Summer Research Projects
Prof. Jayakanth Ravichandran
Assistant Professor, Chemical Engineering and Materials Science
Area of Research: Electronic and Photonic Materials and Devices.
Group description: My group specializes in new materials and device development for electronic and photonic applications. We have a strong synthesis component to our research. We also have specialized electrical transport capabilities. We regularly collaborate with EE-Electrophysics faculty on these projects.
1. Neurmorphic computing devices with Phase change oxides: My group is developing new device designs based on phase change materials for neuromorphic computing applications. We do device modeling, synthesis, device fabrication and electrical studies as a part of this project.
2. Mid-infrared photonic materials: We are developing new infrared responsive materials. Specifically, we are interested in the mid-infrared energies, where there is a dearth of materials. We have recently published a few articles in this area (Nature Photonics, 12 , 392-396 (2018) and Chemistry of Materials, 30 (15) , 4897-4901 (2018)).
3. New materials for Visible Opto-electronics: We have developed new ternary chalcogenides as highly absorptive materials for visible light. The applications include ultra-sensitive photo-detectors, photovoltaics.
Prof. Emilio Ferrara
Information Sciences Institute (ISI)
Lab Website: http://www.emilio.ferrara.name/
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.
Location: Marina del Rey, CA
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.
Location: Marina del Rey, CA
Prof. Hossein Hashemi
Ming Hsieh Faculty Fellow
Website : https://hhlab.usc.edu/
Project Description #1: Millimeter-wave integrated circuits
Current wireless communication transceivers operate at radio frequencies below 6 GHz. The next generation of wireless standards, commonly referred to as 5G, is expected to include operation at millimeter-waves (around 30 and 40 GHz) to enable higher data-rate due to higher available bandwidth. Over the past 15 years, there has been significant research and development towards realization of mm-wave ICs in commercial silicon processes. Current research include reducing the power consumption and form-factor of mm-wave ICs while improving their performance and functionality. The summer intern can work on various aspects of mm-wave ICs including design, simulation, and measurements of components and systems.
Project Description #2: Radiofrequency acoustic integrated circuits
Acoustic radiofrequency (RF) filters are used widely in commercial wireless communication transceivers due to their high selectivity, low loss, and compact form factor. The smaller wavelength of acoustic waves in comparison with their electromagnetic counterpart enables realization of more complex RF acoustic integrated circuits (AIC) in the acoustic domain with low loss and compact form factor. The summer intern can work on various aspects of RF AICs including design, simulation, and measurements of components and systems.
Project Description #3: Silicon photonic integrated circuits
Silicon photonic integrated circuits (PIC) are currently used commercially for high-speed fiber optical links that are used in datacenters. Emerging applications of silicon PICs include lidars including those for self-driving cars, projection displays, holography, and biomedical imaging. The summer intern can work on various aspects of silicon PICs including design, simulation, and measurements of components and systems..
Prof. Justin Haldar
Signal and Image Processing Institute
and Biomedical Engineering
University of Southern California
Magnetic resonance (MR) imaging technologies provide unique capabilities to probe the mysteries of biological systems, and have enabled novel insights into anatomy, metabolism, and physiology in both health and disease. However, while MR imaging is decades old, is associated with multiple Nobel prizes (in physics, chemistry, and medicine), and has already revolutionized fields like medicine and neuroscience, current methods are still very far from achieving the full potential of the MR signal. Specifically, modern MR image methods suffer due to long data acquisition times, limited signal-to-noise ratio, and various other practical and experimental factors — this limits the amount of information we can extract from living human subjects, and often precludes the use of advanced experimental methods that could otherwise increase our understanding by orders-of-magnitude. Our research group addresses such limitations from a signal processing perspective, developing novel methods for data acquisition, image reconstruction, and parameter estimation that combine: (1) the modeling and manipulation of physical imaging processes; (2) the use of novel constrained signal and image models; (3) novel theory to characterize signal estimation frameworks; and (4) fast computational algorithms and hardware. Our methods are often based on jointly designing data acquisition and image reconstruction methods to exploit the inherent structure that can often be found within high-dimensional data, and we do our best to take full advantage of the “blessings of dimensionality” while mitigating the associated “curses.” We are seeking excellent students with a strong background in signal processing, with an interest in developing methods to improve existing advanced MR methods and an interest in enabling/exploring innovative next generation imaging approaches.
More information can be found at my website: http://mr.usc.edu/
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:
Our research group works on artificial intelligence algorithms for search,planning and multi-agent coordination. An example is multi-agent path finding, which we describe in the following.
Teams of agents must often assign target locations among themselves and then plan collision-free paths to their target locations. Examples include autonomous aircraft towing vehicles, automated warehouse systems, office robots, and game characters in video games. For example, autonomous aircraft
towing vehicles will soon tow aircraft all the way from the runways to their gates (and vice versa), thereby reducing pollution, energy consumption,
congestion, and human workload. Today, hundreds of robots already navigate autonomously in Amazon fulfillment centers to move inventory pods all the way
from their storage locations to the inventory stations that need the products they store (and vice versa). Path planning for these robots is NP-hard, yet
one must find high-quality collision-free paths for them in real-time. Shorter paths result in higher throughput or lower operating costs (since fewer robots
are required). Our research group therefore studies different versions of multi-agent path finding (MAPF) problems, their complexities, algorithms for
solving them, and their applications, see http://idm-lab.org/projects.html for more information.
We offer a number of different projects in this area. We are looking for students with a computer science background who are strong in algorithms and
have either a good understanding of artificial intelligence (for example, heuristic search) or robotics (for example, motion planning).
Prof. Viktor Prasanna
Topic areas: Data analytics for smart energy systems,FPGA acceleration of deep neural networks,High performance computing,Smartgrid,
Prof. Michelle Povinelli
Study of microphotonic devices for alternative approaches to memory, logic, and computation at high temperatures. Projects include electromagnetic simulation of microfabricated devices and experimental device testing.
Prof Haipeng Luo
Project Areas: bandit optimization, reinforcement learning, and online learning/optimization.
Prof. Richard Leahy
Project Areas : Brain imaging, signal processing, and machine learning.
The human brain processes rich and complex information in a multifaceted world. This project is focused on developing and validating a mathematical and statistical framework based on the BrainSync transform to address the challenges inherent in analyzing multisubject brain imaging data during resting, naturalistic stimulation and self-paced activity paradigms. The project will involve development of novel mathematical and machine learning algorithms, programming, and experimentation on human brain imaging data.
Prof. Xiang Ren
Project Areas: Machine Learning, Natural language processing, and Data mining
Research happening in USC Intelligence and Knowledge Discovery (INK) Lab focus on weakly-supervised methods for learning from text and graph-structured data. We are particularly interested on computational models and systems that can extract machine-actionable structures and knowledge from massive natural-language text data (e.g., information extraction and knowledge base construction). Projects in iNK lab include sequence modeling under weak supervision (learning with noisy labels, learning from prior knowledge, and zero-shot learning), and graph neural networks for knowledge representation and reasoning.
Prof. Mike Shuo-Wei Chen
1. High performance, low power analog circuit design
2. Machine learning assisted circuit design
3. Bio-inspired computing hardware
Prof. Assad A Oberai
PDE-based models for unsupervised learning: Recent developments have established a close connection between the big(infinite)-data limit of graph-based unsupervised learning algorithms and partial differential operators. This leads one to consider whether computational methods used for solving PDEs can play a role in constructing new, more robust and efficient algorithms for solving unsupervised learning problems. Over the period of a summer, the student will explore this connection and will help develop and implement these PDE-based algorithms for solving unsupervised and semi-supervised learning problems.
Prof. Shinyi Wu
Our project is Markov computational modeling and simulation for affective disorders. This is a multiyear research agenda that would include a theory development part, a computational/simulation part, and an empirical data collection part. In particular, we would like students who are interested in a training in building computational psychiatry Markov models.
Prof. Cyrus Shahabi
Project 1: Empirical Evaluation of Geo-Indistinguishability Mechanisms
Description: Mobile users interact very frequently with location-based apps such as maps, ride-sharing services, geosocial networks, etc. The disclosure of unprotected location data can lead to serious privacy breaches related to one’s health or financial status, political or religious orientation, etc. Geo- Indistinguishability (GeoInd) is emerging as a promising model for protecting location privacy, but existing techniques that implement it are either too slow to be used in mobile apps, or introduce too much data distortion, decreasing apps’ usability. In this project, students will implement and evaluate experimentally the performance and accuracy of existing techniques for GeoInd on a broad set of real- life datasets, and under a diverse set of parameter settings. The objective of the project is to identify which technique is suitable for what specific use-case scenario, and how one should set system parameters to achieve desired system goals. Required Skills: strong coding skills (C/C++, Java), good mathematical background on computational geometry and statistics.
Project 2: Performance Evaluation of Searchable Encryption Techniques
Description: The emergence of cloud computing led to a trend where data are stored and processed at entities that may not always be trusted. Since large amounts of data about individuals are outsourced to the cloud, serious concerns arise regarding privacy. Even in cases where the cloud service provider is not malicious, a security breach by a malicious adversary can lead to the disclosure of private individual data. To address this threat, a number of encryption techniques have been proposed which allow both secure storage of data, as well as query processing directly on the ciphertexts. Some basic operations such as exact match, range queries, or evaluation of inner-products are currently supported by such cryptographic primitives, in either the symmetric or asymmetric cryptography setting. However, all these techniques have a significant performance overhead, which questions their practical applicability. In this project, students will implement several prominent techniques for searchable encryption, and thoroughly evaluate their performance for a broad set of datasets and a diverse set of parameter settings. The objective is to understand the performance overhead of searchable encryption, and attempt to optimize the performance under certain use case scenarios (e.g., by identifying certain parameter settings that reduce overhead, or by employing parallelism where possible).
Required Skills: strong programming skills (Python, C, C++, Java), good background on number theory.
Prof. Sandeep Gupta & Prof. Pierluigi Nuzzo
1. Developing custom computing solutions for inverse problems: Inverse problems span many application domains, including combinatorial optimization and bio-medical imaging. Professors Sandeep Gupta and Pierluigi Nuzzo are working on two different approaches for building new custom computing solutions for SAT (satisfiability), the key inverse problem in the logic domain to which a wide range of important applications can be reduced. Specifically, their current approaches go beyond the common paradigms for hardware acceleration (namely, lower hardware delays, harnessing higher core-/logic-level parallelism, and reducing data movement) and embody new paradigms, especially harnessing electrical-level parallelism (e.g., achieving n-way broadcasts over interconnects and into memories at O(log(n)) delays), and developing new VLSI circuits that directly implement inverse functions.
2 – Self-Driving Vehicle Testbed : The goal of this project is to build an experimental testbed to emulate realistic scenarios for self-driving vehicles and test the effectiveness of different driving algorithms. The testbed will target a traffic intersection and will include a set of scaled-down autonomous cars, a programmable traffic light sequencer to emulate the traffic and pedestrian signals, and a set of robots to emulate pedestrian traffic. The students will closely collaborate with USC Viterbi faculty and Ph.D. students to define the architecture of the testbed, define and assemble the different components, implement the driving scenarios on the testbed, and collect data. Activities will include programming the driving algorithms in a simulation environment as well as on embedded microcontrollers (e.g., on Raspberry Pi boards).
3 – High-Assurance Design of Safety-Critical Autonomous Systems with Machine Learning Components : Autonomous systems are particularly desirable for a variety of applications, such as driverless cars, spaceflight, household maintenance, and delivery of goods and services. To achieve high degrees of autonomy and operation in uncertain environments, these systems will increasingly adopt machine learning algorithms. These algorithms have achieved human-level performance or better on a number of tasks; however, their deployment in safety-critical applications brings additional sources of approximations that require formal analysis and design methods to ensure that the implemented system is safe and avoid undesired outcomes. The goal of this project is to develop, simulate, and test scalable analysis and design procedures that can guarantee correct operation of safety-critical autonomous systems.
4 – Security-Driven Optimized Obfuscation of Integrated Circuits : Integrated circuit obfuscation consists of a set of techniques that are used to prevent the reverse-engineering of integrated circuits and the insertion of hardware Trojans. Several obfuscation techniques have been developed over the years, but there is little agreement on metrics to validate the security claims, or tools to select and assess the effectiveness of obfuscation. The goal of this project is to develop an obfuscation design methodology and unified metrics which treat obfuscation security as a first-class design constraint and enable the selection of an appropriate mix of techniques to satisfy a set of security and performance objectives.
Prof. Shaama Mallikarjun Sharada
Website : https://sharada-lab.usc.edu/
Project 1: Chemistry inspired from biology: While manmade processes for conversion of natural gas resources to useful products require extreme temperatures and pressures and lots of energy, enzymes found in bacteria can carry out highly selective transformations under ambient, mild conditions. Our goal is to demystify these processes and uncover new catalysts using clever quantum chemical strategies for probing mechanisms and predicting catalyst compositions.
Project 2: Machine learning for the inverse problem: While machine learning methods are rapidly and successfully being adopted for predicting material and chemical properties, the inverse problem of predicting novel materials with desired properties is still in its infancy. We are implementing deep learning frameworks to address this problem and to explore the chemical space based on electronic and steric properties of ligands in transition metal complex catalysts.
Project 3: Single atom chemistry: Supported single atom catalysts are touted as being highly efficient and economical compared to supported precious metal nanoparticle catalysts. However, the activity and stability are dependent on operating conditions and reactants/products involved. We use quantum chemical and dynamics approaches to probe these dependencies and design catalysts for various applications including preferential oxidation for fuel cell feed.
Prof. Manuel Monge
Areas of Research:
Integrated Circuits (ICs) for Medical Electronics, Miniature Medical Devices, Neural Interfaces.
We combine and integrate physical and biological principles into the design of integrated circuits to engineer miniature biomedical devices for fundamental research, medical diagnosis and treatment.
1. Bidirectional Neural Interfaces
Neural interfaces directly interact with neurons in our body. Neural recordings of brain activity are one of the key elements for studying the brain, which has led to remarkable breakthroughs in science, engineering and medicine. Similarly, neural stimulation of key brain regions has enabled treatment of medical conditions such as Parkinson’s disease, epilepsy, and others.
Currently approved neural interface devices have limited bandwidth and spatial coverage of the brain, with up to 10s or 100s of channels in the system. The majority of these channels are dedicated to recording and only a few to stimulation. Future high-density, high-bandwidth bidirectional neural interface systems will support thousands of channels for simultaneous recording and stimulation, and could provide real-time visualization of multiple brain-regions with high temporal and spatial resolution.
The summer intern can work on various aspects of neural amplifiers and neural stimulators ICs including design, simulation, and measurements of components and systems.
2. Location-Broadcasting Bio-Chips
The function of miniature wireless medical devices such as capsule endoscopes, biosensors, and drug-delivery systems critically depends on their location inside the body. However, existing electromagnetic, acoustic and imaging-based methods for localizing and communicating with such devices are limited by the physical properties of tissue or performance of imaging modality.
We recently developed a new class of microchips for localization of microscale devices by embodying the principles of nuclear magnetic resonance in a silicon integrated circuit. We mimicked the behavior of nuclear spins and engineered miniaturized RF transmitters that encode their location in space by shifting their output frequency in proportion to the local magnetic field. The application of external field gradients then allows each device to be located precisely from its signal’s frequency. This technology is inherently robust to tissue properties, scalable to multiple devices, and suitable for the development of microscale devices to monitor and treat disease.
The summer intern can work on various aspects of this new technology including design, simulation, and measurements of low-power analog and mixed-signal ICs, and external multi-channel RF receivers for interfacing with these new devices.
3. Wireless Implantable Biosensors
Implantable biosensors are emerging as new devices capable of continuous in vivo monitoring of clinically relevant biomarkers. As these devices become smaller, reducing the power consumption while maintaining or improving performance is paramount.
We focus on developing high-sensitivity, high-dynamic range, and low-power micro-scale electrochemical sensors for measurement of different biomarkers such as glucose, proteins, and ions.
The summer intern can work on various aspects of wireless implantable biosensor ICs including design, simulation, and measurements of components and systems.
Prof. Maja Mataric
We are looking for students to contribute to a project that is developing novel algorithms and supporting software for a robot that will act as a social presence to help populations with exceptional needs to follow a prescribed exercises and activities. The robot will monitor and prompt the user and facilitate activities through demonstration and verbal and expressive feedback. This project will involve activity tracking, designing appropriate feedback, and sensing and adjusting the exercise based on human affect during the workout using appropriate machine learning methods. Students on this project will gain experience in robotics, computer vision, and reinforcement learning. Students should have programming experience and be strong communicators. Ideally, students have experience in human-robot-interaction, experience using Robot Operating System (ROS), and some experience with estimators (e.g., Extended Kalman Filter).
Prof. Constantine Sideris
My research areas are in Analog/RF Integrated Circuits and Photonics for biomedical applications and wireless communications, and computational electromagnetics for modeling and optimization of microwave and photonic devices.
1. We are designing a very dense ultra-sensitive magnetic biosensor array for enabling affordable at-home diagnostics of infectious diseases.
2. We are working on designing biosensors in silicon photonics processes.
3. We are designing and implementing algorithms which can simulate photonic devices much more efficiently (potentially an order of magnitude faster) than existing software and combining them with optimization algorithms to design and fabricate exciting, new photonic devices.
Prof. David R. Traum
Natural Language Dialogue Systems – we are interested and working on many areas involved in improving performance and extending the capabilities of such systems. Unlike many labs that focus strictly on assistant systems or chat, we have extensive research efforts on the following kinds of applications (as well as others):
Virtual Human Dialogue
Dialogue with Robots
Conversations with History and Heroes (video of an actual person)
Dialogue in Games
Extended dialogue interaction
Prof. Alice C. Parker
Area of Research: Neuromorphic Circuits to Model Neurons in the Human Brain.
Group description: My group researches nanotechnologies and analog electronic circuits that capture brain-like behavior.
1. Neuromorphic circuits that model neurons in the cortex that learn new skills without forgetting.
2. Circuits incorporating nanotechnology models (carbon nanotubes, memristors, molybdenum disulfide) to model neurons.
3. Neuromorphic circuits that model neurons in the cortex of a robotic cat.
Previous IIT interns have been lead authors on conference papers resulting from their internships. Background in high-school biology is sufficient for the project.