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
Why Fear of AI Persists Despite Technological Advances
The fear of AI persists because it taps into deep-seated anxieties about job loss, ethical risks, and the unknown. Despite AI’s potential to boost productivity, many people and organizations remain hesitant, often citing historical patterns where technological shifts caused both progress and…Jul 3rd 2026
Voice-Enabled AI Agents Using PSTN
Voice-enabled AI agents using PSTN eliminate friction in human-AI interaction by using the universal reach of traditional phone lines. With the global voice-AI market projected to grow 34.8% annually-reaching $47.5 billion by 2034-businesses adopting this technology gain a competitive edge. These…Jul 3rd 2026
Hierarchical Task Network Planning with Multi Agent Deep RL
Watch: Multi-Agent Hide and Seek by OpenAI Hierarchical Task Network (HTN) Planning with Multi-Agent Deep Reinforcement Learning (MARL) addresses critical limitations in complex decision-making systems. By integrating symbolic planning with deep learning, HTN frameworks reduce exploration…Jul 3rd 2026
Why LLM‑Generated Variables Aren’t Real Observations
Understanding the difference between LLM-generated variables and real observations is critical for ensuring the validity of AI models. LLMs produce synthetic data that often fails to replicate the statistical patterns of real-world populations, leading to flawed inferences. For example, a 2026…Jul 1st 2026
AI Bootcamp Success: Fine-Tuning Instructions for Real-World Application Development
Watch: Prompt Engineering by Thinking Neuron Key Highlights AI bootcamp success is no longer a niche pursuit-it’s a strategic investment with measurable returns for individuals and industries. The global AI market is projected to exceed $1.81 trillion by 2030, driven by businesses adopting…Jul 1st 2026
How a Data Model and Schema Shape Knowledge Graph Ontology
Watch: Data Model vs Ontology | What’s the Difference? | Simple Explanation with Real Examples by Tech Bytes Insights A well-designed data model can improve knowledge graph ontology quality by up to 30%, according to Stanford research. The gain comes from three places: cleaner data integration,…Jul 1st 2026
Why Spatial Priming Outperforms Semantic Prompting in Chart Extraction
Watch: 🔬 Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM A by Observe AI Chart extraction is a critical process in today’s data-driven world, where visual representations like charts and graphs dominate communication across industries. From financial reports…Jun 30th 2026When AI Strategy Takes Over Product Planning
Watch: AI Driven Product Development in 2026: A Practical Guide by Apptunix - #1 App Development Company AI strategy is no longer optional in product planning-it’s a competitive necessity. Companies that integrate AI into their workflows see measurable gains in efficiency, customer alignment, and…Jun 30th 2026
The Next Five Years for Digital Twins: From Mirror to Agent
Over the next five years, digital twins move from passive mirrors to active operators. The shift is easy to state. Twins that once just reflected what a machine or building was doing will start reading live data, suggesting the next move, and taking limited action inside hard rules. In regulated…Jun 29th 2026
Why Most AI Agents Fail in Production
Understanding why AI agents fail in production is critical because these failures cost businesses hundreds of thousands of dollars per project and erode customer trust. Industry data reveals 88% of AI agent projects fail before reaching production, with 61% of these failures tied to preventable…Jun 29th 2026
Why AI Struggles with Practical Optimization Problems
Traditional AI models face significant challenges in practical optimization settings due to their inherent design limitations. These challenges often stem from their inability to efficiently process high-dimensional data, handle noisy or sparse datasets, and adapt to complex problem structures. As…Jun 29th 2026
AI evals vs AI Hype: what wins ?
Watch: The State of AI Code Quality: Hype vs Reality. Itamar Friedman, Qodo by AI Engineer Evals win. The number we keep running into is blunt: about 75% of AI projects fail, and the common thread is skipped evaluation. Hype buys you a demo. Evals buy you something that holds up when real users…Jun 28th 2026
Why Local Models Are Enough for Enterprise AI
Local models moved from hobbyist territory to a real deployment option for one reason: you can run AI without shipping sensitive data to someone else's servers. For a large share of internal enterprise work, that single property settles the decision. You keep the data, you control the cost, and you…Jun 26th 2026
Vector Databases vs Graph RAG: Picking the Right Memory for AI Agents
Watch: VectorDB vs GraphDB for Gen AI Agents | Databases for AI by AWS Events Use vector databases for semantic memory. Use Graph RAG for structured reasoning. Combine them when your agent needs both recall and explainability. That one line covers most decisions. The rest of this section unpacks…Jun 26th 2026
Using ZeRO and FSDP to Scale Large Models on Multiple GPUs
Watch: Ultimate Guide To Scaling ML Models - Megatron-LM | ZeRO | DeepSpeed | Mixed Precision by Aleksa Gordić - The AI Epiphany ZeRO and FSDP solve the same problem the same way: shard the heavy parts of training across your GPUs so no single card has to hold all of it. Where they differ is…Jun 26th 2026
Fine-Tune LLMs 3x Faster with Newline AI Course
Fine-tuning a large language model isn't only a technical chore. For a mid-career developer trying to move into AI work, it's leverage. You take a pre-trained model, point it at a specific problem, and suddenly the outputs actually fit the business instead of sounding like a generic chatbot.…Jun 25th 2026
8-Step Firebase Schema Migration Checklist
Firebase schema migrations don't work like the SQL migrations most of us learned on. Firestore has no ALTER TABLE. You change your data model by writing scripts that read old documents and rewrite them, all while your app keeps serving live traffic. This 8-step checklist breaks that risky work into…Jun 25th 2026
What is production ready AI engineering ?
Watch: OpenAI + @Temporalio: Building Durable, Production Ready Agents - Cornelia Davis, Temporal by AI Engineer Production-ready AI engineering is what happens after the demo works. It's the work of deploying models that survive contact with real users, real traffic, and real failure modes. The…Jun 24th 2026
What is Model distillation and Why Companies are doing it
Model distillation moves knowledge from a large, accurate model into a smaller, faster one. The big model plays "teacher," the compact model plays "student," and the student learns to copy the teacher's outputs while running on a fraction of the compute. You keep most of the accuracy and pay far…Jun 24th 2026
Fable 5 or GLM 5.2 ?
Pick between Fable 5 and GLM 5.2 and you're really picking between two things: reasoning power and cost at scale. Fable 5 wins the benchmarks for reasoning and coding. GLM 5.2 wins on price and carries a slightly larger context window, which makes it the sane default for high-volume work. Both are…Jun 24th 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