Summer Research Projects

Prof. Peter Beeral
1. Software/CAD in the areas of low-power design, latch-based design, and asynchronous design. Students will learn about state-of-the-art techniques to attack power consumption in these domains and contribute to computer-aided-design tools to improve the power. Students should have experience with commercial ASIC flows.
2. Software/HW design in the area of superconducting electronics. With the end of Moore’s law search for the next generation technology is extremely important. We are working to see if superconducting electronics run at ultra-low temperatures (run at 4 degrees Kelvin!) is one area we are looking at and new software tools are circuits are needed to increase the scale of possible designs. Strong programming experience along with knowledge of open-source and/or commercial computer aided design tools is desired.
3. Machine learning acceleration. Students with strong machine learning background and ASIC/FPGA design will be considered to help our team build efficient machine learning accelerators for DNN/CNN/RNN/LSTMs.

Prof. Quan T Nyugen
1. Control, optimization and machine learning for achieving extremely robust locomotion on quadruped robots
2. Design and control of a hybrid wheel-leg robot, toward the future of delivery robots
3. Collision avoidance in emergency cases for self-driving cars

Prof. Jayakanth Ravichandran 
Areas of Research: Electronic and photonic materials and devices for emerging applications
1. Negative capacitance for emerging electronics: This project will involve the design, fabrication and device characterization of heterostructure gate dielectrics showing the negative capacitance effect for use in high mobility field effect transistors for logic and memory applications. There can be a theory component for the project too.
2. Phase change oscillator for neuromorphic applications: This project will leverage metal-to-insulator phase change to achieve high frequency electrical oscillators with small footprint and low power. This device will enable highly scaled neuromorphic circuits. Both theory and experimental studies will be carried out.
3. Development of giant linear and nonlinear photonic materials: This project will develop new linear
and nonlinear photonic materials with giant susceptibilities. We have already demonstrated a world record birefringence in a quasi-1D chalcogenide (BaTiS3). This experimental work will continue and buildon this effort.

Prof. Nanyun Peng
1. Common sense is defined as, “the basic ability to perceive, understand, and judge things that are shared by (‘common to’) nearly all people and can reasonably be expected of nearly all people without the need for debate.” Humans are usually not conscious of the vast sea of commonsense assumptions that underlie every statement or action. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is considered the most significant barrier between the narrowly focused AI applications of today and the more general, human-like AI systems hoped for in the future. Common sense reasoning’s obscure but pervasive nature makes it difficult to articulate and encode. The Machine Common Sense (MCS) program seeks to address the challenge of machine common sense by learning from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language questions about commonsense phenomena, complete and expand commonsense knowledge-bases, and generate natural language descriptions that are compliant to commonsense (e.g. machine should not write an article saying “I saw a pig flying”).

2. Machine translation (MT) has made significant progress in recent years with a shift to neural models and rapid development of new architectures such as the transformer. However, current models trained on little parallel data tend to produce poor quality translations. This challenge is exacerbated in the context of social media, where we need to enable communication for languages with no corresponding parallel corpora or unofficial languages such as romanized versions. The low-resource MT project develops innovative approaches to obtain strongly performing models under low-resource training conditions.

3. Humans make sense of events by organizing them into narrative structures that occur frequently. These structures are abstracted into schemas, which are organized units of knowledge that represent a pattern of memory used in human cognition. With these schemas, human are able to understand and interact with the world, make predictions on what will happen next, and prepare accordingly. The event-schema learning project aims at developing revolutionary ideas that use schema-based AI to comprehend events, their components, and the participants involved by extracting information from natural language texts, images, videos, and audios.

4. The amount of fake and misleading information online is unprecedented due to recent advances in generative adversarial networks (GAN) and manipulation tools for image, video, text, and audio. Statistical detection techniques have been successful, but with the development of media manipulation algorithms, pure statistical detection methods are quickly becoming insufficient for detecting falsified media assets. This project aims to develop a hybrid semantic forensics system that automatically examines single- or multi-modal media inputs to detect, attribute and characterize (DAC) any potential media falsification, as well as provide forensic evidence for suspected falsification.

Prof. Joseph Lim 
Areas of Research: Reinforcement learning, robotics, deep learning, meta learning, imitation learning, and hierarchical reinforcement learning.

Prof. Andrei Irimia
Areas of Research: Biomedical Engineering and Neuroscience
Group Description: My laboratory utilizes magnetic resonance imaging, diffusion tensor imaging, computational neuroanatomy approaches and image processing to study alterations in brain structure and function prompted by traumatic brain injury, Alzheimer’s disease and typical aging. The research would involve one of the areas listed above and require a quantitative background as well as programming skills.

Prof. Emilio Ferrara
Most of the world population is connected to the global information environment. Therefore, it is of paramount importance to understand how information spreads and being able to capture, at scale and in real time, the mechanisms that govern the diffusion of online information in an increasingly-interconnected world. Using machine learning models, and causal inference methods, we will study online social phenomena at different temporal resolutions and at multiple scales, from individual to community to global collective behavior. Special attention will be given to dynamics of manipulation and the use of artificial intelligence agents like bots online.

Prof. Hossein Hashemi

1 .We have been developing silicon photonics optical phased arrays that enable electronic steering of
laser beam (no mechanical movement). An optical phased array enables realization of compact, low-power, solid-state lidar (laser detection and ranging) to create a high-resolution 3D point-cloud data in applications such as self-driving cars, drones, and handheld devices. The summer intern can work on various aspects of this research effort including analysis, design, simulations, modelling, and measurements.
2. We have been working on millimeter-wave integrated circuits for applications that include 3G wireless standard and automotive radar. The summer intern can work on analysis, design and simulations of various circuit building blocks such as low-noise amplifiers, filters, oscillators, phase-locked loops, power amplifiers, etc. Successful designs can be taped-out for experimental validation as well.

Prof. Bhaskar Krishnamachari
Group Description: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. Previous summer interns with this group have gone on to Ph.D. programs at top places including UC Berkeley, Columbia, MIT, Princeton, UCLA, USC, UT Austin, Stanford, U. Michigan, UIUC.

Prof. Justin Haldar 
Group description: 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.

Prof. Sven Koenig
Group description: The IDM artificial intelligence lab ( develops the next generation of search algorithms in the context of the kind of multi-agent path finding that is used in the warehouses of large e-commerce retailers. In general, 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 requires path planning but, different from single-agent path planning, is NP-hard and thus requires extremely smart algorithms to result in good performance, see for more information. We are looking for students interested in this artificial intelligence project, which uses simple simulations instead of robot hardware.

Prof. Viktor Prasanna
Areas of Research: Data analytics for smart energy systems, FPGA acceleration of deep neural networks, High performance computing, Smartgrid.

Prof. Michelle Povinelli
Areas of Research: Study of microphotonic devices for alternative approaches to memory, logic, and computation at high temperatures.
Projects: Electromagnetic simulation of microfabricated devices and experimental device testing.

Prof Haipeng Luo
Areas of Research: 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
Projects: 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
Projects: 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
1. Empirical Evaluation of Geo-Indistinguishability Mechanisms – 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.
2. Performance Evaluation of Searchable Encryption Techniques – 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

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.
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.
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.
Group Description:
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
Projects: 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
Areas of Research: 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
Projects: 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 Roleplay dialogue Conversations with History and Heroes (video of an actual person) Dialogue in Games Extended dialogue interaction Storytelling dialogue

Prof. Alice C. Parker
Areas 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.