Incorta

Providing strategic leadership in the design and execution of machine learning product initiatives, aligning them with overall business objectives and ensuring seamless integration with existing systems.

Principal Machine Learning Engineer

Volvo Cars

Advanced AI & Analytics Team (Production)

Staff Machine Learning Engineer

Senior Machine Learning Engineer (Tech Lead)

Innovation, Tech Strategy & Tech Fund (Corporate VC)

Senior Data Scientist

Jackson State University

• Designed, deployed and supported custom cluster deployments for scientific researchers.
      • Managed a 7-node heterogeneous Cray computing cluster equipped with 7120P Intel ® Xeon Phi ™ coprocessors and InfiniBand network interconnect at Mississippi E-center.
      • Built and managed 3-node HPE ProLiant BL460c Gen10 servers with two E5-2650v5 processors and 64 GB memory each as a testbed Hadoop and Spark cluster.
      • Helped building high-performance visualization systems. 
• Examined the literature on applying complex detection and classification algorithms on image data from multiple aging-related diseases and training deep learning models.
• Applied Machine Learning algorithms to functional magnetic resonance imaging (fMRI) for Alzheimer disease using multi-class, hybrid classifiers.

Graduate Researcher

• Facilitator for the National Science Foundation-funded Extreme Science and Engineering Discovery Environment (XSEDE). Provided information about high-performance computing resources to researchers and educators, including resources available from XSEDE that will help them to advance scientific discovery. Assist the researchers to quickly get access to allocations on national and local high-performance computing resources.
• Facilitated and conducted awareness and training workshops about the use of high-performance computing resources and services. Trained users in using job schedulers, submit jobs, Linux, programming languages such as Python, R, & scala, parallelism, and computational methods.

Campus Champion

• Supervised and mentored undergraduate and graduate students.
• TA for several graduate & undergraduate classes (Applied Programming in Python , OOP C++, Database Systems, and Data Mining)

Graduate Teaching Assistant

- Taught (CSC 115) Digital Computer Principles in the Computer Science Department at College of Science, Engineering & Mathematics.
- Subject matter consists of word processing, spreadsheet, database, graphics, computing, data processing, the organization of a computer, input and output devices, number systems, internal data representation and an introduction to a high-level programming language.

Adjunct Faculty

• Responsible for Simplicity (https://www.symplicity.com/) system integration with the JSU housing system. ETL, data transformation & API consumption.
• Administrated Residence Life Management System; information, changes, and updates.
• Developed and administrated the Residence Life website using WordPress.

Technology & Web Developer

C Spire

• Built complex workflows (automation) that combine various data-flow projects. 
• Gross matched data across multiple common entities, using shared keys.
• Created rollups by grouping user-level data at the business level, cell site, area, etc. 
• Developed rich interactive graphics and data visualizations of large structured data in browser-friendly formats using client-server architecture.
• Conducted R&D related to data differential privacy and data protection.

Data Scientist

• Utilized Incorta's Direct Data Mapping architecture to develop machine learning models and predictive analytics to categorize, block, and remediate targeted attacks and Advanced Persistent Threats (APT)
• Converted PL-SQL Query logic from clients to fit Incorta's PySpark architecture.

Xtrava Health

• Developed a hybrid deep learning approach using deep belief network
and hidden Markov models (DL-HMM) for bio-sensor data from a triaxial accelerometer to recognize human activity & extract intrinsic features.
• The developed model utilizes the massive unlabeled acceleration samples for unsupervised feature extraction and later for classification.

Data Analyst

Illuminate Education, Inc.

• Developed a web-based Internal Support System used by the support team to safely query the main database through a GUI using ASP.NET MVC architecture.
• Developed RESTful Web services for the main product.
• Supervised 2 undergraduate interns.

Web Developer

University of Science and Technology, Yemen

• Taught TQM(1203) Mathematics for Information Technology, International College.
• Taught TIA(1113) Programming Fundamentals (Java), International College.
• Evaluated the Student’s class work and assignments: testes, labs, and reports.

Adjunct Faculty Instructor

Exceed Language Center

• Developed the Institute Management System (IMS) using WinForms as the interface and C# & SQL server as the backend. The system contains different modules; Registration, Management, Teachers' grading, and Reporting.

Software Engineer

• Taught English Language, Level 4 Low Intermediate (LINT) &  Level 5 Hight Intermediate (HINT). Maintained discipline in the classroom – Created a vibrant teaching atmosphere by using the communicative approach.

English as a Second Language (ESL) Instructor

Worked at Mississippi e-center @ JSU on a project to develop an educational Android mobile app to teach “Java” for K-12.

Undergraduate Research Assistant

Multi-agent collaboration has emerged as a key AI agentic design pattern. Given a complex task like writing software, a multi-agent approach would break down the task into subtasks to be executed by different roles -- such as a software engineer, product manager, designer, quality assurance engineer, and so on -- and have different agents accomplish different subtasks.

Different agents might be built by prompting a LLM to carry out different tasks. For example, to build a software engineer agent, we might prompt: "You are an expert in writing clear, efficient code. Write code to perform the task …". 

It might seem counterintuitive that, although we are making multiple calls to the same LLM, we apply the programming abstraction of using multiple agents. I'd like to offer a few reasons:
- It works! Many teams are getting good results with this method, and there's nothing like results! Ablation studies (for example, in the AutoGen paper cited below) show that multiple agents give superior performance to a single agent. 
- Even though some LLMs today can accept very long input contexts, their ability to truly understand long, complex inputs is mixed. An agentic workflow in which the LLM is prompted to focus on one thing at a time can give better performance. 
- It gives us a framework for breaking down complex tasks. When writing code to run on a single CPU, we often break our program up into different processes or threads. This lets us decompose a task -- like implementing a web browser -- into subtasks that are easier to code. Multi-agents roles is, similarly, a useful abstraction.

In companies, managers decide what roles to hire and then how to split complex projects into smaller tasks to assign to employees with different specialties. Using multiple agents is analogous. Each agent implements its own workflow, has its own memory (itself a rapidly evolving area in agentic technologies -- how can an agent remember enough of its past interactions to perform better on upcoming ones?), and may ask other agents for help. Agents themselves can also engage in Planning and Tool Use. While managing people is hard, it's a sufficiently familiar idea that it gives us a mental framework for how to "hire" and assign tasks to our AI agents. 

Frameworks like AutoGen, Crew AI, and LangGraph provide rich ways to build multi-agent solutions. If you're interested in playing with a fun multi-agent system, check out ChatDev, an open source implementation of a set of agents that run a virtual software company. While it may not always produce what you want, you might be amazed at how well it does! 

To learn more, I recommend: 
- Communicative Agents for Software Development, Qian et al. (2023) (the ChatDev paper)
- AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, Wu et al. (2023) 
- MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, Hong et al. (2023) 

[Original text: https://lnkd.in/g7gShRnf ]

Ebrahim Alareqi

deeplearning.ai • 9 min read

AI Agents With Low/No Code, Hallucinations Create Security Holes, and more

It’s finally here! I’m thrilled to introduce you to Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights:
• 8B and 70B parameter openly available pre-trained and fine-tuned models
• Trained on more than 15T tokens, 7x+ larger than Llama 2's dataset!
• State-of-the-art performance across industry benchmarks
• New capabilities, including enhanced reasoning and coding
• 3x more efficient training than Llama 2
• New trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• Training underway on a leading-edge 400B+ parameter model 
We’re also releasing a new version of Meta AI powered by Llama 3. We’re making it available in 13 new countries, integrating enhanced search capabilities and adding new ways to experience it on the web at meta.ai. Our latest updates to image generation on Meta AI is state of the art, allowing you to create, animate and share stunning images with a simple text prompt in seconds. We think Meta AI is the world’s most intelligent AI assistant and we can't wait for you to try it and tell us what you think.

Read more about it here: https://lnkd.in/eB5yzi8R

2024 is the year of small models. GPT-3.5 level on your phone thanks to Phi-3 MIT license just released on Hugging Face by Microsoft!

🌟 Excited to introduce Ebrahim Alareqi  a featured speaker at the AI Tech Stack Masters Exchange in Atlanta! 🌟

Ebrahim serves as Principal Machine Learning Engineer at Incorta, a unified data and analytics platform dedicated to making it quick and easy to unlock the full potential of data from multiple complex source systems.

On the first day of the exchange, Ebrahim will lead the session: Incorta Use Case Showcase: Transforming Analytics with AI. The session will walk attendees through Incorta's technology stack, illustrating its revolutionary impact on analytics across various industries.

Stay in the loop with real-time updates on the AI Tech Stack Masters Exchange by following the Intelligent Enterprise Leaders Alliance on LinkedIn! Join us to stay informed and engaged with the latest insights and developments in the world of AI.

#AITechStack #analytics #datamanagement #aianalytics #tech

Are you at the AI Tech Stack Masters Exchange in Atlanta this week? 🚀

This morning Ebrahim Alareqi shared how Incorta can help boost productivity with no-code GenAI features, customizable dashboards, and AI-driven insights.

Learn more about #IncortaX 👉 https://bit.ly/3vKd6du #AI #ML #IncortaX #GenAI Caroline Jacklin Intelligent Enterprise Leaders Alliance

Welcome to Day 1 of the AI Tech Stack Masters Exchange - Get ready for an electrifying start! 💥 #AITSME

Today, we’ll hear from top thought leaders in the realm of AI. Throughout the day, attendees will engage with industry expert for cutting-edge insights in keynote presentations, dive into thought-provoking discussions during panel sessions on AI strategies, and connect with peers in networking sessions to forge valuable connections.

Here’s a glance at what we have planned for the day!

Day 1 speakers 🎤: Lutz Beck | Ryan Priem | Ebrahim Alareqi | Robert Long | Gu X. | Howard Holton | Natalie G. | Randy Storch | Venkat Vedam | Arun Nandi | Narender Chinthamu MBA,TOGAF® 9, PSM I®, GCP®, AZURE®,SAFe® | Kevin Pho, M.D. | Shola Oyewole

Day 1 Featured sponsors🌟: Glean | Incorta | Apptad Inc. | Soda | Informatica

#AITechStack #FutureofAI #datastrategies #datamaturity #GenAI

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