On April 14 2021, I gave an invited talk at the UVA Human and Machine Intelligence Seminar:
Projects by Year
Title: General Multi-label Image Classification with Transformers
Title: Curriculum Labeling- Self-paced Pseudo-Labeling for Semi-Supervised Learning”
Transfer Learning with Motif Transformers for Predicting Protein-Protein Interactions Between a Novel Virus and Humans
Title: Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
Title: Reevaluating Adversarial Examples in Natural Language
My tutorial talk about jointnets at UCLA computational genomics summer school 2019 for extracting connectomes from heterogeneous samples
Here is the slide of my tutorial talk at UCLA computational genomics summer school 2019.
JointNets R package for Joint Network Estimation, Visualization, Simulation and Evaluation from Heterogeneous Samples
jointNets R package: a Suite of Fast and Scalable Tools for Learning Multiple Sparse Gaussian Graphical Models from Heterogeneous Data with Additional Knowle...
kDIFFNet - Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models
Tool kDIFFNet: Adding Extra Knowledge in Scalable Learning of Sparse Differential Gaussian Graphical Models
On April 23 2019, I gave an invited talk at the ARO Invitational Workshop on Foundations of Autonomous Adaptive Cyber Systems
On December 21 @ 12noon, I gave a distinguished webinar talk in the Fall 2018 webinar series of the Institute for Information Infrastructure Protection (I3P)...
My tutorial talk at UVA-CPHG seminar and healthDynamics workshop 2018 for Making Deep Learning Understandable for Genomics
I gave a tutorial talk at UVA-CPHG Seminar Series 2018.
Tool DeepDIff: DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
Here are the slides of tutorial talk I gave at ACM-BCB 2018.
A Series of Tutorials We wrote to explain the JointS GM tools we built for extracting connectomes from heterogeneous samples
So far, we have released the following Tutorials:
JEEK - Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Tool JEEK: A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Here are the slides of lecture talks I gave at UCLA CGWI and NLM-CBB seminar about our deep learning tools: DeepChrome, AttentiveChrome and DeepMotif.
We are releasing EvadeML-Zoo: A Benchmarking and Visualization Tool for Adversarial Examples (with 8 pretrained deep models+ 9 state-of-art attacks).
Jack’s DeepMotif paper (Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks ) have received the “best paper awar...
Tool DIFFEE: Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure
Tool W-SIMULE: A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs with Additional Prior knowled...
AttentiveChrome-Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Tool AttentiveChrome: Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Tool Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Tool DeepChrome: deep-learning for predicting gene expression from histone modifications
Paper ICLR17 Workshop
Paper ICLR17 workshop
Tool GaKCo-SVM: a Fast GApped k-mer string Kernel using COunting
Tool TSK: Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Tool FASJEM: A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Tool SIMULE: A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models
MUST-CNN- A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Tool MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Title: Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence
Here are the slides of one lecture talk I gave at UVA CPHG Seminar Series in 2014 about our deep learning tools back then.
Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties
Paper1: Learning the Dependency Structure of Latent Factors Y. He, Y. Qi, K. Kavukcuoglu, H. Park (2012) NeurIPS PDF Talk: Slide
Paper0: Learning to rank with (a lot of) word features