Summer Research Projects

Prof. Chao Wang
Associate Professor of Computer Science
University of Southern California

The projects are described as follows:
* Using techniques in compilers and operating systems to improve the security of cyber-physical systems (CPS) and the internet of things (IoT).
* Using techniques in verification and testing to improve the safety of software based on machine learning methods.

Prof. Jayakanth Ravichandran
Assistant Professor, Chemical Engineering and Materials Science

Complex Materials for Visible to Infrared Optoelectronics
Perovskite chalcogenides are an emerging class of opto-electronic materials with tunable band gap in the visible to infrared part of the electromagnetic spectrum. My group is interested in investigating the optoelectronic properties of these materials using advanced optical spectroscopy. Further, we are also interested in fabricating proof of concept optoelectronic devices to understand their device performance.

Electron Transport in 2DEGs of Complex Oxides
Two-dimensional electron gases (2DEGs) have been widely used in high speed and power electronics and the transport physics of the 2DEGs remains a subject of interest. My group is interested in developing novel transport and spectroscopic methods to understand the nature of 2DEGs formed at the interface of complex oxides. This project will involve synthesis of 2DEGs using atomically precise growth techniques, and then characterizing their structural, chemical and transport properties.

Prof. Emilio Ferrara
Research Professor
Information Sciences Institute (ISI)

Computational Social Network Science  
Computational social sciences focus on the study of individual behaviour and society through the lens of computational and statistical tools. The selected candidate will be involved in projects related to the study of technology and society, focusing on online social networks (e.g., Twitter, Reddit), techno-social systems (e.g., mobile communication systems), news platforms, or human mobility and sensor data, by applying a combination of statistical methods, network science models, and data-driven analysis. The ideal candidate is a computationally-minded and strongly motivated student with a clear understanding of network science and statistical modelling methods and applications. Python coding fluency and previous experience with social network mining are preferred.

Social Media Analysis
Manipulation and Abuse Dynamics – Social media have become pervasive tools for planetary scale communication and now play a central role in our society: from political discussion to social issues, from entertainment to business, such platforms shape the real-time worldwide conversation. But with new technologies, also come abuse: social media have been used for malicious activities including public opinion manipulation, propaganda, and coordination of cyber-attacks. The selected candidate will work on projects related to social media analysis, in particular studying the behaviour of social media users, and the dynamics of use and abuse of social platforms for a variety of purposes including spreading of fake news and social bots. The ideal candidate is a computationally-minded and strongly motivated student with a clear understanding of social networks, machine learning, and data science methods and applications. Python coding fluency and previous experience with social media mining are preferred.

Prof. Ellis Meng
Biomedical Engineering

Interested in hosting electrical engineers from Tsinghua or India who have an interest in biomedical applications. 
The Biomedical Microsystems Laboratory invites electrical, mechanical, and biomedical engineers who have an interest in micro- and nano-machined systems for biomedical applications.  The laboratory is developing innovative implantable wireless multi-sensor systems for monitoring hydrocephalus shunts and interfaces to a range of different nerves (brain, retina, and peripheral nerves).  Nerve interfaces are intended for recording and/or stimulation.  Candidates from different engineering disciplines are welcome to apply.

Prof. Hossein Hashemi
Ming Hsieh Faculty Fellow

Project Description #1:
We have been working on monolithic optical phased arrays (a special case of which is electronically-controlled optical beam steering) for applications including lidar, 3D imaging, 3D holography, biomedical implants, etc. Summer interns will be working on various aspects of this research including electronic hardware development, optical hardware development, integration, software, and testing.
Project Description #2:
We have been working on monolithic CMOS integrated circuits for neural implants (recording and stimulation). Summer interns will be working on analysis, design, and simulations of energy-efficient circuits for neural recording implants.

Prof. Justin Haldar
Assistant Professor
Signal and Image Processing Institute
Electrical Engineering
and Biomedical Engineering
University of Southern California

My group works in the field of Computational Imaging (a subfield of Signal Processing), and our goal is to design novel data acquisition techniques and novel signal reconstruction/estimation methods that allow high-quality imaging results from low-quality and/or incomplete data.  As an example, a lot of the work in my group focuses on making biomedical Magnetic Resonance Imaging (MRI) faster and more informative than ever before, thereby improving the usefulness of MRI while also reducing the costs of performing an MRI experiment.  More information can be found at my website:

Prof. Sven Koenig
Computer Science Department

Our artificial intelligence laboratory mostly works on planning technology, often with applications to mobile robotics. See here to find out more about us:

We are looking for students with an interest in artificial intelligence to develop the next generation of AI planning and search algorithms. They should have taken at least an Introduction to Artificial Intelligence class, have strong analytic and algorithmic skills and be able to program in C/C++.  The exact topic of the research will be tailored to the students. An example topic is given below. More topics can be found at

Multi-Agent Path Planning
Consider several agents (such as robots or game characters) that need to move from their current locations on a grid with blocked and unblocked cells to given goal locations without obstructing each others’ movement. This problem, is faced by warehousing robots (like those operating in the Amazon fulfillment centers, see here: and requires path planning but, different from single-agent path planning, is NP-hard and thus requires extremely smart algorithms to result in good performance. We are looking for a student who is interested in helping us to develop the next generation of such algorithms. (This is not a robotics project and does not use robot hardware but simple simulations instead.)

Prof. Jay Kuo
Project Description: You will study the modern deep learning technology, and apply it to some computer vision and medical imaging problems.
Areas of Research: Big Data Analytics, Deep Learning, Machine Learning, Computer Vision, Medical Image Analysis
Lab Website:

Prof. Shaama Mallikarjun Sharada
Project descriptions:
* Machine learning approaches to predict structure-activity relationships in the selective conversion of natural gas to methanol. The predictive model will be constructed using descriptors for activity and selectivity calculated using density functional theory for biomimetic dicopper catalysts.

* Automated discovery of active sites in single atom catalysts using surface hopping methods. Single atom catalysts are gaining significance due to their promising applications in selective CO removal in fuel cell feeds.

Here is my website: It is a bit basic, but I hope to improve on that soon. My research areas include: density functional theory, computational catalysis, metal-oxide interfaces, methane activation chemistry, and machine learning for chemistry.

Prof. Viktor Prasanna
Topic areas: parallel computing, FPGA, HPC, data science, big data, data analytics.

Prof. Bhaskar Krishnamachari
The Autonomous Networks Research Group (,  Directed by Prof. Bhaskar Krishnamachari, seeks bright undergraduate students with backgrounds in electrical engineering, computer science and mathematics for research into the Internet  of Things, wireless robotic networks, connected and autonomous vehicles, and other next generation wireless networks. Projects will involve a mix of mathematics, simulation and testbed experiments, tailored to student background and interest, and provide a strong experience in graduate-level research.

Prof. Leana Golubchik
My areas of research are broadly in design and evaluation of large scale distributed systems, such as cloud computing. My group’s web page is at:

Prof. Salman Avestimehr
Coded Computation for Large-Scale Graph Analytics
Coded computing is a recently developed framework that leverages tools from information theory and coding to alleviate some of the key bottlenecks in large-scale distributed computing. The key idea behind coded computing framework is to inject computation redundancy in an unorthodox coded manner that can be leveraged to alleviate stragglers/failures and reduce the data shuffling load in distributed computing.

We have developed the principles of coded computing framework, and have demonstrated its impact in several scenarios. For example, we have demonstrated that coded computing can speed up machine learning algorithms, e.g. the widely-used gradient descent algorithm and the TeraSort algorithm, for large-scale data analytics. Coded computing can also provide order-wise improvement over the state-of-the-art methods for large-scale distributed matrix multiplication, which is underlying many data analytics algorithms. The goal of this research project is to expand the application of coded computing framework, in particular to the area of large-scale graph analytics.

For more information please visit the webpage of Prof. Avestimehr’s research group at

Prof. Meisam Razaviyayn
The project is about designing and analyzing algorithms for training deep neural networks. The student will obtain experience on running various algorithms for training neural networks and compare their performance. Here is the link to my website:

Prof. Aleksandra Korolova
Areas of research: Privacy and Algorithmic Fairness
Website link:
Brief description of projects: Interested in developing algorithms that enable data-driven innovations while preserving individual privacy? Using your technical and data-mining chops to identify bias and unfairness in the workings of machine learning-based tools? Investigating ways to empower individuals to protect their privacy and avoid filter bubbles? Come work with us this summer! Requirements: solid knowledge of algorithms and a passion for using Computer Science to make a difference in the world.

Prof. Yan Liu
The USC Melady Lab led by Prof. Yan Liu develops machine learning models for solving important problems involving data with special inherent structures, such as time series, spatiotemporal data and relational data. We work closely with domain experts to solve challenging problems and have made significant contributions to a variety of applications, such as smart health and biology domains, sustainability, and social media analysis. Some of our most recent research projects include Interpretability in Deep Learning, Traffic forecasting with Graph Convolutional Recurrent Neural Nets, Medical Time Series to Concept Captions, and Adversarial Deep Reinforcement Learning for Security Games. More information can be found at

Prof. Rafael Ferreira da Silva
Project #1: Building a Workflow Management System Simulation Workbench
Overview — Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. The broad objective of this work is to provide foundational software, the Workflow Simulation Workbench (WRENCH), upon which to develop an experimental science approach that will transform scientific workflow development, research, and education. WRENCH provides a software framework that makes it possible to simulate large-scale hypothetical scenarios quickly and accurately on a single computer, obviating the need for expensive and time-consuming trial and error experiments. WRENCH enables scientists to make quick and informed choices when executing their workflows, software developers to implement more efficient software infrastructures to support workflows, and researchers to develop novel efficient algorithms to be embedded within these software infrastructures.
Summary of Activities — The student will implement scheduling and optimization algorithms using WRENCH as the simulation platform. The goal of this study is to compare different workflow scheduling and optimization approaches from the literature that have been used in the past independently.
Required Skills: C++, Git
Desired Skills: Unix, Google Test
Faculty: Rafael Ferreira da Silva

Project #2: Enabling Scientific Workflow executions via Jupyter Notebooks
Overview — Scientific workflows are a mainstream solution to process such large-scale scientific computations in distributed systems, and have supported traditional and breakthrough researches across several domains. They provide an abstraction above the individual application components and often abstract the details of the execution environment. Workflows allow scientists to easily express multi-step computational tasks, for example retrieve data from an instrument or a database, reformat the data, and run an analysis; while automating data movement between workflow processing stages. The Jupyter Notebook is a web application that allows users to create and share documents that contain live code, equations, visualizations and explanatory text. Its flexible and portable format resulted in a rapidly adoption by the research community to share and interact with experiments. Jupyter Notebooks has a strong potential to reduce the gap between researchers and the complex knowledge required to run large-scale scientific workflows via a programmatic high-level interface to access/manage workflow capabilities.
Summary of Activities — The student will extend/develop a Pegasus’ Python API for Jupyter to manage workflow executions (submission and monitoring), as well as collecting workflow performance information (e.g., statistics).
Required Skills: Python, Git
Desired Skills: Jupyter, Unix
Faculty: Rafael Ferreira da Silva and Ewa Deelman

Project #3: Mining statistical usage and download data from the Pegasus software via the ELK Stack
Overview — Understanding software usage performance data is crucial for improving the understand of users behaviors and functionalities usage. Since its inception, the Pegasus workflow management system has been widely used worldwide for enabling scientific discoveries among several disciplines. The Pegasus website and the software have mechanisms to automatically collect metrics (e.g., number of downloads, locations, usage, etc.), which are stored in an SQL database. Understanding the relations among this data is crucial for driving our next development efforts.
Summary of Activities — The student will use the ELK Stack (Elastic Search) tools to extract insightful knowledge from Pegasus’ usage data. The ultimate goal of this project is to improve the understand of the metrics data via visual graphs using the Kibana dashboard.
Required Skills: Python, Git
Desired Skills: Elasticsearch, Logstash & Kibana (ELK Stack), Unix
Faculty: Rafael Ferreira da Silva and Ewa Deelman