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Bo Li
Assistant Professor
,
Siebel School of Computing and Data Science
Assistant Professor
,
Electrical and Computer Engineering
Assistant Professor
,
Information Trust Institute
Assistant Professor
,
National Center for Supercomputing Applications (NCSA)
Email
lbo
illinois
edu
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Research & Scholarship
(210)
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%
Machine Learning
71%
Adversarial Example
69%
Adversarial Machine Learning
68%
Attackers
40%
Backdoors
39%
Deep Learning
37%
Training Data
26%
Neural Network
26%
Reinforcement Learning
26%
Language Modeling
23%
Federated Learning
20%
Autonomous Driving
18%
Learning Systems
17%
Generative Model
16%
Graph Neural Network
14%
Deep Learning Model
14%
Autonomous Vehicles
14%
Adversarial Setting
13%
Generative Adversarial Networks
12%
World Application
12%
Learning Algorithm
12%
Experimental Result
12%
Sensitive Informations
12%
Data Augmentation
11%
Deep Reinforcement Learning
11%
Object Detection
10%
Privacy Preserving
10%
Sufficient Condition
10%
Large Language Model
10%
Black-Box Attack
10%
Attack Strategy
9%
Personal Data
8%
obstacle detection
8%
Artificial Intelligence
8%
Defense Strategy
8%
Detection Algorithm
7%
Baseline Method
7%
Data Distribution
7%
Mutual Information
7%
Machine Learning Approach
7%
Multimedia
7%
Graph Convolutional Network
7%
Watermarking
7%
Feature Space
6%
Convolutional Neural Network
6%
Watermarking Technique
6%
Edge Detection
6%
Generative Pre-Trained Transformer 4
6%
Image Captioning
6%
Keyphrases
Deep Neural Network
90%
Adversarial Examples
61%
Adversary
39%
Adversarial Attack
36%
Attacker
27%
Backdoor Attack
26%
Backdoor
24%
Machine Learning
23%
Certified Robustness
23%
Machine Learning Models
21%
Training Data
21%
Adversarial Perturbation
19%
Black Hole Attack
19%
Large Language Models
19%
Poisoning Attack
17%
Safety-critical
17%
Federated Learning
17%
Deep Learning
16%
Adversarial Training
16%
Attack Strategy
15%
Neural Network
15%
Attack Defense
15%
Vulnerability
15%
Generative Adversarial Networks
14%
CIFAR-10
14%
MNIST
13%
Transferability
13%
Training Model
13%
Reinforcement Learning
13%
Robustness Certification
13%
Language Model
13%
Deep Learning System
13%
Adversarial Learning
13%
Autonomous Vehicles
13%
Graph Neural Network
12%
Gradient Estimation
12%
Scenario Generation
11%
Autonomous Driving
11%
Deep Learning Model
11%
Attack Success Rate
11%
Deep Reinforcement Learning (deep RL)
10%
ImageNet
10%
Adversarial Setting
10%
Data Augmentation
10%
Real-world Application
10%
Shapley Value
10%
Large-scale Dataset
9%
Generative Models
9%
Model Robustness
9%
Adversarial Robustness
8%
