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Alfred Hull reposted thisAlfred Hull reposted thisHuge: You can now run a 2.8 trillion parameter AI model on a normal PC with only 25GB of RAM 😳 No GPU. No cluster. Just a pure-C engine called Colibrì. It keeps only the dense core in memory and streams the rest of the experts straight from disk the moment the router needs them. → Dense weights stay resident (~35 GB int4 for Kimi K3) → 16-of-896 experts (or 8-of-256 on GLM-5.2) get pulled on demand → Per-layer LRU + learned pinning + prefetch hide most of the latency → Works with the original MXFP4 weights - zero conversion required for Kimi This is how a 2.8T model (and a 744B one) suddenly fits on consumer hardware that used to max out at 70B. The project just shipped v1.4.0, hit 21.6K GitHub stars, and now officially supports four model families including full Kimi K3, GLM-5.2, and others. Oh, and it’s 100% free and open-source 🤖 But perhaps the biggest thing here is the realization that the real constraint was never the model size - it was the assumption that every single parameter had to live in fast memory at the same time. Of course, ot’s still slow when the cache is cold. You still need a fat NVMe (1.6 TB for the full Kimi checkpoint). And expert hit rates make or break the experience. But the direction is now clear. The future of frontier AI isn’t bigger models in bigger GPUs. It’s engines that only wake up the tiny fraction of the model that is actually thinking. P.S. also check out The Ultimate Guide to Kimi K3 🤖: https://lnkd.in/dTjiNm6v
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Alfred Hull reposted thisAlfred Hull reposted thisClaude is not just another chatbot anymore. 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 𝗻𝗼𝘄 → aiforleaders.com __________ Claude is becoming a work platform. That changes how leaders should evaluate it. Most teams still compare Claude to ChatGPT. That is the wrong diagnostic. The better question is: What job does each layer perform? → Claude Chat handles the obvious front door. Ask questions. Draft content. Analyze documents. Turn messy thinking into a clearer first pass. → Claude Workspaces and Projects change the collaboration model. Instead of every employee starting from zero, teams can keep files, instructions, and shared context together. That makes repeated work easier to govern. → Claude Code moves the product into execution. Developers can refactor codebases, debug applications, generate tests, and turn a rough plan into a working branch faster. → Claude's model stack creates choice. Opus is for maximum reasoning. Sonnet is the daily workhorse. Haiku is for fast, high-volume tasks. The point is not to pick one model forever. The point is to route the task correctly. → Extended Thinking matters when the cost of a bad answer is high. Use it for planning, risk review, technical tradeoffs, and decisions with multiple dependencies. → Claude in Excel and Claude in Chrome push AI into live workflows. That is where the value starts to move from "help me write" to "help me operate." → Connectors, Plugins, Artifacts, Skills, and Prompt Templates are the scaling layer. They turn one-off prompting into repeatable systems. That is what separates experiments from operating capability. The practical audit is simple. If a Claude workflow cannot be reused by another teammate, measured against a business outcome, or governed with clear permissions, it is still a personal productivity hack. The lesson is simple. Claude is strongest when it is treated as an AI work architecture, not a single text box. Leaders should map where Claude fits: Context. Execution. Governance. Automation. Team reuse. The competitive advantage is not just model access. It is the quality of the context system around the model. Where would Claude create the most leverage in your team's workflow?
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Alfred Hull reposted thisAlfred Hull reposted thisInstead of watching 2 hours of Netflix tonight, watch this in-depth interview with Kimi founder & CEO Zhilin Yang. It’s the clearest and most thoughtful breakdown I’ve seen of why AI agents beat pure reasoning models and how frontier labs outside the West are actually building them. He explains why everyone’s racing on reasoning while Claude quietly bet on agents - but the real hard part isn’t the agent itself, it’s the model underneath. His memorable metaphor: a pure reasoning model is like “a brain in a fish tank” - it can think forever but never touches the world. An agent is that brain wired to tools, memory, and action. Yang shares how they’re thinking about K2 helping build K3 through strong agent skills, the biggest bottlenecks in generalization, test-time scaling in two dimensions, and even how RL-style thinking applies to both models and running teams. Whether you’re building agents, scaling models, or just want to understand frontier AI thinking beyond the usual Western narratives, this is one of the clearest and most thoughtful long-form discussions available right now. Bookmark it & watch the whole interview (link: https://lnkd.in/e5_hBAgB) - it might be the most valuable 100 minutes you spend on real AI engineering and strategy this month. P.S. also check out The Ultimate Guide to Kimi K3 🤖: https://lnkd.in/dTjiNm6v
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Alfred Hull reposted thisAlfred Hull reposted thisAnnouncing OpenWorker! An open-source agent that doesn't just chat with you, but delivers finished work -- like hand you a polished document, send a slack message, or update a calendar entry. Ask it to prepare a customer brief, untangle your calendar, draft a report, or triage a Slack alert. It works across your files and everyday tools, produces the deliverable, and checks in before doing anything consequential. OpenWorker runs on your Mac, with Windows support coming soon. It does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama to keep your data local. Your data does not leave your machine except through an LLM provider and integrations that you choose. Rohit Prsad and I are building OpenWorker because AI coworkers are an important way to get work done, and we want there to be an open, privacy-preserving, model-independent option. Check it out and let us know what you think! Try it out: http://openworker.com (requires your own API key) Source code: https://lnkd.in/gvZYspRv
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Alfred Hull shared this👀This Autonomous Micro Drone Just Killed a Moth in Mid-Air — And Mosquitoes Are NextThis Autonomous Micro Drone Just Killed a Moth in Mid-Air — And Mosquitoes Are Next
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Alfred Hull reposted thisAlfred Hull reposted thisBillions of people unlock their phone with their face every day. Apple Face ID. Google face unlock. Neither would pass the identity standard the EU is about to enforce 😳 And here's the uncomfortable part: the verification most banks use today wouldn't either. Document-based identity verification was never designed for the digital world. Passports have UV and infrared security features that can only be checked physically. Online, you're verifying a photograph of a secure document. A photograph. That was fine when faking an identity required skill, time, and resources. In 2026, AI does it in seconds. → Synthetic identities pass document capture → AI-generated faces clear liveness checks → Deepfakes defeat the "blink twice, turn your head" routine → Full identity packages are assembled faster than compliance teams can update their rules Verifying a document no longer means you've verified a person. And that's a fraud problem, not just a compliance one. Governments see it. That's why they're not patching the old model - they're replacing it with digital-native identity. Government-backed eIDs that are cryptographically assured, not photographed. You're not trusting a scan. You're trusting a digitally signed identity that AI can't fake. And regulation is now forcing the pace. eIDAS 2.0 is law. The AML Regulation update hits in July 2027. Banks, insurers, and telecoms are being pushed toward higher-assurance identity - fast. But 150+ eID schemes exist worldwide, each with different standards. Integrating them individually doesn't scale. That's what Hopae is building - a global eID network connecting 100+ government-backed identity schemes through a single integration. One API. Access to the highest-assurance digital identities globally. This is how fraud actually goes down. Not by adding another layer to a broken process, but by replacing the process entirely. If you want to see how this is being built in practice, take a look here: https://lnkd.in/dW9pa6wV
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Alfred Hull reposted thisAlfred Hull reposted thisIKEA used AI to replace 8,500 customer service roles. Then did something most companies wouldn't. They retrained the people. Instead of removing jobs, they moved employees into interior design advisory roles. The result? A new service line that reportedly generated $1.4 billion in revenue. That's the part worth paying attention to. AI is very good at repetitive work. Humans are still better at judgment. Relationships. Taste. Trust. The companies that win with AI won't be the ones that automate everything. They'll be the ones that automate the right things. And then move their people closer to where they create the most value. That's a much more interesting future than humans versus AI. It's humans with AI. How is your company thinking about AI: cost reduction or value creation? Share this with someone navigating AI at work. Follow Helene Guillaume Pabis for human first leadership and smarter systems. If you're looking to get started with AI 1o1, book your free discovery call here: https://lnkd.in/eSKqyEA3 . What you'll learn in a few hours will make you confident you don't have to just wait and see
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Alfred Hull reposted thisAlfred Hull reposted thisMost companies spend millions on AI initiatives. 20 years in technology has shown me why 80% fail: Everyone wants to be "AI-first" but nobody wants to fix the data. 1/ The real state of enterprise data: ↳ Zero governance and zero lineage tracking. ↳ Pipelines that nobody maintains or monitors. ↳ Duplicate data scattered across tons of different systems. ↳ Data lakes that are actually drowning in data in practice. 2/ Why AI makes this worse, not better: ↳ AI doesn't work well on messy data, it learns on incorrect data. ↳ Bad inputs create confidently wrong outputs at scale. ↳ And automation amplifies existing data quality issues. ↳ Trust then erodes when AI systems produce unreliable results. 3/ The real problem is hiding in plain sight: ↳ This isn't an engineering problem; it's a prioritization problem. ↳ Someone has to postpone the roadmap and delay the AI pilots. ↳ And leadership must choose unglamorous cleanup over flashy AI features. ↳ Data governance requires ongoing commitment, not one-time fixes. Ask this question in your next AI strategy meeting: "When did we last actually fix this data?" Not migrate it. Not move it to a new tool. Actually, cleanse and fix it. Watch what happens next. Most organizations don't have an AI problem. They have a data problem masquerading as a technical challenge. Data is the root of AI and has to be a key aspect of all AI projects. Until that changes, every AI initiative is just expensive automation built on data that nobody trusts. ♻️ Share with someone who needs to understand how data is the foundation of AI. ➕ Follow me, Ashley Nicholson, for more tech insights.
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Alfred Hull reposted thisAlfred Hull reposted thisWe are locked in a multi-billion-dollar race to deploy defense AI. Yet, 60–70% of technology programs still fail to deliver what they promised. That failure rate hasn't moved in decades. It is not because the technology is flawed. It is because the operational problem was never translated into a baseline that a system could actually be built against. Here is the hard truth about digital transformation: Deploying AI over unmapped, chaotic workflows does not fix broken processes. It simply automates dysfunction at machine speed. The discipline required to translate operational reality into an algorithmic baseline has a name: Lean Six Sigma. But in the rush to deploy commercial AI, this critical step is being skipped. The greatest strategic tragedy in this race? The Department of Defense already possesses the exact workforce required to fix this. Over the last two decades, the military trained thousands of Lean Six Sigma Black Belts—the exact personnel trained to quantify problems, map data lineage, and set the strict control limits an AI must meet. Where are they? Trapped in administrative billets managing SharePoint sites and SOPs. Meanwhile, the GAO reports the Department cannot even fully identify who is in its AI workforce. We are running out of runway. Congress has set a hard statutory deadline: a clean DoD financial audit by December 31, 2028. You cannot pass an audit—or deploy trustworthy operational AI—on fragmented, ungoverned data. Article 2 of my series is live. We are breaking down the exact competency required to turn "we know the system is broken" into a rigorous, auditable spec you can actually build against. If you work in defense, federal IT, or government transformation, this one's for you. 👇 Read it here: https://lnkd.in/eiNez8Zz #ArtificialIntelligence #LeanSixSigma #DefenseInnovation #DataGovernance #FederalIT #AuditReadiness #TrustworthyAI #CDAO #KnowledgeManagementTHE FOUR COMPETENCIES FOR TRUSTWORTHY AI — ARTICLE 2 OF 4THE FOUR COMPETENCIES FOR TRUSTWORTHY AI — ARTICLE 2 OF 4
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Alfred Hull liked thisAlfred Hull liked thisMy team at Horizon3 is hiring! Fully remote senior+ software engineers. We are building out the largest architectural changes in Horizon3's history - a real-time processing platform to process rich sets of data coming from hundreds of thousands of real pentests, and providing customers a high-fidelity view of attack-paths, vulnerabilities, and insights into their security platform all in real time. We deal with enterprise networks at a size no one else is doing, with a richness no one else can compete with. Now we are on the bleeding edge of taking those results that previously take days to process after a pentest completes, and shifting it left to become minutes after the vulnerabilities are found. For massive enterprise networks with tens of thousands of devices, this is the difference of days, potentially weeks. We are also working on solving the incredibly hard data problem of taking network connected devices with IP churn, MAC address churn, behind firewalls that intentionally obfuscate identity - and using sophisticated techniques to match the identity of these devices over time in real-time. This is a unique blend of data-science, applied AI and attack-engineering in interpreting pentest results. If this piques your interest, please reach out to me! No job posting yet exists. I will be at #blackhat in LV this upcoming week and happy to anyone who will be present on job oppertunities.
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Alfred Hull reacted on thisAlfred Hull reacted on thisHuge: You can now run a 2.8 trillion parameter AI model on a normal PC with only 25GB of RAM 😳 No GPU. No cluster. Just a pure-C engine called Colibrì. It keeps only the dense core in memory and streams the rest of the experts straight from disk the moment the router needs them. → Dense weights stay resident (~35 GB int4 for Kimi K3) → 16-of-896 experts (or 8-of-256 on GLM-5.2) get pulled on demand → Per-layer LRU + learned pinning + prefetch hide most of the latency → Works with the original MXFP4 weights - zero conversion required for Kimi This is how a 2.8T model (and a 744B one) suddenly fits on consumer hardware that used to max out at 70B. The project just shipped v1.4.0, hit 21.6K GitHub stars, and now officially supports four model families including full Kimi K3, GLM-5.2, and others. Oh, and it’s 100% free and open-source 🤖 But perhaps the biggest thing here is the realization that the real constraint was never the model size - it was the assumption that every single parameter had to live in fast memory at the same time. Of course, ot’s still slow when the cache is cold. You still need a fat NVMe (1.6 TB for the full Kimi checkpoint). And expert hit rates make or break the experience. But the direction is now clear. The future of frontier AI isn’t bigger models in bigger GPUs. It’s engines that only wake up the tiny fraction of the model that is actually thinking. P.S. also check out The Ultimate Guide to Kimi K3 🤖: https://lnkd.in/dTjiNm6v
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Alfred Hull liked thisAlfred Hull liked thisKimi CEO Zhilin Yang nailed it: “AI is making previously impossible tasks now possible. You used to need a team, a budget, and months. Now you need a weekend and a $15 plan.” In this 11-minute keynote, the Moonshot AI founder breaks down exactly how agent swarms, Attention Residuals, and open-source scaling collapse the old requirements of startup teams, big budgets, and months of work into something a single person can drive. One of the clearest technical frameworks for why the barriers to shipping your idea have never been lower. P.S. also check out The Ultimate Guide to Kimi K3 🤖: https://lnkd.in/dTjiNm6v
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Alfred Hull liked thisAlfred Hull liked this>> The smartest person Jensen Huang met? He refused to answer. When asked who the smartest person he has ever met was, Jensen smiled and challenged the premise. That response was the real answer. Because what we call smart is changing. Coding fast. Solving equations. Optimizing systems. AI is already doing that at scale. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆 The differentiator is shifting. Not speed of cognition. Depth of perception. Jensen pointed toward emotional depth, adaptability, navigating ambiguity. Skills that are messy, contextual, human. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗛𝘂𝗺𝗮𝗻𝘀 𝗰𝗿𝗲𝗮𝘁𝗲 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 That is the upgrade. In the AI age, knowing more is easy. Deciding better is rare. So here is the uncomfortable question. Are we training people to compete with AI Or to complement it? Agree or disagree? ---- --- ♻️ Repost to share with your network ➕ Follow me Paul Storm for cutting edge AI insights. Join 600,000+ professionals using AI to stay ahead: 🔗 https://lnkd.in/eFNvmcYa
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Alfred Hull liked thisAlfred Hull liked thisClaude is not just another chatbot anymore. 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 𝗻𝗼𝘄 → aiforleaders.com __________ Claude is becoming a work platform. That changes how leaders should evaluate it. Most teams still compare Claude to ChatGPT. That is the wrong diagnostic. The better question is: What job does each layer perform? → Claude Chat handles the obvious front door. Ask questions. Draft content. Analyze documents. Turn messy thinking into a clearer first pass. → Claude Workspaces and Projects change the collaboration model. Instead of every employee starting from zero, teams can keep files, instructions, and shared context together. That makes repeated work easier to govern. → Claude Code moves the product into execution. Developers can refactor codebases, debug applications, generate tests, and turn a rough plan into a working branch faster. → Claude's model stack creates choice. Opus is for maximum reasoning. Sonnet is the daily workhorse. Haiku is for fast, high-volume tasks. The point is not to pick one model forever. The point is to route the task correctly. → Extended Thinking matters when the cost of a bad answer is high. Use it for planning, risk review, technical tradeoffs, and decisions with multiple dependencies. → Claude in Excel and Claude in Chrome push AI into live workflows. That is where the value starts to move from "help me write" to "help me operate." → Connectors, Plugins, Artifacts, Skills, and Prompt Templates are the scaling layer. They turn one-off prompting into repeatable systems. That is what separates experiments from operating capability. The practical audit is simple. If a Claude workflow cannot be reused by another teammate, measured against a business outcome, or governed with clear permissions, it is still a personal productivity hack. The lesson is simple. Claude is strongest when it is treated as an AI work architecture, not a single text box. Leaders should map where Claude fits: Context. Execution. Governance. Automation. Team reuse. The competitive advantage is not just model access. It is the quality of the context system around the model. Where would Claude create the most leverage in your team's workflow?
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Alfred Hull liked thisAlfred Hull liked thisYesterday, NVIDIA founder and CEO Jensen Huang joined leadership at the Naval Postgraduate School in Monterey to commission the first NVIDIA DGX GB300 AI supercomputer in the U.S. military. This system now gives more than 1,500 students and 600 faculty on-premises access to one of the world’s most powerful AI platforms. This will enable them to train foundation models, run high-fidelity simulations, and develop real-world applications in weather prediction, cybersecurity, ocean modeling, digital twins, and disaster response. As Admiral Sam Paparo, Commander of U.S. Pacific Command, put it: “As we modernize our technology, we must also modernize how we educate our leaders.” That’s exactly what this partnership delivers. NPS is training officers (From across all Service Branch Components) who will be commanding in AI-enabled environments. Huge congratulations to the NPS team, the NVIDIA AI Technology Center on campus, and our partners at Dell Technologies, DDN, VAST Data, and Vertiv who helped bring this capability online. This is what it looks like when advanced computing meets the mission. In this photo: U.S. Navy Adm. Samuel J. Paparo, commander, U.S. Pacific Command; retired U.S. Navy Vice Adm. Ann E. Rondeau, president of the Naval Postgraduate School (NPS); Jensen Huang, founder and CEO of NVIDIA; and Hank Plain, board chair of the Naval Postgraduate School Foundation & Alumni Association. To learn more, read the internal blog here -> https://lnkd.in/eeg8ce_h NPS Blog here -> https://lnkd.in/e3iCrt6F #NVIDIA #AI #Defense #NPS #NavalPostgraduateSchool
Experience & Education
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NorthStar-Insight Inc.
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Licenses & Certifications
Volunteer Experience
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Chair Academic Advisory Council (Dept. of IT and Decision Sciences)
Old Dominion University
- 2 years 8 months
Education
The aim is to serve the advisory council of the Department of IT, Decision Sciences, Maritime & Supply Chain Management. The advisory council meets once a year to provide insight for IT / Business Analytics / Maritime and Supply Chain curricula, review the industry's supply chain and business analytics needs, and suggest expected skill sets of our current and future graduates.
https://www.odu.edu/business/departments/itds -
Co-Chair Artificial Intelligence (AI) and Autonomy Workshop (Defense and Security Applications)
Military Operations Research Society
- 2 years 1 month
Science and Technology
The mission of MORS is to enhance the quality of analysis that informs national and homeland security decisions.
MORS has a long and distinguished history, dating back over fifty years. The First Military Operations Research Symposium (MORS), sponsored by the Office of Naval Research (ONR) - Pasadena, was held at Corona Naval Ordnance Lab, Corona, California, in August of 1957. By the Eighth MORS Symposium, the event became a nationally-oriented joint-service meeting. In April 1966…The mission of MORS is to enhance the quality of analysis that informs national and homeland security decisions.
MORS has a long and distinguished history, dating back over fifty years. The First Military Operations Research Symposium (MORS), sponsored by the Office of Naval Research (ONR) - Pasadena, was held at Corona Naval Ordnance Lab, Corona, California, in August of 1957. By the Eighth MORS Symposium, the event became a nationally-oriented joint-service meeting. In April 1966, the Military Operations Research Society was incorporated.
Link: https://www.mors.org/ -
Community Volunteer
AAAS
- Present 6 years 4 months
Science and Technology
The AAAS seeks to "advance science, engineering, and innovation throughout the world for the benefit of all people." To fulfill this mission, the AAAS Board has set the following broad goals:
Enhance communication among scientists, engineers, and the public;
Promote and defend the integrity of science and its use;
Strengthen support for the science and technology enterprise;
Provide a voice for science on societal issues;
Promote the responsible use of science in public…The AAAS seeks to "advance science, engineering, and innovation throughout the world for the benefit of all people." To fulfill this mission, the AAAS Board has set the following broad goals:
Enhance communication among scientists, engineers, and the public;
Promote and defend the integrity of science and its use;
Strengthen support for the science and technology enterprise;
Provide a voice for science on societal issues;
Promote the responsible use of science in public policy;
Strengthen and diversify the science and technology workforce;
Foster education in science and technology for everyone;
Increase public engagement with science and technology; and
Advance international cooperation in science.
https://www.aaas.org/
Publications
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Federal Government Trends: Contracting, HR, Budget & IT
Careers in Government
See publicationTransitioning Article
Courses
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Business And Public Policy
MBAD 6284
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Business Ethics
MBAD 6281
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Business Law: Enterprise Organization
ACCY 6602
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Calculus & Matrices-Business & Econometrics
MATH 200
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Data Analysis and Decisions
MBAD 6222
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Executive Decision Making
DNSC 6258
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Exploring Finance with Simulation
FINA 6290
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Financial Accounting
MBAD 6211
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Financial Management
MBAD 6234
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Financial Markets
MBAD 6233
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Global Perspectives
MBAD 6241
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International Business in Operations
MGMT 361
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International Management
MBAD 6244
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International Shipping
MSCM 370
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Judgment/Uncertainty & Decisions
MBAD 6221
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Leadership/Executive Development
MGT 6253
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Legal Environment of Business - Business Law
FINA 331
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Linear Models for Data Science
STAT 6021
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Macro Economics For The Global Economy
6243
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Management Science
DSCI 407
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Managerial Accounting
6213
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Managerial Economics
ECON 301
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Marketing Decisions
MBAD 6273
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Micro Economics For The Global Economy
MBAD 6242
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Nature of Markets
MBAD 6272
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Operations Management
MBAD 6223
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Operations Management/Technology
OPMT 303t
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Optimization Models for Decision Making
DNSC, 6251
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Port Management
MSCM 472
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Precalculus I
MATH 162
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Principles of Accounting
ACCT 201
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Principles of Accounting
ACCT 202
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Principles of Macroeconomics
ECON 201s
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Probably-Decisions-Analysis-Business-Statistics,
DSCI 206
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Programming and Systems for Data Science
CS 5010
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Real Estate Valuation & Investment
FINA 6242
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Simulation Modeling/Analysis-Business Systems
DSCI 476
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Social Network Analysis/Managers
DNSC 6290
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Spreadsheet Modeling/Analysis Business Decisions
DSCI 406
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Statistical Data-Analysis-Management Science
DSCI 306
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Statistics for Management
DNSC 6202
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Strategic Sourcing/Purchasing
MSCM 430
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Strategy Formulation & Implementation
MBAD 6286
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Supply Chain Management & Logistics
MSCM 441
Projects
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wrangling_api_data
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See projectThis project involves a series of efforts taken @ Springboard's Data Science: Machine Learning Bootcamp program.
In this overview, I will venture into the space of the Application Program Interface (API). The following exercise will require us to pull some data from the Qunadl API. Qaundl is currently the most widely used aggregator of financial market data.
An application program interface (API) is an establishment of routines, protocols, and tools for creating software…This project involves a series of efforts taken @ Springboard's Data Science: Machine Learning Bootcamp program.
In this overview, I will venture into the space of the Application Program Interface (API). The following exercise will require us to pull some data from the Qunadl API. Qaundl is currently the most widely used aggregator of financial market data.
An application program interface (API) is an establishment of routines, protocols, and tools for creating software applications. An API stipulates how software components ought to interact. APIs built into programming graphical user interfaces (GUI) elements save developers time. Good APIs make it easier to develop programs by front-loading most of the necessary building blocks needed to interact with what it does. A programmer then assembles the blocks in the program he or she was developing. There are several types of APIs for operating systems, applications, or websites. Microsoft Windows, for example, has many API sets that are utilized by system hardware and software applications. For example, when you copy and paste text from one form into another, it is the API that allows that integration to work. -
wrangling_.json_filetypes
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See projectIn this overview, I will venture into the lightweight data-interchange format JavaScript Object Notation (JSON) data structure.
JSON is effortless for humans to read and write while also being accessible for machines to parse from and generate. JSON is a lightweight data-interchange format working between clients, servers, and embedded in programming languages. It is a subset of the JavaScript object and distinct from any other programming language. JSON is a subset of the JavaScript…In this overview, I will venture into the lightweight data-interchange format JavaScript Object Notation (JSON) data structure.
JSON is effortless for humans to read and write while also being accessible for machines to parse from and generate. JSON is a lightweight data-interchange format working between clients, servers, and embedded in programming languages. It is a subset of the JavaScript object and distinct from any other programming language. JSON is a subset of the JavaScript Programming Language Standard: ECMA-262 3rd Edition - December 1999. It has a text format that is completely language independent but utilizes analogs that are accustomed to programmers of the “C- family” of languages: C, C++, C#, Java, JavaScript, Perl, Python, and many others 1.
JSON’s human-readable data structures are efficient for more extensive data exports that contain a hierarchically structured format.
The basic structure of the JSON object is as follows:
i. Data is by name/value pairs with colons.
ii. Commas split data-objects.
iii. Curly braces {} hold Data-Objects.
iv. Square brackets [] can be utilized to indicate a list of objects in a group.
v. Data-Element enclosed with quotes for characters, and without quotes for a numeric value. -
https://github.com/ahull002/wrangling_csv.git
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See projectIn this overview, I will venture into the domain-specific Structured Query Language (SQL) utilized in programming and designed for managing data residing in relational database management systems (RDBMS), or even for stream processing in a relational data stream management system (RDSMS).
Structured Query Language, or is commonly called SQL (pronounced "ess-que-el"), is a domain-specific programming language. SQL utilized for communicating with relational database management systems…In this overview, I will venture into the domain-specific Structured Query Language (SQL) utilized in programming and designed for managing data residing in relational database management systems (RDBMS), or even for stream processing in a relational data stream management system (RDSMS).
Structured Query Language, or is commonly called SQL (pronounced "ess-que-el"), is a domain-specific programming language. SQL utilized for communicating with relational database management systems (RDMS). According to the ANSI (American National Standards Institute), it is the standard language for RDMS. SQL statements are used to carry out tasks such as revise data in a database or retrieve data information from a database. Traditional RDMS that make use of SQL are Oracle, Sybase, Microsoft SQL Server, Access, and Ingres. Although nearly all database systems use SQL, most of them also have their additional exclusive extensions that are usually only used on their respective networks. SQL commands such as "Select," “From,” "Insert," "Update," "Delete," "Create," “Group,” “Order,” and "Drop" can be employed to accomplish almost everything that someone needs to do in a database. This overview utilizes SQL to conduct Exploratory Data Analysis (EDA) on simulated data from a Country Club that mimics a real-world example of what Data Scientists will face in the workforce when confronted with various data sets or databases.
Honors & Awards
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Letter of Appreciation
Department of the Navy, Space and Naval Warfare Systems(SPAWAR): Norfolk, VA
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Letter of Appreciation
Department of the Navy, Naval Audit Service (NAVAUDSVC): Norfolk, VA
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2nd place Award: The Climate Change – Sea Level Rise initiative
Old Dominion University
The Climate Change – Sea Level Rise initiative is an effort to identify the multi-faceted impact climate change and rising sea levels will have on our region.
The initiative is led by James V. Koch, Board of Visitors Professor of Economics and President Emeritus, and Oceanography Professor Dr. Larry Atkinson. The project pulls together the university and region's foremost experts to find solutions to the anticipated effects on the economy, housing, ports, and infrastructure…The Climate Change – Sea Level Rise initiative is an effort to identify the multi-faceted impact climate change and rising sea levels will have on our region.
The initiative is led by James V. Koch, Board of Visitors Professor of Economics and President Emeritus, and Oceanography Professor Dr. Larry Atkinson. The project pulls together the university and region's foremost experts to find solutions to the anticipated effects on the economy, housing, ports, and infrastructure. Facilitating faculty engagement in Climate Change and Sea Level Rise research is a priority of Old Dominion University. Our goal is to make ODU a recognized leader in this area in order to address the challenges that will significantly affect not only the Hampton Roads region but also the country and the world. To achieve this goal, we seek to strengthen relationships among faculty, colleges, centers and departments working in and across disciplines that address Climate Change and Sea Level Rise impacts. In an effort to promote and support faculty interested in forming teams to address Climate Change and Sea Level Rise issues facing a metropolitan environment, the Office of Research accepted proposals for short-term seed funding.
The 2011 On Target: Case Study Competition was hosted by the Career Management Center and the Undergraduate Research Program. Student teams were asked to resolve a corporate problem that spans several functional areas. Teams addressed the following questions about sustainable business practices at TARGET;
1) how can Target fulfill the guests' wants to support sustainability through its product offerings?
2) How can Target communicate its commitment to sustainability in a meaningful, clear, and concise way?
Each team prepared a written executive report. Based on an evaluation of the reports, the top three teams presented their conclusions in 15-minute presentations before a panel of Target Corporation Executives.
Languages
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Spanish
Professional working proficiency
Organizations
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Military Operations Research Society (MORS)
Co-Chair Artificial Intelligence (AI) and Autonomy Workshop (Defense and Security Applications)
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Old Dominion University
Chair Academic Advisory Council (Dept. of IT and Decision Sciences)
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Association of Military Comptrollers
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Chesapeake Bay Foundation
Volunteer
- Present
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Krissy Tripp
I work with influencers 💅 I think if you asked them both, they sort of hate being called influencers 😆 Regardless of the semantics, they're innovators. Rather than sticking with the status quo tools they're most familiar with, they're constantly diving into what's new and exciting in the world of business intelligence. If you'll be at the Sigma Summit conference next week, be sure to ask them what they're working on.
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