Faculty working in this area
| Faculty | website | |
|---|---|---|
| Patrick H. Chen | patrickchen@binghamton.edu | Chen's Group |
| Kenneth Chiu | kchiu@binghamton.edu | Chiu's Group |
| Weiying Dai | wdai@binghamton.edu | Dai's Group |
| Zeyu Ding | dding1@binghamton.edu | Ding's Group |
| Adnan Siraj Rakin | arakin@binghamton.edu | Rakin's Group |
| Sujoy Sikdar | ssikdar@binghamton.edu | Sikdar's Group |
| Zhen Xie | zxie3@binghamton.edu | Xie's Group |
| Ping Yang | pyang@binghamton.edu | Yang's Group |
| Lijun Yin | lyin@binghamton.edu | Graphics and Image Computing Laboratory |
| Shiqi Zhang | zhangs@binghamton.edu | Autonomous Intelligent Robotics Group |
| Yingxue Zhang | yzhang42@binghamton.edu | Zhang's Group |
| Zhongfei (Mark) Zhang | zzhang@binghamton.edu | Multimedia Research Lab |
Highlights in this area
Patrick H. Chen 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.
Weiying Dai 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.
Zeyu Ding 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.
Adnan Siraj Rakin 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?
Sujoy Sikdar 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.
Zhen Xie 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.
Ping Yang researches information and systems security, privacy, AI-based security, trustworthy AI and virtual machine security. She is the director of the Center for Information Assurance and Cybersecurity at Binghamton University. Her recent research projects focus on improving the accuracy, real-time responsiveness, robustness and explainability of AI-based security solutions.
Lijun Yin 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.
Shiqi Zhang 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.
Yingxue Zhang 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.
Zhongfei (Mark) Zhang 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.
