Research Lab

Teaching

Tue, 02 Jun 2026 00:00:00 +0000

Autonomous Self-Driving Lab for Robot-Assisted Scientific Experimentation

Mon, 01 Jun 2026 00:00:00 +0000

Project Mission

This project develops autonomous self-driving laboratory systems that integrate robotics, AI, sensing, and scientific instrumentation for end-to-end experimental execution.
We focus on building robotic workflows that can handle samples, transport materials across laboratory spaces, operate analytical instruments, monitor experimental conditions, and generate feedback for the next experimental cycle.

The long-term goal is to move beyond isolated laboratory automation toward a physically embodied discovery platform, where robotic agents can connect experimental planning, sample handling, measurement, analysis, and decision-making in a unified closed-loop system.

Scientific Motivation

Modern scientific experiments often depend on repetitive manual operations, fragile sample handling, instrument-specific procedures, and delayed analysis. These constraints limit throughput, reduce reproducibility, and make it difficult to run experiments continuously across long time horizons.

Self-driving laboratories provide a new framework for scientific discovery by combining AI-driven planning with robotic execution and automated characterization. In this project, we extend that idea from benchtop automation to a broader robotic laboratory architecture that includes dexterous manipulation, mobile sample logistics, real-time monitoring, and future integration with surgical and in vivo experimental systems.

Research Approach

Dexterous Manipulation and Touch Sensing

Touch-Aware Grasping: Developing robotic manipulation policies that use tactile feedback to handle fragile samples, containers, tools, and experimental materials.
Vision-Tactile Fusion: Combining camera-based perception with contact sensing to improve grasp stability, object recognition, and manipulation under uncertain laboratory conditions.
Low-Light Robotic Operation: Designing touch-centered control strategies for dark-lab or lights-out environments where visual sensing alone is insufficient.
Slip and Force Monitoring: Using tactile signals to detect unstable grasps, excessive force, surface contact changes, and potential sample damage.
Skill Learning for Lab Manipulation: Training robots to perform reusable laboratory skills such as picking, placing, uncapping, loading, aligning, and transferring samples.

Robotic Sample Transportation

Quadruped Sample Delivery: Exploring legged robots for transporting samples across laboratories, hallways, stairs, and uneven indoor environments.
Secure Payload Handling: Designing sample carriers that preserve orientation, reduce vibration, and maintain traceability during robotic transport.
Multi-Floor Laboratory Navigation: Planning robot routes that account for stairs, doors, elevators, restricted areas, and handoff points between workstations.
Chain-of-Custody Tracking: Connecting barcodes, RFID, or vision-based sample identification with automated experiment logs.
Human-Robot Lab Coordination: Developing safe interaction protocols for robots operating around researchers, instruments, and sensitive materials.

Automated Experiment Execution and Analysis

Instrument Integration: Connecting robotic arms, liquid handlers, incubators, microscopes, spectrometers, and environmental sensors into a coordinated experimental workflow.
Automated Sample Preparation: Standardizing preparation steps such as dispensing, mixing, incubation, transfer, and measurement setup.
Real-Time Experimental Monitoring: Tracking instrument status, sample condition, environmental variables, and abnormal events during autonomous operation.
AI-Based Data Interpretation: Applying machine learning to analyze imaging, spectroscopy, sensor streams, assay outputs, and experimental metadata.
Closed-Loop Optimization: Using experimental results to update hypotheses, refine protocols, and select the next set of experimental conditions.

System Design

The proposed system is structured as an embodied self-driving lab pipeline. An AI planning module selects experimental objectives and schedules tasks, robotic manipulators execute local operations, a quadruped robot transports samples between physical stations, and analytical instruments produce measurements that are automatically processed by data models.

The laboratory operating layer coordinates these components by managing task queues, robot availability, sample identity, instrument readiness, safety constraints, and data provenance. This architecture allows the platform to operate as a connected experimental ecosystem rather than a collection of independent automation devices.

Current Implementation

At the current stage, this project is structured around modular robotic capabilities rather than a fully autonomous dark lab. The initial focus is on developing robot manipulation policies, tactile sensing modules, sample transportation strategies, and software interfaces for experimental monitoring.

The near-term implementation will evaluate how dexterous manipulators and quadruped robots can coordinate with automated instruments for sample preparation, transport, analysis, and data logging. This stage is designed to establish robust physical execution before expanding toward more complex closed-loop scientific workflows.

Future Research Directions

Future work will extend the platform toward supervised robotic experimentation in biological and biomedical settings. One direction is to integrate surgical robotic systems with automated sample handling, imaging, physiological monitoring, and post-experiment analysis.

The long-term vision is to connect in vitro experiments, animal studies, and in vivo experimental workflows through a unified robotic pipeline. In this framework, surgical robots would support controlled intervention or sample acquisition, mobile robots would manage sample transfer, analytical systems would process measurements, and AI models would monitor outcomes under strict ethical, safety, and regulatory constraints.

This direction is not intended to replace scientific oversight. Instead, the goal is to develop a supervised autonomous experimentation system that improves precision, reproducibility, traceability, and continuity across the full experimental cycle.

Join Our Team

Mon, 01 Jun 2026 00:00:00 +0000

Vision-Language-Action Models for Quadruped and Humanoid Robots

Sat, 30 May 2026 00:00:00 +0000

Project Mission

This project develops Vision-Language-Action models for quadruped and humanoid robots. We focus on building embodied AI systems that can understand visual scenes, interpret natural-language instructions, reason about physical environments, and generate robot-specific actions for legged mobility, whole-body movement, and long-horizon planning.

The goal is to move beyond isolated perception, navigation, or control modules toward a unified robot intelligence framework. In this method, quadruped robots and humanoid robots use vision-language reasoning to understand what is happening in the environment, decide what actions are physically possible, and execute specific tasks.

Scientific Motivation

Recent advances in vision-language models (VLMs) have shown strong capabilities in recognizing objects, describing scenes, and reasoning over images and text. However, robots require more than visual reasoning. They must connect perception and language to physical action. A robot must know not only what an object is, but also whether it can approach it, grasp it, avoid it, open it, move it or use it as part of a larger task.

Quadruped and humanoid robots make this problem especially important. A quadruped robot may be able to traverse stairs, uneven terrain, narrow passages, and large indoor or outdoor spaces, but it has limited manipulation capability. A humanoid robot may be able to open doors, operate tools, pick up objects, and interact with human-designed environments, but it requires more complex whole-body balance, motion planning, and manipulation control. The same language instruction may therefore require different interpretations depending on the robot body.

For example, the instruction “check the object on the upper shelf” may require a quadruped robot to navigate to the area, inspect the shelf from multiple viewpoints, and report the object state. For a humanoid robot, the same instruction may involve walking to the shelf, adjusting body posture, reaching with an arm, grasping the object, and possibly relocating it. A general VLA system must understand both the shared task meaning and the embodiment-specific action requirements.

Research Approach

VLA Robot Intelligence

Language-Guided Visual Understanding: Developing models that ground natural-language instructions in robot camera observations, object locations, spatial relations, and task-relevant visual cues.
Embodied Action Reasoning: Connecting visual-language understanding to physically executable robot behaviors such as walking, turning, inspecting, reaching, grasping, and interacting.
Long-Horizon Task Decomposition: Translating high-level human instructions into structured action sequences that can be executed over time by quadruped or humanoid robots.
Affordance-Aware Perception: Estimating what can be walked through, avoided, climbed, reached, grasped, opened, moved, or manipulated from visual observations.
Failure-Aware VLA Control: Detecting when a task cannot be completed from the current observation and triggering replanning, additional perception, or alternative robot actions.

Quadruped Robot Intelligence

Language-Guided Locomotion: Enabling quadruped robots to follow natural-language commands for navigation, inspection, search, and spatial exploration.
Terrain-Aware Navigation: Combining visual perception, proprioception, and environmental cues to move across stairs, slopes, cluttered spaces, narrow paths, and uneven terrain.
Active Visual Inspection: Allowing quadruped robots to move their body and camera viewpoint to inspect objects, rooms, structures, obstacles, or uncertain regions.
Semantic Exploration: Mapping and exploring environments not only by geometry, but also by object categories, room functions, landmarks, and task-relevant regions.
Human-Centered Mobility: Developing navigation behaviors that allow quadruped robots to move safely around people, furniture, doors, corridors, and dynamic obstacles.

Humanoid Robot Intelligence

Whole-Body VLA Control: Developing VLA models that connect language and visual perception to coordinated head, torso, arm, hand, and leg movements.
Bimanual Manipulation: Training humanoid robots to use both hands for object handling, tool use, carrying, opening, pushing, pulling, and coordinated manipulation.
Human-Scale Environment Interaction: Enabling humanoids to interact with doors, handles, shelves, switches, cabinets, tables, chairs, tools, and other objects designed for human bodies.
Posture and Balance-Aware Action: Generating actions that account for center of mass, reachability, foot placement, support surfaces, and whole-body stability.
Socially Situated Robot Action: Studying how humanoid robots should move, gesture, approach, hand over objects, and interact with people in shared spaces.

SLAM and Spatial Understanding

Semantic SLAM for VLA Robots: Using SLAM as one component of the VLA robot system to support localization, spatial memory, object mapping, and long-horizon task execution.
Language-Conditioned Map Querying: Connecting language expressions such as “the room near the stairs,” “the object behind the table,” or “the door on the left” to map-based spatial representations.
Multi-View Scene Understanding: Combining observations from different robot viewpoints to improve object localization, scene reconstruction, and environmental awareness.
Map-Based Task Planning: Using spatial maps to support navigation, inspection, object search, path planning, and task sequencing for both quadruped and humanoid robots.
Dynamic Map Updates: Updating spatial and semantic maps when objects move, doors open, paths become blocked, or robot actions change the environment.

Current Implementation

At the current stage, this project focuses on defining the VLA framework for quadruped and humanoid robot intelligence. The initial implementation is organized around robot vision, natural-language instruction grounding, semantic mapping, and embodiment-specific action generation.

For quadruped robots, the near-term focus is on language-guided navigation, active visual inspection, scene exploration, terrain-aware movement, and semantic SLAM integration. These capabilities provide a foundation for robots that can move through complex environments while interpreting instructions in relation to physical space.

For humanoid robots, the near-term focus is on vision-language-guided reaching, object interaction, whole-body task execution, and manipulation-oriented scene understanding. These capabilities provide a foundation for robots that can operate in human-designed environments and perform tasks requiring arms, hands, posture control, and physical interaction.

Future Research Directions

Future work will extend this project toward general-purpose VLA robot systems that can operate across different embodiments, environments, and task domains. One direction is to develop shared robot foundation models that can interpret language and vision across quadruped and humanoid platforms while producing actions through embodiment-specific control heads.

A further direction is to build simulation-to-real training pipelines for VLA robot policies. Simulation can provide diverse environments, rare failure cases, and scalable task variations, while real-robot experiments can test whether the learned policies remain robust under physical constraints, sensor noise, contact uncertainty, and dynamic environments.

Molecular Dynamics for Semiconductor Phonon and Thermal Dynamics

Fri, 29 May 2026 00:00:00 +0000

Project Mission

This project develops molecular dynamics methods for understanding semiconductor materials at finite temperature. We focus on how atomic motion, lattice vibrations, and phonon damping influence thermal transport, optoelectronic stability, and device-relevant material behavior.

The goal is to build Hamiltonian- and DFT-informed simulation workflows that connect atomistic trajectories with semiconductor performance, especially for perovskites and optoelectronic materials.

Scientific Motivation

Semiconductor behavior is not determined only by static band structures. Real materials operate under temperature, strain, illumination, interfaces, defects, and structural disorder. These conditions create dynamic atomic environments where phonon scattering, lattice anharmonicity, carrier relaxation, defect migration, and interfacial heat transfer can strongly affect semiconductor device performance.

Molecular dynamics provides a direct way to study these time-dependent processes. Instead of only predicting isolated material properties, this project investigates how semiconductor structures evolve, dissipate energy, and respond to thermal perturbations over time.

Research Approach

Hamiltonian-Informed Molecular Dynamics

Ab Initio Molecular Dynamics: Using DFT-based forces to simulate atomic motion without relying only on empirical force fields.
Hamiltonian Modeling: Constructing electronic-structure-aware representations that connect atomic configurations with energy landscapes, forces, and dynamical responses.
Born-Oppenheimer Dynamics: Simulating nuclear trajectories on electronic ground-state potential energy surfaces for finite-temperature materials analysis.
Car-Parrinello Dynamics: Exploring coupled electron-ion propagation for efficient first-principles molecular dynamics.
Trajectory-Based Electronic Analysis: Evaluating how band edges, local structure, bonding environments, and defect states fluctuate along atomic trajectories.

Phonon Dynamics and Damping

Phonon Lifetime Analysis: Estimating how long vibrational modes persist before scattering or dissipating energy.
Phonon Damping in Semiconductors: Studying damped lattice vibrations caused by anharmonicity, defects, disorder, and electron-phonon interactions.
Thermal Conductivity Modeling: Connecting phonon transport and heat dissipation to semiconductor device reliability.
Anharmonic Lattice Dynamics: Modeling non-harmonic atomic motion that becomes important at finite temperature and under structural instability.
Hot-Carrier and Energy Relaxation: Investigating how lattice vibrations mediate energy loss in photovoltaic materials.

Semiconductor and Perovskite Materials

Halide Perovskites: Studying soft lattice motion, ion migration, octahedral tilting, phonon broadening, and thermal instability.
Optoelectronic Semiconductors: Modeling materials used in solar cells, LEDs, photodetectors, and light-emitting devices.
Defect Dynamics: Simulating how vacancies, interstitials, grain boundaries, and local disorder evolve under thermal conditions.
Interface Stability: Investigating atomic-scale behavior at semiconductor interfaces, heterostructures, and contact layers.
Strain and Thermal Response: Evaluating how mechanical deformation changes phonon transport and lattice stability.

AI-Accelerated Molecular Dynamics

Machine Learning Interatomic Potentials: Training neural potentials from DFT data to extend molecular dynamics to larger systems and longer timescales.
Active Learning for Force Fields: Selecting informative atomic configurations for new DFT calculations during simulation.
Uncertainty-Aware Trajectories: Detecting when a learned potential leaves its reliable domain during semiconductor dynamics.
Graph-Based Atomic Representations: Encoding local atomic neighborhoods for transferable force and energy prediction.
Multiscale Simulation: Bridging first-principles accuracy with device-relevant length and time scales.

System Design

The proposed workflow begins with DFT and ab initio molecular dynamics calculations for representative semiconductor structures. These simulations generate atomic trajectories, forces, stresses, local electronic features, and finite-temperature structural information.

The next stage trains machine learning interatomic potentials and Hamiltonian-aware models from first-principles data. These models enable longer molecular dynamics simulations for phonon transport, damping behavior, defect motion, and interface evolution.

The analysis layer extracts physical quantities such as phonon spectra, mode lifetimes, thermal conductivity, energy dissipation pathways, defect migration barriers, and structural stability indicators. This creates a simulation pipeline that connects atomistic dynamics with semiconductor material design.

Current Implementation

At the current stage, this project focuses on building molecular dynamics workflows for semiconductor lattice dynamics and thermal transport. The initial implementation uses DFT calculations, ab initio molecular dynamics, classical molecular dynamics, and phonon analysis tools to study finite-temperature behavior in semiconductor systems.

The near-term focus is on perovskite and optoelectronic materials, where soft lattice motion, phonon damping, defect dynamics, and electron-phonon interactions are critical to material stability and performance.

Future Research Directions

Future work will extend this project toward Hamiltonian-informed and AI-accelerated molecular dynamics. One direction is to develop learned DFT Hamiltonian models that can approximate electronic-structure information along molecular dynamics trajectories more efficiently than repeated self-consistent DFT calculations.

Another direction is to combine molecular dynamics with nonadiabatic and electron-phonon coupling models to study carrier relaxation, hot-carrier cooling, and phonon-mediated recombination in semiconductors.

The long-term objective is to build a semiconductor dynamics platform that can predict how materials behave under realistic operating conditions, including temperature gradients, illumination, defects, interfaces, and mechanical strain. This platform will support the design of more stable perovskites, efficient optoelectronic materials, and thermally robust semiconductor devices.

Quantum Computing for Quantum Chemistry and Material Discovery

Fri, 29 May 2026 00:00:00 +0000

Project Mission

This project develops hybrid quantum-AI methods for quantum chemistry, drug discovery, and semiconductor materials design.
We focus on quantum neural networks and quantum-compatible learning architectures that can represent molecular interactions, electronic structures, and materials properties in ways that are
difficult to capture with conventional AI models.

The long-term goal is to connect AI-driven screening with quantum computing resources to enable more accurate and scalable discovery
of drug-like molecules and next-generation semiconductor materials, especially perovskites and optoelectronic materials.

Scientific Motivation

Drug and materials discovery often requires modeling quantum-level phenomena such as electron correlation, molecular binding, charge transport, band gaps, excited states, and defect behavior. These properties are central to molecular design and semiconductor discovery, but they are expensive to predict across large chemical spaces.

Quantum-AI hybrid methods provide a practical pathway by combining the scalability of machine learning with quantum-inspired or quantum-compatible representations. Rather than replacing classical simulation, this project uses quantum neural networks as a bridge between data-driven prediction and future quantum-enhanced computation.

Research Approach

Quantum Neural Networks

Parameterized Quantum Circuits: Designing trainable quantum circuit models for molecular and materials property prediction.
Quantum Feature Encoding: Representing molecular descriptors, features, and structure information in quantum-compatible forms.
Hybrid Quantum-Classical Learning: Combining quantum neural network layers with classical ML/DL models.
Hardware-Compatible Architectures: Developing noise-aware
QNN models that can be deployed on real quantum processors.

Quantum Chemistry for Drug Discovery

Molecular Property Prediction: Predicting stability, reactivity, toxicity features, and electronic properties of candidate molecules.
Drug-Target Interaction Modeling: Learning representations relevant to binding affinity, selectivity, and interaction patterns.
Quantum Chemistry-Informed Screening: Prioritizing drug-like compounds using electronic-structure-aware descriptors.
Reaction and Conformation Modeling: Studying molecular configurations and reaction-relevant quantum effects.

Perovskite Semiconductor Material Discovery

Band Gap Prediction: Modeling electronic properties relevant to photovoltaic and optoelectronic performance.
Perovskite Materials Design: Exploring halide, hybrid, and lead-free perovskites for stable and efficient semiconductor applications.
Defect and Interface Modeling: Understanding how atomic-scale defects influence charge transport, stability, and device behavior.
Qunatum AI-Guided Candidate Selection: Screening materials for solar cells, LEDs, photodetectors, and next-generation devices.

Hybrid System Design

The current framework is designed as a hybrid discovery pipeline. Classical AI models generate, encode, and prioritize candidate molecules, while quantum neural network components learn quantum-compatible representations for property prediction. In future stages, quantum computing modules will be introduced to support electronic-structure estimation, molecular simulation, and quantum-enhanced learning.

This structure allows the project to begin with simulator-based QNN development while keeping the model architecture compatible with future quantum hardware processors.

Current Implementation

At the current stage, this project focuses on quantum neural network models without direct use of quantum computing hardware. The initial implementation uses quantum circuit simulators and hybrid neural architectures to evaluate how QNN layers can encode molecular and semiconductor materials information.

This stage is designed to establish model architecture, data representation, training stability, and benchmark performance before moving toward hardware-based quantum computation.

Future Research Directions

Future work will integrate actual quantum computing resources into the discovery pipeline. The focus will be on making quantum neural networks more compatible with real quantum hardware by improving circuit depth, noise robustness, qubit encoding, and hybrid optimization.

The long-term direction is to build a quantum-AI discovery workflow in which classical AI models identify promising molecular and materials candidates, while quantum computing resources provide more direct support for electronic-structure modeling, molecular simulation, and quantum-enhanced property prediction.

Keynote: AI for Scientific Discovery

Sun, 15 Dec 2024 14:00:00 +0000

About the Talk

Prof. Jane Smith will deliver the opening keynote at the prestigious International AI in Science Symposium, highlighting our lab’s pioneering work in computational biology and drug discovery.

Key Topics

AI-Powered Drug Discovery

Machine learning platforms for molecular design
Accelerating clinical translation from years to months
Real-world case studies and clinical outcomes

Protein Structure Prediction

DeepFold architecture and breakthrough results
Comparison with AlphaFold and novel improvements
Applications in understanding disease mechanisms

Future of AI in Science

Emerging opportunities in materials science and climate research
Challenges in model interpretability and validation
Building interdisciplinary research partnerships

Expected Impact

This keynote will position our lab as a thought leader in AI-driven scientific research and attract potential collaborators from leading institutions worldwide.

Conference Details

Venue: Stanford University Memorial Auditorium
Date: December 15, 2024
Time: 2:00-3:00 PM PST
Audience: 500+ researchers, industry leaders, and policymakers
Format: 45-minute talk + 15-minute Q&A

Media Coverage

The talk will be livestreamed and recorded, with proceedings published in the Journal of AI in Science special issue.

Monthly Lab Seminar: Recent Advances in Quantum Materials

Wed, 20 Nov 2024 16:00:00 +0000

Seminar Overview

Our monthly lab seminar series brings together the research community to share latest findings and foster collaboration. This month, Dr. Maria Rodriguez will present groundbreaking results from our quantum materials discovery project.

About the Speaker

Dr. Maria Rodriguez is a leading researcher in computational materials science with expertise in high-throughput screening and machine learning applications to materials discovery.

What You’ll Learn

Latest advances in quantum materials prediction
Machine learning models for materials properties
Experimental validation of computational predictions
Opportunities for collaboration and future research

Who Should Attend

Graduate students and postdocs in materials science, physics, or related fields
Faculty interested in computational materials research
Industry researchers working on quantum technologies
Anyone curious about the intersection of AI and materials science

Format

Duration: 45 minutes presentation + 15 minutes Q&A
Attendance: Hybrid (in-person + virtual)
Recording: Available to registered participants
Refreshments: Coffee and pastries provided for in-person attendees

Registration

Required: Please register to receive Zoom link and calendar invitation
Cost: Free for all attendees
Contact: for questions

Hands-On Workshop: Machine Learning for Computational Biology

Fri, 08 Nov 2024 09:00:00 +0000

Workshop Overview

This intensive one-day workshop provides hands-on experience with machine learning techniques specifically designed for computational biology applications.

Learning Objectives

By the end of this workshop, participants will be able to:

Set up ML environments for biological data analysis
Implement deep learning models for protein structure prediction
Analyze genomic datasets using neural networks
Evaluate model performance and biological relevance

Schedule

9:00-10:30 AM: Introduction to ML for Biology
10:45-12:00 PM: Hands-on: Setting up DeepFold
1:00-2:30 PM: Genomic Data Analysis with Python
2:45-4:00 PM: Building Custom Neural Networks
4:15-5:00 PM: Project Presentations & Wrap-up

Prerequisites

Basic Python programming experience
Undergraduate-level biology or chemistry background
Laptop with Python 3.8+ installed
GitHub account for accessing materials

Instructors

Dr. Michael Chen - Postdoctoral researcher specializing in ML for protein structure prediction
Sarah Johnson - PhD student with expertise in genomic data analysis

Registration

Capacity: Limited to 20 participants for optimal hands-on experience
Cost: Free (materials and lunch included)
Deadline: November 1, 2024

Contact

Questions? Email or contact Dr. Michael Chen directly.

Research Facilities & Infrastructure

Mon, 20 May 2024 00:00:00 +0000

Materials Science

Fri, 12 Jan 2024 00:00:00 +0000

Overview

We design and discover materials using computational modeling, machine learning, and high-throughput simulation, validated through experimental collaborations.

Themes

Quantum materials for next-generation electronics
Energy storage materials and solid-state batteries
Catalysts for sustainable chemical processes

Collaborations

Partnerships with national labs and industry to accelerate translation from computation to synthesis.

Machine Learning

Thu, 11 Jan 2024 00:00:00 +0000

Overview

We build robust and interpretable ML models to accelerate scientific discovery across domains, with emphasis on generalization, uncertainty, and physical constraints.

Focus Areas

Physics-informed neural networks (PINNs)
Graph neural networks for structured scientific data
Interpretable models and reliability assessment

Selected Publications

See our recent work in Nature Machine Intelligence and NeurIPS.

Computational Biology

Wed, 10 Jan 2024 00:00:00 +0000

Overview

We develop algorithms and tools to analyze complex biological data at scale, from genomes to proteomes, integrating multi-omics data for systems-level understanding.

Current Projects

DeepFold: AI-powered protein structure prediction
Multi-omics integration for disease subtyping
Single-cell analysis pipelines

Resources

Datasets: Public genomics repositories and lab-curated datasets
Tools: Custom pipelines built on Python, Nextflow, and cloud computing

Contact

Mon, 01 Jan 2024 00:00:00 +0000

Our Team

Mon, 01 Jan 2024 00:00:00 +0000

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Charts

Hugo Blox supports the popular format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

Interactive Buttons

Add engaging call-to-action buttons to your data visualization posts:

Basic Buttons

Learn Plotly

{{< button url="/" >}}Contact Us{{< /button >}}

{{< button url="https://plotly.com/python/" new_tab="true" style="secondary" >}}Learn Plotly{{< /button >}}

Styled Buttons for Data Actions

View Dashboard

Download Data

View Source Code

{{< button url="#" style="primary" size="lg" align="center" icon="chart-bar" >}}View Dashboard{{< /button >}}

{{< button url="/data/results.csv" style="outline" icon="document-arrow-down" >}}Download Data{{< /button >}}

{{< button url="https://github.com/HugoBlox" new_tab="true" style="ghost" icon="arrow-top-right-on-square" icon_position="right" >}}View Source Code{{< /button >}}

Multiple Aligned Buttons

Try Jupyter

Open in Colab

{{< button url="https://jupyter.org/" new_tab="true" style="secondary" rounded="full" align="center" >}}Try Jupyter{{< /button >}}

{{< button url="https://colab.research.google.com/" new_tab="true" style="primary" rounded="full" align="center" icon="rocket-launch" >}}Open in Colab{{< /button >}}

👩🏼‍🏫 Teach academic courses

Tue, 24 Oct 2023 00:00:00 +0000

is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Citation

Here’s an example of citing a publication using the cite shortcode:

Me (2019). An example preprint / working paper.

Preprint Code Slides Dataset Poster Source Document Video Custom Link Preprint

You can also use the default view by omitting the view parameter:

Equal contribution

, Robert ford

Equal contribution

(2013). An example conference paper. In ICW.

PDF Code Dataset Slides Source Document Video PDF DOI

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube D2vj0WcvH5c >}}

Bilibili:

{{< bilibili BV1WV4y1r7DF >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your , and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. Enable math by setting the math: true option in your page’s front matter, or enable math for your entire site by toggling math in your config/_default/params.yaml file:

features:
 math:
 enable: true

To render inline or block math, wrap your LaTeX math with $...$ or $$...$$, respectively.

Example math block:

$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math $\nabla F(\mathbf{x}_{n})$ renders as $\nabla F(\mathbf{x}_{n})$.

Example multi-line math using the math linebreak (\\):

$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

python Python

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- Hugo Blox
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

An example preprint / working paper

Sun, 07 Apr 2019 00:00:00 +0000

This work is driven by the results in my on LLMs.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

An example journal article

Tue, 01 Sep 2015 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

An example conference paper

Mon, 01 Jul 2013 00:00:00 +0000

Note

Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software.

Note

Create your slides in Markdown - click the Slides button to check out the example.

Add the publication’s full text or supplementary notes here. You can use rich formatting such as including .

Mon, 01 Jan 0001 00:00:00 +0000

Publications Maintenance Guide

This folder contains the content and data used for the Publications page.

Files

- Controls the page title and the short introduction shown at the top of the Publications tab.
- This is the main source of truth for the publications list.
- Add, edit, or remove publications here.
- These are sample/template content files from Hugo Blox.
- They are not the source used for the current custom publications list.

Rendering Template

- This file controls how the publications page is rendered.
- It reads and formats the list on the page.
- Keep this file in layouts/publications/. Hugo expects templates there.

How To Update Publications

Open .
Add a new item under publications:.
Save the file and rebuild/run Hugo.

Example:

publications:
 - type: C
 title: "Example Conference Paper"
 link: "https://example.com"
 authors: "Author A, Dong-Hee Shin*, Author B"
 venue: "Example Conference, 2026"
 note: "Oral presentation"
 subnote: "Best paper nominee"

Field Meaning

type
- J = Journal
- C = Conference
- Any other value is treated as preprint/other
title
- Paper title
link
- Paper URL, DOI, PDF, or publisher page
authors
- Author list as a single string
- <br> can be used if you want a manual line break
- * is rendered as corresponding-author mark
venue
- Journal or conference name, usually ending with the year
note
- Green highlight badge
subnote
- Secondary outline badge

Important Notes

The current publications page does not read from data/full_publications.yaml.
The current publications page does not build its list from the sample journal-article or conference-paper entries.
The list is driven by only.