SAIL-Research

We are now hiring Research Assistants for an innovative project on Green Serverless Computing for AI at VinUniversity! 🌍💻
If you're passionate about AI, cloud computing, and sustainability, this is your chance to contribute to cutting-edge research while reducing the environmental impact of AI systems.

Position: Full-Time (Undergraduate RA positions also available)
Location: On-site at VinUniversity Campus
Duration: Initial contract of 6-12 months (extendable)
Project Focus: Energy-efficient AI training with serverless computing
Skills Needed: AI/ML basics, cloud platforms (AWS, Azure), Python programming, data analysis

[More on Job Descriptions]: https://drive.google.com/file/d/1zTdqJJkBVw_z4UZ11V2T3IKkfabvezVZ/view?usp=drive_link

📄 How to Apply: Send your CV and cover letter to [email protected] and [email protected].

Job Description_Research Assistant.pdf

As the time is closing in, we're very nervous but also feeling very proud of the team and the collaborators of our Farm2Vet solution to reduce Antimicrobial Resistance (AMR) with Generative AI (read about the solution here https://solve.mit.edu/.../trinity.../solutions/81996 and the official announcement here https://www.linkedin.com/.../the-trinity-challenge... ).
A pat on the shoulder to MAIL-Research and SAIL-Research (and of course everyone else on the solution). Very luck that we have a chance to collaborate with everyone!!!

From our Founder and Chair, Dame Sally Davies, our Director, Prof Marc Mendelson and all the team - we want to wish all our finalists good luck for this afternoon!
Your innovative thinking and commitment is key in mitigating the .

From our Founder and Chair, Dame Sally Davies, our Director, Prof Marc Mendelson and all the team - we want to wish all our finalists good luck for this afternoon!
Your innovative thinking and commitment is key in mitigating the .

Contact us in case you cannot find your "front door".....😁

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions Federated learning (FL) is an approach within the realm of machine learning (ML) that allows the use of distributed data without compromising personal…

Happy to share that our Survey of Backdoor Attacks and Defenses on Federated Learning is accepted to the Q1 journal Engineering Applications of Artificial Intelligence. Special thanks to Thuy Dung Nguyen and Minh Tuan Nguyen, who did an unbelievable amount of work for this submission.

If you are interested in Safe/Trustworthy ML/AI research in Federated Learning, or about to develop a Federated ML application in practice, this survey will give a comprehensive discussion on various vulnerabilities when deploying such FL systems.

Thuy Dung Nguyen, Tuan Nguyen, Phi Le Nguyen, Hieu H Pham, Khoa D Doan, Kok-Seng Wong. Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions. EAAI 2023. [Arxiv version: https://arxiv.org/abs/2303.02213]



SAIL-Research MAIL-Research VinUniversity

Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy. However, the heterogeneous distribution of data among clients in FL can make it difficult for the orchestration server to validate the integrity of local model u...

Backdoors in Deep Learning Research on Deep Learning Backdoors