Computer Science and Artificial Intelligence Laboratory (CSAIL)

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A Taste of Programming with SICP JS
Jan/18 Tue 10:00AM–12:00PM

With visiting professor Martin Henz. We can understand some computer programs in the way we solve math equations: by performing one simple algebraic step after another, until we reach an answer. This Independent Activity introduces programming in this way, inspired by the first chapter of Structure and Interpretation of Computer Programs, JavaScript edition (SICP JS). We start from first principles, by looking at functions that you know from mathematics, but before long, you will program interesting graphics and sound patterns using the Source Academy, a website built for SICP JS. The Activity offers entertaining and thought-provoking insights into the essence of computation, and at the same time an introduction to programming using the popular programming language JavaScript.


Day 1: The elements: See the basic ingredients of all computer programs
Day 2: A picture language: Program graphical patterns by wishful thinking
Day 3: Functions: Experience the magic of higher-order programming
Day 4: A curve language: Program fractals and three-dimensional curves with functions
Day 5: The lambda calculus: Explore the essence of computation
Day 6: Functional sound processing: Make some noise

For more details and to register see

AI in Finance - NLP, Graphs & Personalization
Jan/19 Wed 04:00PM–05:00PM

MIT - Capital One Tech Talk
Register in advance for this meeting:

Speakers: Abhijit Bose, Aamer Charania & Giri Iyengar

Join us to hear how AI is transforming the financial services industry with firsthand views from Capital One.

A few years ago, Abhijit Bose was leading Facebook’s East Coast AI Research, leading computer vision models to spot sunglasses on people’s faces. Now he is heading Capital One Machine Learning! Hear what attracted him to Capital One and how he envisions AI transforming the financial services industry. Abhijit will be joined by two MIT alumni - Aamer Charania and Giri Iyenga. Aamer, who leads development of machine learning services at Capital One, will talk about NLP/Conversational AI and Graphs. Giri, who leads enterprise marketing technology, will share his views on Personalization at scale.

The talk will be beneficial for folks interested in AI applications for finance in general, or like to hear about NLP/Conversational AI, Graphs or Personalization at scale.

Speaker Bios:

Abhijit Bose (

Abhijit Bose is the Managing Vice President for Capital One’s Center for Machine Learning (C4ML). Prior to joining Capital One, Abhijit served as Facebook’s Head of Engineering (Montreal, NYC, Pittsburg) for Facebook AI Research.

With over 20+ years of data science expertise, Abhijit encompasses an impressive technical and academic career history. He obtained a Bachelor's degree in Mechanical Engineering, received his dual-Masters in Mechanical Engineering as well as Computer Science, and received his dual-Ph.D in Computer Science and Engineering Mechanics.

Before joining Facebook, Abhihit was the Managing Director of Data Science for JP Morgan's Digital Organization. He’s also worked for IBM, Google, and American Express. Abhijit and his wife live in New Jersey with their 6-year-old twins. When he’s not working, Abhijit
enjoys spending time volunteering with his family at their local animal shelter, as well as hiking and touring state parks.

Aamer Charania (

Aamer Charania leads the development of enterprise Machine Learning products and services at Capital One. Before joining Capital One, he led AI initiatives at Humana and Verizon. Prior to that, he was a research assistant at MIT.

Aamer enjoys giving back to the community. He is the founder of Dallas AI (, the largest nonprofit AI meetup group in North Texas, with over 3,700 members. Aamer is also a board member of the Southern Methodist University (SMU) Big Data Advisory. In addition, he is an MIT Alumni Career Advisor, MIT Educational Counselor and former President of the MIT Alumni Club of Dallas & Fort Worth.

Aamer has been awarded over 10 patents. He holds a Masters in Engineering from MIT, a Masters in Computer Science from University of Illinois at Urbana-Champaign, and an MBA from Southern Methodist University.

Giri Iyengar (

Giri Iyengar leads the Enterprise Marketing Tech teams as part of Enterprise Products and Platforms Technology Organization. He is responsible for the Capital One Site, Messaging, Content Management, and Experimentation Technology Platforms at Capital One.

Prior to Capital One, Giri was the head of Engineering for eBay's Advertising group where he led research and development of several innovative ML driven advertising products for eBay's buyers and sellers. He also created the first ever Computer Vision team at eBay that allows billions of products to be searched using your smartphone pictures. After his PhD from MIT, he started his career as a Machine Learning Researcher at IBM Watson Research Center where he worked on Speech Recognition, Computer Vision and other Machine Learning technologies.

Big Data and Machine Learning in Investing
Jan/26 Wed 04:00PM–05:00PM

Abstract: We outline how we use Big Data and Machine Learning in our everyday investing process at BlackRock. On the Data side, we strive to incorporate a vast amount of granular information – while making the availability of this information fast and scalable for downstream models. We will give a few examples of what challenges we encounter in our data process and how we address them. On the Machine Learning side, we will discuss what makes the financial domain so special when applying ML, and which situations are more favorable for applying machine learning models. We will also present a few live applications of Machine Learning in investing, including liquidity and trading modelling, managing risk, as well as extracting alpha insights.



Ganeshapillai Gartheeban, PhD, Director, is a member of the Global Equity Research team within BlackRock's Systematic Active Equity group where he focuses on extracting patterns from large scale heterogeneous datasets.

Prior to joining the firm in October 2014, he was doing in his PhD at Massachusetts Institute of Technology, where he worked on developing machine learning algorithms for various problems in medicine, systemic risk in financial systems, and sports. Primary motivation of his research is to discover novel approaches to automatically recognize patterns in large datasets and develop tools to answer questions that affect people. His PhD thesis was on learning cross-sectional connections in financial time series, and was supervised by Professor John Guttag and Professor Andrew Lo.


Stefano Pasquali, Managing Director, is the Head of Liquidity Research Group at BlackRock Solutions. As Head of Liquidity Research, Mr. Pasquali is responsible for market liquidity modelling both at the security and portfolio level, as well as estimating portfolio liquidity risk profiles. His responsibilities include defining cross asset class models, leveraging available trade data and developing innovative machine learning based approaches to better estimate market liquidity. Mr. Pasquali is heavily involved in developing methodologies to estimate funding liquidity and better estimate funds flows. These models include: the cost of position or portfolio liquidation, time to liquidation, redemption estimation, and investor behavior modelling utilizing a big data approach.

Previous to Blackrock, Mr. Pasquali oversaw research and product development for Bloomberg's liquidity solution, introducing a big data approach to their financial analytics. His team designed and implemented models to estimate liquidity and risk across different asset classes with a particular focus on OTC markets. Before this he led business development and research for fixed income evaluated pricing.

Mr. Pasquali has more than 15 years of experience examining and implementing innovative approaches to calculating risk and market impact. He regularly speaks at industry events about the complexity and challenges of liquidity evaluation? particularly in the OTC marketplace. His approach to risk and liquidity evaluation is strongly influenced by over 20 years of experience working with big data, data mining, machine learning and data base management.

Prior to moving to New York in 2010, Mr. Pasquali held senior positions at several European banks and asset management firms where he oversaw risk management, portfolio risk analysis, model development and risk management committees. These accomplishments include the construction of a risk management process for a global asset management firm with over 100 Billion AUM. This involved driving projects from data acquisition and normalization to model development and portfolio management support.

Mr. Pasquali, a strong believer in academic contribution to the industry, has engaged in various conversations and collaborations with universities from the US, UK, and Italy. He also participates as a supervisor in the Experiential Learning Program and Master of Quantitative Finance Program based at Rutgers University, along with tutoring students in research activities.

Before his career in finance, Mr. Pasquali was a researcher in Theoretical and Computational Physics (in particular Monte Carlo Simulation, Solid State physics, Environment Science, Acoustic Optimization). Originally from Carrara (Tuscany, Italy), he grew up in Parma. Mr. Pasquali is a graduate of Parma University and holds a master’s degree in Theoretical Physics, as well as research fellowships in Computational Physics at Parma University and Reading University (UK).

Stefano lives in New York since 2010. In his spare time, he tries to devote to his passions which are music, traveling and spending more time as possible to the sea and sailing boat.

Alex Remorov, PhD, is a Vice President at BlackRock's Systematic Active Equities (SAE). In this role, Alex builds systematic alpha strategies for hedge funds and long-only portfolios by leveraging machine learning, alternative data, and investment intuition.

Alex earned a BSc in Mathematics and Statistics from the University of Toronto and a PhD in Operations Research from MIT. He has carried out academic research on systematic trading strategies, behavioural biases, and investor decision-making. During this time Alex also did short stints at Manulife Investment Management in Toronto, as well as at Goldman Sachs in New York and London.