Work with thought leaders and academic experts in Data science

Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](https://www.dainamic.ai/), a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](https://www.livingopera.org/), a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.

As a dynamic and growth-oriented Data Scientist, I have extensive experience in data analytics, research and development, and engineering, with a particular focus on cognitive science and psychophysiology. Throughout my career, I have consistently demonstrated a goal-directed approach, leveraging advanced statistical techniques, machine learning algorithms, and data visualization tools to extract meaningful insights from complex datasets. <br> Being a forward-thinking professional, I led and managed end-to-end cognitive science experiments, from participant recruitment to the execution of data acquisition processes, ensuring the integrity and efficiency of research initiatives. My expertise extends to conducting advanced generative modeling and sophisticated graph theoretical analyses, providing unparalleled insights into dynamic fluctuations within large-scale networks. I am known for my ability to streamline data processing workflows through the implementation of automated pipelines in MATLAB and Python, resulting in a significant increase in team productivity. As a compassionate and motivational team member, I coordinate with multidisciplinary teams, including neuroscientists, clinicians, and engineers, to synthesize findings and integrate perspectives. Additionally, I have facilitated multimodal neuroimaging studies by creating efficient experimental protocols for the simultaneous use of electroencephalography and transcranial electrical stimulation, navigating complex regulatory processes while always adhering to ethical practices. Effective communication and collaboration are at the core of my approach, allowing me to work seamlessly with colleagues from diverse backgrounds. My problem-solving abilities have been instrumental in overcoming complex challenges, and my commitment to data ethics and privacy ensures that research initiatives are conducted responsibly and with the utmost integrity. Some of my skills are listed below: Data Visualization \| Statistical Data Analysis \| Experimental Design & Research \| Computational Modeling \| Machine Learning & AI \| Forecasting & Benchmarking

I am an **AI and Data Science expert** with two decades of experience specializing in machine learning, data engineering, and generative AI (GenAI). I hold an **MSc in Data Science & Predictive Analytics from** **Northwestern University**, where I honed my skills in advanced analytics, predictive modeling, and data-driven decision-making. Throughout my career, I have transformed vast datasets into actionable insights that drive innovation and efficiency for organizations. I have successfully developed and implemented enterprise-level AI strategies, designed advanced machine learning models, and created data-driven solutions for a range of industries. With a deep understanding of customer behavior, perception, and quality of experience, I excel at uncovering patterns in data that unlock business potential. My work spans from predictive modeling to designing recommendation systems and search algorithms that improve user engagement and operational performance. I have a proven track record of delivering clear, compelling narratives to both technical and non-technical audiences, making complex concepts accessible and actionable. I am passionate about bridging the gap between academia and industry, helping organizations leverage cutting-edge AI technologies to solve real-world problems. My expertise is complemented by a collaborative approach, working closely with cross-functional teams to deliver impactful, scalable solutions. I look forward to partnering with organizations seeking to accelerate innovation through AI and data science.

Team lead in machine learning, neuroscience. <br> I have captured and executed projects for DARPA, NASA, and several international clients (GM, Toyota, Fujitsu). \* 12 projects transitioned--$10M revenue captured (31+ publications, 32+ patents) \* 9 awards including those from NASA, GM, and HRL Laboratories \* I also have startup, contracting and consulting experience \* US citizen (authorized to work) <br> Last updated: Aug 30, 2023

**Results-driven AI leader with 20+ years of success spearheading model development and optimization initiatives in the energy industry and academia. Proven track record in leveraging computational data science, scientific machine learning, and AI to drive breakthrough physics-data solutions and deliver tangible business value. Adept at translating complex scientific concepts into robust AI models. Skilled in numerical simulation, scientific machine learning, and bilingual communication to optimize project outcomes.**

As a Postdoctoral Researcher at UC Irvine, I apply my PhD in Cognitive Neuroscience to investigate the neural mechanisms of learning and memory. I use advanced data management, analytics, techniques to process and analyze large-scale brain imaging data sets. I have over seven years of research experience, including as a Graduate Research Assistant at Florida International University, where I contributed to multiple publications and projects on cognitive and affective neuroscience. I am passionate about advancing the scientific understanding of the brain and its functions, and collaborating with other researchers and experts in the field. I am also committed to sharing my knowledge and skills with the next generation of neuroscience students and professionals.

Dr Jo Boaler is a Professor of Mathematics Education at Stanford University, and the co-founder of youcubed. Her PhD won the national award for educational research in the UK and her book: Experiencing School Mathematics won the ‘Outstanding Book of the Year’ award for education in Britain. She is an elected fellow of the Royal Society of Arts (Great Britain), and a former president of the International Organization for Women and Mathematics Education (IOWME). She is the recipient of a National Science Foundation ‘Early Career Award’ and the NCSM Kay Gilliland Equity Award (2014). She is the author of nine books and numerous research articles. Her latest book is Mathematical Mindsets: Unleashing Students’ Potential through Creative Math, Inspiring Messages and Innovative Teaching (2016), and is published by Wiley. She serves as an advisor to several Silicon Valley companies.

Dr. Justin P. Whalley was educated in the UK (M.Sci. Mathematics, University of Bristol) and France (Ph.D. Bioinformatics, University of Évry). He moved to Spain to work as a postdoc at the National Center for Genomic Analysis (CNAG). During his time there, he ran the Quality Control working group for the Pan-Cancer Analysis of Whole Genomes project to assess the data coming in and reduce batch effects. This involved collaboration with researchers from the Broad Institute of MIT and Harvard, the German Cancer Research Center and the Wellcome Sanger Institute in the UK. He returned to the UK to work as a [Senior Bioinformatician at the University of Oxford](https://www.well.ox.ac.uk/people/jpw/). His time there coincided with the global pandemic and he was deeply involved in the COvid-19 Multi-omics Blood ATlas (COMBAT) consortium as the lead for the Integration (Tensor) working group. Dr. Whalley became a member of the [faculty of the Chicago Medical School at Rosalind Franklin University of Medicine and Science](https://www.rosalindfranklin.edu/academics/faculty/justin-p-whalley/) in January 2023.

My interest in interdisciplinary approach to solving problems has led me to working on research projects in a range of engineering applications. By education and work experience, I am proficient in applying computational mechanics and multi-physics analysis for scientific research and R&D projects. <br> I am also driven by data science to create new ways of approaching difficult problems. I envision a future where the application of machine and deep learning leads to a more connected world where tasks are accomplished more efficiently and decisions are made more knowledgeably. The mega-project that I am currently working on as a Data Scientist is applying deep learning and time series analysis for pricing financial options.

I am a lecturer at the University of California, Davis, teaching undergraduate students in physics and engineering basic and advanced physics. I also collaborate with several experimental and theory groups in condensed matter physics all over the globe focussing on critical phenomena, quantum magnetism, topological materials, transport properties, and driven quantum systems. My current interests lie in Hilbert Space fragmentation and associated critical phenomena for systems with subsystem symmetries. Additionally, I use the numerical linked cluster expansion to study heavy fermion compounds of interests to groups in John's Hopkins and Princeton Universities.