Heewook Lee
Assistant Professor, School of Computing and Augmented Intelligence
Heewook Lee is an assistant professor in the School of Computing and Augmented Intelligence and the Biodesign Institute’s Center for Biocomputing, Security and Society. His research interest lies broadly in computational biology, focusing on genetic variation. His current research develops computational techniques to study largely varying types of immunological components in organisms. Prior to joining ASU, he was a Lane Fellow in the School of Computer Science at Carnegie Mellon University.
Spring 2024
Ryan Connolly-Kelley
Computer science
Understanding the Root Causes for catELMo’s Superior Performance Embedding T-Cell Receptors With Respect to the Downstream TCR-Epitope Binding Affinity Prediction Task
Studying embedding techniques for T-cell receptors will enable the recommendation of embedding methods for other types of biological data
Program: FURI
Uttam Kumar
Computer science
Towards Automated Selection of Embedding Models: Identifying the Optimal Parameters for the Baseline Model for TCR Embedding
Automating embedding model selection optimizes TCR research, accelerating therapeutic target discovery for improved health outcomes.
Program: MORE
Sonal Sujit Prabhu
Computer science
Developing an Antibody Language Model for Generating Missing Amino Acid Residues to Complete Partial BCR Sequences
Our model studies immune cells, which hold promise as treatments for cancer and can extend to other infectious diseases.
Program: MORE
Benjamin Hanson
Computer science
Next-Gen Immunotherapies: Harnessing Evolutionary Diffusion Models for Enhanced TCR-Epitope Sequence Generation
Exploring diffusion models to design TCR-Epitope sequences can lead to groundbreaking immunotherapies for cancer and disease treatment.
Program: FURI
Akshata Ashok Jedhe
Computer science
Enhancing Single-Cell RNA Sequencing Analysis for Neurodegenerative Disease Research
Transforming neurodegenerative care: Advanced cell analysis enables precise diagnoses and targeted therapies, improving patient outcomes.
Program: MORE
Summer 2024
Muhammed H. Topiwala
Computer science
Next-Gen Immunotherapies: Analysis of Large TCR Repertoires to Create a TCR Cluster Framework Using the catELMO Embedding Technique
Developing a machine learning technique for clustering large-scale empirical tokenized host-pathogen interaction datasets to advance cancer immunotherapy.
Program: FURI
Fall 2023
Aiko Muraishi
Computer science
Identifying the Optimal Orientation for Selecting Embedding Models for TCR-Epitope Binding Affinity Prediction
Developing GPT-based protein embeddings will help determine suitable types of machine learning models for TCR-epitope binding prediction.
Program: FURI
Summer 2023
Aiko Muraishi
Computer science
Identifying the Optimal Orientation for Selecting Embedding Models for TCR-Epitope Binding Affinity Prediction
Developing GPT-based protein embeddings can determine suitable types of machine learning models for TCR-epitope binding prediction.
Program: FURI
Spring 2023
Saajan Maslanka
Computer science
Developing Meaningful Protein Embeddings Using Structural Information For Use in TCR-Epitope Binding Affinity Prediction Tasks
Utilizing 3D structural information in protein binding prediction can help accelerate vaccine development and improve patient outcomes.
Program: FURI
Shreyas Sharma
Computer science
PiTE II: TCR-epitope Binding Affinity Prediction with Contribution From CDR3-Alpha, V, and J Genes
Having an accurate prediction of whether or not one's immune system can identify and fight a disease can allow for a plethora of treatments.
Program: FURI
Ajay Kannan
Computer science
Few Shot Learning for TCR-Epitope Binding Affinity Prediction
Modeling accurate prediction of TCR-epitope binding could aid in developing effective immunotherapies and vaccines for cancers and pandemics.
Program: MORE
Zishen Wei
Computer science
Impact of Position Information on TCR-epitope Binding Affinity Prediction Model and Its Biological Significance
Studying the effect of position information on the prediction of TCR-epitope binding affinity will help the development of immunotherapy.
Program: FURI
Fall 2022
Rishik Kolli
Computer science
Identifying the Physical Identity of an Individual through Wastewater-Based Epidemiology
Determining the possibility of successfully identifying individuals with genetic information from wastewater data will help understand the security of wastewater-based epidemiology.
Program: FURI
Spring 2022
Shayna Mallett
Computer science
Graph-Guided Assembly and Typing of the HLA Alleles from RNA-seq Data
Accurately and efficiently typing human leukocyte antigens is essential to successful organ transplants and has potential in the field of precision medicine.
Program: FURI
Nicholas A. Moran
Computer science
Boosting TCR and Epitope Binding Affinity via Supervised Contrastive Loss
Boosting binding affinity leads to highly effective patient-specific responses to historically hard-to-treat ailments like cancer.
Program: FURI
Fall 2021
Shayna Mallett
Computer science
Graph-Guided-Assembly and Typing of the HLA Alleles from RNA-seq Data
Accurately and efficiently typing human leukocyte antigen alleles is essential to successful organ transplants and has potential for precision medicine.
Program: FURI
Spring 2021
Mojca Stampar
Computer science
Alternative Promoter Usage Using Transcription Start Sites
Developing an innovative tool for analyzing tissue- and disease-specific gene expression will help improve understanding and research in this area.
Program: MORE
Fall 2020
Michael Ray Cai
Computer science
Towards Automated Identification of Cancer Immunotherapy Targets: Prediction of Binding Affinity of T Cell Receptor and Antigens Using Graph-Guided Deep Neural Network
Utilizing machine learning to make predictions will enable immunologists to aid our bodies in fighting cancer.
Program: FURI
Summer 2020
Michael Ray Cai
Computer science
Towards Automated Identification of Cancer Immunotherapy Targets: Prediction of Binding Affinity of T Cell Receptor and Antigens Using Graph-Guided Deep Neural Network
Using machine learning to predict immune responses will help immunologists design treatments to make our own bodies fight cancer.
Program: FURI
Needs Review
Muhammed H. Topiwala
Computer science
Next-Gen Immunotherapies: Analysis of Large TCR Repertoires to Create a TCR Cluster Framework Using the catELMO Embedding Technique
Developing a machine learning technique for clustering large-scale empirical tokenized host-pathogen interaction datasets to advance cancer immunotherapy.
Program: FURI