Fellowships for PHD Students

The JHU + Amazon Initiative for Interactive Artificial Intelligence (AI2AI) invites applications from outstanding PhD students for fellowships in the 2024-2025 academic year. Awardees, who will be designated Amazon Fellows, will receive a full stipend, 20% tuition, and student health insurance for the fall 2024 and spring 2025 semesters. Additionally, they will be nominated for a paid summer internship at Amazon in 2025, during which they will gain valuable industry insights and experiences via engagement with Amazon researchers.

Amazon builds and deploys AI across three technology layers: the bottom layer consists of Amazon’s own high performance and cost-effective custom chips, as well as a variety of other computing options including from third-parties. The middle layer focuses on the customer’s choice by providing the broadest selection of Foundation Models—both Amazon-built as well as those from other leading providers. At the top layer Amazon offers generative AI applications and services to improve every customer experience.

There are three things that distinguish Amazon’s approach to the development and deployment of AI:
1) Maintaining a strategic focus on improving the customer and employee experience through practical, real-world applications of AI;
2) marshaling world-class data, compute, and talent resources to drive AI innovation; and
3) committing to the development of responsible, reliable, and trustworthy AI.

Topics of interest include, but are not limited to, those below. Please feel free to bring your own unique viewpoint and expertise to these topics:

  • AWS Security:
  • Low-cost computer vision techniques for object interactions for physical security.
  • Near-real-time anomaly detection at scale (e.g., streaming petabytes per hour) to identify malicious activity in Linux cloud-based environments.
  • Running distributed big data, AI/ML, and/or streaming models with minimal latency and cost using host, network, and audit telemetry sources. Such data engineering can be used for selecting interesting subsets of data including complex, multi-event sequences, improving matching and micro-summarization efficiencies for continuous log processing, and improving event summarization processing efficiency while maintaining accuracy.
  • Use of AI/ML (e.g., GenAI) to automate and improve security response workflows (for example, from written reports or threat detections), specifically through incident summarization, response planning, and communication management.
  • Artificial General Intelligence & Search:
  • Large Language Models (LLMs):
  • Retrieval augmented generation (RAG), fine-tuning and alignment (SFT, RLHF), and efficient inference: ensuring accuracy and reducing hallucinations; maintaining privacy and trust; reasoning over long contexts
  • Long form context methods
  • Improving data efficiency; effectively distilling models for real-time inference, data quality checks
  • Multi-lingual LLMs and challenges for cross-language defects (e.g. cross-language hallucinations)
  • Synthetic data generation for LLM learning
  • Adapting LLMs for dynamic content (e.g., feeds, web content) in knowledge-augmented scenarios
  • Tool and Code Empowered LLM
  • External Knowledge and Domain Knowledge Enhanced LLM and Knowledge Updating
  • Vision-Language:
  • Multimodal learning and video understanding: retrieval with multimodal inputs (e.g., video, image, text, speech)
  • Adversarial ML with multimodal inputs
  • Comprehensive video understanding with diverse content (open-vocabulary)
  • Shared multimodal representation spaces, aligned codecs
  • LLM and VLM based Intelligent Agents
  • Search and Retrieval:
  • Personalization in Search, semantic retrieval, conversational search: understanding descriptive and natural language queries for product search; retrieving information using LLMs’ output
  • Search page optimization (ranking) using heterogeneous content such as related keywords, shoppable images, videos, and ads
  • Tool Learning for Proactive Information Seeking
  • Efficient Generative AI:
  • Novel model architectures for improved performance (accuracy & efficiency)
  • Training large neural network models with efficiency: High performance distributed training and inference algorithms for Generative AI systems, quality metrics and evaluations
  • Responsible Generative AI:
  • This may include, but is not limited to measurement and mitigation, guardrail models, privacy concerns, detecting and mitigating adversarial use cases, and machine unlearning and model disgorgement

  • Responsible AI for audio, image and video generation
  • Privacy preserving continual learning/self-learning
  • Fact Checking and Factual Error Correction for Truthful LLMs

Applicants must be enrolled full time and in good standing in a WSE PhD program or PhD program in a WSE affiliated department, be in their third year or higher in AY 2024-2025, and have exhibited outstanding academic performance. Exceptionally outstanding students in their second year in AY 2024-2025 also are eligible. Applications from those working in AI who are women and/or identify as members of underrepresented groups are particularly encouraged.

Application Instructions

All applicants must complete steps 1 and 2, listed below, for full consideration. Both steps must be completed no later than Monday, May 6.

1. AI2AI Fellowship Recommendation Letter Request Form

Submit the AI2AI Fellowship Recommendation Letter Request Form as soon as possible, but no later than Monday, May 6.

On this form, applicants will be asked to provide the names of 1) a WSE faculty member and 2) an additional mentor/supervisor (may be from industry), who will send detailed letters of recommendations in support of their applications directly to AI2AI. Letters of recommendation must be received by Monday, May 13 at 11:59 p.m. ET.

2. AI2AI Fellowship Application Form

Submit the AI2AI Fellowship Application Form by Monday, May 6.

Materials required for this form include:

a) A resume listing the applicant’s educational history, awards/honors, refereed publications, and a link to their web page describing scholarly accomplishments and/or other relevant considerations

b) A personal statement describing the applicant’s research interests and plan (two pages maximum)

All documents should be submitted in PDF format through the AI2AI submission portal.



Monday, May 6 – Applicant submits AI2AI Fellowship Recommendation Letter Request Form (recommended to submit earlier)

Monday, May 6 – Applicant submits AI2AI Fellowship Application Form

Monday, May 13 – References submit letters of recommendation for applicant