{"id":18734471,"url":"https://github.com/callmequant/callmequant","last_synced_at":"2026-01-24T05:35:32.821Z","repository":{"id":157651362,"uuid":"633582186","full_name":"CallmeQuant/CallmeQuant","owner":"CallmeQuant","description":null,"archived":false,"fork":false,"pushed_at":"2023-09-13T14:36:55.000Z","size":10,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-09-14T04:51:10.640Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CallmeQuant.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-04-27T20:34:54.000Z","updated_at":"2023-09-13T14:32:36.000Z","dependencies_parsed_at":"2024-08-21T03:03:10.919Z","dependency_job_id":null,"html_url":"https://github.com/CallmeQuant/CallmeQuant","commit_stats":null,"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CallmeQuant%2FCallmeQuant","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CallmeQuant%2FCallmeQuant/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CallmeQuant%2FCallmeQuant/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CallmeQuant%2FCallmeQuant/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CallmeQuant","download_url":"https://codeload.github.com/CallmeQuant/CallmeQuant/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239606097,"owners_count":19667168,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-07T15:13:30.903Z","updated_at":"2026-01-24T05:35:32.802Z","avatar_url":"https://github.com/CallmeQuant.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"## Hello 👋\n\nI am an independent researcher working across **mathematical statistics**, **machine learning**, and **time-series analysis**.\nMy work focuses on developing and analyzing methods that blend **rigorous statistical theory** with **modern computational modeling**, particularly for high-dimensional or structured data.\n\nI am interested in collaborations involving **theoretical analysis**, **modeling frameworks**, and **computational methodology** motivated by real-world complex data.\n\n---\n\n## 🔬 Research Interests\n\n### **Statistical Methodology**\n\n* High-dimensional statistics\n* Model selection and regularization\n* Nonparametric and semiparametric inference\n* Missing-data mechanisms (MAR/MNAR)\n* Robustness and contamination models\n\n### **Learning Theory \u0026 Generative Modeling**\n\n* Empirical processes, concentration, and generalization\n* Approximate inference: variational Bayes, MCMC, stochastic approximations\n* Deep generative models: flows, diffusion models, energy-based models\n\n### **Time-Series \u0026 Stochastic Systems**\n\n* State-space models (linear, nonlinear, SSMs, SDEs)\n* Diffusion-based forecasting\n* Representation learning for sequential data\n\n---\n\n## 🧰 Technical Stack\n\n**Languages:**\n![Python](https://img.shields.io/badge/-Python-333333?style=flat\\\u0026logo=python)\n![R](https://img.shields.io/badge/-R-333333?style=flat\\\u0026logo=R)\n![Julia](https://img.shields.io/badge/-Julia-333333?style=flat\\\u0026logo=julia)\n\n**Computational Frameworks:**\n![PyTorch](https://img.shields.io/badge/-PyTorch-333333?style=flat\\\u0026logo=pytorch)\n![JAX](https://img.shields.io/badge/-JAX-333333?style=flat\\\u0026logo=jax)\n![Scikit-Learn](https://img.shields.io/badge/-Sklearn-333333?style=flat\\\u0026logo=scikit-learn)\n\n**Probabilistic Programming:** NumPyro - Pyro - PyMC3\n\n---\n\n## 📘 Research Perspective\n\nMy work aims to build connections between:\n\n* **Statistical theory** (empirical processes, concentration, asymptotics)\n* **Modeling and computation** (variational inference, SDEs, diffusion-based models)\n* **Applications** where structure and uncertainty play a central role (biostatistics, forecasting, high-dimensional regimes)\n\nI am particularly motivated by methodologies that offer **both statistical guarantees and practical applicability**, and I enjoy collaborations where theory, computation, and application inform each other.\n\n---\n\n## 📫 Contact\n\n[![Linkedin](https://img.shields.io/badge/linkedin-black?logo=Linkedin\\\u0026logoColor=white)](https://www.linkedin.com/in/binh-ho-899390193/)\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcallmequant%2Fcallmequant","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcallmequant%2Fcallmequant","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcallmequant%2Fcallmequant/lists"}