Sunday, February 19, 2017

Exciting new book

My long term collaborator, thinker, and a theory researcher Ramesh Hariharan has put together a book that sounds fascinating: Genomic Quirks: The Search for Spelling Errors.

"This is a book of real stories about the search for genomic spelling errors that have stark consequences -- infants who pass away mysteriously, siblings with misplaced organs, a family with several instances of vision loss, sisters whose hearts fail in the prime of their youth, a boy whose blood can’t carry enough oxygen, a baby with cancer in the eye, a middle-aged patient battling cancer, and the author’s own color blindness. The search in each case proves to be a detective quest that connects the world of medical practice with that of molecular biology, traversing the world of computer algorithms along the way."


Friday, February 03, 2017

9th annual NYCE meeting

The NY area CS+Econ meeting, the 2017 year version, will be held May 19th at NYU. More info:

The New York Computer Science and Economics Day is an annual meeting of researchers in the NY area working on the interplay between computer science and economics. As usual, NYCE will feature invited talks by leading researchers -- this year's keynote speakers include David Rothschild (Microsoft), Emin Gun Sirer (Cornell) and Assaf Zeevi (Columbia)  - as well as contributed talks and a poster session.

We are soliciting contributed short talks and posters. Topics of interest to the NYCE community include (but are not limited to) the economics of Internet activity such as search, user-generated content, or social networks; the economics of Internet advertising and marketing; the design and analysis of electronic markets; algorithmic game theory; mechanism design; and other subfields of algorithmic economics.  We welcome posters and short talks on theoretical, modeling, algorithmic, and empirical work.

Please see the website for registration and the call for papers.

Organizers: Amy Greenwald (Brown University), Ilan Lobel (NYU Stern), Renato Paes Leme (Google Research) and  James Wright (Microsoft Research)


Experiments with Reviewing

This is not an experiment with truth but an experiment with reviewing truth.  The WSDM PCs experimented with double-blind reviewing. Here is the PDF of preliminary analysis.


Sunday, January 22, 2017

Job/Intern Announcements

Following job/intern announcements are of interest to folks.

1. NYT: The Data Science Group at The New York Times is expanding, and we are hiring in data scientist / machine learning roles ( The group focuses on developing and deploying machine learning solutions to meet newsroom and business challenges throughout the company. These challenges include prediction and prescription problems (e.g., supervised learning, targeting), resulting in a variety of internal data products: e.g., webapps, APIs, and slackbots. For examples of public-facing details on machine learning at The New York Times, see the URLs below for an interview [1], talk [2], news [3], or blog [4].


2. Adobe:  Full-Time Positions in Big Data Experience Lab (BEL) at Adobe Research, San Jose, CA

Big Data Experience Lab (BEL) at Adobe Research ( in San Jose is looking for full-time researchers to define and execute next generation machine learning and AI research for digital marketing applications and services. Adobe Marketing Cloud ( is one of the largest data collection platforms in the world, managing approximately 35 petabytes of customer data and processing one trillion transactions per quarter. But it's not just the quantity of data - it's the quality of the work that makes this an amazing time to be at Adobe Research. BEL has excellent publication record with dozens of papers at top-tier machine learning and AI conferences and journals in recent years. Join us to turn data into impact as you analyze unique problems, draw inferences, test theories, and see your theories come to life in solutions that help our customers rack up business successes. If you're interested in the problems related to finding information hidden in large data sets, then Adobe Research is your opportunity to make a huge impact on the academic community as well as our customers, who represent the top 10,000 biggest web and mobile businesses.

We accept applications throughout the year. The application should include a brief description of the applicant's research interests and past experience, plus a CV that contains the degrees, GPAs, relevant publications, names and the contact information of references, and other relevant documents. To apply, please send your application to

3. Adobe: Machine Learning Internship at Adobe Research, San Jose, CA

Machine Learning Group in Big Data Experience Lab (BEL) at Adobe Research ( in San Jose is looking for interns to work on a range of problems in machine learning, deep learning, digital marketing, and analytics. Our interns will have opportunity to work on real-world terabyte-scale problems in Adobe Marketing Cloud ( The interns will be supervised by researchers in the group who have excellent publication record with dozens of papers at top-tier machine learning and AI conferences and journals in recent years.  The internship will be in San Jose, California, at the heart of the Silicon Valley. The duration of the internship is 12 weeks and it can start any time from April 1, 2017.

The successful candidate will be mentored and work closely with one or more of the following Adobe researchers:
- Yasin Abbasi Yadkori (
- Branislav Kveton (

The deadline for the application is January 31, 2017.  To apply, please send your application to


Tuesday, January 17, 2017

Ballet and Gender Roles, A Discussion

Can ballet express modernist view of sexes and gender roles? This is a much needed discussion, and NYT steps up. Good to see friend Amar make cameos in pictures. 

Saturday, January 07, 2017

Lines of NY

Here are the lines of NY in a snowing morning.

Monday, November 28, 2016

Dreading when Data is Unleashed.

For past 2 decades we have believed that web will free information, anyone can report it,  pass it on, take it down, etc. But there was always a nuclear outcome that seemed plausible but the optimists thought humans as a crowd will control its misuse. We are learning it is tricky:

Obama said, “If we are not serious about facts and what’s true and what’s not … if we can’t discriminate between serious arguments and propaganda, then we have problems.”

"If everything seems to be the same and no distinctions are made, then we won’t know what to protect. We won’t know what to fight for. And we can lose so much of what we’ve gained in terms of the kind of democratic freedoms and market-based economies and prosperity that we’ve come to take for granted,” he said. 

Now I have a mental exercise for folks. We are as a society trying to set Data free. Data (say tables, observations, measurements) can be presented, analyzed, depicted, mined in so many ways that there is true and what's not will (need to) be debated in web scale. 

Undergrad vs Graduate Work

What is the difference between undergrad and grad students in professional interaction? I explained it recently to a student as follows:

  •  In undergrad times, the interactions are transactional/negotiation-oriented: professor gives you HWs, you do them, you get points;  professor poses a problem, you answer it, get points, you dont answer it, say the problem was confusing, get partial points; at the end of the course, you get a grade, forget the professor,  approach the professor again when you need a reference letter, professor writes a more or less generic letter saying you were one of the top X students in the class or whatever; your life moves on, the professor stays behind. 
  • In grad times, the interactions are relational: you take a course with a professor and do well or not; you might continue research with the professor or have them on your qualifier exam; you will TA one of their courses later or even be a GA if that professor has funds and your advisor needs some gap funding; that professor might have a contact in a company looking for a research intern for a summer and they might connect you;  they might ask you to referee papers or recommend you for a travel grant; even if your research diverges from that professor, you might remember something about that professor's research which helps you during an interview, just being world-aware; etc. your life moves on, so does the professor's, and two professional research lives, even if parallel, will cross in unexpected ways. 

Saturday, November 26, 2016

Amazon Postdoc at IISc, India

This Amazon postdoc with IISc, Bangalore, India sounds very interesting, focused on data science/machine learning, and theory CS. IISc has very strong theory researchers. Amazon India is an incredible business success story and has terrific data science/Machine learning researchers. Bangalore is a nice place to spend some time in ones life, and explore India.