Computer Science

Students may apply to one or more of the below projects, indicating this in their statement of interest, or they may apply for "Computer Science: General," indicating in their statement of interest their skills and background and some faculty with whom they would be interested in working.  Computer Science Faculty List

Title Name Email Project Name Project Description Requirements
Prof. Suresh Jagannathan Distributed Systems Verification Modern-day distributed systems are built on a complex stack of hard-to-understand components.  We have developed a number of novel techniques to facilitate automated verification and specification of these systems.  This project will entail an exploration of these ideas on realistic protocols and frameworks. Knowledge of programming languages, formal methods, and systems are desirable.
Asst. Prof. Hemanta K. Maji Balls and Bins Problems Balls and Bins problems are powerful models for several stochastic processes in theoretical computer science. For example, consider the problem of allocating (batch) jobs to servers? Here we model the jobs as balls and the servers as bins. Furthermore, hashing algorithms also model allocating balls (the inputs) to the bins (the outputs). Our objective is to understand interesting properties of how many balls land up in a bin. For example, representative questions include: what is the maximum number of balls in a bin, how many balls need to be thrown to ensure that no bin is empty?

The objective of the project is to theoretically and numerically study some specialized ball-and-bins problems that arise in theoretical computer science. 
Strengths: Combinatorics, Probability. 
Assoc. Prof. David Gleich Data Driven Scientific Computing Our research team attempts to solve pressing challenges in science through data-driven approaches by seeking to match the right data to the right questions to the right algorithms. These projects are customized to the interests of the participants and the current set of challenges we have. In practice, they can range from running a set of intricate experiments on HPC clusters, to developing novel types of features for machine learning, to creating new theory to explain the success (and failure) of data to help solve a problem.  Experience with numerical computing in terms of Matlab, Julia, Python (NumPy) is required. HPC experience (MPI, etc.) would be helpful. C and C++ expertise are also helpful.

Working knowledge of linear algebra and matrix computations is a must! 

Asst. Prof.

Jeremiah Blocki Memory Hard Functions In the last few years over a billion user passwords have been exposed to the dangerous threat of offline attacks through breaches at organizations like Yahoo!, Dropbox, LinkedIn, LastPass, AdultFriend Finder and Ashley Madison. Password hashing is a crucial `last line of defense’ against an offline attacker. An attacker who obtains the cryptographic hash of a user’s password can validate password guesses offline by comparing the hashes of likely password guesses with the stolen hash value. There is no way to lock the adversary out so the attacker is limited only by the cost of computing the password hash function millions/billions of times per use. A strong password hashing algorithm should have the property that (1) it is prohibitively expensive for the attacker to compute the function millions or billions of times (2) it can be computed on a standard personal computer in a reasonable amount of time so that users can still authenticate in a reasonable amount of time. Memory hard functions (MHFs) are a crucial cryptographic primitive in the design of key-derivation functions which transform a low-entropy secret (e.g., user password) into a cryptographic key. Data-Independent memory hard functions (iMHFs) are an important variant due to their natural resistance to side-channel attacks. Argon2, the winner of the password hashing competition, initially recommended the data-independent mode Argon2i for password hashing, but this recommendation was later changed in response to a series of space-time tradeoff attacks against Argon2i showing that the amortized Area-Time complexity of this function was significantly lower than initially believed (CRYPTO 2016). In this project students will be exposed to cutting edge research on the design and analysis of memory hard functions and will have the opportunity to help implement and evaluate state of the art constructions (e.g., EUROCRYPT 2017, CCS 2017, 2018).  An ideal student should have a strong background in mathematics and theoretical computer science (e.g., graph theory, data-structures and algorithms) and should be comfortable writing code (e.g., C, C++, C#, Python). The project can be tailored to the student's strengths. One aspect of the research will involve running intensive computational experiments. Another aspect will involve modifying current implementations of memory hard functions and evaluating these implementations. For students with an exceptionally strong background in theoretical computer science there are several challenging open problems to work on.
Assoc Prof.  Daniel Aliaga Urban Modeling and Design Dr. Aliaga’s lab performs research in the area of 3D computer graphics but overlaps with computer vision and visualization while also having strong multi-disciplinary collaborations outside of computer science. His research activities are divided into three groups: a) his pioneering work in the multi-disciplinary area of inverse modeling and design; b) his first-of-its-kind work in codifying information into images and surfaces, and c) his compelling work in a visual computing framework including high-quality 3D acquisition methods. Dr. Aliaga’s inverse modeling and design is particularly focused at digital city planning applications that provide innovative “what-if” design tools enabling urban stake holders from cities worldwide to automatically integrate, process, analyze, and visualize the complex interdependencies between the urban form, function, and the natural environment.

The lab is seeking research participation from exciting undergraduate and graduate students to assist with the aforementioned projects. Dr. Aliaga has worked closely with over 20 undergraduate research students over the years.

Good CS, math and programming skills, and a plus would be prior computer graphics experience.
Asst. Prof. Gaurav Chopra Artificial Intelligence (AI) in Chemistry

We really live in special times where we have so much data, e.g. Jean-Louis Reymond at U. Berne in Switzerland has collected a list of 166 Billion compounds that are chemically feasible organic molecules. Our lab is interested in developing methodology to model chemical systems. We discover “chemical rules” from large datasets and known databases of experiments and chemical reactions along the use of machine learning to guide further experiments. We develop CPU, GPU (graphical processing unit) based cheminformatics, chemical/structural modeling, machine learning methods for retrosynthesis prediction, docking and drug design tools for our software suite, namely, CANDIY (Computational Algorithms for Novel Drug Identification/Informatics for You). Undergraduate students in my lab will have the opportunity to work on projects in the area of using machine learning and chemical data to do retrosynthetic route prediction before doing synthesis, developing “computational assays” that are essential for predicting biological activity of the molecules in cells, etc. We are interested in developing molecules that perturb the immune cells for the treatment of neurological (Alzheimer's disease) and cancers.

(Listed primarily in the Department of Chemistry. Choose CHM while applying.)

Some experience in scripting (perl, python, etc) and programming (C++) will be very useful with a basic knowledge of tools existing in machine learning (optional). Basic knowledge of organic synthesis, chemical interactions, or experiments done to test compounds in biological assays will be preferred but not required.

Students in Chemistry, Physics, CS, Math and ECE are all welcome to apply.

Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, (765) 494-4600

© 2015 Purdue University | An equal access/equal opportunity university | Copyright Complaints | Maintained by Office of Corporate and Global Partnerships

Trouble with this page? Disability-related accessibility issue? Please contact Office of Corporate and Global Partnerships at