You will get AI research mentoring for deep learning projects and papers
Top Rated
Project details
You will get practical, PhD-level research mentoring for deep learning projects and papers.
I help students, researchers, labs, and startups move from vague ideas or stuck experiments to a clearer research plan and concrete next actions. This project is designed for people who need strong research judgment plus hands-on ML understanding, not generic tutoring.
I can support literature review, problem formulation, experiment design, baseline and ablation planning, PyTorch debugging, result interpretation, paper polishing, and rebuttal feedback. My background is in computer vision, diffusion models, large language models, and workflow-oriented AI systems, and I have supported more than 10 research clients on advanced AI projects.
What sets this project apart is that the feedback is structured and action-oriented. I do not give generic comments. I review your current stage, identify the main technical risks, and return clear written guidance tailored to your goals, constraints, and package level. Whether you need an idea audit, an experiment roadmap, or a deeper review of your drafts, results, or code, the goal is always the same: help you make faster, better research decisions.
I help students, researchers, labs, and startups move from vague ideas or stuck experiments to a clearer research plan and concrete next actions. This project is designed for people who need strong research judgment plus hands-on ML understanding, not generic tutoring.
I can support literature review, problem formulation, experiment design, baseline and ablation planning, PyTorch debugging, result interpretation, paper polishing, and rebuttal feedback. My background is in computer vision, diffusion models, large language models, and workflow-oriented AI systems, and I have supported more than 10 research clients on advanced AI projects.
What sets this project apart is that the feedback is structured and action-oriented. I do not give generic comments. I review your current stage, identify the main technical risks, and return clear written guidance tailored to your goals, constraints, and package level. Whether you need an idea audit, an experiment roadmap, or a deeper review of your drafts, results, or code, the goal is always the same: help you make faster, better research decisions.
Machine Learning Tools
BERT, ChatGPT, Deeplearning4j, GPT-3, NVIDIA AI Platform, OpenCV, Python, PyTorch, Stanford CoreNLP, TensorFlow, Word2vecWhat's included
| Service Tiers |
Starter
$99
|
Standard
$229
|
Advanced
$449
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 0 | 0 | 1 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$29 - $99
Additional Revision
+$39
Additional Model Variation
(+ 1 Day)
+$79
Source Code
(+ 2 Days)
+$149
Paper Polishing
(+ 2 Days)
+$99
Rebuttal Support
(+ 2 Days)
+$99Frequently asked questions
5 reviews
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AA
Ahmed A.
May 6, 2026
AI generated image detection
Good mentor and well-written report
AA
Ahmed A.
Jan 6, 2026
AI generated image detection
KV
Kunal V.
Aug 16, 2025
Research Paper Publish (ML, LLM)
MM
Muhammad Ahmed M.
Jan 2, 2025
Model Development for Detecting AI-Generated Text
BS
Ben S.
Nov 1, 2024
ML Engineer for Training Models
Liu did an incredible job of this project. His communication was always great, and he went above and beyond to complete a complex task that had many different variables. I will be working with Liu again on several new projects.
About Liu
Diffusion & CV ML Engineer | AI Research Mentor | LLM Workflows
100%
Job Success
Hefei, China - 5:06 pm local time
I help students, researchers, labs, and startups build deep learning projects that require both strong research judgment and solid implementation. My work covers literature review, problem formulation, experiment design, training and debugging, performance optimization, paper polishing, and rebuttal support. I am particularly comfortable with diffusion models, controllable generation, image/video editing, radiology report generation, and LLM-based workflows.
My publications include work in AAAI, IEEE Transactions on Multimedia, IEEE Transactions on Image Processing, and IEEE Transactions on Circuits and Systems for Video Technology. I have also privately mentored more than 10 clients on research projects targeting venues such as CVPR, ICML, AAAI, NeurIPS, TIP, and TMM. This background helps me judge what is technically sound, what is novel enough, and what is worth investing time in.
I also maintain public open-source resources in generative AI and computer vision, with 2.1k+ total GitHub stars across my repositories. My GitHub profile is under the name AlonzoLeeeooo, where you can find curated resources and project code related to text-to-image generation, video generation, image inpainting, and diffusion-based editing.
Representative work you can search by title includes:
- Bootstrapping Large Language Models for Radiology Report Generation
- Toward Interactive Image Inpainting via Robust Sketch Refinement
- LaCon: Late-Constraint Controllable Visual Generation
- StableV2V: Stabilizing Shape Consistency in Video-to-Video Editing
- A Systematic Review of Deep Learning-based Research on Radiology Report Generation
You can also find my full publication list by searching my Google Scholar profile under Chang Liu (USTC).
If you have a project in mind, send me your topic, current stage, and target outcome, and I can help define the most practical next step.
Steps for completing your project
After purchasing the project, send requirements so Liu can start the project.
Delivery time starts when Liu receives requirements from you.
Liu works on your project following the steps below.
Revisions may occur after the delivery date.
Review your topic and current stage
I first review your research goal, current progress, and constraints so I can focus on the most useful support: idea audit, literature review, experiment planning, debugging, or paper feedback.
Identify the main technical risks
I look for unclear assumptions, missing baselines, weak evaluation design, implementation bottlenecks, or writing issues that may slow down your progress.