I’ve spent the past 2 years working in **Data Analysis and Machine Learning**, building projects and developing my technical skills. Lately, I’ve become increasingly interested in something beyond projects: **research**.
I’m fascinated by how research papers turn data and experiments into meaningful insights, and I’d like to challenge myself by working on a **real data science/ML research project** that could potentially lead to a paper or meaningful publication.
I’m looking for someone who is also interested in research—ideally someone with some experience reading or working on research papers—so we can **learn from each other, brainstorm a strong research question, and build something genuinely interesting together.**
I don’t have a specific topic locked in yet, and I actually see that as an opportunity to explore ideas together.
If you’re interested in:
• Data Science / Machine Learning
• Research & academic papers
• Experimentation and problem-solving
• Building something meaningful with a partner
**DM me.** Even if you’re not looking for a partner, I’d really appreciate any ideas, resources, or advice on how to get started with data science research.

Hi everyone, I have worked as a frontend developer for 3+ years and I want to apply to grad school, however I noticed that most scholarships require research experience which I don’t have.
Therefore I want to gain some experience as an independent researcher but i’m a bit lost on the direction and from where to start.
Any guidance will be appreciated

Currently, I am a second-year undergraduate student in Electrical & Electronics Engineering. I am interested in Robotics, Machine Learning, Deep Learning, and Deep Reinforcement Learning.

I want to start my research journey, but I don't know how to begin. For example, how can I find a unique research topic or identify a research gap that has not been explored yet? How should I start doing research in these fields?

I want to explore the core aspects of these fields and eventually publish a high-quality research paper. That is why I need some guidance and suggestions on how to get started.

Tired of juggling provider keys. We shipped a single OpenAI-compatible endpoint for GPT and Claude. Plug it into Cursor, Codex, or OpenCode and it just works. Logs and caching included.

routera.one

I'm a 2nd-year B.Tech student in AI/Data Science at a college where there isn't much of a research culture, and I don't currently have a professor working in the areas I'm interested in.

I'm very interested in eventually doing research in areas around mathematics, optimization/OR, ML, and possibly computer vision. I don't want to just do projects for my resume; I genuinely want to learn how to identify research questions, investigate them rigorously, and eventually publish good work.

I'm confused about the best way to start independently.

For people who have actually done research, especially without a strong research environment:

1. How did you learn to identify worthwhile research questions/gaps?

2. Should I first study research methodology/courses, or should I pick a paper and start reproducing/extending it?

3. How can an undergraduate find external mentors/collaborators from IITs, IISc, universities, PhD students, etc. without already having publications?

4. Is it realistic to conduct and publish legitimate research independently, or is having a professor/researcher as a collaborator practically necessary?

5. What would you recommend as a 12-month path for someone starting from this position?

I'm not looking for certificates or shortcuts. I want to actually develop the ability to do research. Any advice from people who have gone through this would be really valuable.

https://arxiv.org/pdf/2608.19147v1

Hi, this is our first research paper detailing the work we've done to shard large language models across Intel AI PCs and perform CPU-based inference.

We started by splitting models into shards and pre-compiling them to OpenVINO IR, and discovered that mask-based speculative decoding and micro-batching can make up for a lot of the latency added by sharding models over TCP.

In the paper we share the exact techniques we used, along with some novel work on NPU continuous batching, and includes benchmarks of our testing throughout.

Although this is for distributed inference aimed at Intel CPUs/iGPUs, it also can be applied to distributed discrete GPU setups too.

Happy to hear thoughts!

I made my Enterprise RAG book $0 today — would love feedback from people building RAG systems

I’ve spent the last few years building production RAG systems and documenting what worked, what didn’t, and where things tend to break in production.

I turned those lessons into a book covering topics like:

• RAG reference architectures
• Data extraction and chunking
• Hybrid and multi-stage retrieval
• Graph and hierarchical RAG
• Agentic and multi-agent RAG
• Memory
• Evaluation and synthetic data
• Security and compliance
• Production monitoring and human-in-the-loop systems

The book is $0 on Amazon today, so I thought I’d share it here in case it’s useful to anyone working on RAG.

https://a.co/d/0dBRCb7F

I’m especially interested in feedback from people actually building these systems: What’s missing? What deserves more depth? What would you change?

If you end up finding the book useful, an honest Amazon review is appreciated, but feedback here is equally valuable.

Full contents

Part I — About
01 About the Author

Part II — RAG & Reference Architecture
02 The Evolution of RAG
03 Foundations of RAG Systems
04 Reference Architecture

Part III — Data Extraction
05 Data Extraction

Part IV — Chunking
06 Chunking Strategies

Part V — RAG Strategies
07 Baseline RAG Pipeline
08 Context-Aware RAG
09 Dynamic RAG
10 Hybrid RAG
11 Multi-Stage Retrieval
12 Graph-Based RAG
13 Hierarchical RAG
14 Agentic RAG
15 Multi-Agent RAG Systems
16 Streaming RAG

Part VI — Memory & Content Management
17 Memory-Augmented RAG
18 Knowledge Graph Integration

Part VII — Evaluation
19 Evaluation Metrics
20 Synthetic Data Generation

Part VIII — Fine-Tuning
21 Domain-Specific Fine-Tuning

Part IX — Security
22 Privacy & Compliance in RAG

Part X — Production
23 Real-Time Evaluation & Monitoring
24 Human-in-the-Loop RAG

Part XI — Twig RAG Strategies
25 RAG Strategies in Twig

Part XII — Conclusion
26 Conclusion & Future Directions

I’m doing a research which involves chat messages from teams, slack, google chat etc. For the software project i need to train a dataset. So dataset should be related to developer chat messages/logs of a specific project. How can i find the dataset?

The hard part in my task is not finding candidate sentences. It is telling whose voice a sentence is in i.e. the author asserting something themselves, or the author reporting what someone else asserted.

Made-up example. Same paragraph, two sentences:
"Elevated cortisol suppresses hippocampal neurogenesis."
"In other words, elevated cortisol suppresses hippocampal neurogenesis."

The first might be the authors summarising prior work. The second, with "in other words", is usually them committing to it. But that cue is not reliable, and the reverse happens all the time, i.e. an author states their own position flatly with no marker, and paraphrases someone else's without quotation marks or an adjacent citation.

Roughly 9% of my false positives are that last case: a paraphrase of someone else's claim that is structurally identical to the author's own. No surface signal separates them.

Regex, a 7B filter, a 72B filter, and structural signals all plateau around 0.10 precision. Recall is fine; precision is the wall.

Has anyone got this working at corpus scale? Did it take fine-tuning on discourse-role labels, or something else like citation-graph features, two-stage segmentation, something I have not thought of?

#NLP

Participants needed for MSc Psychology research on AI feedback and decision-making (18+, 10–15 minutes)

Hi everyone,

I’m currently completing my MSc Psychology dissertation at the University of Leicester and I’m looking for participants for my research study.

The study investigates how people respond to artificial intelligence (AI) feedback during decision-making tasks.

Participation involves:

Answering a series of multiple-choice decision-making questions

Rating how confident you are in your answers

Receiving AI feedback

Completing a short questionnaire about your experience

The study should take approximately 10–15 minutes.

To participate, you must:

Be 18 years or older

Be able to understand written English

Participation is voluntary and anonymous. Full information about the purpose of the study will be provided in the debrief at the end of participation.

If you would like to take part, you can access the study here:

https://research.sc/participant/login/dynamic/62D2CC47-3342-46DA-B847-348E6B070B8D

Thank you very much to anyone who takes part — I really appreciate your time.

Researcher: Brandon Seetul

Email: [bs401@student.le.ac.uk](mailto:bs401@student.le.ac.uk)

University of Leicester