Ruirui (Annie) Huang

Ruirui (Annie) Huang

she/her/hers

CS + AMS BS & Robotics MSE at JHU. Interests: control & simulation, computational biology, vision models.

Editorials

2024

From AI to Cancer pathology: Andrew Song’s path to AI-enhanced cancer diagnostics

Published:

Andrew H. Song, a postdoctoral research fellow at Harvard Medical School, presented his work on applying deep learning tools to cancer pathology at a talk titled “Taming Large-Scale Pathology Data for Clinical Outcome Prediction” on Nov. 13. In his talk, Song delved into his efforts to leverage AI in improving cancer diagnostics, explaining how machine learning models can fundamentally alter the landscape of clinical outcome prediction.

Multi-agent learning for safe and efficient autonomous vehicles

Published:

Fei Miao, Pratt & Whitney Associate Professor at the University of Connecticut’s School of Computing, delivered a talk titled “Learning and Control for Safety, Efficiency, and Resiliency of Embodied AI” on Nov. 8. Her presentation explored her team’s recent efforts to advance Multi-agent Reinforcement Learning (MARL) for Connected and Automated Vehicles (CAVs), which models multiple autonomous vehicles that can send and receive real-time information from nearby vehicles and infrastructure to enhance driving decisions.

Aiming for the stars: HopSat’s mission to solar sailing

Published:

In a buzzing corner of campus, a group of students is pushing the limits of student-led space innovation. The newly established student organization, JHU CubeSat Club (HopSat), is gearing up to launch a nanosatellite powered by passive solar propulsion, with the ambitious goal of deploying the largest solar sail ever sent into space.

Adnan Munawar on open simulation platform for surgical robotics research

Published:

Adnan Munawar, an assistant research scientist at the Laboratory for Computational Sensing and Robotics (LCSR), shared his progress on the open-source Asynchronous Multi-Body Framework (AMBF) simulator used for several applications in surgical robotics on Sept. 11. The talk shed light on the use of reactive digital twins for surgical environments. His paper on AMBF was published in the Intelligent Robots and Systems (IROS) program.

A Gift of Fire: Guiding the AI-driven Healthcare Revolution for a More Equitable Future

Published:

This editorial examines the role of artificial intelligence (AI) in healthcare, a field where it has become a significant driving force. It establishes AI as a “gift of fire” with the potential to create a safer, more equitable future for healthcare but also become a source of danger. The editorial argues fror a balanced under standing of AI’s role in healthcare and emphasizes the need for informed public discourse and responsible technology use.

Biophotonics imaging transforms studies of neuronal activities

Published:

Yuehan Liu is a fifth-year doctoral candidate affiliated with the Biophotonics Imaging Technology Lab (BIT) advised by Xingde Li. She recently gave a talk at SPIE Photonics West BiOS entitled ‘Two-photon fiberscope with a proactive optoelectrical commutator for rotational resistance-free neuroimaging in freely-behaving rodents.’ Her talk focused on the recent progress of non-invasive imaging technologies that could revolutionize the study of brain function and diseases.

The ‘realness’ of computer simulation: A conversation with Saikat Dan

Published:

Saikat Dan is a research fellow affiliated with the Computational Mechanics Research Laboratory (CMRL) and is advised by Somnath Ghosh in the Civil Engineering Department. As a PhD student this past fall, he taught a HEART course titled Computer Simulations: How Real are They? in which he gave a high-level overview of the field as well as applications of his research.

2023

SneakyPrompt: Revealing the vulnerabilities of text-to-image AI

Published:

In the rapidly evolving field of artificial intelligence (AI), understanding and improving AI security is increasingly crucial. Yuchen Yang, a third-year doctoral student advised by Yinzhi Cao, employed an automated attack framework to reveal the vulnerabilities in text-to-image generative models such as DALL·E 3 and Stable Diffusion. The paper, ‘SneakyPrompt: Evaluating Robustness of Text-to-image Generative Models’ Safety Filters,’ formerly titled ‘SneakyPrompt: Jailbreaking Text-to-image Generative Models,’ will be presented at the 45th Institute of Electrical and Electronics Engineers (IEEE) Symposium on Security and Privacy.

Revolutionizing medicine: Aimon Rahman on enhancing health care with deep learning

Published:

Aimon Rahman, a third-year doctoral student in the Vision & Image Understanding (VIU) Lab in the Electrical and Computer Engineering Department, is making significant contributions to the field of medical artificial intelligence (AI). In her Hopkins Engineering Applications and Research Tutorials (HEART) course titled ‘Introduction to Deep Learning for Medical Imaging,’ Rahman introduces students to the practical applications of computer vision in medical image analysis.

From CLSP to HEART: A conversation with Orion Weller on information retrieval systems

Published:

Orion Weller is a third-year doctoral student affiliated with the Center for Language and Speech Processing (CLSP) advised by Benjamin Van Durme and Dawn Lawrie. He is currently teaching a Hopkins Engineering Applications and Research Tutorials (HEART) course titled Reasoning with ChatGPT in which he discusses the contexts and relevance of his research.

Stefano Soatto demystifies large language models as ChatGPT advances

Published:

In the Sept. 26 Department of Computer Science (CS) Distinguished Lecture Series, Stefano Soatto, a CS professor from the University of California, Los Angeles, and Vice President of Applied Science for Amazon Web Services AI, spoke about the learning and controllability of large language models (LLMs) and computer vision. His talk, titled ‘Foundational Issues in AI: Views from the Real and Ideal Worlds,’ used analytic methods to address several concerns about the controllability of LLMs.

Renee Brady on patient-specific models for cancer response prediction

Published:

In the Institute for Computational Medicine (ICM) special seminar series on Oct 3., Assistant Professor at the Integrated Mathematical Oncology Department at H. Lee Moffitt Cancer Center & Research Institute Renee Brady shared insights on using minimally invasive biomarkers to predict treatment responses. The talk shed light on using dynamics of patient-reported outcomes (PROs) as alternative treatment strategies, ultimately contributing to the reduction of cancer health disparities. Her team’s research findings were recently published in Clinical Cancer Research.

Revolution in microscopy: Roarke Horstmeyer on multi-camera array microscopes

Published:

The Department of Electrical and Computer Engineering (ECE) hosted a seminar on Sept. 26 to showcase the research conducted by Assistant Professor of Biomedical Engineering at Duke University Roarke Horstmeyer. The talk, titled ‘Computational 3D Video Microscopy with Multi-camera Arrays,’ explained the design and algorithm behind the state-of-the-art multi-camera array microscopes (MCAMs) and several use cases. The findings were published recently in Optica.