Alex Morehead

Ph.D. Candidate in Machine Learning & Computational Biology @Mizzou


Welcome! :wave:

I am a Computer Science Ph.D. candidate at the University of Missouri. I work as a graduate research assistant in Dr. Jianlin Cheng’s Bioinformatics & Machine Learning Lab. Prior to my Ph.D., I completed my B.S. in Computer Science at Missouri Western State University. My interests in computer science and machine learning began through many fun experiences building and experimenting with bleeding-edge software early on, and competing in mathematics Olympiads in high school.


My current research interests include machine learning, deep learning, computational biology, and generative modeling. In particular, I have developed new geometric graph neural network architectures such as GCPNet and the Geometric Transformer for modeling biomolecules of various sizes (e.g., proteins), introduced GCDM for diffusion generative modeling of 3D molecules, and curated large open-source molecular datasets (e.g., DIPS-Plus).


I enjoy sharing thoughts on Machine Learning research and applications on Twitter and (occasionally) my blog.


Aug 18, 2023 Wrapped up my research internship with ⭐
Jul 25, 2023 Published and presented DProQA at ISMB :sparkles:
May 5, 2023 Presented GCDM at ICLR 2023’s Machine Learning for Drug Discovery (MLDD)
Apr 30, 2023 Wrapped up my internship with Absci as a Deep Learning Research Intern 🧬
Feb 14, 2023 Presented GCPNet and GCDM at AAAI 2023’s Deep Learning on Graphs (DLG-AAAI’23) workshop as well as the AI to Accelerate Science and Engineering (AI2ASE-AAAI’23) workshop!
Feb 10, 2023 Served as a reviewer for both Nature Machine Intelligence and IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI)!
Dec 20, 2022 Published GCPNet at AAAI 2023’s Deep Learning on Graphs (DLG-AAAI) and AI to Accelerate Science & Engineering (AI2ASE-AAAI) workshops!
Jan 20, 2022 One paper accepted by ICLR :sparkles:
Aug 24, 2021 Served as a reviewer for NeurIPS for the first time :sparkles:
Nov 9, 2019 One paper accepted by IEEE BigData

selected publications

  1. Low Cost Gunshot Detection using Deep Learning on the Raspberry Pi
    Morehead, Alex, Ogden, Lauren, Magee, Gabe, Hosler, Ryan, White, Bruce, and Mohler, George
    IEEE International Conference on Big Data (Big Data) 2019
  2. DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
    Morehead, Alex, Chen, Chen, Sedova, Ada, and Cheng, Jianlin
    Scientific Data 2023
  3. Geometric Transformers for Protein Interface Contact Prediction
    Morehead, Alex, Chen, Chen, and Cheng, Jianlin
    International Conference on Learning Representations (ICLR) 2022
  4. Geometry-Complete Perceptron Networks for 3D Molecular Graphs
    Morehead, Alex, and Cheng, Jianlin
    AAAI Workshop on Deep Learning on Graphs: Methods and Applications 2023
  5. Geometry-Complete Diffusion for 3D Molecule Generation
    Morehead, Alex, and Cheng, Jianlin
    ICLR 2023 - Machine Learning for Drug Discovery workshop 2023
  6. Semi-Supervised Graph Learning Meets Dimensionality Reduction
    Morehead, Alex, Chantapakul, Watchanan, and Cheng, Jianlin
    IEEE ICMLA 2023
  7. 3D-equivariant graph neural networks for protein model quality assessment
    Chen, Chen, Chen, Xiao,  Morehead, Alex, Wu, Tianqi, and Cheng, Jianlin
    Bioinformatics 2023
  8. A gated graph transformer for protein complex structure quality assessment and its performance in CASP15
    Chen, Xiao*, Morehead, Alex*, Liu, Jian, and Cheng, Jianlin
    Intelligent Systems for Molecular Biology (ISMB) & Bioinformatics Jun 2023
  9. DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning
    Soltanikazemi, Elham, Roy, Raj S, Quadir, Farhan, Giri, Nabin,  Morehead, Alex, and Cheng, Jianlin
    ICIBM Jun 2023