MeiqiYang

Postdoc at Rice University in Civil and Environmental Engineering, Water-Energy Nexus, Mineral Extraction, Machine Learning for Science

[HPC-AI Tech @HPCAITech]

HPC-AI is heading to #ICML2026! 🚀

Looking for top-tier compute efficiency or building the next generation of AI Agents? We’ve got you covered! Stop by Booth B301 to talk cutting-edge technology, supercharge your projects, and grab awesome perks!

🎁 Exclusive ICML Swag & Perks:
📱 Interact & Win: Engage with this post to grab our custom caps & eco-friendly canvas bags for FREE! 🧢💼
📝 1-Min Survey: Complete a quick questionnaire and claim your exclusive ICML badges & tech stickers! 🧲✨
🔋 Top-up Bonus: Top up your account during ICML and take home a premium Bluetooth speaker! 🔊
📝 Accepted Author Special: Got a paper at ICML? Unlock our Author Incentive Program to get exclusive vouchers for GPU compute and Model APIs! 🎓💡

⚡ Why Choose HPC-AI?
1️⃣ GPU Cloud Compute | High Performance & Elasticity
- Full-Spectrum GPU Coverage: From H200 to B200, we provide top-tier GPUs for training, fine-tuning, and inference.
- Flexible & Cost-Effective: Pay-as-you-go billing with ultimate price-to-performance ratio.
- Stable & Ready-to-Use: Instant deployment, elastic scaling, and enterprise-grade reliability for both corporate R&D and academic research.
2️⃣ Model APIs | One-Click Access to Frontier LLMs
- Mainstream Models Covered: Seamlessly access DeepSeek, GLM, Kimi, MiMo, and more, with continuous updates.
- High-Performance Inference: Built on enterprise GPU infrastructure, ensuring accelerated inference and high availability for massive workloads.
- Seamless Ecosystem Integration: Compatible with 40+ mainstream AI frameworks and tools (Dify, Cursor, Open Claw, Open Code, etc.). Connect in 3 easy steps!

📍 Booth B301 📅 July 7-10 🏢 ICML Conference (Korea Seoul)

#ICML2026 #HPCAI #MachineLearning #DeepLearning #GPU #ModelAPIs #LLM #AICompute

[Junjie Zhu @Jun_Jie_Zhu]

Excited to share the EMBRACE Checklist, aiming to support all the members in the environmental ML community. Please check the viewpoint for more details. Thanks to @zjasonren, Professor Boehm. Also @MeiqiYang2, @sinaborzooei, and @ZhonghuaZheng.
https://t.co/aF7o7mJk2j

[Junjie Zhu @Jun_Jie_Zhu]

We’re also invited to share the work using the Interactive poster. @MeiqiYang2 and I will also be Bluebird Ballroom 3A - Colorado Convention Center on Sunday, August 18, 2024, 4:00 PM - 5:00 PM. #ACSFall2024

[Princeton PLI @PrincetonPLI]

Research on LLMs in ESE fields remains a gap. A new study provides the first fundamental analysis in the ESE field using expert-level ESE domain-specific textbooks.

Read our latest blog post from @Jun_Jie_Zhu, @zjasonren and @MeiqiYang2: https://t.co/NeC5zwhoTa

[Junjie Zhu @Jun_Jie_Zhu]

I will give a talk about more details of our LLM research in environmental fields in the upcoming #ACS Fall 2024 #Denver, CO. Come to my talk on 8/20 8:05 am @ Room 106 - Colorado Convention Center. @MeiqiYang2

[MeiqiYang @MeiqiYang2]

Thrill to share our recent study😀

[Junjie Zhu @Jun_Jie_Zhu]

Data leakage management makes the model more robust! @MeiqiYang2

[Environmental Science & Technology Journals @EnvSciTech]

In the latest issue of ES&T: @zjasonren and team at @AndlingerCenter @Princeton employ #machinelearning models to identify high potential polymers for accelerating tailored membrane designs for #pervaporation based organic recovery.

Read more: https://t.co/bliS4rrLyg

[MeiqiYang @MeiqiYang2]

Thrilled to share our recent paper @EnvSciTech: https://t.co/ZzE8Qeecef where we discussed using ML to boost polymer design for organic recovery, result in identified polymers with good promise for future experimental studies.#MachineLearning
@Jun_Jie_Zhu @zjasonren

[Junjie Zhu @Jun_Jie_Zhu]

Special Issue @EnvSciTech on #DataScience #MachineLearning #ArtificialIntelligence is out today! Really happy to see our cover + 2 highly accessed articles! Haven't read them? Check it now: https://t.co/e4gifuslQK
@zjasonren
@MeiqiYang2
@yinxisang
@EPrinceton
@AndlingerCenter

[Junjie Zhu @Jun_Jie_Zhu]

Two weeks ago we were awarded a mini-grant ($10,000) of Azure Cloud Computing Credits from @PrincetonSML for our ML/LLM research. It definitely helps us to test larger models. Thanks for the support! @zjasonren @MeiqiYang2 @yinxisang

[Nature Water @NatureWaterJnl]

Our September issue is now live, with a beautiful cover from @zjasonren and colleagues
https://t.co/9nUq9d0HMy

[Andlinger Center @AndlingerCenter]

September newsletter highlights:
• A new, environmentally friendly approach to lithium extraction
• Studying the benefits of tandem solar cells
• E-ffiliates expands
• Annual Meeting on next decade energy technologies
Read more & subscribe: https://t.co/3XHHJFeZIq

[Andlinger Center @AndlingerCenter]

The method, developed in the lab of @zjasonren, could significantly reduce the amount of land, time, and water needed to meet the world's booming lithium demand!

[Andlinger Center @AndlingerCenter]

#PrincetonU researchers led by @zjasonren have developed an environmentally friendly approach to lithium extraction that could help to meet booming demand for the silvery-white metal. @NatureWaterJnl

https://t.co/BGFIsSrBqM

[MeiqiYang @MeiqiYang2]

I am thrilled to share our just published review paper on #MachineLearning in #environmental research led by @Jun_Jie_Zhu. We summarized common pitfalls from 148 highly cited papers, and provide practical guidance. @zjasonren @AndlingerCenter @EPrinceton
https://t.co/BDq3GIEhbV

[Junjie Zhu @Jun_Jie_Zhu]

I'm very excited to share our just published review article of #ML applications in #environmental in @EnvSciTech! We evaluated 148 high-cited studies based on >30 ML items, provided reference paradigms.
@zjasonren @MeiqiYang2 @AndlingerCenter @EPrinceton
https://t.co/sVAAUt1Vr5

[MeiqiYang @MeiqiYang2]

@XiaoxiaoLi8 @TaoThuan @Shipei_Xing @UBCChem @ECEUBC 👏

[Xiaoxiao Li @XiaoxiaoLi8]

Thrilled to share this amazing work on ML-based molecular formula discovery published at Nature Methods led by my colleague @TaoThuan and his excellent team @Shipei_Xing et al. 👏Thanks for the great multidisciplinary collaboration opportunities at @UBCChem + @ECEUBC.

[MeiqiYang @MeiqiYang2]

@Tao_Ye_ @EnvSciTech @zjasonren @Jun_Jie_Zhu Great point, theoretically, data from the same group possibly contains hidden information (i.e., personnel, equipment, environment). Would be great to extend these methods step by step in the future, considering the affiliation/ authors ...😀

[MeiqiYang @MeiqiYang2]

Machine learning provides efficient tools for performance prediction, but is data leakage an issue during model development? Check out our recently published article 😊https://t.co/Kyb99c7z0L @EnvSciTech @zjasonren @Jun_Jie_Zhu