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USC SURE student develops prototype algorithm to help automate spacecraft docking
As a New Jersey native attending boarding school in Canada, Derek Chibuzor spent a lot of time on planes as a young student. The travel sparked an enduring interest in flight, and when he enrolled at the University of Southern California (USC), Chibuzor chose to major in aerospace engineering.
Chibuzor attended the prestigious Ridley College boarding school in Ontario, Canada, enrolling in an International Baccalaureate diploma program with a focus on STEM. After graduating from Ridley, he considered becoming a fighter pilot but ultimately chose to enroll in USC’s aerospace engineering program — in part because it would give him access to future internships at large aerospace companies.
His experience already has gone beyond the classroom. Through a unique summer fellowship in 2022, Chibuzor (who was then an undergrad) gained real-world experience conducting research that could help change how spacecraft dock with one another.
What impressed me most was his thirst for knowledge. He’s a student in aerospace but he wants to know about large language models, he wants to know about computer science — he really wants to know everything about everything.
Chibuzor is one of dozens of students who have enrolled in the Amazon-sponsored Summer Undergraduate Research Experience (SURE). The fellowship provides students from historically underrepresented backgrounds with a unique research experience at top-tier universities including USC.
As a SURE fellow in 2022, Chibuzor spent the summer after his first year at USC’s Space Engineering Research Center working on a research project, with visits to Amazon. As part of the program, he was also mentored by Gérard Medioni, Amazon vice president and distinguished scientist, and emeritus professor at USC.
“What impressed me most was his thirst for knowledge,” Medioni said. “He’s a student in aerospace but he wants to know about large language models, he wants to know about computer science — he really wants to know everything about everything.”
Medioni, who previously served as the chair of the computer science department at USC, was able to offer Chibuzor advice on “a set of different options that he could follow while trying to get a dual curriculum of aerospace engineering and computer science.”
“I’ve always been passionate about aerospace and engineering. But I think what the SURE program helped me realize is that I’m also interested in computer science and computer engineering,” Chibuzor said.
Valuable research experience
During USC’s 2022 SURE program at the Space Engineering Research Center, which is part of the school’s Information Sciences Institute and affiliated with the university’s Viterbi School of Engineering, Chibuzor participated in a project called “CLING-ERS”. The CLING-ERS project goal was to develop an autonomous spacecraft docking solution for the International Space Station.
Chibuzor, who was later joined by two other interns to take on the ambitious challenge, was tasked with developing a computer vision algorithm.
“To achieve autonomous rendezvous and docking, each CLING-ERS device is equipped with an infrared camera and a set of four infrared LEDs,” Chibuzor explained. “By detecting the location and orientation of the IR LEDs attached to the opposing device, each CLING-ERS device is able to determine its position, attitude, and range with respect to the other — enabling the pair of to navigate toward one another and dock. To facilitate this IR LED detection, my team developed a computer vision algorithm using OpenCV.”
The team utilized SimpleBlobDetector, a tool which can extract blobs — a region in an image that is distinct from the rest in terms of brightness or color. “When docking, the IR camera of each CLING-ERS device captures live video of the IR LEDs attached to the opposing device,” he noted. “Our algorithm could then continually analyze this live video to determine the position, attitude, and range of the four IR LEDs.
“This proved challenging for a few reasons, but one of the more interesting problems involved lens flare,” he continued. “As CLING-ERS approached to dock, lens flare caused the shape of the light emitted by the IR LEDs to distort from circular to rectangular — limiting the efficacy of the algorithm at close range. To remedy this, we developed a feature that dynamically tuned the detection constraints of the algorithm as docking progressed.”
Chibuzor and his team presented their research and prototype solution to the SURE faculty team and their peer interns in August of 2022.
Expanding STEM representation
Chibuzor’s experience as a USC SURE intern demonstrates the potential of exposing undergraduates from underrepresented groups to real-world scientific research and Amazon’s culture of innovation.
“Amazon’s SURE program at USC aims to fulfill the mission of diversifying the pipeline of students in STEM, focusing on an array of projects including machine learning, AI and other areas that Amazon is focused on,” said Andy Jones-Liang, associate director of Viterbi Academic Services at USC’s Viterbi School of Engineering and the school’s SURE program lead. “We had a smaller SURE program at USC before Amazon’s partnership, but thanks to Amazon’s gift and sponsorship, we’ve been able to triple the number of students enrolling in the program.”
While I’ve always been interested in computers and computer programming, SURE was really amazing because I got exposure to an aerospace research project in a professional research environment.
“SURE is about purposefully looking at the talent in a very broad section of our community and understanding both how can we can become more aware of that talent and also help those students realize that their talents are useful to companies like Amazon,” Medioni added.
Each year, USC faculty volunteer to host SURE students for the summer. Students apply for projects based on their personal and academic interests. USC faculty and Jones-Liang then work together to select students who match up well with faculty projects based on their backgrounds and research potential.
Accepted students work on their research projects for eight weeks and prepare a poster presentation for SURE faculty and peer interns. They also participate in weekly professional development events, join weekly lunch-and-learns to hear about graduate fellowship opportunities, and participate in weekend socials to build relationships, expand their networks, and explore the local area. The fellowship includes the opportunity to visit Amazon’s local offices and receive direct mentorship from an Amazonian.
“SURE, and Amazon’s involvement in it, helps address the broader societal goals of increasing representation when it comes to solving the STEM problems we’re encountering in the world,” Jones-Liang said. “Students can see what a future in STEM might be like — whether in academia or industry.
“For many of these SURE students like Derek, this is typically their first or second research experience,” he continued. “So a lot of the program is designed to familiarize them with the research environment and give them exposure to a real-world research project.”
“While I’ve always been interested in computers and computer programming, SURE was really amazing because I got exposure to an aerospace research project in a professional research environment,” Chibuzor agreed.
SURE projects also help students understand the iterative, trial-and-error nature of research.
“Much of the program is about fostering that intrinsic motivation to want to learn and get over the hurdles that always present themselves when you’re innovating and doing something new,” said Jones-Liang.
As part of his SURE fellowship, Chibuzor also visited Amazon’s Culver City office — meeting employees, listening to guest speakers from both Amazon and academia, and immersing himself in the Amazon environment.
“I was a student myself and as a student, you have a very limited visibility on the workforce and the environment where you may find yourself,” Medioni observed. “SURE opens the windows on the experience at Amazon in a way that is useful to those students.”
In the summer of 2023, Chibuzor furthered his experience in aerospace engineering through a second internship with Northrop Grumman. Chibuzor was recently admitted to USC’s graduate engineering school to pursue a masters in electrical engineering with an emphasis in machine learning and data science. Longer term, he would like to exercise his entrepreneurial muscles and found a new business or startup.
“The SURE internship exposed me to a lot of computer science and computer engineering, sparking an interest for me to further that and create something of my own,” he said.
Events & Conferences
An inside look at Meta’s transition from C to Rust on mobile
Have you ever worked is legacy code? Are you curious what it takes to modernize systems at a massive scale?
Pascal Hartig is joined on the latest Meta Tech Podcast by Elaine and Buping, two software engineers working on a bold project to rewrite the decades-old C code in one of Meta’s core messaging libraries in Rust. It’s an ambitious effort that will transform a central messaging library that is shared across Messenger, Facebook, Instagram, and Meta’s AR/VR platforms.
They discuss taking on a project of this scope – even without a background in Rust, how they’re approaching it, and what it means to optimize for ‘developer happiness.’
Download or listen to the episode below:
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The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
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And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.
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Amazon Research Awards recipients announced
Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 73 award recipients who represent 46 universities in 10 countries.
This announcement includes awards funded under five call for proposals during the fall 2024 cycle: AI for Information Security, Automated Reasoning, AWS AI, AWS Cryptography, and Sustainability. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.
Recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.
“Automated Reasoning is an important area of research for Amazon, with potential applications across various features and applications to help improve security, reliability, and performance for our customers. Through the ARA program, we collaborate with leading academic researchers to explore challenges in this field,” said Robert Jones, senior principal scientist with the Cloud Automated Reasoning Group. “We were again impressed by the exceptional response to our Automated Reasoning call for proposals this year, receiving numerous high-quality submissions. Congratulations to the recipients! We’re excited to support their work and partner with them as they develop new science and technology in this important area.”
“At Amazon, we believe that solving the world’s toughest sustainability challenges benefits from both breakthrough scientific research and open and bold collaboration. Through programs like the Amazon Research Awards program, we aim to support academic research that could contribute to our understanding of these complex issues,” said Kommy Weldemariam, Director of Science and Innovation Sustainability. “The selected proposals represent innovative projects that we hope will help advance knowledge in this field, potentially benefiting customers, communities, and the environment.”
ARA funds proposals throughout the year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.
The tables below list, in alphabetical order by last name, fall 2024 cycle call-for-proposal recipients, sorted by research area.
AI for Information Security
Recipient | University | Research title |
Christopher Amato | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
Bernd Bischl | Ludwig Maximilian University of Munich | Improving Generative and Foundation Models Reliability via Uncertainty-awareness |
Shiqing Ma | University Of Massachusetts Amherst | LLM and Domain Adaptation for Attack Detection |
Alina Oprea | Northeastern University | Multi-Agent Reinforcement Learning Cyber Defense for Securing Cloud Computing Platforms |
Roberto Perdisci | University of Georgia | ContextADBench: A Comprehensive Benchmark Suite for Contextual Anomaly Detection |
Automated Reasoning
Recipient | University | Research title |
Nada Amin | Harvard University | LLM-Augmented Semi-Automated Proofs for Interactive Verification |
Suguman Bansal | Georgia Institute of Technology | Certified Inductive Generalization in Reinforcement Learning |
Ioana Boureanu | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
Omar Haider Chowdhury | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
Stefan Ciobaca | Alexandru Ioan Cuza University | An Interactive Proof Mode for Dafny |
João Ferreira | INESC-ID | Polyglot Automated Program Repair for Infrastructure as Code |
Sicun Gao | University Of California, San Diego | Monte Carlo Trees with Conflict Models for Proof Search |
Mirco Giacobbe | University of Birmingham | Neural Software Verification |
Tobias Grosser | University of Cambridge | Synthesis-based Symbolic BitVector Simplification for Lean |
Ronghui Gu | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
Alexey Ignatiev | Monash University | Huub: Next-Gen Lazy Clause Generation |
Kenneth McMillan | University of Texas At Austin | Synthesis of Auxiliary Variables and Invariants for Distributed Protocol Verification |
Alexandra Mendes | University of Porto | Overcoming Barriers to the Adoption of Verification-Aware Languages |
Jason Nieh | Columbia University | Scaling Formal Verification of Security Properties for Unmodified System Software |
Rohan Padhye | Carnegie Mellon University | Automated Synthesis and Evaluation of Property-Based Tests |
Nadia Polikarpova | University Of California, San Diego | Discovering and Proving Critical System Properties with LLMs |
Fortunat Rajaona | University of Surrey | Phoebe+: An Automated-Reasoning Tool for Provable Privacy in Cryptographic Systems |
Subhajit Roy | Indian Institute of Technology Kanpur | Theorem Proving Modulo LLM |
Gagandeep Singh | University of Illinois At Urbana–Champaign | Trustworthy LLM Systems using Formal Contracts |
Scott Stoller | Stony Brook University | Restricter: An Automatic Tool for Authoring Amazon Cedar Access Control Policies with the Principle of Least Privilege |
Peter Stuckey | Monash University | Huub: Next-Gen Lazy Clause Generation |
Yulei Sui | University of New South Wales | Path-Sensitive Typestate Analysis through Sparse Abstract Execution |
Nikos Vasilakis | Brown University | Semantics-Driven Static Analysis for the Unix/Linux Shell |
Ping Wang | Stevens Institute of Technology | Leveraging Large Language Models for Reasoning Augmented Searching on Domain-specific NoSQL Database |
John Wawrzynek | University of California, Berkeley | GPU-Accelerated High-Throughput SAT Sampling |
AWS AI
Recipient | University | Research title |
Panagiotis Adamopoulos | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
Vikram Adve | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
Frances Arnold | California Institute of Technology | Closed-loop Generative Machine Learning for De Novo Enzyme Discovery and Optimization |
Yonatan Bisk | Carnegie Mellon University | Useful, Safe, and Robust Multiturn Interactions with LLMs |
Shiyu Chang | University of California, Santa Barbara | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
Yuxin Chen | University of Pennsylvania | Provable Acceleration of Diffusion Models for Modern Generative AI |
Tianlong Chen | University of North Carolina at Chapel Hill | Cut the Crap: Advancing the Efficient Communication of Multi-Agent Systems via Spatial-Temporal Topology Design and KV Cache Sharing |
Mingyu Ding | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
Nikhil Garg | Cornell University | Market Design for Responsible Multi-agent LLMs |
Jessica Hullman | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
Christopher Jermaine | Rice University | Fast, Trusted AI Using the EINSUMMABLE Compiler |
Yunzhu Li | Columbia University | Physics-Informed Foundation Models Through Embodied Interactions |
Pattie Maes | Massachusetts Institute of Technology | Understanding How LLM Agents Deviate from Human Choices |
Sasa Misailovic | University of Illinois at Urbana–Champaign | Fellini: Differentiable ML Compiler for Full-Graph Optimization for LLM Models |
Kristina Monakhova | Cornell University | Trustworthy extreme imaging for science using interpretable uncertainty quantification |
Todd Mowry | Carnegie Mellon University | Efficient LLM Serving on Trainium via Kernel Generation |
Min-hwan Oh | Seoul National University | Mutually Beneficial Interplay Between Selection Fairness and Context Diversity in Contextual Bandits |
Patrick Rebeschini | University of Oxford | Optimal Regularization for LLM Alignment |
Jose Renau | University of California, Santa Cruz | Verification Constrained Hardware Optimization using Intelligent Design Agentic Programming |
Vilma Todri | Emory University | Generative AI solutions for The Spillover Effect of Fraudulent Reviews on Product Recommendations |
Aravindan Vijayaraghavan | Northwestern University | Human-Aligned Uncertainty Quantification in High Dimensions |
Wei Yang | University of Texas at Dallas | Optimizing RISC-V Compilers with RISC-LLM and Syntax Parsing |
Huaxiu Yao | University of North Carolina at Chapel Hill | Aligning Long Videos and Language as Long-Horizon World Models |
Amy Zhang | University of Washington | Tools for Governing AI Agent Autonomy |
Ruqi Zhang | Purdue University | Efficient Test-time Alignment for Large Language Models and Large Multimodal Models |
Zheng Zhang | Rutgers University-New Brunswick | AlphaQC: An AI-powered Quantum Circuit Optimizer and Denoiser |
AWS Cryptography
Recipient | University | Research title |
Alexandra Boldyreva | Georgia Institute of Technology | Quantifying Information Leakage in Searchable Encryption Protocols |
Maria Eichlseder | Graz University of Technology, Austria | SALAD – Systematic Analysis of Lightweight Ascon-based Designs |
Venkatesan Guruswami | University of California, Berkeley | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
Joseph Jaeger | Georgia Institute of Technology | Analyzing Chat Encryption for Group Messaging |
Aayush Jain | Carnegie Mellon | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
Huijia Lin | University of Washington | Large Scale Multiparty Silent Preprocessing for MPC from LPN |
Hamed Nemati | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
Karl Palmskog | KTH Royal Institute of Technology | Trustworthy Automatic Verification of Side-Channel Countermeasures for Binary Cryptographic Programs using the HoIBA libary |
Chris Peikert | University of Michigan, Ann Arbor | Practical Third-Generation FHE and Bootstrapping |
Dimitrios Skarlatos | Carnegie Mellon University | Scale-Out FHE LLMs on GPUs |
Vinod Vaikuntanathan | Massachusetts Institute of Technology | Can Quantum Computers (Really) Factor? |
Daniel Wichs | Northeastern University | Obfuscation, Proof Systems, and Secure Computation: A Research Program on Cryptography at the Simons Institute for the Theory of Computing |
David Wu | University Of Texas At Austin | Fast Private Information Retrieval and More using Homomorphic Encryption |
Sustainability
Recipient | University | Research title |
Meeyoung Cha | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
Jingrui He | University of Illinois at Urbana–Champaign | Foundation Model Enabled Earth’s Ecosystem Monitoring |
Pedro Lopes | University of Chicago | AI-powered Tools that Enable Engineers to Make & Re-make Sustainable Hardware |
Cheng Yaw Low | Max Planck Institute | Forest-Blossom (Flossom): A New Framework for Sustaining Forest Biodiversity Through Outcome-Driven Remote Sensing Monitoring |
Events & Conferences
Independent evaluations demonstrate Nova Premier’s safety
AI safety is a priority at Amazon. Our investment in safe, transparent, and responsible AI (RAI) includes collaboration with the global community and policymakers. We are members of and collaborate with organizations such as the Frontier Model Forum, the Partnership on AI, and other forums organized by government agencies such as the National Institute of Standards and Technology (NIST). Consistent with Amazon’s endorsement of the Korea Frontier AI Safety Commitments, we published our Frontier Model Safety Framework earlier this year.
During the development of the Nova Premier model, we conducted a comprehensive evaluation to assess its performance and safety. This included testing on both internal and public benchmarks and internal/automated and third-party red-teaming exercises. Once the final model was ready, we prioritized obtaining unbiased, third-party evaluations of the model’s robustness against RAI controls. In this post, we outline the key findings from these evaluations, demonstrating the strength of our testing approach and Amazon Premier’s standing as a safe model. Specifically, we cover our evaluations with two third-party evaluators: PRISM AI and ActiveFence.
Evaluation of Nova Premier against PRISM AI
PRISM Eval’s Behavior Elicitation Tool (BET) dynamically and systematically stress-tests AI models’ safety guardrails. The methodology focuses on measuring how many adversarial attempts (steps) it takes to get a model to generate harmful content across several key risk dimensions. The central metric is “steps to elicit” — the number of increasingly sophisticated prompting attempts required before a model generates an inappropriate response. A higher number of steps indicates stronger safety measures, as the model is more resistant to manipulation. The PRISM risk dimensions (inspired by the MLCommons AI Safety Benchmarks) include CBRNE weapons, violent crimes, non-violent crimes, defamation, and hate, amongst several others.
Using the BET Eval tool and its V1.0 metric, which is tailored toward non-reasoning models, we compared the recently released Nova models (Pro and Premier) to the latest models in the same class: Claude (3.5 v2 and 3.7 non-reasoning) and Llama4 Maverick, all available through Amazon Bedrock. PRISM BET conducts black-box evaluations (where model developers don’t have access to the test prompts) of models integrated with their API. The evaluation conducted with BET Eval MAX, PRISM’s most comprehensive/aggressive testing suite, revealed significant variations in safety against malicious instructions. Nova models demonstrated superior overall safety performance, with an average of 43 steps for Premier and 52 steps for Pro, compared to 37.7 for Claude 3.5 v2 and fewer than 12 steps for other models in the comparison set (namely, 9.9 for Claude3.7, 11.5 for Claude 3.7 thinking, and 6.5 for Maverick). This higher step count suggests that on average, Nova’s safety guardrails are more sophisticated and harder to circumvent through adversarial prompting. The figure below presents the number of steps per harm category evaluated through BET Eval MAX.
The PRISM evaluation provides valuable insights into the relative safety of different Amazon Bedrock models. Nova’s strong performance, particularly in hate speech and defamation resistance, represents meaningful progress in AI safety. However, the results also highlight the ongoing challenge of building truly robust safety measures into AI systems. As the field continues to evolve, frameworks like BET will play an increasingly important role in benchmarking and improving AI safety. As a part of this collaboration Nicolas Miailhe, CEO of PRISM Eval, said, “It’s incredibly rewarding for us to see Nova outperforming strong baselines using the BET Eval MAX; our aim is to build a long-term partnership toward safer-by-design models and to make BET available to various model providers.” Organizations deploying AI systems should carefully consider these safety metrics when selecting models for their applications.
Manual red teaming with ActiveFence
The AI safety & security company ActiveFence benchmarked Nova Premier on Bedrock on prompts distributed across Amazon’s eight core RAI categories. ActiveFence also evaluated Claude 3.7 (non-reasoning mode) and GPT 4.1 API on the same set. The flag rate on Nova Premier was lower than that on the other two models, indicating that Nova Premier is the safest of the three.
Model | 3P Flag Rate [↓ is better] |
Nova Premier | 12.0% |
Sonnet 3.7 (non-reasoning) | 20.6% |
GPT4.1 API | 22.4% |
“Our role is to think like an adversary but act in service of safety,” said Guy Paltieli from ActiveFence. “By conducting a blind stress test of Nova Premier under realistic threat scenarios, we helped evaluate its security posture in support of Amazon’s broader responsible-AI goals, ensuring the model could be deployed with greater confidence.”
These evaluations conducted with PRISM and ActiveFence give us confidence in the strength of our guardrails and our ability to protect our customers’ safety when they use our models. While these evaluations demonstrate strong safety performance, we recognize that AI safety is an ongoing challenge requiring continuous improvement. These assessments represent a point-in-time snapshot, and we remain committed to regular testing and enhancement of our safety measures. No AI system can guarantee perfect safety in all scenarios, which is why we maintain monitoring and response systems after deployment.
Acknowledgments: Vincent Ponzo, Elyssa Vincent
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