Academia

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

Datacast Episode 65: Chaos Theory, High-Frequency Trading, and Experimentations at Scale with David Sweet

David Sweet was a quantitative trader at GETCO, where he used experimental methods to tune trading strategies, and a machine learning engineer at Instagram, where he experimented on a large-scale recommender system. He is currently writing a book called "Tuning Up," an extension of lectures given at NYU Stern on tuning high-frequency trading systems. Before working in the industry, he received a Ph.D. in Physics and published research in Physical Review Letters and Nature. The latter publication – an experiment demonstrating chaos in geometrical optics -- has become a source of inspiration for computer graphics artists, undergraduate Physics instructors, and an exhibit called TetraSphere at the Museum of Mathematics in New York City.

Datacast Episode 60: Algorithms and Data Structures For Massive Datasets with Dzejla Medjedovic

Datacast Episode 60: Algorithms and Data Structures For Massive Datasets with Dzejla Medjedovic

Dzejla Medjedovic earned her Ph.D. in the Applied Algorithms Lab of the Computer Science department at Stony Brook University in 2014. Dzejla has worked on a number of projects in algorithms for massive data, taught algorithms at various levels, and spent some time at Microsoft. Dzejla is passionate about teaching, promoting computer science education and technology transfer. She now works as an assistant professor of Computer Science at the International University of Sarajevo.

Datacast Episode 56: Apprehending Quantum Computation with Alba Cervera-Lierta

Datacast Episode 56: Apprehending Quantum Computation with Alba Cervera-Lierta

Alba Cervera-Lierta is a postdoctoral researcher at the Alán Aspuru-Guzik group at the University of Toronto. She obtained her Ph.D. at the University of Barcelona in 2019. Her background is in particle physics and quantum information theory. She has focused on quantum computation algorithms in the last years, particularly those suited for noisy-intermediate scale quantum computation.

Datacast Episode 51: Research and Tooling for Computer Vision Systems with Jason Corso

Datacast Episode 51: Research and Tooling for Computer Vision Systems with Jason Corso

Dr. Jason Corso is the new director of the Stevens Institute for AI. He is also the co-founder and CEO of Voxel51, an AI software company creating development tools for improving the performance of computer vision and machine learning systems. Previously, he was a professor of electrical engineering and computer science at the University of Michigan. A veteran in the field of computer vision, Jason has dedicated over 20 years to academic research and has authored nearly 150 academic papers and hundreds of thousands of lines of open-source code on video understanding, robotics, and data science. He received his Ph.D. and MSE degrees from Johns Hopkins University and his bachelor’s degree from Loyola University Maryland, all in computer science.

Datacast Episode 45: Teaching Artificial Intelligence with Amita Kapoor

Datacast Episode 45: Teaching Artificial Intelligence with Amita Kapoor

Amita Kapoor is an Associate Professor in a college at the University of Delhi. She has 20+ years of teaching experience. She is the co-author of various best-selling books in the field of Artificial Intelligence and Deep Learning. A DAAD fellow, she has won many accolades, with the most recent Intel AI Spotlight award 2019 in Europe. As an active researcher, she has more than 50 publications in international journals and conferences. She is extremely passionate about using AI for the betterment of society and humanity in general.

Recommendation System Series Part 3: The 6 Research Directions of Deep Recommendation Systems That Will Change The Game

Recommendation System Series Part 3: The 6 Research Directions of Deep Recommendation Systems That Will Change The Game

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 3 addresses the limitations of using deep learning-based recommendation models by proposing a couple of research directions that might be relevant for the recommendation system scholar community.

Datacast Episode 20: Managing Data Scientists with Sunanda Parthasarathy

Datacast Episode 20: Managing Data Scientists with Sunanda Parthasarathy

Sunanda Koduvayur Parthasarathy is an Associate Director of Data Science at Wayfair Inc., where she leads a team of data scientists and engineers to build machine learning solutions that lead to a better shopping experience for all Wayfair customers. Prior to this, she was leading data science innovation R&D projects at the ad-tech startup DataXu. Through her career in data science, she has enjoyed bringing quantitative definition and clarity to unwieldy strategic business questions that have led to multiple high-revenue products.

Datacast Episode 17: Computer Vision Research with Genevieve Patterson

Datacast Episode 17: Computer Vision Research with Genevieve Patterson

Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her Ph.D. from Brown University in 2016 under the direction of James Hays.

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Datacast Episode 15: Thoughts on Data Science from the perspective of a behavioral scientist with Nick Gaylord

Nick Gaylord has worked as a data scientist in the Bay Area for about the last 5 years. Currently, he’s a member of the Johnson & Johnson Health Technology team, and prior to that, he has worked in different fields ranging from small business revenue analytics to enterprise machine-learning-as-a-service platforms. Like many data scientists, he started out as an academic before transitioning to industry, in his case earning a Ph.D. in Psycholinguistics from the University of Texas at Austin in 2013.

Datacast Episode 1: From Molecular Biologist to Data Scientist with Dr. Jon Leslie

Datacast Episode 1: From Molecular Biologist to Data Scientist with Dr. Jon Leslie

Dr. Jonathan Leslie obtained his Ph.D. in Biology from the University of London, studying blood vessel formation at the Cancer Research UK London Research Institute. After 20 years of researching the molecular processes underlying cancer, he turned to data science and founded a freelance consultancy business. He is passionate about promoting open-source software and routinely volunteers as a mentor in the R-programming and data science communities.