SGX Analytics Team has worked in multiple data science development, data strategy consulting, quantitative analysis, and technology projects, with expertise acquired in the field working for, advising, designing, and implementing data science and technology solutions for institutions such as AIG, AIA, Willis Group, Tokyo Marine, PDVSA, the Federal Reserve Bank of Boston, the World Bank, the Inter-American Development Bank, Deutsche Bank, Lehman Brothers, Sense.ai, Algorhythm.ai, PreHype, Verizon Communications, Intel Labs, Swiss Re, Pauli.ai and others.
Luis is a seasoned professional with an MBA in Finance and over 20 years of experience in capital markets, insurance, and data science. His career began as Director of Quantitative Analysis for hedge funds in New York, after which he joined AIG to co-develop their first weather risk model. Moving on to Deutsche Bank, Luis led the bank's first catastrophic risk transfer structure.
He later served as a Senior Vice President at Lehman Brothers and then founded Ttwick Inc., a tech startup whose assets were acquired by Elliott Management Associates. With 12 patents in machine learning, Luis has also been a featured speaker at multiple international forums and is proficient in a wide array of programming languages and tools. The scope of Luis’s influence extends beyond corporate roles; he’s a thought leader in the industry. He has been featured in “The Data Science Handbook,” which showcases the top 25 data science practitioners in the USA. He’s also an active contributor to conferences and forums, discussing topics from alternative investments to artificial intelligence. Luis is a top-ranked coder on Stack Overflow and a LASPAU scholar, a Fulbright scholarship administered by Harvard University. His academic credentials include an MBA earned in 1992 and a B.Sc. in Civil Engineering obtained in 1987. He also has undergraduated degrees in Programming & Computer Science, and graduate professional certificates in General Artificial Intelligence and Natural Language Processing from Stanford University.
Kosrow is on the faculty of Financial Engineering of Columbia University and the Department of Finance and Risk Engineering of New York University. He is also the founder of the New York Institute and Laboratory for Artificial Intelligence. Previously, Kosrow was Assistant General Manager and Head of Analytic and Quantitative Trading and Co-Head of the Hedge Fund Group at Samba Financial, where he initiated the use of Big Data and Data Mining to develop quantitative trading strategies.
Before joining Samba, he was a Managing Director in the Structured Credit Products at Citigroup, in charge of Exotic Credit Trading. Previously, he was head of Fixed Income Derivatives Structuring and New Products at the same firm. Previous to that, he joined the Derivatives Marketing and Trading Group of Chase Manhattan Bank, as an Executive Director. Kosrow started his career on Wall Street at the quantitative trading firm of D.E. Shaw, in charge of developing and back testing statistical arbitrage trading strategies for equities and commodities. Dr. Dehnad received his BSc. in Mathematics with first class honors from the University of Manchester in England and his PhD in Mathematics from the University of California, Berkeley. After receiving his second PhD in Applied Statistics from Stanford University, he joined AT&T Bell Labs where he published the book “Quality Control and Taguchi Method” and more recently, his latest book “Blockchain Investors’ Manual” was published in 2020. Kosrow has been on both the sell and buy side of the financial markets. He has structured, traded, and risk managed FX, Fixed Income, Equities, Credit, Commodities and Alternative Investments.
Natalia Díaz Rodríguez holds dual Ph.D. degrees from the University of Granada in Spain and Åbo Akademi University in Finland. She is currently a Marie Curie postdoctoral researcher at the DaSCI Andalusian Research Institute and has had diverse professional experiences ranging from Silicon Valley to NASA's FDL Programme.
Natalia has also served as an Assistant Professor of Artificial Intelligence at ENSTA, Institut Polytechnique Paris. Her research interests lie in deep learning, explainable AI, and AI for social good, with a particular focus on neural-symbolic approaches to responsible and ethical AI. Earlier in her career, she has worked with esteemed organizations like CERN, Philips Research, and the University of California Santa Cruz. Natalia’s work has not only been influential but also widely recognized. She is the winner of the Andalusian of the Future award in the Science category and has received the Leonardo Grant Award by BBVA Foundation for her project on explainable AI. Other notable recognitions include the Google Research Scholar Grant, an Outstanding Contribution award from FDL- NASA-SETI for her work on climate change visualization, and the UNIVERGEM UGR Emprendedora prize for providing support to women suffering from gender violence. She has also been a finalist for the Ada Byron Outstanding Woman Technologist award and has been selected as a Heidelberg Laureate Forum Fellow twice. Additional accolades include the Kavli Fellow and Summer Institute on Cognitive Neurosciences Fellow awards, as well as the Google Anita Borg Award for being among the top 40 women in EMEA for academic strength, leadership, and passion in computer science.
Eric is the co-founder and Chief Data Scientist of SGX Analytics with a Ph.D. in Economics. With several years of experience in capital markets, his focus has been in research and data science. Before co-founding SGX Analytics, Eric worked as a Data Scientist and Quantitative Analyst at Ttwick Inc; where he developed time-series analyses and computational linguistic algorithms to decode the stock market.
Earlier in his career, Eric served as a Teaching Assistant at NYU, specialized in Econometrics, and worked for the Federal Reserve Bank of Boston in econometric analysis and macroeconomic forecasting. His coding proficiency extends across multiple languages and tools, from Python and R to Fortran 90 and Java. Eric’s credentials are further highlighted by his academic achievements. He earned his Ph.D. in Economics from NYU, focusing on Macroeconomics, International Economics, Econometrics, and Computational Economics. He also graduated Summa Cum Laude with a Major in Economics and History from UC San Diego. Eric has been a featured speaker at Bloomberg NY, specifically on the subject of “Data Investing: Using Python and Machine Learning to gain insights into the dynamics of stockprice returns around earnings announcements.” These accomplishments demonstrate his blend of academic rigor and practical expertise in the realm of economics and data science.