I am recruiting graduate students for Spring and Fall 2023. Multiple RA positions are available in the areas of 1) deep learning, 2) ML applications in chemistry/material science, and 3) ML applications in medical data analysis.
- Our paper titled "Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions" was accepted for publication in Journal of Physics:Energy. (Nov 2022)
- Akshay will be presenting our paper titled "Do Domain Generalization Methods Generalize Well?" at the NeurIPS ML Safety workshop 2022. (Oct 2022)
- We are hosting the NeurIPS 2022 Social Proposal: Gulf Coast AI on Dec 30 at 6PM in Room 393. (Oct 2022)
- I gave a talk at the Chonnam University on "Towards Adversarially Robust Machine Learning" (Oct 2022)
- I gave a talk at the International Symposium of SNUH Biomedical Engineering and Transdisciplinary Advanced Medical Technology on "Towards Robust Machine Learning" (Aug 2022)
- Allan (PI) and I have received the 2nd-year LAMDA Seed Funding for "Automatic Detection of Fractures in X-ray Material Tomography using Weakly-supervised Machine Learning." (Aug 2022)
- Our paper titled "Backdoor Attack on Machine Learning Models of Electronic Health Records: Exploiting Missing Value Patterns" was accepted for publication in JMIR Medical Informatics. (July 2022)
- Our paper titled "A Spectral View of Randomized Smoothing under Common Corruptions: Benchmarking and Improving Certified Robustness" was accepted for publication in European Conference on Computer Vision (ECCV). Congrats, Jianchen! (July 2022)
- Gloria won the 3d place for her presentation at LAMDA Student Retreat with our work titled "Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions." (July 2022)
- I gave a talk on "Toward Certifying Generalization to Unseen Domains" at KAIST (July 2022)
- Our paper titled "Augmented Multi-Modality Fusion for Generalized Zero-Shot Sketch-based Visual Retrieval" was accepted for publication in IEEE Transactions on Image Processing. Congrats, Taotao! (May 2022)
- I served as a panelist at NSF CISE IIS RI (2022)
- Akshay will be spending his summer at Apple Machine Learning Research (Summer 2022)
- Welcome Yunsung and Yunbei to the machine learning group! (Fall 2022)
- Congrations to Batu for successfully defending his honors thesis on "Parameterizing Chains in Generalized Involutions", and also receiving the Peery Society Award! (Apr 2022)
- I gave a short presentation on "ML predictions of stable phases of high-entropy alloys" at 2022 LAMDA Symposium (Apr 2022).
- Our paper titled "Online Evasion Attacks on Recurrent Models: The Power of Hallucinating the Future" was accepted for publication in International Joint Conference in AI (IJCAI). Congrats, Byunggill! (April 2022)
- Allan (PI) and I have received the LAMDA Seed Funding for "Automatic Detection of Fractures in X-ray Material Tomography using Weakly-supervised Machine Learning." (Jan 2022)
- I'm teaching a new undergraduate/graduate course "Deep Learning" (CMPS 4660) this Spring. This course will focuss on the practices and techniques for using modern neural networks. (Dec 2021)
- Our paper titled "Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix GAN image translation" was accepted for publication in Biomedical Optics Express. It's been selected as Editor's Pick! Well done, Joy! (Nov 2021)
- Our paper titled "Defeating Traffic Analysis via Differential Privacy:A Case Study on Streaming Traffic" was accepted for publication in International Journal of Information Security . Great job, Xiaokuan! (Nov 2021)
- Akshay Mehra will be presenting our paper titled "Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning" at the Neural Information Processing Systems (NeurIPS) 2021. The final version will be linked here soon. (Sep 2021)
- Akshay Mehra will be presenting our paper titled "Penalty Method for Inversion-Free Deep Bilevel Optimization" at the Asian Conference on Machine Learning (ACML) 2021. The final version will be uploaded here soon. (Sep 2021)
- I'm serving on the Senior Program Committee at the AAAI Conference on Artificial Intelligence 2022. (Aug 2021)
- I gave a talk on "Understanding Out-of-distribution Robustness of Machine Learning through Data Poisoning" at KAIST and KIAS (July 2021).
- Akshay Mehra will be presenting our paper titled "On the Effectiveness of Poisoning against Unsupervised Domain Adaptation" at the ICML Workshop on Adversarial Machine Learning 2021. The paper can be found here (Jul 2021).
- I gave a talk on "Data poisoning attack on certifiably robust classifiers" at the LATech's ECE seminar (May 2021).
- The prospect of AI's impact from our faculty's point of view was recently featured in Tulanian.
- Riley Juenemann (co-advised by Scott McKinley who is the primary) will be pursuing her PhD at Stanford University with the prestigious NSF Graduate Research Fellowship. She also received the William Wallace Peery Medal for Academic Excellence. Congratulations!!!
- Caroline Mayberry and Samantha Rothman will be presenting our work on "A Machine Learning Exploration of Early Handwriting Development" at the Association for Psychological Science (APS) 2021 Virtual Convention. The poster and the paper will be released soon. (May 2021)
- I gave a short presentation on "ML predictions of mixing enthalpy of complex alloys " at 2021 LAMDA Technical Conference (Apr 2021).
- Akshay Mehra will be presenting our paper titled "How Robust are Randomized Smoothing based Defenses to Data Poisoning?" at the Computer Vision and Pattern Recognition (CVPR) 2021. The paper can be found here. (June 2021)
- Chanho Lim will be presenting our current work on "Machine Learning Approaches in Digital Health" at the 14th Annual Western Afib Symposium. (Feb 2021)
- I gave a tutorial talk on Adversarial Machine Learning (slides are here) at the Pattern Recognition and Machine Learning Winter School 2021. (Feb 2021)
- Byunggill Joe will be presenting "Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks" at the AAAI 2021 Workshop on Trustworthy AI for Healthcare. (Feb 2021)
All aspects of Machine Learning. Specific topics include the following:
- Foundations of Deep Learning
- Adversarial Learning and Optimization
- Private and Secure Machine Learning
- Medical Image Analysis and Computational Anatomy
- Nonlinear Dimensionality Reduction and Manifold Learning
You can find the list of publications from Google scholar.