Robert Corwin is the founder and CEO of Austin Artificial Intelligence, Inc., a premier data science, machine learning and AI consultancy serving clients in the technology, financial, and industrial sectors. He is responsible for leading the firm and setting its policies and standards with respect to client relationships, customer success, quality control, sales and marketing, safety, fund-raising, partnerships, and investments. Founded in 2020, the firm raised venture funding from a Northern California-based IT firm, The Silicon Partners, and has been rapidly growing ever since. Rob frequently contributes to actual client work from both technical and managerial perspectives.
Prior to Austin Ai, Rob was the Co-Chief Investment Officer and Co-Founder at EVA Capital Management, a data-based, quantitative asset management firm which offers systematic investment management strategies that include actively managed equity products and passive alternatively-weighted equity indexes. Prior to that, he was Head of Quantitative Research at EVA Dimensions, a research firm that provided investing insights using its proprietary accounting framework and database, which corrects distortions in GAAP accounting. It was acquired by Institutional Shareholder Services (ISS), the global leader in corporate governance & responsible investment.
Earlier in his career, Rob was employed at The Rohatyn Group, a multi-billion dollar emerging markets hedge fund, where he held a wide array of responsibilities in quantitative modeling, trading, and programming. Prior to The Rohatyn Group, Rob worked in the quantitative unit at an internal fund of funds at Nomura Securities in both New York and Tokyo. His responsibilities there included risk analysis, performance attribution and the development of manager selection and asset allocation models.
Rob holds a Masters in Financial Engineering from the University of California at Berkeley, a BS degree in Chemical Engineering from Cornell University and is a Chartered Financial Analyst. His interests include windsurfing, windfoiling, wingfoiling, wakefoiling, surfing, surf foiling, running, poker, piano, music production, and real estate investing.
Andrew Hong is a seasoned analytics product leader with more than 20 years of experience in business intelligence, advanced analysis, and AI/ML. His expertise is in designing, implementing, and optimizing mission-critical systems that use data analytics and optimization to provide business benefits, particularly in the supply chain and manufacturing industries.
Andrew is passionate about transforming supply chains by combining advanced analytics and machine learning. Throughout his career, he has developed and implemented BI Analytics, Big Data, and AI solutions for major manufacturers at IBM and SAP. As a leader, he is recognized for promoting thought leadership, innovation, and customer engagement among global teams. He also frequently speaks at major SAP and BI conferences, where he shares his knowledge and expertise.
Andrew is currently the managing director of Austin Artificial Intelligence, Inc., where he oversees the expansion of AI consulting services with a focus on the financial services industry. Previously, he was the VP of Analytics and Data Science at LeanDNA Inc., where he oversaw the design, development, and implementation of supply chain analytics solutions. His previous positions included Senior Principal at Infosys, Associate Partner at IBM Global Business Services, and Director at Answerthink and Factory Logic.
Andrew earned a Master of Science in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign, where he conducted research on computer vision and robotics. His technical skills include SAP HANA, AI, and machine learning on the SAP Leonardo Platform, SAP BW, SAP BusinessObjects, SAP ERP, SAP MDM, AWS, Python, Java, R, Octave, Oracle, MySQL, MS SQL, and OpenCV.
Joey Rowley is a talented data scientist working at Austin Ai in Chicago, Illinois. He has a strong academic background and extensive experience in data science, which he brings to his current role.
Joey earned his PhD in physics from Ohio University. His research focused on hadron spectroscopy and the hyperon-baryon interactions that impact the equation of state of neutron stars. Through his work, he became skilled at examining both structured and unstructured data to find key observables necessary for physics discoveries. Joey is excellent at gathering, cleaning, and organizing data for both technical and non-technical users.
Before joining Austin Ai in June 2022, Joey worked as a data scientist at Sav.com and spent time at Jefferson Lab. He then developed and improved machine learning software, increasing the speed and precision of data analysis. He also worked with GUI-driven Java calibration applications and created new software to model secondary beam scatterings inside an accelerator detector.
Joey also has a strong background in teaching and mentoring. As a teaching assistant at Ohio University, he prepared lessons, mentored students, and helped develop their critical thinking skills. His ability to explain complex information and support student learning highlights his excellent communication and analytical skills.
Joey's proficiency in statistical, algebraic, and other analytical techniques, along with his experience with machine learning algorithms, makes him a valuable member of the Austin Ai team. He is highly organized, motivated, and diligent, with a proven track record of contributing to scientific research and practical applications in data science.
Kahlil Wehmeyer is a solutions architect at Austin Ai. He helps clients quickly create AI-based solutions and guides them through the entire software development process, from the initial idea to full production. With a solid background in data science, machine learning, and cybersecurity, Kahlil always aims to deliver innovative and effective results.
He is a strong advocate for decentralization, privacy, freedom of speech, and open-source software. Among Kahlil's noteworthy projects are the Twitter analysis web app created in association with Florida Polytechnic University and the Spotify.jl package, an open-source interface for utilizing the Spotify web API in Julia.
Kahlil has multiple certifications, such as the Confluent Fundamentals Accreditation and the AWS Certified Cloud Practitioner, and a bachelor's degree in data science from Florida Polytechnic University. His work experience includes positions in prestigious organizations as a data scientist, solutions analyst, and engineer.
Fun fact About Kahlil: In his free time, Kahlil enjoys racing motorcycles and working on them, combining his love for speed with his hands-on mechanical skills.
John Zhou is a professional web developer and graphic designer at Austin Ai. He leads the development of the company's main website, ensuring a user-friendly experience and a visually appealing interface. His work also includes creating design assets for marketing efforts, such as PDFs and Figma files, event campaigns, name tags, and other branding materials.
John plays an important role in maintaining and enhancing the company’s brand identity through consistent and creative design solutions. In addition, he provides essential marketing and technical support, assisting with HubSpot and LinkedIn campaigns to boost the company’s online presence and streamline marketing processes. His innovative approach and meticulous attention to detail have significantly contributed to the company's branding efforts.
Prior to Austin Ai, John worked on many notable projects ranging from custom website development to cross-functional collaboration with graphic designers, content creators, and marketing specialists for various agencies like Reinhardt Creative LLC, Aumentar Consulting, TrueOps, and 402Lab. John's technical proficiency spans Figma, Webflow, HTML, CSS, and JavaScript, enabling him to deliver seamless user experiences.
John holds a Bachelor of Science in Physics from the University of South Florida. His background in science and a love for tinkering and perfecting things have enabled him to craft visually stunning and high-performing websites. John's commitment to excellence and his ability to exceed project expectations make him a valuable asset to Austin Artificial Intelligence.
Fun fact about John: He often finds inspiration in the creative use of colors by other artists, which he happily takes notes on. He also really enjoys checking out the designs and layouts of random things like restaurant menus, TV channels, company logos, etc.
David Davalos is a distinguished machine learning engineer and quantum physicist with extensive expertise in artificial intelligence and machine learning. His profound knowledge and innovative approach have been pivotal in driving advancements and implementing cutting-edge machine learning solutions.
David’s professional journey is marked by his dedication to bridging the gap between complex scientific theories and their practical applications. He has a strong background in mathematics and physics, including quantum mechanics, quantum information, and quantum computation. His expertise extends to mathematical applications such as machine learning and advanced statistics. David is also proficient in scientific computing with a wide array of tools, including Python, Julia, Mathematica, C++, Git, and Anaconda.
David has substantial experience leading academic research, culminating in publications in peer-reviewed journals, some with practical applications in quantum computing. Currently, he is a Research Fellow at the Research Center for Quantum Information under the framework of the Slovak National Research Platform for Quantum Technologies (QUTE), working on initiatives to create a quantum network in Slovakia.
In addition to his role as a researcher, David has also worked as a specialist and developer on machine learning algorithms and quantum computing at TechIsland. He brings a unique perspective and understanding of quantum-enhanced machine learning algorithms, making significant contributions to the field.
David is passionate about challenges and intriguing problems, leveraging a diverse set of tools to solve them. His ability to hit the ground running and quickly adapt to new business rules and codebases has been highly praised by his peers.
Fun fact about David: David enjoys video gaming and flight simulation in his free time.
Nephtali Garrido-Gonzalez is a seasoned data scientist, quantitative researcher, quantum physicist, and consultant with a solid track record in the financial sector. He represented Mexico twice at the International Olympiad in Informatics, showcasing his strong background in the overlapping fields of science, technology, computing, and business.
At Austin Artificial Intelligence, Inc., Nephtali is a Data Science Specialist. He uses his expertise in AWS, Agile methodologies, and cloud computing to create innovative solutions. His job is all about developing and implementing machine learning models that help guide business decisions and improve predictive analytics.
Nephtali also works as a consultant for Toptal, where he applies his deep knowledge in quantitative research and machine learning to solve complex problems for clients worldwide. Before this, he developed investment strategies at Q.ai, a Forbes company, and designed prediction models as a Senior Data Scientist at Kavak.com.
He earned his PhD in Physics from the University of Nottingham, where he did experimental research on quantum systems and devices. He also holds a Master’s degree in Physics from Universidad Nacional Autónoma de México and was part of the Faculty of Sciences' Choir.
Fun fact about Nephtali: He is a lifelong learner who loves diving deep into challenging problems. In his free time, he enjoys classical singing, chess, long-distance running, and street photography. He's been passionate about street photography for nearly a decade and shares some of his favorite shots on his Instagram.
With over 7 years of experience in Data Science and Quantitative Research, Oleksandr has worked across e-commerce, telecommunications, maritime, and quantitative finance. In each of these fields, he's developed machine learning models—either independently or collaboratively—from initial hypothesis testing through to production and ongoing monitoring, consistently driving impact on core business operations.
In quantitative finance, Oleksandr's work includes researching, deploying, and maintaining trading strategies with a track record, researching various strategies, and building backtesting frameworks for robust hypothesis testing.
In the maritime sector, he developed predictive models for vessel fuel consumption and optimized pathfinding, creating efficient routes that were core elements of the product platform.
In telecommunications, he made significant business outcomes by building credit scoring models that generated over one-third of the annual income for the data science department, along with customer segmentation models used in targeted marketing campaigns.
Oleksandr's skill set includes research, development, and productionizing of predictive models, big data preprocessing pipelines creation, and experience with cloud platforms (AWS, Databricks) and distributed systems (Hadoop, Spark).
Oleksandr holds a master's degree in Data Science and a bachelor's degree in Applied System analysis. His interests include sailing, riddles, and rock climbing.
Fun fact about Oleksandr: He enjoys solving riddles, and one of them, considered among the world's hardest ("The Three Gods Riddle"), took him over 1.5 years to solve.
Ahmed Ata is a skilled AI Python developer and data scientist with over five years of coding experience, as well as three years of data analysis and Python experience. He currently works at Austin Artificial Intelligence, Inc., where he started as a Python AI developer and now serves as the Egypt office manager. Ata's expertise spans various fields, including data scraping, data cleaning, LLM frameworks, and creating dashboards with Tableau.
Ata has worked as a freelancer on sports analysis projects, including predicting soccer match results and player sponsorship value. He is also a freelancing mentor at Udacity, training graduates to kickstart their freelancing careers in data analysis.
Ata is proficient in Python, Pandas, NumPy, and various visualization tools like Matplotlib and Seaborn. He has hands-on experience with machine learning using Sklearn, deep learning with TensorFlow/Keras, and working with platforms like LangChain and Haystack for LLMs. Additionally, he is well-versed in cloud services such as AWS and Azure.
Ata holds multiple certifications from Udacity, including Nanodegrees in Natural Language Processing and Deep Learning. He is committed to delivering excellent service and enjoys sharing his knowledge through mentoring and teaching.
Fun fact about Ata: He used to play Snooker professionally, which is a billiards table sport. He speaks Arabic, English, and a bit of German. Ata loves to travel and has visited about 13 countries around the world.
Fares is a skilled data scientist based in Alexandria, Egypt, with a robust engineering background and extensive experience in AI and data science projects. He holds a Master’s degree in Electrical and Computer Engineering, specializing in AI and Data Science, and is passionate about leveraging his expertise to address real-world challenges through innovative data-driven solutions.
Currently, Fares is making strides as a Data Scientist at Austin Ai, where he applies his expertise in data analysis, ETL processes, and web development to drive impactful solutions. His experience at multinational companies has honed his skills in software development and automation.
Fares is proficient in various data science tools and technologies, including Python, R, SQL, and cloud services like AWS, enabling him to develop scalable solutions and manage deployments effectively.
Known for his technical prowess and effective communication, Fares is regarded by peers as a reliable resource for resolving complex issues. With a keen interest in innovative technologies, he is excited to continue exploring the intersection of data science and engineering to make meaningful contributions.
Fun fact about Fares: He is an avid swimmer with a deep passion for the water, earning him the affectionate nickname "Baby Shark" from friends. In addition to swimming, he enjoys trying out new games and often plays padel to keep things fresh and exciting.
Mohamed focuses on leveraging advanced natural language processing to tackle complex, data-driven challenges as a data scientist specializing in large language models (LLMs). His work with LLMs involves developing and fine-tuning models to improve text comprehension, information retrieval, and interactive AI applications, including chatbots and intelligent data retrieval systems. This approach enables his team to create adaptive, context-aware solutions that provide valuable insights across various domains, aligned with specific client needs and project goals.
In addition to his expertise with LLMs, Mohamed brings practical experience in building retrieval-augmented generation (RAG) systems and designing custom tagging solutions. He recently collaborated with a notable asset management firm to discuss RAG-based applications, focusing on efficient document organization and metadata management. This process included exploring LangGraph as a potential framework for optimizing workflows and enhancing document handling. His skills in combining LLMs with structured data management allow him to help his firm deliver high-impact, automated solutions that streamline data retrieval, support decision-making, and address the complex requirements of client-centered applications. This integrated approach keeps their systems adaptive and competitive, positioning the firm at the forefront of data science and AI advancements.
Fun fact about Mohamed: In his free time, he dives into anime and American movies to explore different cultures and pick up unique insights!
Abdallah is a highly skilled AI Python developer and data scientist with extensive experience in machine learning, deep learning, and full-stack development. He is currently working at Austin Ai as a Data Scientist, where he is involved in developing advanced chatbots and Retrieval-Augmented Generation (RAG) systems using LangChain, tailored to meet client-specific needs.
He also worked as a research assistant position at The American University in Cairo (AUC), where he focused on modeling brine desalination, a niche domain. Abdallah actively trains and compares machine learning and deep learning regression models, collaborates with non-technical teams to extract insights, and contributes to research papers.
His technical proficiencies include Python, TensorFlow, Pandas, NumPy, and visualization tools like Matplotlib and Seaborn. Abdallah is well-versed in cloud services, particularly AWS, and has worked with frameworks like LangChain and Flask. His academic background includes an M.Eng. in Artificial Intelligence from the University of Ottawa, with a strong focus on applied machine learning, NLP, and smart cities.
Fun fact about Abdallah: When Abdallah isn't busy wrangling data or training AI, he's either leveling up in video games or getting lost in a good book. He swears that his epic gaming sessions have trained him to think faster, but he's still waiting for his "gamer reflexes" to kick in when it comes to dodging deadlines!
Rafiq Nazir is a seasoned software engineer, specializing in the development of mobile and web applications that seamlessly integrate with advanced AI systems. With a robust background in computer science and a degree from the National Institute of Technology, Srinagar, Rafiq has cultivated a deep expertise in creating production-ready applications. His professional journey includes notable stints as a software development engineer at Reflektive and a software developer at Algoscale, where he led critical projects and ensured the efficient development and deployment of software solutions.
Rafiq's technical proficiency spans a wide array of technologies, including React.js, Flutter, SQL, Elasticsearch, and Amazon Web Services (AWS). His work as a Research Intern at the Indian Institute of Science in Bangalore involved pioneering projects like Obstacle Detection and Avoidance for UAVs using Computer Vision, showcasing his ability to blend innovative research with practical applications.
Committed to continuous learning and sharing knowledge, Rafiq holds a HackerRank Problem Solving Certificate and has actively contributed to significant projects such as M-OCR and Namaz. His project M-OCR, associated with the National Institute of Technology, focused on solving handwritten linear equations using computer vision and deep learning.
Fun fact about Rafiq: He loves to read about economics and binge-watch movies and TV shows.
Austin Artificial Intelligence offers packaged data science, Ai and ML solutions that are neither pure services nor pure product.
Using a hybrid approach, we retain the empathetic nature of consulting engagements in order to thoroughly understand client problems - and then deploy sophisticated automated algorithms to solve them. Our size and experience allows us to deploy these solutions at scale.
Whereas many times a purely consultative or purely product approach fails to give the desired results, our solutions are the most efficient and effective way to achieve practical results in organizations of any size.
Our comprehensive approach ensures that your business can leverage the full potential of Ai and ML to drive efficiency, innovation, and growth.
Close collaboration with the business stakeholders and front office
Deeply understanding the specific dynamics of the organization
In addition to data scientists, Ai engineers, and IT professionals, we employ management consultants and industry experts
Understanding human behavior
Customization of models and output to the client’s needs
Ensuring continuous model improvement and adaptation
Selection of inputs based on industry-specific dynamics
Transformation of inputs based on fundamental business relationships
Integrating ethical considerations and regulatory compliance in Ai solutions
Removal of forward-looking and other biases in data sets
Resistance to overfitting and excessive data mining
We use Automated ML for efficiencies – but never for initial model setup – and always sanity-check the results
Leveraging the power of Artificial Intelligence (Ai) and Machine Learning (ML), we drive innovation and efficiency for businesses. Our solutions are designed to tackle the most complex challenges and unlock new opportunities for growth.
Our Ai solutions are tailored to meet the specific needs of your business.
Data-Driven Insights
Automated Processes
Predictive Analytics
We work closely with your team to understand your business objectives.
Custom Solution Design
Ongoing Support
Lean teams of engineers and scientists as opposed to SaaS-company bloat. Our staff includes multiple PhDs, with expertise in fields such as physics and quantum computing.
