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Bo Li
Associate Professor
,
Siebel School of Computing and Data Science
Associate Professor
,
Electrical and Computer Engineering
Associate Professor
,
Information Trust Institute
Associate Professor
,
National Center for Supercomputing Applications (NCSA)
Email
lbo
illinois
edu
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Research & Scholarship
(221)
Honors
(5)
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Dive into the research topics where Bo Li is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Weight
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Computer Science
Deep Neural Network
100%
Learning System
72%
Adversarial Machine Learning
72%
Machine Learning
66%
Adversarial Example
66%
Backdoors
40%
Attackers
40%
Deep Learning Method
37%
Neural Network
28%
Reinforcement Learning
28%
Training Data
25%
Language Modeling
24%
Federated Learning
22%
Autonomous Driving
18%
Large Language Model
18%
Autonomous Vehicles
16%
Generative Model
15%
Privacy Preserving
15%
Generative Adversarial Networks
15%
Graph Neural Network
13%
Deep Learning Model
13%
Information Retrieval
13%
Adversarial Setting
12%
World Application
11%
Experimental Result
11%
Learning Algorithm
11%
Data Augmentation
10%
Deep Reinforcement Learning
10%
Object Detection
10%
Sensitive Informations
9%
Black-Box Attack
9%
Attack Strategy
8%
Sufficient Condition
8%
Artificial Intelligence
8%
Personal Data
8%
obstacle detection
8%
Copyright Protection
8%
Diffusion Model
8%
Generative Pre-Trained Transformer 4
7%
Defense Strategy
7%
Detection Algorithm
7%
Data Distribution
7%
Mutual Information
7%
Machine Learning Approach
6%
Multimedia
6%
Graph Convolutional Network
6%
Watermarking
6%
Pre-Trained Language Models
6%
Baseline Method
6%
Feature Space
6%
Keyphrases
Deep Neural Network
88%
Adversarial Examples
57%
Adversary
38%
Adversarial Attack
34%
Attacker
28%
Backdoor Attack
25%
Certified Robustness
24%
Backdoor
23%
Large Language Models
22%
Machine Learning Models
22%
Machine Learning
21%
Adversarial Perturbation
20%
Training Data
20%
Safety-critical
19%
Neural Network
18%
Black Hole Attack
18%
Poisoning Attack
16%
Federated Learning
16%
Deep Learning
16%
Adversarial Training
16%
Reinforcement Learning
16%
Language Model
15%
Adversarial Learning
15%
Autonomous Vehicles
15%
Attack Strategy
14%
Vulnerability
14%
Attack Defense
14%
Scenario Generation
13%
Generative Adversarial Networks
13%
Training Model
13%
CIFAR-10
13%
Transferability
13%
MNIST
13%
Robustness Certification
12%
Deep Learning System
12%
Graph Neural Network
11%
Attack Success Rate
11%
Gradient Estimation
11%
Autonomous Driving
10%
Deep Learning Model
10%
Watermarking
10%
Differentially Private
10%
Deep Reinforcement Learning (deep RL)
10%
ImageNet
9%
Adversarial Setting
9%
Data Augmentation
9%
Real-world Application
9%
Privacy-preserving
9%
Shapley Value
9%
Robust Learning
9%
