CHENWEI ZHANG | University of British Columbia

About Me

Think big. Think different.

I am Chenwei Zhang (张晨伟 in Chinese). I am a Ph.D. student in the Department of Computer Science at the University of British Columbia under the co-supervision of Prof. Anne Condon and Prof. Khanh Dao Duc. I am also affiliated with the Algorithm Lab (Beta Lab). I obtained my MSc degree under the supervision of Prof. Pavle Radovanovic from the University of Waterloo in 2021. I earned a dual Bachelor’s degree from the University of Waterloo (Dean’s Honours List) and Beijing Jiaotong University (China) in 2018. From June 2023 to June 2024, I have been undertaking my one-year internship at Amgen Canada as a role of machine learning scientist.

Research Interests

Machine Learning: Generative models (diffusion models, GANs, VAEs, etc.), Large Language Models, AI Agent, Multimodal AI, Visualization
AI4Science: Protein structure modelling, AI4Healthcare, Drug discovery, DNA kinetics

News

[📌Pinned📌] I made a paper list that collects different SOTA ML, DL or other advanced computational approaches for protein atomic model building.
[📌Pinned📌] I made a python package to find a specified number of shortest paths (top K) in a weighted graph by Dijkstra’s algorithm.
[Sept. 2024] 🗣️ I presented a poster at DNA30 conference at Johns Hopkins University, Baltimore, USA.
[Jul. 2024] 🗣️ I will attend Oxford Machine Learning Summer School (OxML 2024) at University of Oxford, Oxford, UK, from 6 - 14, July, 2024.
[Mar. 2024] 🗣️ I represented the Amgen Interns to attend the Mitacs Roundtable Discussion with new BC Minister of Post-Secondary Education and Future Skills, Lisa Beare..
[Dec. 2023] 🗣️ I will give a poster presentation at Machine Learning in Structural Biology (MLSB) workshop at NeurIPS 2023 conference in New Orleans, US.
[Nov. 2023] 🗣️ I will give an oral presentation at Machine Learning in Computational Biology (MLCB 2023) conference in Seattle, US.
[Nov. 2023] 🎉 Our paper on visualizing hybridization trajectories with biophysics-informed deep graph embeddings is accepted to MLCB 2023, PMLR, selected as an oral presentation.
[Oct. 2023] 🎉 Our paper on partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence is accepted to MLSB, NeurIPS2023.

Previous news

[Sept. 2023] 🗣️ I presented a poster at DNA29 conference at Tohoku University, Sendai, Japan.
[Sept. 2023] 🎉 I gave a talk at BIRS workshop in Banff, Canada.
[Jul. 2023] 🎉 Our paper on revisiting hybridization kinetics with Bayesian inference is accepted to DNA29.
[Jul. 2023] 🎉 Our abstract poster on visualizing DNA kinetics using deep graph embeddings is accepted to DNA29.
[Jun. 2023] 👨‍💻 I am starting my internship at Amgen focused on employing deep learning in drug discovery.
[Jan. 2023] 🎉 Our paper on revisiting plasmonic properties of complex semiconductor nanocrystals using Magnetic Circular Dichroism Spectroscopy is accepted to The Journal of Physical Chemistry C.
[Dec. 2022] 🗣️ I presented a poster at NeurIPS2022 conference in New Orleans, US.
[Oct. 2022] 🎉 Our workshop paper on using semi-VAE for visualization of DNA reaction trajectories is accepted to MLSB, NeurIPS2022.
[Oct 2022] ✍️ I PASSED my PhD qualification exam!
[Aug 2022] 🗣️ I attended DNA28 conference at University of New Mexico, Albuquerque, US.
[Jul. 2022] 🎉 Our abstract poster on faster elementary steps in DNA reaction simulators is accepted to DNA28.
[Dec. 2021] 🎉 Our paper on the study of origin of d⁰ magnetism in transparent metal oxide nanocrystals is accepted to The Journal of Physical Chemistry C.
[Sept. 2021] 📕 I am beginning my doctoral journey in the Department of Computer Science at the University of British Columbia！
[Feb. 2021] 👨‍🎓 I obtained my MSc degree from the University of Waterloo！
[Sept. 2020] 🎉 Our paper on manipulating carrier polarization in semiconductor nanocrystals is accepted to ECS Transactions.
[Sept. 2018] 📕 I am beginning my MSc study at the University of Waterloo！
[Sept. 2015 - Aug. 2018] 👨‍🎓 I obtained a dual Bachelor's degree from the University of Waterloo (Dean’s Honours List) [BSc] and Beijing Jiaotong University [BEng] within 3 years!
[Sept. 2015] 🐣 Academic life starts!

Publications

Submitted

Synthetic High-resolution Cryo-EM Density Maps with Generative Adversarial Networks

Chenwei Zhang, Anne Condon, Khanh Dao Duc

Submitted

PDF Code BibTex
Generating synthetic cryogenic electron microscopy (cryo-EM) 3D density maps from molecular structures has potential important applications in structural biology. Yet existing simulation-based methods cannot mimic all the complex features present in experimental maps, such as secondary structure elements. As an alternative, we propose struc2mapGAN, a novel data-driven method that employs a generative adversarial network (GAN) to produce high-resolution experimental-like density maps from molecular structures. More specifically, struc2mapGAN uses a U-Net++ architecture as the generator, with an additional L1 loss term and further processing of raw experimental maps to enhance learning efficiency. While struc2mapGAN can promptly generate maps after training, we demonstrate that it outperforms existing simulation-based methods for a wide array of tested maps and across various evaluation metrics. Our anonymous code is available at https://github.com/chenwei-zhang/struc2mapGAN.

Submitted

A comprehensive survey and benchmark of deep learning-based methods for atomic model building from cryo-EM density maps

Chenwei Zhang, James Chen, Khanh Dao Duc, Anne Condon

Submitted

Code
Recent advancements in deep learning (DL) have significantly enhanced the automated construction of atomic models from single-particle cryogenic electron microscopy (cryo-EM) density maps, addressing challenges posed by noise, varying resolutions, and complex biological structures. We conduct a comprehensive survey of DL-based approaches for building atomic models and evaluate cutting-edge methods against traditional physics-based approaches using 50 cryo-EM density maps at varying resolutions. In addition, we introduce improved evaluation metrics that overcome the limitations of existing metrics for model-building methods. Our findings demonstrate that DL-based methods outperform their traditional physics-based counterparts, especially when integrated with AlphaFold, which improves the completeness and accuracy of the constructed atomic models. Furthermore, we provide insightful future directions in this field and highlight the importance of collaboration between machine learning scientists and structural biologists.

PMLR

ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings

Chenwei Zhang, Jordan Lovrod, Boyan Beronov, Khanh Dao Duc, Anne Condon

Machine Learning in Computational Biology. PMLR , 2023.

PDF Code BibTex Oral Presentation
Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DNA reaction trajectories that uses a 2D embedding of the secondary structure state space underlying the CTMC model. To this end, we integrate a scattering transform of the secondary structure adjacency, a variational autoencoder, and a nonlinear dimensionality reduction method. We augment the training loss with domain-specific supervised terms that capture both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA hybridization reactions. Our results demonstrate that the domain-specific features lead to significant quality improvements over the state-of-the-art in DNA state space visualization, successfully separating different folding pathways and thus providing useful insights into dominant reaction mechanisms.

NeurIPS

EMPOT: partial alignment of density maps and rigid body fitting using unbalanced Gromov-Wasserstein divergence

Aryan Tajmir Riahi, Chenwei Zhang, James Chen, Anne Condon, Khanh Dao Duc

Machine Learning for Structural Biology Workshop, NeurIPS (MLSB), 2023.

PDF Code BibTex Poster Presentation
Aligning EM density maps and fitting atomic models are essential steps in single particle cryogenic electron microscopy (cryo-EM), with recent methods leveraging various algorithms and machine learning tools. As aligning maps remains challenging in the presence of a map that only partially fits the other (e.g. one subunit), we here propose a new procedure, EMPOT (EM Partial alignment with Optimal Transport), for partial alignment of 3D maps. EMPOT first finds a coupling between 3D point-cloud representations, which is associated with their so-called unbalanced Gromov Wasserstein divergence, and second, uses this coupling to find an optimal rigid body transformation. Upon running and benchmarking our method with experimental maps and structures, we show that EMPOT outperforms standard methods for aligning subunits of a protein complex and fitting atomic models to a density map, suggesting potential applications of Partial Optimal Transport for improving Cryo-EM pipelines.

DNA29

Revisiting Hybridization Kinetics with Improved Elementary Step Simulation

Jordan Lovrod, Boyan Beronov, Chenwei Zhang, Erik Winfree, Anne Condon

29th International Conference on DNA Computing and Molecular Programming (DNA29), 2023.

PDF Code BibTex Best Oral Presentation
Nucleic acid strands, which react by forming and breaking Watson-Crick base pairs, can be designed to form complex nanoscale structures or devices. Controlling such systems requires accurate predictions of the reaction rate and of the folding pathways of interacting strands. Simulators such as Multistrand model these kinetic properties using continuous-time Markov chains (CTMCs), whose states and transitions correspond to secondary structures and elementary base pair changes, respectively. The transient dynamics of a CTMC are determined by a kinetic model, which assigns transition rates to pairs of states, and the rate of a reaction can be estimated using the mean first passage time (MFPT) of its CTMC. However, use of Multistrand is limited by its slow runtime, particularly on rare events, and the quality of its rate predictions is compromised by a poorly-calibrated and simplistic kinetic model. The former limitation can be addressed by constructing truncated CTMCs, which only include a small subset of states and transitions, selected either manually or through simulation. As a first step to address the latter limitation, Bayesian posterior inference in an Arrhenius-type kinetic model was performed in earlier work, using a small experimental dataset of DNA reaction rates and a fixed set of manually truncated CTMCs, which we refer to as Assumed Pathway (AP) state spaces. In this work we extend this approach, by introducing a new prior model that is directly motivated by the physical meaning of the parameters and that is compatible with experimental measurements of elementary rates, and by using a larger dataset of 1105 reactions as well as larger truncated state spaces obtained from the recently introduced stochastic Pathway Elaboration (PE) method. We assess the quality of the resulting posterior distribution over kinetic parameters, as well as the quality of the posterior reaction rates predicted using AP and PE state spaces. Finally, we use the newly parameterised PE state spaces and Multistrand simulations to investigate the strong variation of helix hybridization reaction rates in a dataset of Hata et al. While we find strong evidence for the nucleation-zippering model of hybridization, in the classical sense that the rate-limiting phase is composed of elementary steps reaching a small nucleus of critical stability, the strongly sequence-dependent structure of the trajectory ensemble up to nucleation appears to be much richer than assumed in the model by Hata et al. In particular, rather than being dominated by the collision probability of nucleation sites, the trajectory segment between first binding and nucleation tends to visit numerous secondary structures involving misnucleation and hairpins, and has a sizeable effect on the probability of overcoming the nucleation barrier.

JPCC

Revisiting Plasmonic Properties of Complex Semiconductor Nanocrystals Using Magnetic Circular Dichroism Spectroscopy A Cautionary Tale

Aaron Kenny-Wilby, Gyorgy Jaics, Chenwei Zhang, Penghui Yin, Pavle V. Radovanovic

The Journal of Physical Chemistry C (J. Phys. Chem. C), 2023.

PDF BibTex
Plasmonic semiconductor nanocrystals (NCs) have emerged as an attractive alternative to noble metal nanoparticles. They typically exhibit localized surface plasmon resonance (LSPR) in the infrared range, which can be tuned by controlling the type and concentration of charge carriers. Compositionally and electronically complex semiconductor NCs have attracted significant attention due to their reported plasmonic properties, such as bipolar behavior (off-stoichiometric spinel-type gallium iron oxide or GFO), wide tunability (copper iron chalcogenide or Cu_xFe_yS_2-zSe_z), and extremely broad LSPR (oxygen-deficient WO_3-x). However, the origin of these unusual properties remains unclear. Here, we comparatively investigate the plasmonic properties of these semiconductor NC materials using magnetic circular dichroism (MCD) spectroscopy, owing to the highly specific response of LSPR to the magnetic field and light polarization. In all systems, we find indisputable evidence that the LSPR absorption bands have been mischaracterized. MCD signals that coincide with the absorption bands previously assigned to LSPR of GFO and Cu_xFe_yS_2-zSe_z NCs show rapid saturation with the magnetic field and independence on the sign of charge carriers, indicating that the corresponding absorption bands are not due to LSPR. This behavior is consistent with intra-ionic and inter-band transitions for GFO and Cu_xFe_yS_2-zSe_z NCs, respectively. The MCD signal corresponding to the absorption spectrum of WO_3-x NCs shows Brillouin function dependence on the magnetic field at high photon energies (attributed to d–d transitions of W5+) and linear dependence in the near-infrared range (attributed to LSPR), indicating that the broad absorption spectrum of WO_3-x NCs is only partly associated with LSPR. The results of this work help resolve notable discrepancies in the literature, underline the challenges in assigning absorption bands of complex semiconductor NCs to LSPR, and demonstrate that MCD spectroscopy is an invaluable tool for characterization of these materials.

NeurIPS

Visualizing DNA Rection Trajectories with Deep Graph Embedding Approaches

Chenwei Zhang, Khanh Dao Duc, Anne Condon

Machine Learning for Structural Biology Workshop, NeurIPS (MLSB), 2022.

PDF Code BibTex Poster Presentation
Synthetic biologists and molecular programmers design novel nucleic acid reactions, with many potential applications. Good visualization tools are needed to help domain experts make sense of the complex outputs of folding pathway simulations of such reactions. Here we present ViDa, a new approach for visualizing DNA reaction folding trajectories over the energy landscape of secondary structures. We integrate a deep graph embedding model with common dimensionality reduction approaches, to map high-dimensional data onto 2D Euclidean space. We assess ViDa on two well-studied and contrasting DNA hybridization reactions. Our preliminary results suggest that ViDa's visualization successfully separates trajectories with different folding mechanisms, thereby providing useful insight to users, and is a big improvement over the current state-of-the-art in DNA kinetics visualization.

JPCC

On the Origin of d⁰ Magnetism in Transparent Metal Oxide Nanocrystals

Chenwei Zhang, Penghui Yin, Whenhuan Lu, Victor Galievsky, Pavle V. Radovanovic

The Journal of Physical Chemistry C (J. Phys. Chem. C), 2021.

PDF BibTex
Controlling magnetic and magneto-optical properties of transparent metal oxide semiconductors has a significant potential for spintronics and photonics. Although ferromagnetism has been reported for several nanostructured transparent metal oxides in the absence of magnetic dopants, its origin and the nature of the exchange interactions remain controversial. Here, we report a variable-temperature–variable-field magnetic circular dichroism study of ZnO and SnO2 nanocrystals prepared under oxidizing and reducing conditions. We observe the band splitting in ZnO and SnO2 nanocrystals induced by localized doublet (S = 1/2) and triplet (S = 1) ground states, respectively. Photoluminescence measurements suggest that these states are associated with oxygen vacancies, either as isolated paramagnetic sites in ZnO or as local vacancy-based complexes in SnO2 nanocrystals. The results of this work demonstrate the ability to tune carrier polarization in metal oxide nanocrystals by in situ control of the native defect formation and attest to the anomalous Zeeman splitting of the band states, which may play an important role in generating ferromagnetism in this class of materials.

THESIS

Manipulating Carrier Polarization in Pure and Doped Metal Oxide Semiconductor Nanocrystals

Chenwei Zhang

MSc Thesis at the University of Waterloo (MSc Thesis), 2021.

PDF BibTex
Moore's Law will soon be inadequate to keep up with the needs of technological developments as the currently used materials have reached their natural performance limits. Hence, the development of a new technology to replace traditional semiconductor electronics is imminent. Spintronics has been emerged as a promising alternative to control charge properties as well as electron spin to improve the device performance. Metal oxide and chalcogenide semiconductor nanocrystals (NCs) have generated much research due to their unique properties and relatively low cost. In contrast to noble metal nanoparticles (NPs), the semiconductor NCs with tunable free charge carrier concentration are regarded as promising materials in technological applications including multifunctional quantum devices and next-generation electronic devices.

ECST

Manipulating Carrier Polarization in Semiconductor Nanocrystals

Chenwei Zhang, Penghui Yin, Pavle V. Radovanovic

ECS Transactions (ECST), 2020.

PDF BibTex
We investigated the role of the synthesis method and post-synthesis processing on the plasmonic properties of antimony-doped SnO2 nanocrystals. The nanocrystal samples having variable doping concentrations were prepared by coprecipitation and solvothermal methods, and subsequently thermally annealed for different durations. We found that solvothermally-synthesized Sb-doped SnO2 nanocrystals exhibit strong localized surface plasmon resonance in the near-infrared region, which is distinctly absent in the nanocrystals synthesized by the coprecipitation method. Upon thermal annealing, the plasmon absorption emerges in the nanocrystals prepared by the coprecipitation method, and increases in intensity in solvothermally-synthesized nanocrystals. Using X-ray photoelectron spectroscopy, we correlated the plasmon intensity to the oxidation of Sb3+ to Sb5+. These results demonstrate that synthesis methodology and post-synthesis treatment can dramatically influence the plasmonic properties of aliovalently doped semiconductor nanocrystals via dopant oxidation state, and can be effectively used to design semiconductor nanocrystals with targeted plasmonic properties.

Work Experience

Industry Internship

[Jun. 2023 - Jun. 2024] Research machine learning intern at Amgen Canada, focused on leveraging artificial intelligence for drug discovery.

Teaching Assistant

Activities

Conference & Workshop

Photography

Photograph Portfolio

Services

Conference Reviewers

Public Services

Mitacs Roundtable with BC Minister of Post-Secondary Education and Future Skills

Onsite teaching assistant for Oxford Machine Learning Summer School, 2024.

Contact

Address: ICICS/CS Building 201-2366 Main Mall, Vancouver, BC V6T 1Z4
Office Location: ICICS/CS Building X560
Email: cwzhang (at) cs.ubc.ca

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