
Use ChatGPT to generate topics, refine headings, and organize literature for an AI-focused course, including a case study on generative AI in structured finance.
Learn to use essential publishing tools—texstudio for LaTeX, arrow for reference, and git—plus Zotero basics, chart creation, template conversion, and conference and journal search.
Master collaborative publishing with LaTeX, Python, Zotero, and GitHub by learning bibliography libraries, version control, and notebook workflows to publish papers, journals, books, and articles quickly.
Explore this intro level profile building strategies course for EB2 NIW and EB1A. Learn how to prepare a strong application, including papers and citations, across 39–41 lectures and quizzes.
Identify topics at the intersection of Gen AI and big data for financial risk and outline literature review approaches, case studies, and surveys using PySpark and MapReduce.
Explore how ChatGPT maps topics, subtopics, models, and code for structured finance, using Gen AI to create synthetic data, stress tests, and real-time risk insights.
Use ChatGPT to craft an academic title, refining the synergy of gen ai and big data for financial risk applications and eb2 niw alignment.
Learn to perform literature reviews with chatgpt, organize references by date, generate flow and gap diagrams from a bib file, and create ieee-compatible latex code.
Fix a paper created by deep sea perplexity and Google Gemini by merging code, reorganizing sections, and addressing hallucinations with literature review and mitigation strategies.
Manually fix hallucinated keys and missing citations in a literature review, using JNI tools, and organize sections with 5–6 subsections while verifying topics like fixed income and SEC rules.
Fix the paper introduction and keywords using gen ai latex code, retaining artificial fixed income generative data as core keys and converting abstract sections into a reader-friendly introduction.
Export and clean a bib file from Zotero using deep seq, convert to APA or IEEE style, and generate a LaTeX boilerplate with cited keys, while avoiding hallucinations.
Learn how to merge gen ai generated latex sections and subsections into balanced 3–5 bullet point blocks, organizing introduction, analytics, risk management, and ai applications in fixed income.
Explore effective prompts for literature review with ChatGPT, grouping references, extracting quantitative data, and generating LaTeX code and BibTeX in three technical categories.
Group papers, pull numbers, and generate code for a literature review using ChatGPT, with prompts that categorize references, extract findings, and preserve LaTeX formatting.
Organizing reference and literature review created by ChatGPT by headings, grouping, quantitative info and future scope
Learn to use ChatGPT to find journals, verify links, compare price ranges, and curate data to identify three journals under €500 for information science and AI applications.
Course in New York City
Using Chat GPT for Research Papers for EB1A Profile Building
Accelerated Build your profile for EB2 NIW and EB1A
About the Course
Introduction:
This Course focuses on how to use ChatGPT for Research paper writing.
ChatGPT is very important part of literature review.
It can be also used for error handling for software installation.
It can be also a co-pilot.
This is a handon course feel free to pause where needed.
Once we search the papers we have import it in zotero and then export the biblatex and remove the abstract.
We also will show how we can still use the free ChatGPT version.
Using Chat GPT and Gen AI for Research, Review and Results
Using Copilot notebook for pycharm and ChatGPT
Copilot in Jupiter notebooks running In pycharm is going to make things lot easier
Using Chat GPT and Gen AI for Research, Review and Results
Using ChatGpt to learn do fix coding error, how to use chatgpt as copilot for your reserach journey.
No Experience Needed
Conclusion:
With new ideas about ChatGPT now we can accelerate our research.
We found that we need 4-6 prompts to get to the right code.
Width in two columns remains a big prob.
With right prompts we can extract the numbers and quant output form the paper but need to be verified.
Future work can be adding copilot on vscode.