News
- I had a short interview with the host of the local radio channel WGSO about the NeurIPS outreach program and the future of AI.
- The NeurIPS 2023 Outreach Program has reached out to 12 local colleges in Louisiana. In the end we invited 94 student participants and 5 faculty participants from 6 colleges and offered a tutorial program on Dec 11 at NeurIPS. (Dec 2023)
- Akshay will be presenting our paper titled "Analysis of Task Transferability in Large Pre-trained Classifiers" at NeurIPS 2023 workshop. (Dec 2023)
- We have announced NeurIPS 2023 outreach program. More details of the program will follow soon. (Oct 2023)
- We will be receiving continued support for NIH R33 "MIND-the-SKIN" with Yotsu as PI. (Oct 2023)
- I served as a panelist for NSF CISE IIS III. (2023)
- Allan (PI) and I have received the 3rd-year LAMDA Seed Funding for "Automatic Detection of Fractures in X-ray Material Tomography using Weakly-supervised Machine Learning." (Aug 2023)
- Akshay will be presenting our paper titled "On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization" at WACV 2024. (Aug 2023)
- Yunsung will be presenting our paper titled "FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation" at MICCAI'23 workshop MILLanD . (Aug 2023)
- The LAMDA team had a successfull NSF site visit at LSU, and will have a continued funding for Y4-5. (Aug 2023)
- Congrats to Taotao for successfully defending the phd theses! (July 2023)
- I will be giving a keynote talk on "Analyzing Transfer Learning Bounds through Distributional Robustness" at ICML 2023 Workshop AdvML-Frontiers. (Jul 2023)
- Akshay will be presenting our paper titled "Risk-Averse Predictions on Unseen Domains via Neural Style Smoothing" at ICML 2023 Workshop AdvML-Frontiers 2023. (Jul 2023)
- I gave a short presentation on "Bayesian Optimization for stable phase prediction" at 2023 LAMDA Symposium. (July 2023)
- I will be giving an invited talk at on "Transferability of Large Pre-trained Classifiers" at Korea Institute for Advanced Study (KIAS). (Jul 2023)
- Our paper titled "Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: a pilot study" was accepted for publication in PLOS Neglected Tropical Diseases. (June 2023)
- Janet Wang is joining the machine learning group. Welcome!
- Congrats Karthik and Haifeng for successfully defending your phd theses! (Apr 2023)
- Congrats Charles, Chenyu, Jamie, and Reagan for receiving the SSE Capstone Design Expo Expert Award on "Skin Disease Diagnosis on Darker Skin Tones"! (Apr 2023)
- I will be serving as the NeurIPS 2023 Outreach Chair at the New Orleans Convention Center in December.
- Our paper titled "Marginalized Augmented Few-shot Domain Adaptation" was accepted for publication in IEEE TNNLS. Congrats, Taotao! (Apr 2023)
- I gave a short presentation on "ML predictions of stable phases of complex concentrated alloys" at 2023 LAMDA Technical Conference. (Feb 2023)
- Peter Stone (SONY/UT Austin) is visiting us and will give a talk on "Outracing Champion Gran Turismo Drivers with Deep Reinforcement Learning" on Friday Dec 12 at 4PM in ST302. (Dec 2022)
- Masashi Sugiyama (RIKEN/Univ of Tokyo) is visiting us and will give a talk on "Recent Advances in Robust Machine Learning" on Monday Nov 28 at 11AM in Boggs 600. (Nov 2022)
- 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 for 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)
Research Interests
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
Publications
You can find the list of publications from Google scholar.