Yue Zhao
Avatar of Yue Zhao
Assistant Professor
Thomas Lord Department of Computer Science
School of Advanced Computing

University of Southern California

Los Angeles, CA, USA
Email:

FORTIS Lab Logo FORTIS Lab

FORTIS stands for Foundations Of Reliable, Transparent, and Intelligent Systems. Inspired by the Latin word fortis — meaning strong or resilient — the FORTIS Lab aims to build AI that is robust in performance, reliable under uncertainty, and responsible in deployment. Our work spans the full spectrum from foundational algorithms to open, scalable systems, integrating core advances in anomaly and out-of-distribution detection, LLM safety and trust evaluation, and graph and multimodal learning. Through open-source tools and interdisciplinary collaboration, FORTIS drives AI research that is both scientifically rigorous and societally impactful — enabling reliable intelligence for science, security, and society.

1. PhD Openings and Application Guidelines I will recruit 0 or 1 Ph.D. student for Fall 2026 -- however, I usually admit the students that I (or my close coauthors) have worked with. We also have openings for undergraduate and graduate interns all the time, both at USC and elsewhere (preferably in North America for time zone compatibility).

  • Research Focus: Candidates should align with my core research areas. For details, please visit my research interests.
    Update (2025–2026): For Fall 2026, I prefer Ph.D. students whose interests align with a new research direction on cryptographic systems, distributed systems, ML systems. This includes but is not limited to (these are just examples): Candidates with system or cryptography backgrounds (e.g., Cosmos SDK, EVM, ZK, consensus protocols) are especially encouraged to apply.
  • Prerequisites for Ph.D.: Successful candidates typically have:
    • Multiple published, relevant top-tier publications in ML (e.g., NeurIPS, ICML, ICLR), Systems (e.g., MLSys, VLDB, OSDI), or LLMs (ACL, EMNLP, NAACL). This list is not exhaustive.
    • Strong programming skills, demonstrated through research projects or significant open-source contributions.
  • Prerequisites for Research Interns: Successful candidates typically have:
    • At least one top-tier publication in ML (e.g., NeurIPS, ICML, ICLR), Systems (e.g., MLSys, VLDB, OSDI), or LLMs (ACL, EMNLP, NAACL). This list is not exhaustive.
    • Strong programming skills, demonstrated through research projects or significant open-source contributions.
    • I do not have any summer interns -- I am also enjoying summer for fun :)

2. Email for Connection and Form Submission - For all positions, please fill out this Google Form: Application Form and also email the following details (subject line: "Interested in PhD/Intern at Fall 26"): Magic Words to fortis@usc.edu - please include Trojan in the email title to demonstrate that you have read these instructions carefully.

  • CV: Provide a concise summary of your background and your future plans.
  • Why FORTIS?: Mention any of my research papers or topics that have caught your interest. Additionally, feel free to propose new topics you are interested in exploring. I am always open to innovative ideas and fresh perspectives.

3. Compensation. Ph.D. students will receive the full support (tuition waiver + stipend is $40,000 in 2024-25) outlined by the CS department. TA is always guaranteed, and we will try to give you as many RA as possible. We do not have paid opportunities for undergraduate and master RA; it is not allowed to pay remote interns at USC :( Please consider this before reaching out.

4.1. GPU Clusters. In November 2023, we deployed fortis-prima, a high-performance GPU server equipped with dual AMD EPYC 7763 ("Milan") CPUs, 1TB DDR4 memory (64×16GB), 15TB SSD storage, and 8× NVIDIA RTX 6000 Ada GPUs (released Q4 2022; up to 1.5× faster than the previous A6000 Ampere). In Fall 2025, we are deploying fortis-nova, a second GPU server with support for 8× NVIDIA RTX 6000 Blackwell Max-Q GPUs (96GB each, released in 2025). It will launch with 4 GPUs installed, and the remaining 4 will be added in 2026/27 to complete the full 8-GPU configuration.

4.2. Cloud Credits. We recently (Aug 2024) get $20,000 AWS credit; thank you, Amazon. We also get generous computing credits from NSF :)


Our Team (Alphabetical by Last Name)

Meet the real bosses of the lab: Our bosses ensure the lab stays stress-free, doors are scratched to perfection, 3D printers are sparkling clean, and all visitors are greeted with enthusiasm. They might not be applying for Ph.D. positions, but they are certainly accepting treat applications!

Lion

Lion (莱恩)

Affiliated with Bourne Li

Expertise: Scratching doors

Leffo

Leffo (来福)

Affiliated with Bourne Li

Expertise: Cleaning 3D printers

Ryan

Ryan (小面包)

Affiliated with Tiankai Yang

Expertise: Barking and running

Huhu

Labubu (虎虎)

Affiliated with Chenxiao Yu

Expertise: Electronics Slayer


Connect with them: many of my Ph.D. students are looking for internships for Summer 2026 -- please reach out to them (or me for an intro).

Jiate Li

Jiate Li

1st year, Joined Fortis in Aug 2025

GNN, AI Robustness

Ph.D. Student (jiateli@usc.edu)

Li Li

Shawn Li (黎力)

2nd year, Joined Fortis in Aug 2024

Multimodal Learning, Trustworthy ML

Amazon ML Fellowship, Capital One Fellowship

Ph.D. Student (li.li02@usc.edu)

Yuehan Qin

Yuehan Qin

4th year, Joined Fortis in Jan 2024

OOD Detection, Time Series

Ph.D. Student (yuehanqi@usc.edu)

Haoyan Xu

Haoyan Xu

4th year, Joined Fortis in Jan 2024

GNN, Anomaly Detection

Capital One Fellowship

Ph.D. Student (haoyanxu@usc.edu)

co-advised by Mengyuan Li

Tiankai Yang

Tiankai Yang

2nd year, Joined Fortis in Aug 2024

Anomaly Detection, LLM

Ph.D. Student (tiankaiy@usc.edu)


Connect with them: many of the Undergraduate/Master RA are looking for Ph.D./full time job for Fall 2026 -- please reach out to them (or me for an intro).

Peilin Cai

Peilin Cai

Multimodal / Generative AI

Master Student (peilinca@usc.edu)

Publications with us:
📄 Secure On-Device Video OOD Detection Without Backpropagation, ICCV 2025
Jerry Chen

Jerry Chen

CECS, AI Applications

Undergraduate RA (jchen570@usc.edu)

Huixian Gong

Huixian Gong

ML, Software Dev.

Master Student (huixian@usc.edu)

Publications with us:
📄 DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection, CVPR 2025
Deyang Hsu

Deyang Hsu

Software Engineer, ML/AI

Master Student (deyanghs@usc.edu)

Bourne Li

Bourne Li

NLP, Distributed Systems

Master Student (jli77629@usc.edu)

Publications with us:
📄 NLP-ADBench: NLP Anomaly Detection Benchmark, Findings of EMNLP 2025
📄 AD-LLM: Benchmarking Large Language Models for Anomaly Detection, Findings of ACL 2025
Jinbo Liu

Jinbo Liu

Multi-Agent, Time Series

Master Student (jinboliu@usc.edu)

Ojas Nimase

Ojas Nimase

Mathematics, CS

Undergraduate RA (nimase@usc.edu)

Publications with us:
📄 Navigating Between Explainability and Extractability in Machine Learning as a Service, IEEE ICDM BlueSky Track, 2025, Second Prize CCC Award.
Phillip Huang

Phillip Huang

AI/ML, LLM

Master Student (huangphi@usc.edu)

Dacheng Shen

Dacheng Shen

Software Engineering, ML

Master Student (jasonshendc@gmail.com)

Michael Siu

Michael Siu

CS, Applied Math

Undergraduate CURVE Fellow (siuw@usc.edu)

Publications with us:
📄 PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection, The Web Conference (Demo Track), 2025
📄 AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection, Findings of IJCNLP-AACL, 2025
Jiale Tan

Jiale Tan

Multimodal/Generative AI

Master Student (jialetan@usc.edu)

Andrew Yu

Andrew Yu

Generative Engine Optimization

Master Student (ayyu@usc.edu)

Ziyi Wang

Ziyi Wang

Generative AI, Human-centered AI

Master Student (zoewang@umd.edu)

Publications with us:
📄 Mitigating Hallucinations in Large Language Models via Causal Reasoning, AAAI 2026
📄 Few-Shot Graph Out-of-Distribution Detection with LLMs, ECML PKDD 2025
📄 JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language Model, COLM 2025
Zheng

Zheng Yu

Robust and Trustworthy AI, AI4Science

Master Student (zheng.yu.24@ucl.ac.uk)

Xiaolin Zhou

Xiaolin Zhou

Multimodal/Generative AI/LLM

Master Student (xzhou733@usc.edu)


Past Members

We greatly appreciate the contributions of our past members (see their placement and papers with us):

Sihan Chen (Master Student → Now Ph.D. Student at CMU)
📧 sihanch2@andrew.cmu.edu

Publications with us:
📄 PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection, The Web Conference (Demo Track), 2025
Yuangang Li (Master Student → Now Ph.D. Student at UCI)
📧 yuangangli.cs@gmail.com

Publications with us:
📄 Mitigating Hallucinations in Large Language Models via Causal Reasoning, AAAI 2026
📄 NLP-ADBench: NLP Anomaly Detection Benchmark, Findings of EMNLP 2025
📄 AD-LLM: Benchmarking Large Language Models for Anomaly Detection, Findings of ACL 2025
Yi Fan (Master Student)
📧 fanyi@usc.edu
Xingcan Hu (Undergraduate RA)
📧 xh_186@usc.edu

Publications with us:
📄 PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection, The Web Conference (Demo Track), 2025
Phillip Huang (Master Student)
📧 huangphi@usc.edu
Leo Lee (Master Student)
📧 leolee.developer@gmail.com
Alex Qian (Undergraduate CURVE Fellow)
📧 alexqian@usc.edu

Publications with us:
📄 PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection, The Web Conference (Demo Track), 2025
📄 AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection, Findings of IJCNLP-AACL, 2025
Zhuo Xiao (Master Student → Now Intern at NVIDIA)
📧 zhuoxiao@usc.edu

Publications with us:
📄 NLP-ADBench: NLP Anomaly Detection Benchmark, Findings of EMNLP 2025
📄 AD-LLM: Benchmarking Large Language Models for Anomaly Detection, Findings of ACL 2025
Yichi Zhang (Master Student)
📧 yzhang42@usc.edu

Publications with us:
📄 MetaOOD: Automatic Selection of OOD Detection Models, ICLR 2025
📄 PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection, The Web Conference (Demo Track), 2025

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