Faculty working in this area
| Faculty | website | |
|---|---|---|
| Patrick H. Chen | patrickchen@binghamton.edu | |
| Kenneth Chiu | kchiu@binghamton.edu | |
| Weiying Dai | wdai@binghamton.edu | |
| Zeyu Ding | dding1@binghamton.edu | |
| Nancy Guo | nguo1@binghamton.edu | |
| Adnan Siraj Rakin | arakin@binghamton.edu | |
| Sujoy Sikdar | ssikdar@binghamton.edu | |
| Zhaohan Xi | zxi1@binghamton.edu | |
| Zhen Xie | zxie3@binghamton.edu | |
| Ping Yang | pyang@binghamton.edu | |
| Lijun Yin | lyin@binghamton.edu | |
| Shiqi Zhang | zhangs@binghamton.edu | |
| Yingxue Zhang | yzhang42@binghamton.edu | |
| Zhongfei (Mark) Zhang | zzhang@binghamton.edu |
Highlights in this area
focuses on the fundamental, uncertainty and efficiency aspects of machine learning.
To make models reliably applicable, his research group studies the fundamental aspects
of ML, such as factualness and robustness, and analyzes uncertainty issues in various
applications, such as continual learning. To make models more efficient for practical
usage, his research focuses on compressing machine learning models to make them deployable
on devices with limited memory, and accelerating the training and inference time of
machine learning models to meet latency requirements.
researches medical imaging, healthcare bioinformatics, biomedical image processing, functional magnetic resonance imaging (fMRI), machine learning and pattern recognition. She co-directs the Center for Advanced Magnetic Resonance Imaging Sciences (CAMRIS). She is working on the aging-related brain patterns, imaging biomarkers for schizophrenia and diabetes, formation of brain folding patterns, automatic sleep stage learning, and LLM and deep learning on fMRI image registration and image reconstruction.
researches the intersection of data privacy, software security, machine learning and algorithmic fairness. The overarching goal of his work is to protect sensitive personal information from being leaked in unintended ways. His current research focuses on differential privacy and its interactions with software security, formal verification, numerical optimization, statistical inference and machine learning.
Nancy Guo develops AI/ML algorithms that utilize big data for prediction and decision-making.
Her research includes software reliability and precision medicine. Her recent research
focuses on developing and applying foundation AI models for biomarker and drug discovery
for cancer treatment. Examples of her algorithms include Dempster-Shafer belief networks
and non-negative matrix factorization/Monte Carlo Simulation for modeling multi-omics
pathways and networks.
focuses on unsupervised machine learning. His research group is dealing with three important research questions:
- How to improve the performance of deep learning model with limited data in a collaborative environment? The investigation also looks into the challenges of domain shift and domain generalization of data.
- What are the security challenges in such a collaborative un-supervised training scheme? His group is investigating potential defensive solutions as well.
- How to incorporate a wide range of hardware fault injection techniques from CPU, GPU and FPGA to evaluate ML security and privacy threats?
researches the intersection of computer science, artificial intelligence, economics and social science in understanding individual and group preferences, how preferences are aggregated in systems composed of multiple agents, and designing algorithms to make good decisions for groups of heterogeneous agents. Some examples:
- Designing fair and efficient algorithms for group decision-making problems like fair division and voting.
- Learning and modeling preferences from data.
- Understanding human behavior in a variety of social contexts including in social media streams.
At CRAFT Lab, develops AI agents for cross-disciplinary scenarios, such as healthcare decision support, with a strong emphasis on responsibility, reliability, trustworthiness and transparency. Xi stays at the forefront of technological innovation while bridging the gap between advancements and real-world implementation. Our approach integrates humans throughout the entire lifecycle of these AI systems to ensure ethical and effective deployment. CRAFT Lab is seeking new members passionate about cross-disciplinary research and responsible AI development. Our current focus includes LLM-as-Agent frameworks, multimodality, multi-agent systems and LLM security.
researches high-performance computing (HPC) with a focus on the interaction between machine learning algorithms and system-level performance optimization.
- System for Machine Learning: building modern ML/DL algorithms and systems on heterogeneous and parallel HPC architectures (e.g., GPUs and AI accelerators).
- High-Performance Computing: automatic performance optimization on HPC applications with the aid of machine learning.
- Scientific Machine Learning: accelerating HPC applications using machine learning-based approximation.
researches information and systems security, privacy, AI-based security, trustworthy AI and virtual machine security. She is the director of the . Her recent research projects focus on improving the accuracy, real-time responsiveness, robustness and explainability of AI-based security solutions.
performs research on affective computing, human emotion analysis, biometrics and human computer interaction. He leads the Graphics and Image Computing (GAIC) Laboratory. He is working on the automatic detection of emotion and behavior status using multimodal approaches for health-care in collaborating with a medical practitioner.
researches robotics, artificial intelligence (AI) and human-robot interaction (HRI).
He leads the Autonomous Intelligent Robotics (AIR) research group, whose goal is to
develop intelligent mobile robots that are able to interact with people, provide services
to people, and learn from this experience, in human-inhabited, collaborative environments.
researches:
- Spatial-temporal data science, AI, with applications on urban intelligence and smart cities.
- Human behavior analysis and human decision making analysis with data-driven AI approaches including imitation learning and offline reinforcement learning.
She is working on theoretical research on offline reinforcement learning, and applications on contrastive learning, model pretraining and offline reinforcement learning related to smart cities.
researches machine learning and artificial intelligence, data mining and knowledge discovery, multimedia indexing and retrieval, computer vision and image understanding, and pattern recognition. He is working on several projects in these areas including LLM compression, multimodal data learning, out of domain learning, learning with noise and novelty learning.