Complete Guide to 2025 AI Conference Participation and Paper Submission: Essential Tips Every AI Researcher Should Know

The Complete 2025 Guide to AI Conference Participation and Paper Submission: Essential Tips Every AI Researcher Should Know

If you are conducting research in artificial intelligence (AI) or want to keep up with the latest trends, understanding how to participate in AI conferences and the paper submission process is absolutely essential. In 2025, AI technologies are driving innovation across industries and academic interest is at an all-time high. This guide provides a detailed overview of 2025 AI conferences and step-by-step instructions for submitting your research, helping you effectively share your work and expand your academic network.

1. Noteworthy AI Conferences and Events in 2025

For AI researchers, staying aware of major domestic and international AI conferences is crucial. In 2025, a wide range of AI events are being held worldwide, serving as vibrant platforms for sharing the latest research results and industry applications.

• ICLR 2025 (International Conference on Learning Representations)
  One of the most influential conferences in deep learning and machine learning, ICLR 2025 will be held in April at Singapore Expo. Expect innovative research presentations, including AI-based drug discovery.
• AI EXPO KOREA 2025
  Held in May at COEX in Seoul, this is Korea’s largest AI exhibition, featuring about 350 companies showcasing AI solutions and convergence technologies.
• Korean Operations Research and Management Science Society (KORMS) Spring Conference (June 18–21, 2025, Jeju Haevichi Hotel)
  Focused on data-driven decision-making and AI-driven industry innovation, this conference accepts both paper abstracts and presentation materials (PPT) for submissions[1].

Other notable global AI events include AWS Summit Seoul and AI4 2025 in Las Vegas, USA, which continue throughout 2025.

2. How to Submit a Paper to an AI Conference: Step-by-Step Guide

The process of submitting a paper to an AI conference is systematic and rigorous. Below is a summary of the 2025 paper submission process for major Korean AI conferences.

2-1. Preparing and Writing Your Paper

• Selecting a Topic
  AI conferences generally prefer topics related to the latest trends, such as generative AI, machine learning, AI ethics, data fusion, and AI service innovation. Clearly state your research motivation and purpose, and ensure you reference the latest literature.
• Adhering to Submission Guidelines
  Always check the conference’s paper format and submission guidelines. For example, KORMS requires a clear abstract (within 300 characters), author names, affiliations, contact information, and presentation field, and also accepts full papers or presentation materials (PPT) for submission[1].
• Utilizing AI Tools
  Use AI-based writing tools like ChatGPT to efficiently draft, structure, and proofread your paper. However, always follow ethical guidelines and ensure that all AI-generated content is reviewed and edited by the researcher[2].

2-2. Submission and Review Process

• Abstract and Full Paper Submission
  Most conferences require an abstract submission, followed by a full paper if your abstract is accepted. For example, KORMS requires abstracts by April 25 and full papers or presentation materials by May 14[1].
• Review and Feedback
  Submitted papers are reviewed by experts, who evaluate originality, reproducibility, logical structure, and up-to-date literature references.
• Final Submission and Presentation Preparation
  Accepted papers must submit a final manuscript and prepare materials for presentation at the conference. Presentation times are typically limited to about 15 minutes.

3. Essential Checklist for Writing an AI Research Paper

To successfully submit a paper to an AI conference, be sure to check the following points.

• Clear Research Question and Motivation
  Clearly explain why your research is important and what problem it aims to solve.
• Transparent Methodology
  Describe your data collection, experiment design, and analysis methods in detail, but keep it concise and avoid unnecessary information.
• Strong Results Interpretation and Discussion
  Go beyond simply listing data; connect your results to your research questions and analyze them in depth, clearly stating any limitations.
• Up-to-Date References
  Reference the latest AI research trends and literature to enhance the credibility of your work.
• Compliance with Submission Guidelines
  Strictly follow the conference’s paper template and formatting requirements to minimize the risk of rejection[1].

4. Practical Tips for Successful AI Conference Paper Submission

• Peer Review
  Have your draft reviewed by your advisor or fellow researchers to improve its quality.
• Stick to Deadlines
  Strictly adhere to submission deadlines and allow enough time to prepare your presentation.
• Appropriate Use of AI Tools
  Use AI tools like ChatGPT for drafting and editing, but always review and verify all content yourself[2].
• Understand Conference Characteristics
  Research each conference’s topic preferences, review criteria, and presentation format in advance to tailor your submission accordingly.

5. Conclusion: Start Preparing for AI Conference Participation and Paper Submission Now

In 2025, the AI field is growing faster than ever. AI conferences are the best platforms for sharing your latest research, expanding your network, and making your work known to the world. By understanding the paper submission process and conference schedules in advance, you can strengthen your position as a researcher.

Check the 2025 AI conference schedules that interest you and start selecting topics and writing your papers now. We support your journey as a leader in AI research!

This post is based on the latest 2025 AI conference schedules and paper submission guidelines.

References

• 2025 Spring Joint Conference Paper Submission Guidelines – Korean Operations Research and Management Science Society (KORMS)
• Ethical Use of ChatGPT in Academic Writing: 2025 Guidelines – Editverse
• 2025 Korea Software Engineering Conference Call for Papers
• Guidelines for Using ChatGPT and Other LLMs in University Research and Thesis Writing (2025)

Tags

AI Conference, Paper Submission, AI Research, Artificial Intelligence Conference, AI Paper, Academic Writing, AI Conference Tips, Paper Review, Academic Conference, AI Trends, Research Guidance, Conference Presentation, Paper Preparation

Understanding DevOps, MLOps, ModelOps, DataOps, and AIOps with Real-World Workflows

In today’s fast-moving tech landscape, Ops-related practices like DevOps, MLOps, ModelOps, DataOps, and AIOps are more than just buzzwords—they're essential frameworks for automating operations, improving efficiency, and maintaining governance across software, data, and AI systems. Each “Ops” serves a distinct purpose depending on the domain, from code deployment to model lifecycle management and infrastructure automation.

🔧 DevOps Workflow & Real-World Use Case

📊 Workflow Diagram:

[Code] → [Build] → [Test] → [Release] → [Deploy] → [Operate] → [Monitor]

CI/CD tools: Jenkins, GitHub Actions, GitLab CI
Monitoring tools: Prometheus, Grafana

💼 Use Case: Fintech App Feature Deployment

• Developers push new code to Git
• Jenkins triggers automatic build and unit testing
• Code is deployed to a QA server and then production using Blue/Green deployment
• Grafana and Prometheus monitor error logs and traffic in real-time
• Multiple releases per day become possible using CI/CD pipelines

🤖 MLOps Workflow & Real-World Use Case

📊 Workflow Diagram:

[Data Prep] → [Model Train] → [Model Validation] → [Model Registry] → [Model Deployment] → [Monitor & Re-train]

Key tools: MLflow, Airflow, SageMaker, Kubeflow, Feast

💼 Use Case: Auto Finance Credit Risk Model

• Data pipeline built using Airflow and Spark
• Model trained with XGBoost, tracked using MLflow
• Validated models deployed via SageMaker Endpoints
• Performance metrics (KS, AUC) continuously monitored
• If model degradation is detected, automatic retraining is triggered

🧾 ModelOps Workflow & Real-World Use Case

📊 Workflow Diagram:

[Model Development] → [Independent Validation] → [Approval Committee] → [Production Release] → [Monitoring & Governance]

Key tools: ModelOp Center, IBM Watson OpenScale
Focus: Governance, documentation, regulatory compliance (e.g., SR11-7, KSOX)

💼 Use Case: Loss Forecasting in Financial Institutions

• Models developed in Python/SAS with clear documentation
• Independent Model Risk team performs validation (KS, stress testing)
• Results submitted to Risk Committee for approval
• Version control managed via Git and SharePoint
• Production results are matched against UAT to ensure alignment

🔄 DataOps Workflow & Real-World Use Case

📊 Workflow Diagram:

[Ingest] → [Transform] → [Validate] → [Publish] → [Monitor]

Key tools: dbt, Airflow, Apache Nifi, Snowflake, Great Expectations

💼 Use Case: Real-Time Customer Behavior Analysis

• Events collected using Kafka → stored in Snowflake
• Data transformation performed using dbt
• Data validation using Great Expectations
• Published to BI tools like Tableau or Looker
• Failures in DAGs trigger Slack alerts to data engineering team

📡 AIOps Workflow & Real-World Use Case

📊 Workflow Diagram:

[Log/Metric Collection] → [Anomaly Detection] → [Root Cause Analysis] → [Automated Remediation] → [Feedback Loop]

Key tools: DataDog, Splunk, Dynatrace, Moogsoft

💼 Use Case: Cloud Infrastructure Monitoring for SaaS

• Logs collected via ELK Stack and DataDog
• AI models (e.g., LSTM, Isolation Forest) detect anomalies in system metrics
• CPU or memory threshold breaches trigger alerts and automated scaling
• Root cause reports automatically generated
• Feedback used to improve future alerting models

🔚 Summary: Ops Comparison Table

Ops Type	Core Focus	Main Users	Example Tools
DevOps	Code to service delivery	Dev & QA teams	Jenkins, GitHub Actions
MLOps	ML lifecycle automation	Data Science & Eng	MLflow, Airflow, SageMaker
ModelOps	Governance & compliance	MRM, Risk, Strategy	ModelOp Center, OpenScale
DataOps	Data pipeline automation	Data engineers, analysts	dbt, Airflow, Snowflake
AIOps	IT anomaly detection	Cloud/IT Ops teams	Splunk, Dynatrace, DataDog

As technology stacks grow more complex, embracing the right "Ops" strategy can dramatically boost performance, agility, and governance. Whether you're building models, deploying code, or monitoring infrastructure, these frameworks bring structure and efficiency to every stage of the lifecycle.

Easy Guide to LLM, RAG, MCP

🔍 Easy Guide to LLM, RAG, MCP (With Real-World Analogies)

What do terms like LLM, RAG, and MCP actually mean? Here’s a simple breakdown using real-life analogies so even non-tech readers can understand.

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✅ 1. LLM (Large Language Model)

🧠 Analogy: A super-smart librarian who has read thousands of books.

An LLM is an AI trained on a huge amount of text—books, articles, websites. It answers questions based on patterns it learned, without using external info. It uses neural networks and NLP techniques to generate the most likely response.

• Trained on massive datasets (Wikipedia, books, forums, etc.)
• Answers only with what it learned during training
• Recent trend: SLM (Small Language Models) for specific industries like healthcare or finance

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✅ 2. RAG (Retrieval-Augmented Generation)

🔍 Analogy: A librarian who not only remembers books but also Googles or searches PDFs in real-time.

RAG models enhance LLMs by pulling live data from external sources—PDFs, internal databases, web search—before generating a response. This allows more accurate, up-to-date answers.

• Combines pre-trained knowledge with real-time retrieval
• Useful for document Q&A, PDF summary, and web-connected AI
• Modern GPTs use RAG-like architecture for document uploads and search

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✅ 3. MCP (Model Context Protocol)

📚 Analogy: An AI assistant that remembers your past questions and continues the conversation naturally.

MCP allows AI models to retain context—your identity, previous inputs, task history—making conversations and actions more relevant and personalized. It enables long-term memory across sessions.

• Understands past conversation flows
• Improves multi-step interactions like follow-up questions or recurring tasks
• Great for automation with tools like Make or Zapier

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📊 Summary Table

Concept	Analogy	Function	Trends
LLM	Librarian with thousands of books memorized	Generates answers based on trained knowledge	SLM (Small Language Models)
RAG	Librarian + real-time searcher	Fetches live data before generating answers	Used in GPTs, PDF/website search
MCP	Memory-enabled smart assistant	Maintains context, remembers conversation history	Contextual automation, task memory

✨ Stay curious—AI is evolving fast, and understanding these concepts will help you use tools like ChatGPT more effectively!

Understanding SHAP in XAI: Game Theory

What Are SHAP Values in Explainable AI?

SHAP (SHapley Additive exPlanations) values are a cornerstone of Explainable AI (XAI), offering a transparent way to interpret complex model predictions. SHAP leverages game theory to fairly attribute the impact of each feature on a model’s output, making it invaluable for GenAI validation, regulatory compliance, and building trust in AI systems.

The Game Theory Behind SHAP: Shapley Value Origins

The mathematical foundation of SHAP comes from the Shapley value, introduced by Lloyd Shapley in 1951. In cooperative game theory, the Shapley value provides a fair way to distribute the total "payout" among players based on their individual contributions. SHAP adapts this by treating each feature as a "player" and the model prediction as the "payout," distributing credit for the prediction among all features.

• Efficiency: Total contributions sum to the prediction.
• Symmetry: Identical features get equal attribution.
• Dummy: Features with no impact get zero.
• Additivity: Attributions combine logically across models.

How Does SHAP Work? (With Example)

SHAP explains an individual prediction by quantifying how much each feature contributed to moving the model's output from the baseline (average prediction) to the actual prediction for that instance.

Example: Loan Default Prediction

• Base value: 0.45 (average default risk across all applicants)
• High debt-to-income ratio: +0.25 (increases risk)
• Low credit score: +0.15 (increases risk)
• High income: -0.05 (decreases risk)
• Total SHAP contribution: +0.35
• Final model score: 0.45 + 0.35 = 0.80 (80% default probability)

This breakdown makes the model's decision transparent, showing exactly how each feature pushed the prediction higher or lower.

SHAP in Practice: Python Example

import xgboost as xgb
import shap

# Train your model (example with XGBoost)
model = xgb.XGBClassifier()
model.fit(X_train, y_train)

# Explain predictions with SHAP
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# Visualize global feature importance
shap.summary_plot(shap_values, X_test, plot_type="bar")

# Explain a single prediction
shap.force_plot(explainer.expected_value, shap_values[0,:], X_test[0,:])
                

Types of SHAP Explainers

Explainer	Best For
TreeExplainer	Tree-based models (XGBoost, LightGBM, CatBoost)
DeepExplainer	Neural networks (Deep Learning)
KernelExplainer	Any model (model-agnostic, slower)
PermutationExplainer	Exact SHAP for small feature sets

SHAP vs. Other XAI Methods

Method	Approach	Pros	Cons
SHAP	Game theory, fair attribution	Consistent, local/global, direction & magnitude	Computationally intensive
LIME	Local surrogate models	Model-agnostic, easy to use	Less consistent, less global insight
Feature Importance	Global ranking	Simple, fast	No direction, no local insight

Real-World Applications of SHAP

• Finance: Credit risk, loan approvals
• Healthcare: Disease risk prediction
• Customer Analytics: Churn, segmentation
• Fraud Detection: Transaction analysis

Quick Insights: Chaos Theory, Fibonacci, and Trimmed Mean

Chaos Theory

Chaos theory studies systems that are highly sensitive to initial conditions, leading to seemingly random but deterministic behavior. Famous for the "butterfly effect," chaos theory helps explain unpredictable patterns in weather forecasting, stock market modeling, population dynamics, encryption, and signal processing. Despite the randomness, these systems follow mathematical rules and display hidden patterns, like the unique structure of snowflakes.

Fibonacci Sequence

The Fibonacci sequence (0, 1, 1, 2, 3, 5, 8, ...) appears in nature (sunflowers, pinecones), art, and finance. Each number is the sum of the two preceding ones, and the ratio between numbers approaches the golden ratio (~1.618), which is often associated with aesthetically pleasing proportions.

Trimmed Mean

The trimmed mean is a robust statistical measure where a fixed percentage of the highest and lowest values are removed before calculating the mean. This approach reduces the impact of outliers and is widely used in sports judging, economic indicators, and data analysis for more reliable averages.

Conclusion: Why SHAP and Math Matter in AI

SHAP values bridge advanced mathematics and real-world AI, making black-box models transparent and trustworthy. Understanding foundational concepts like game theory, chaos, and robust statistics empowers data scientists to build better, more explainable AI systems.

As AI continues to shape critical decisions, explainability and mathematical rigor will remain at the heart of responsible, impactful innovation.

How to Handle Categorical Variables in Machine Learning

In machine learning, many algorithms require numerical input. Since categorical variables (e.g., 'red', 'blue', 'green') are non-numeric, we need to transform them before feeding them into models.

1. Why Convert Categorical Variables?

Models like linear regression, logistic regression, SVM, and neural networks require numerical input. Using raw text labels causes errors or misleading results, as the model might treat them as ordinal or continuous.

2. Basic Encoding with pd.get_dummies()

import pandas as pd

df = pd.DataFrame({'color': ['red', 'green', 'blue']})
pd.get_dummies(df)

This will return:

   color_blue  color_green  color_red
0           0            0          1
1           0            1          0
2           1            0          0

Downsides: High cardinality can create dimensionality issues, and unseen categories in the test set cause problems.

3. Using Scikit-Learn's Encoders

OneHotEncoder

from sklearn.preprocessing import OneHotEncoder

encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
X_encoded = encoder.fit_transform(df[['color']])

handle_unknown='ignore' prevents errors from unseen categories during prediction.

OrdinalEncoder

from sklearn.preprocessing import OrdinalEncoder

encoder = OrdinalEncoder()
X_encoded = encoder.fit_transform(df[['color']])

Warning: This assumes order exists between categories, which can mislead models.

4. Advanced Encoders (category_encoders)

Using target encoding, binary encoding, or leave-one-out encoding can improve results:

import category_encoders as ce

encoder = ce.TargetEncoder()
X_transformed = encoder.fit_transform(df[['color']], [1, 0, 1])

5. Automatic Categorical Handling

LightGBM

import lightgbm as lgb
df['color'] = df['color'].astype('category')  # Critical step!

train_data = lgb.Dataset(X_train, label=y_train)
lgb.train(params, train_data)

Important: Even though LightGBM internally maps categories to integers, it does not treat them as ordered. It splits categories by groupings, not by numerical size.

XGBoost (v1.3+)

import xgboost as xgb
df['color'] = df['color'].astype('category')

model = xgb.XGBClassifier(tree_method='hist', enable_categorical=True)
model.fit(X_train, y_train)

tree_method='hist' and enable_categorical=True are mandatory for native categorical support.

CatBoost

CatBoost natively supports categorical variables, including string values, and uses powerful techniques like target encoding internally.

6. CatBoost Categorical Handling vs OneHot Comparison

Experiment Setup:

- Dataset: Titanic (with 'Sex' and 'Embarked' as categorical features)
- Models: CatBoost with:

• Auto categorical handling
• One-hot encoded inputs

Results:

CatBoost (auto cat handling):  ~83.2% accuracy  
CatBoost (one-hot encoded):   ~78.9% accuracy

Conclusion: CatBoost’s native categorical handling often performs better due to target-based encoding and internal optimizations.

7. Summary Table

Model	Auto Categorical Support	Setup	Need Encoding?
LightGBM	✅	astype('category')	❌
XGBoost	✅ (v1.3+)	tree_method='hist', enable_categorical=True	❌
CatBoost	✅ (best)	cat_features list or raw string columns	❌
Scikit-Learn	❌	OneHotEncoder, OrdinalEncoder	✅

✅ Final Takeaways

• For linear models → Use OneHotEncoder
• For tree models → Use native handling in LightGBM, XGBoost, or CatBoost
• For many categories → Try TargetEncoder or CatBoost
• Always avoid using OrdinalEncoder unless the variable has real order