1. Charalampos (Babis) E. Tsourakakis 3 rd Year Graduate Student Open House February 26 th, 2010

2.  Carnegie Mellon University is a historical university with living history. Alen Newell Herbert Simon Manuel Blum Avrim Blum Stephen Fienberg John Lafferty Alan Frieze Larry Wasserman Carlos Guestrin Admission highly competitive! Tom Mitchell, Head of MLD

3.  International Student (Hellene, ECE NTUA)  2007-2009 worked with Christos Faloutsos on Data Mining (Tensors, Graphs and Large Scale Data Mining using Hadoop)  Switched at the end of the second year.  Now, Gary MillerRussell Schwartz Algorithm Design (TCS) Manifold Learning (ML) Breast Cancer Evolution (App) e.g., Miller Primality Test e.g., decoding human genome

4. Immigration course: faculty gives talks, you choose advisor 0 1 2 3 4 5 … time Coursework: 5 core courses: Intermediate statistics, ML Stat ML, Algorithms, Data mining +3 electives (1 from statistics) TA for 2 classes Change/add advisor (if you want) Start doing research! (publish 1 st paper) Propose Conduct research Finish! Enjoy dept. tea gathering and TGs Data analysis project, speaking skills (  get MS) Summer internships

5.  Very critical choice for your career.  Great faculty to choose from.  Make a good choice by:  choosing a project that excites you  making sure that you and your advisor have the same research “mentality” (Ask yourself, do you like more applications, theory, a mixture of them and what proportion from each)  reading papers of your potential advisors

6.  Year 1:  Intermediate statistics  Intro machine learning  Stat. machine learning  Algorithms  Year 2:  Data mining  Advanced Discrete Math: Additive Number Theory  Advanced Discrete Math: Mixing times and Markov Chains  Computational Methods for Biological Modeling and Simulation  Many ML classes  Graduate ML  Statistical ML  Graphical models  Convex optimization  Graduate AI  Learning theory  Bayesian methods  Comp bio + learning  Computational Complexity  Randomized algorithms  Approximation algorithms  Lots of area-specific machine learning (text, bio, brain, …) (Some of my electives)

8.  MLD Seminars:  MLD/Google seminar  Intelligence seminar  Machine learning lunch ▪ organized by students  Many other seminars:  Theory, LTI, Robotics,…  MLD weekly tea gathering  TGs (Thank Goodness It’s Friday)

9.  A city which has two main advantages which are typically inversely proportional:  Many things to do around, good restaurants, nice coffee shops, bars, pubs, dancing places etc.  Inexpensive.  Easy to travel to other beautiful cities which are near, e.g., Philadelphia, New York City, Toronto.

10. You can feel all four seasons while in Pittsburgh

11.  Different Health Plans  Basic (<~1K for a year)  Enhanced  +Dental  +Vision  CMU Health Services  UPMC  My personal experience so far has been more than good.

12.  Most of the students live in Squirrel Hill, Shadyside, Oakland.  Other neighborhoods: Bloomfield, Point Breeze, Regent Square.  If you do not own a car, make sure that you pick a place with many buses coming by from there (e.g., Squirrel Hill, Shadyside)  Great places to live for really good prices.

13.  An excellent environment for conducting research, having pleasant breaks (great coffee on the 3 rd floor), interesting research discussions on a whiteboard, Tea Parties.

14. Swimming Tennis Biking Canoeing Caving Climbing Hiking Kayaking Scuba DivingSkydiving Ski (Seven Springs Resort) with student prices ~15$

15. Chess Club Dancing Gym Volleyball, Basketball Badminton

16.  Publications Travelling.  I have visited Belgium, France, Greece, Italy, Stanford, New York.  Meeting Scientists from all over the world  Attend highly interesting talks (some of them).  Communicate your ideas, get people to learn about your research.  See and explore new places.

17. When you set out on your journey to Ithaca, pray that the road is long, full of adventure, full of knowledge…. …Always keep Ithaca in your mind. To arrive there is your ultimate goal. But do not hurry the voyage at all… Welcome to CMU and Congratulations again!

18.  Joseph Bradley for sharing previous year’s slides.  Jernej Barbic http://www-rcf.usc.edu/~jbarbic/cmu-start.html http://www-rcf.usc.edu/~jbarbic/cmu-start.html

19. Diane Stidle “Mother” of MLD