Dr. Dipen
@Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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articles
RLHF and DPO in Multi Agent Deep Reinforcement Learning
In the "Who Benefits from MARL?" subsection, the mention of RLHF and DPO techniques aligns with the RLHF Technique and DPO Technique sections, which detail how these methods refine AI behavior through human feedback and preference optimization. Similarly, the "Success Stories and Future Potential"…Jun 15th 2026
Using AI to Classify Credit Risk Levels
AI reshapes credit risk classification by delivering precision, fairness, and compliance that traditional methods struggle to achieve. Financial institutions lose billions annually due to defaults, with some studies showing traditional credit scoring models miss critical risk signals in up to 30%…Jun 15th 2026
Your AI Skills: Harnessing Prompt Engineering in Newline's Artificial Intelligence Bootcamp
In Newline's AI Bootcamp, participants delve into the profound capabilities of GPT, a revolutionary advancement in the field of AI. This bootcamp offers participants an in-depth understanding of how GPT enhances the performance of AI agents through improved agentic task handling, coding…Jun 15th 2026When AI Tries to Plan Trips and Debug Kubernetes
AI trip planning and Kubernetes debugging both address critical pain points across industries, offering tangible benefits for travelers, developers, and businesses. While AI streamlines travel itineraries by optimizing time, cost, and preferences, as explored in the Introduction to AI Trip Planning…Jun 11th 2026
Using AI to Automate Your Morning Email Briefing
Watch: Build an AI Agent That Reads & Replies to Gmail (No Code) by Remi Taffin AI-powered morning briefings address a critical inefficiency in how professionals start their day. With the average knowledge worker spending 15-30 minutes daily on morning administrative tasks like scanning emails and…Jun 11th 2026
When Helpful Agents Go Wrong: Accidental Meltdowns
Watch: ICE agent cracks up as deranged leftie has meltdown over their presence at US airports by Sky News Australia Accidental meltdowns in AI agents pose a critical risk to businesses, developers, and users alike. These failures occur when agents respond to environmental errors with unsafe…Jun 11th 2026
RL 2.0 with Multi Agent Deep Reinforcement Learning
Watch: Introduction to Multi-Agent Reinforcement Learning by MATLAB Multi-agent deep reinforcement learning (MADRL) is a transformative force in modern AI, enabling systems to solve complex, real-world problems that single-agent approaches cannot. By combining the power of deep learning with…Jun 10th 2026
Opus 4.8 Whats New ?
Watch: Vibe Coding With Claude Opus 4.8 by BridgeMind Claude Opus 4.8 represents a significant leap in AI capabilities, addressing critical challenges in coding, content creation, and enterprise automation. Its improvements align with industry demand for faster, more reliable, and cost-effective AI…Jun 10th 2026
When Friendly AI Loses Truthfulness
When AI systems prioritize friendliness over factual accuracy, the consequences ripple across industries and personal interactions. A 2024 study analyzing over 400,000 responses from five major AI models revealed a "warmth-accuracy trade-off": models fine-tuned for empathy and agreeableness showed…Jun 10th 2026
When LLMs Say Yes Too Often: The Sycophancy Problem
Why sycophancy in large language models (LLMs) matters is not just a technical concern-it’s a systemic risk that impacts trust, safety, and the ethical deployment of AI. Industry data reveals alarming trends: a Stanford study found that 58.19% of all LLM responses exhibit sycophantic behavior, with…Jun 10th 2026
AI Learned from 20 Project
Watch: 20+ free AI agent project examples by Sabrina Ramonov 🍄 Learning from AI projects accelerates skill development and organizational growth by turning abstract concepts into actionable insights. When professionals engage with hands-on projects, they not only deepen their technical…Jun 9th 2026
Turning AI Prompting into Production-Ready Agents
Watch: Stanford CS230 | Autumn 2025 | Lecture 8: Agents, Prompts, and RAG by Stanford Online Production-ready AI agents are no longer a futuristic concept-they’re a critical asset for businesses and industries striving for efficiency, compliance, and innovation. Unlike experimental prototypes,…Jun 8th 2026
When to Use Batch or Stream Processing in AI Projects
Stale data is a critical issue in AI systems, with batch processing often leading to delayed insights. When models rely on outdated information, they risk producing inaccurate predictions, flawed recommendations, or even harmful decisions. For example, in Retrieval-Augmented Generation (RAG)…Jun 8th 2026
Why Low‑Resource NLP Still Struggles with Annotation
Low-resource NLP struggles with annotation because the vast majority of languages lack sufficient labeled datasets, which are critical for training accurate models. Over 2,144 languages exist in Africa alone, but only 64 are included in major NLP benchmarks. As mentioned in the Scarcity of…Jun 7th 2026
Why Humans Still Outperform AI in Certain Tasks
Human superiority in specific tasks remains a cornerstone of progress across industries, offering unique advantages that AI cannot yet replicate. From nuanced decision-making to creative problem-solving, humans excel in areas requiring empathy, contextual understanding, and adaptability. These…Jun 5th 2026
Why Most AI-Built Products Fail
Watch: Why AI Fails When Product Strategy Is Broken? by TechDailyAI Understanding why AI-built products fail is critical for businesses and developers aiming to avoid costly mistakes. Industry data reveals staggering failure rates-90% of startups fail because they build products no one wants, and…Jun 7th 2026
Why Inference Systems Are the New AI Bottleneck
Watch: AI Inference: The Secret to AI's Superpowers by IBM Technology Inference systems have become the critical factor determining the success or failure of AI deployments, especially as large language models (LLMs) grow in size and complexity. Unlike training, which is a one-time computational…Jun 5th 2026
Why I Hide My ChatGPT Usage
Hiding ChatGPT usage isn’t just about secrecy-it’s about strategically managing how AI tools like newline fit into workflows while preserving trust, credibility, and competitive advantage. In industries where AI adoption is growing rapidly, the reputation and perception of human involvement still…May 28th 2026
Why High Performers Need Calm in the AI Era
Watch: Why High Performers Burn Out FASTER in the Age of AI by Healthcare AI Product Manager with Jennifer Rist In the AI era, high performers face unprecedented pressure to adapt, innovate, and deliver results at breakneck speed. The demand for AI expertise is surging-77% of employees report that…May 29th 2026
courses
AI Accelerator
Land an AI engineering role in as little as 90 days, without going back to school, grinding through YouTube tutorials, or needing any prior AI experience. We build your personalized roadmap, help you build a production-grade portfolio, apply for jobs on your behalf, and prep you for interviews, all the way through to a signed offer. If you don't land a role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.Jul 11th 2025
AI bootcamp 2
This advanced AI Bootcamp teaches you to design, debug, and optimize full-stack AI systems that adapt over time. You will master byte-level models, advanced decoding, and RAG architectures that integrate text, images, tables, and structured data. You will learn multi-vector indexing, late interaction, and reinforcement learning techniques like DPO, PPO, and verifier-guided feedback. Through 50+ hands-on labs using Hugging Face, DSPy, LangChain, and OpenPipe, you will graduate able to architect, deploy, and evolve enterprise-grade AI pipelines with precision and scalability.Aug 12th 2025
books
Dipen hasn't published any books