business Development; Partnerships | Align outreach to both technical evaluators and institutional buyers. |
| Seniority | Director; VP; CXO; Head; Manager; Professor-equivalent seniority where discoverable | Prioritize leaders who influence research direction and budgets. |
| Job titles | Professor, Principal Investigator, Research Director, Lab Director, Dean, Department Chair, CIO, IT Director, AI Director, Machine Learning Director, Research Scientist, Partnerships Director, University Relations Manager, Procurement Manager | Focus on decision-makers and technical champions. |
| Geography | Beijing; China; Singapore; Japan; South Korea; India; Hong Kong; Taiwan; broader APAC; selected Europe and North America research hubs | Mirror likely conference origin profile. |
| Employee size | 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Covers research institutes, universities, and major technology firms. |
| Keywords | machine learning, artificial intelligence, computer vision, NLP, data science, deep learning, high performance computing, research lab, academic computing, IEEE, conference proceedings | Refine toward event-theme-aligned contacts and institutions. |
| Technologies | Cloud infrastructure, GPU compute, ML platforms, data analytics stacks, notebook platforms, model training environments | Useful if selling technical infrastructure or developer tooling. |
| Revenue range | Use flexible range; prioritize institution scale over revenue for universities and research entities | Avoid over-filtering non-corporate targets. |
| Company type | Educational institutions, research institutes, public sector entities, large technology companies, AI startups | Build segmented lists by buyer environment. |
Suggested Apollo Search Logic: ("machine learning" OR "artificial intelligence" OR "AI" OR "deep learning" OR "computer vision" OR "NLP" OR "data science") AND ("research" OR "lab" OR "institute" OR "department" OR "university" OR "cloud" OR "compute"). Prioritize Beijing- and China-based higher education and research organizations first, then expand to APAC technology firms with active AI research or university partnership functions.
Client Fit Review Required
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ACMLC 2026 Official Website | Official event website | Event name, dates, city, country, conference purpose, target participant groups, deadlines, keynote/invited structure, publication pathway, and submission contact details | High |
| User-supplied official website text extract | Primary source excerpt | Confirmed the official page title and homepage wording for “Beijing, China | July 10-12, 2026,” plus conference description and deadlines | High |
| User-provided known event details | Supplemental input | Venue listed as Renmin University of China; used because venue name was not visible in the supplied official text excerpt | Medium |
| Conference contact information on official website | Official event contact | Contact email acmlc@iacsitp.com suggests conference secretariat or organizer affiliation, but does not fully confirm the organizer name in the supplied text | Medium |
B2B attendee list building suitability: Suitable for niche, research-led B2B targeting and partnership prospecting, but not ideal for high-confidence current-year attendee list extraction because public participant rosters were not available in the supplied official source set.
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