(I avoid mugshots!)

I am a PhD candidate with broad interests in Machine Learning with an emphasis to applications in Computer Vision. In particular, I have a predilection for (deep and otherwise) representation learning, structured prediction
and general semi/weakly supervised learning. Currently however, I have been exploring problems in Discriminative (Linear/Non-linear) Metric/Similarity Learning and Semantic Hashing (i.e. Learning to Hash). I also dabble in
Combinatorics and have a weakness for all things Spectral; where my interests remain very strong.

For my research I consider myself very fortunate to be working under the supervision of Prof. Gregory Shakhnarovich. Prior to this I completed a MS (focusing on Machine Learning) while on the way to candidacy. Before that, in what now seems like a past life, I worked on problems in Educational Analytics, Clustering and Ensemble Learning under the supervision of Professors Neil T. Heffernan and Gábor N. Sárközy
earning another MS (in Computer Science, here's the proof! ) with a thesis (Prof. Sonia Chernova was the reader) that presented a new clustering algorithm based on the Szemerédi Regularity Lemma and also a method somewhat similar to mixture
of experts using clustering for ensemble learning. My work that led to the MS thesis was awarded the best in the university in the Science category both in 2011 and 2012, in an annual competition in which MS and PhD students presented their work as posters. Further afield, I worked in the industry in the signal processing domain (Application Specific Integrated Circuits) for roughly one year after acquiring an undergraduate degree in Electronics and Communications
Engineering. While working I also helped my undergraduate advisor, Dr. (Mrs) K. R. Joshi, in teaching three senior year courses. During my undergrad, I worked on biometrics (face and speech recognition - using subspace projection methods for the former and dynamic programming for the latter. This work, that also involved implementing a low cost (~$1) speech recognizer on a microcontroller, was awarded the best undergraduate project in the institute that year). At the same time I also worked on blind
source separation with applications to Magnetic Resonance image denoising.

I can be reached on email at shubhendu@{cs.uchicago or ttic}.edu

Shubhendu Trivedi, David McAllester, Greg Shakhnarovich.

Shubhendu Trivedi, Jialei Wang, Samory Kpotufe, Greg Shakhnarovich.

Fei Song, Shubhendu Trivedi, Yu Tao Wang, Gábor N. Sárközy, Neil T. Heffernan.

Gábor N. Sárközy, Fei Song, Endre Szemerédi, Shubhendu Trivedi.

Zachary A. Pardos, Qing Yang Wang, Shubhendu Trivedi.

Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan

Zachary A. Pardos, Shubhendu Trivedi, Neil T. Heffernan, Gábor N. Sárközy

Shubhendu Trivedi, Zachary A. Pardos, Gábor N. Sárközy, Neil T. Heffernan

Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan

Shubhendu Trivedi.

Shubhendu Trivedi.

Shubhendu Trivedi, Zachary A. Pardos, Neil T. Heffernan.

arXiv version: arXiv 1509.06163

Shubhendu Trivedi.

Shubhendu Trivedi.

-- CS 4120 Analysis of Algorithms (Instructor: Dr. Gábor N. Sárközy, Textbook: CLRS/Kleinberg-Tardos)

-- CS 2223 Introduction to Algorithms wih Lua (Instructor: Dr. Joshua D. Guttman, Textbook: CLRS)

-- CS 3133 Foundations of Computer Science i.e Automata Theory (Instructor: Dr. Gábor N. Sárközy, Textbook: Sudkamp)

-- CS 4341 Introduction to Artificial Intelligence (Instructor: Dr. Neil T. Heffernan, Textbook: Russell and Norvig)

-- MA 2201 Discrete Mathematics (Instructor: Dr. Gábor N. Sárközy, Textbook: Kenneth Rosen)

-- CS 2223 Introduction to Algorithms wih Lua (Instructor: Dr. Joshua D. Guttman, Textbook: CLRS)

-- CS 3133 Foundations of Computer Science i.e Automata Theory (Instructor: Dr. Gábor N. Sárközy, Textbook: Sudkamp, Dexter Kozen)

-- CS 2011 Introduction to Machine Organization and Assembly Language (Instructor: Dr. Hugh C. Lauer, Textbook: Bryant and Halloran)

-- STAT 27725/CMSC 25400 Machine Learning (Instructor: Dr. Imre Risi Kondor) (

(Slides from some lectures I gave in this course:

Discrete Probability Tutorial | Maximum Likelihood Estimation and Multivariate Gaussians

Artificial Neural Networks I | Artificial Neural Networks II)

Graduate Courses:

-- CS 534 Artificial Intelligence (Instructor: Dr. Neil T. Heffernan, Textbook: Russell and Norvig)

-- TTIC 31020 Introduction to Statistical Machine Learning (Instructor: Dr. Gregory Shakhnarovich)

-- Introduction to Digital Image Processing (Textbook: Gonzalez and Woods)

-- Image and Signal Processing Lab

-- Introduction to Bioinformatics (mostly covered the part on data mining)

Graduate Courses:

-- Deep Learning (CMSC 35246, Textbook: Bengio, Goodfellow, Courville)

Coming soon

My Erdos Number is 2*. My Bacon Number is ∞. I don't eat Bacon.

**( Paths: ST <--> Gábor N. Sárközy <--> Paul Erdos and ST <--> Endre Szemerédi <--> Paul Erdos*)

-- Onionesque Reality (a blog, mostly on random things)

-- Goodreads (again, not too frequently updated, it is hard to catch up with my own reading speed ;)

-- Twitter (use twitter mostly as an advanced RSS feed to follow more recent work in Machine Learning)