Blockchain network security: Network attackers have been shown able to violate the security properties of cryptocurrencies such as Bitcoin. Can we automatically find such vulnerabilities using ML-based techniques? Can we build networks with appropriate security properties?

Buffer management on network devices: To absorb transient drops, network devices are equipped with memory that is shared across all queues in the device. Can we optimize the sharing of the buffer for certain traffic patterns or deployments? Can we leverage this sharing to attack a device?

Network Function Profiling: Networking resources are becoming more sparse especially for programmable devices. Can we use profiling to optimize the resource allocation of a network (device)?

I am happy to discuss ideas that are initiated by students, especially if they are related to Networks, Security and Blockchain.

Although I have not yet listed specific project ideas, please feel free to contact me if you are interested in exploring possibilities.

We explore all different ways to “electrify” the world. We manage electrical power for sensing, computing, and actuation. The research topics that may fall in my group’s expertise include:

• Power management in all scale: from electric vehicles to more electric aircrafts/drones, from cellphones to IoT sensors
• Power delivery in all frequency: from dc to 60Hz ac, from sub-Hz to radio frequency
• Electrical actuation in all formats: mechanical, sound, wave, light, heat
• Fundamentals of power electronics: device, circuits, systems, control, power magnetics
• Design methods for power electronics: device modeling and abstraction, machine learning guided design tools

Although I have not yet listed specific project ideas, please feel free to contact me if you are interested in exploring possibilities.

• New qubits for quantum communication: We are exploring making new defects in diamond to act as long-lived quantum memories for long distance communication. This is highly interdisciplinary project across quantum optics, atomic spectroscopy, confocal microscopy, materials science, and device physics.
• Quantum nanophotonics: To enhance atom-photon interactions, we will also embed these defects in nanophotonic cavities, integrated in functional nanophotonic chips. This effort will explore novel fabrication methods in diamond to create these integrated nanophotonic devices, as well as methods for controllable interfaces between the nanophotonic cavity and the diamond color center.
• Nanoscale sensing: Diamond color centers can have spin degrees of freedom with long coherence times at room temperature, and they are localized to the nm scale, making them excellent point sensors. We are developing technologies for using color centers for nanoscale NMR and MRI, and this project involves spin resonance and quantum control techniques, optics and microscopy, and surface science and device physics.

• Big sound from small speakers. We will use new methods of signal processing to improve the sound quality of small speakers, such as those in cell phones.
• Removing speckle in biomedical ultrasound. This project focuses on enhancing images in clinical ultrasound.
• Imaging blood vessels. This project will image blood vessels and flow using visible light.
• 3D photography. We are developing methods to convert existing cameras into 3D imaging devices.

Our lab develops bioimaging, bioelectronics and computational tools to quantitatively understand living organisms, from the molecular to multicellular scales.

Bioimaging: We work on the development of novel optical microscopy technologies, including super resolution, light sheet, and adaptive optics, for imaging and manipulation of living organisms with high resolution and minimum invasiveness.

Bioelectronics: We develop polymer based multifunctional flexible electronics and apply them to seamlessly interface with multicellular organisms.

Computational tools: We apply cutting edge machine learning methods to extract quantitative principles of large-scale bioimaging and electrophysiology datasets.

• Wireless communication and sensing in the mmWave and THz range. Our lab focuses on the design and implementation of novel devices (e.g., antennas) and wideband beam steering solutions for directional link discovery and real-time link adaptation above 100 GHz. We are interested in architectures for joint communication and sensing that can realize non-coherent millimeter-scale localization accuracy together with terabit/sec wireless connectivity. 

• Wireless Security. Resiliency against eavesdropping and other security threats has become one of the key design considerations for communication systems. As wireless systems become ubiquitous, there is an increasing need for security protocols at all levels, including the physical layer. We are interested in exploring the security vulnerabilities of next-generation wireless systems and introducing cross-layer counter-measure solutions. 

• Smart and adaptive wireless networks. We are interested in building intelligent surfaces that can enhance the coverage, reliability, and security of mmWave networks. We design new data-driven AI protocols in order to learn and predict wireless channel dynamics via distributed low-cost passive components. Through experimental evaluations, we investigate new cross-layer PHY/MAC protocols to dynamically reprogram the channel properties and create favorable transmission characteristics.   

• ML/AI for Enhanced Wireless Communication: Exploiting a data-driven approach to learn and predict the structures in the wireless environments and user mobility patterns to enhance the communication metric, such as data rate and resilience, particularly when the conventional physics-based models are not comprehensive. 

• I am also open to advising projects where the ideas are initiated by students. 

   

• Design and testing of Quantum Cascade lasers
• Design, fabrication, and characterization of semiconductor devices, especially devices based on intersubband transitions
• Mid-infrared photonics (materials, devices, systems)
• Possibilities for “suggest-your-own” projects in the fields of semiconductors, lasers, optics, etc.

I am broadly interested in quantum information and statistical physics, particularly the phases and phase transitions of far-from-equilibrium quantum many-body systems. Here are some descriptions of potential projects:

• Measurement and decoherence in quantum systems. One can think of an open quantum system as a system that is continuously being "measured" by its environment. Information about the system leaks into the environment, and some fraction of it can be retrieved (e.g., by an eavesdropper). I have a set of projects that involve characterizing how much these effects degrade the ability of a system to store quantum information, and another set of computational projects studying the persistence of entanglement in realistic open-system experiments, e.g., in quantum optics and superconducting circuits.
• Finding compressed representations of quantum states. To represent a general quantum state of N qubits, one requires an amount of data that is exponential in N. However, much of this data is irrelevant for computing realistic experimental observables. I am interested in ways of compressing quantum states that do not compromise our ability to predict realistic observables.
• Electronic structure, phases, and phase transitions of quasicrystals, as well as other structured random systems (e.g., random hyperuniform patterns). 

• Building new circuit elements for quantum computers. We are interested in building the pieces for a quantum computer, and in particular need to build new and improved qubits to store quantum information, high quality directional couplers to route quantum information around a chip, and new types of isolators to protect fragile quantum information from environmental noise.
• Optical quantum processors. We are beginning to look into optical properties of defects in SiC which could act as room temperature quantum sensors or quantum bits
• I am always interested in exploring new directions and in engineering more broadly. If you have a crazy idea that you'd like to try, let me know and we might be able to put together a good and feasible project

• Predictive, preventive, and personalized healthcare
• Transformer synthesis, acceleration, and accelerator-model co-design
• Causal inference: Synthetic control and intervention
• Prospective learning: continual learning, knowledge priors, curiosity, counterfactuals
• Differentiable logic networks
• Graph language models and knowledge graphs

My group focuses on developing better machine learning foundations and algorithms for decision making/Reinforcement Learning (RL). Potential projects for undergraduate thesis/independent work include but not limited to:

1. Multiagent decision making: develop multiagent RL algorithms for strategic games such as street fighter; leverage the power of LLM for decision making problems that require extensive knowledge such as Pokemon games.
2. AI for math: develop learning-based methods for proving inequality/combinatorics.
3. Large-scale optimization: develop faster optimization algorithms for machine learning.
4. LLM fine-tuning and alignment using reinforcement learning with human feedback.
5. Mathematical foundations of reinforcement learning, transfer learning, and game theory.

Please feel free to contact me if you are interested in any of these directions or exploring other possible related topics.

NOTE: Professor Kahn will be on sabbatical academic year 2025-2026 and is unable to supervise IW/ST during that time.

Work in my lab revolves around new materials (semiconductors, insulators) for organic and other types of thin film electronics, and ways these materials come together in devices. Below are two possible topics for independent work.

• Molecular dopants in organic semiconductors: investigation of new molecular dopants (n- and p-type) for polymer semiconductors. Fabrication of doped structures and investigation of carrier transport as a function of dopant concentration and temperature.
• 2D and 3D Metal Halide Perovskites: fascinating new materials for PV and other thin film electronics; electronic structure, charge carrier transport.
• Study of electronic structure, carrier transport and surface doping in transition metal dichalcogenides.

Happy to talk with students to hear their research ideas in the broad areas of machine learning and pattern recognition.

NOTE: Professor Kulkarni will be on sabbatical fall 2025 and is unable to supervise IW during that time.

With the information explosion in big data, there is an eminent need of new machine learning methods and novel computational paradigms to make the artificial intelligence tools more actionable, personalized and contextually relevant. More specifically, some relevant and relatively new research problems are as follows:

• Dimension-Reduction techniques, Feature Selection and subspace projection are vital for big data analyses.
• Unsupervised Cluster Discovery with application to the segmentation of social and media networks.
• A hybrid learning model, named Ridge-SVM, which combines two classical models: KRR (Kernel Ridge Regressors) and SVM (Support Vector Machines).
• Non-imputed kernel approaches to incomplete data analysis (IDA), where the data is likely to be collected from a divergent of sources and may be highly incomplete.

Generally speaking, the research projects will embrace their theoretical and/or application aspects. The preferred (though not necessarily required) academic backgrounds will involve multiple disciplinaries including matrix theory, signal processing, regression analysis, discrete mathematics, and optimization theory.

• Happy to talk to students to hear their research ideas.

Much of the work in my lab is centered on figuring out how to build a quantum computer. We have a variety of projects, ranging from more EE-like to more physics-like. For example:

• Designing and testing cryogenic silicon circuits. The quantum computers we are building will operate very close to absolute zero. We need regular silicon chips to work at these temperatures. We design specialized CMOS circuits, which we then need to measure, and we also test regular commercial CMOS chips to see which ones can be made to work at low temperature
• Automating experiments and data acquisition. Lots of times we need to take a bunch of data or upload complicated instructions to instruments. Some of these projects are mostly software (we use Matlab to run some of the equipment), and some are a combination of software and hardware. Recently we used a Raspberry Pi (small single-board Linux computer) for automating a measurement, which worked well, and we plan to keep playing with the Pi's.
• Simulating electron transport on superfluid helium. One of the approaches we are taking to building the quantum computer uses electrons "floating" on the surface of superfluid helium. As part of that we have been developing programs (combination of Python and C) to simulate their motion as we change the voltages on gate electrodes. Projects in this are would mostly be programming, though a longer project could involve both programming and experiments on electron motion.
• Low-noise and precision circuits. We need to measure very small signals (for example, sensing the charge of individual electrons), and must control voltages with very high precision. We need to design and build circuits to do this, and then control them (probably with a Raspberry Pi).
• Designing and building new resonators for Electron Spin Resonance experiments. Electron spin resonance is one of the ways we measure the "quantum bits" we might use in the quantum computer. These experiments use microwave resonators, and we want to develop new ones, both large "bulk" resonators(~ few inches) and superconducting micro-resonators. For example, we want to make resonators which allow us to excite our samples with circularly-polarized microwaves.

My research deals with methods for designing emerging computing systems. In particular, my focus is on designing correct and secure systems. This involves studying and developing techniques for debugging, finding security flaws, and proving functional and security correctness for these systems. Research projects typically involve system modeling, developing and implementing algorithms that can work for industrial scale designs, and running experiments to improve and check these algorithms.

I am also open to advising projects where the ideas are initiated by students-these could involve hardware, software or theory-I am very open!

Organic Electrochemical Transistor Fabrication and Characterisation.  We will use new organic mixed conducting materials synthesized in our group, to fabricate electrochemical transistors and measure their electrical performance as well as exploring other applications including metabolite sensing.

Photocatalytic and Photoelectrocatalytic water splitting and CO2 valorization.  We will fabricate nanoparticles comprising organic semiconductors and explore their performance in photocatalytic reactions in the field of solar fuels.

• Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
• Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
• Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Although I have not yet listed specific project ideas, please feel free to contact me if you are interested in exploring possibilities.

NOTE: Professor Poor will be on sabbatical fall 2025 and is unable to supervise IW during that time.

• Low latency analog photonic interfacing and adaptation using an FPGA
• Information security in optical networks: trustworthy communication with “noise”
• Photonic neuron and neural networks: brain-inspired computing with lasers
• Interference cancellation in wireless communications: optical noise-cancellation for radios
• Adaptive beam forming with phased array antennas: making smart antennas using light
• Also willing to supervise “non-research” project

• Machine learning both theory and applications
• Analysis of functional MRI data or other biological data
• Please feel free to contact me if you are interested in exploring specific projects in machine learning.

• Work on a new class of photovoltaic materials: metal halide perovskite semiconductors. We have >20% efficient devices in our lab, and are investigating ways to improve this as well as understand material and device stability.
• Study the crystallization processes of organic semiconductors and their thin films - linking these material properties to electronic and optical properties
• Study epitaxy (growth) of crystalline organic semiconductors
• Within the "Campus as a lab" initiative, I would like to analyze data from Princeton's solar farm on the other side of the lake, looking at variability and intermittency issues. Let me know if interested. Could be joint with various other departments.
• If you have other ideas within thin film electronic and optoelectronic devices, please feel free to contact me.

We engage theoretical questions in the field of nanophotonics—the study of light in wavelength-scale structures—at the intersection of classical and quantum electromagnetism, by employing a combination of analytical and computational techniques. Our interests revolve around new kinds of electromagnetic effects and device functionalities, including light emission/absorption, light–matter coupling in quantum and fluctuating media, dispersion interactions, and low-power nonlinear devices. Current research directions/projects include:

• Deriving physical limits on both naturally occurring and technologically relevant optical phenomena (device and wave performance bounds derived from Maxwell's equations) through the machinery of quadratic optimization
• Inverse design (large-scale optimization) of photonic nano-structures for controlling light-matter and nonlinear devices
• Investigation of thermal emission and radiative heat transfer in structured media

Project 1: Exploiting computer vision and machine learning we are working to understand different components of the basketball game. Students will be involved in a component of this large and challenging project.

Project 2: We are investigating how to change attributes of videos to preserve privacy while keeping features critical for clinical screening and diagnosis. Students will be involved in a component of this large and challenging project.

Currently, we focus on the next-generation integrated electronic and photonic circuits and systems to address various emerging and high-impact applications, including high-frequency and high-speed communications, sensing, imaging, and onchip bio-sensing and actuation. Everything which is integrated, small yet powerful and sophisticated, that pushes the boundary of science and engineering through innovations, often by exploring the spaces between traditionally different fields, interest us. Our research approach is to leverage concepts from different fields and merge them together to create high-performance systems. The broad themes of research are:

• AI-enabled Wireless Chip Synthesis: We are developing new design techniques that allow new circuit discovery and rapid chip synthesis. This opens up new dimensions in radio-frequency chip design.
• Programmable EM Mirrors and Exotic Electromagnetic Fields for Wireless Communication: We are exploring a new class of topological fields (that connects to the mathematics of topology) to explore future wireless communication with transformative properties. 
• Biomedical Micro-robots; We are interested in exploring very tiny (less than 1 mm2) chips (microrobots) that can sense and move. How to move and power them is unknown, but exciting to explore. 

• Fabrication and characterization of low-dimensional semiconductor structures (quantum wells, quantum wires, quantum dots, etc.)
• Low temperature magneto transport measurements of low-dimensional semiconductor structures.

My lab's mission is to develop generalist robots that learn and plan to help people. We often use techniques from task and motion planning, program synthesis, neuro-symbolic ML, reinforcement learning, and foundation models. Potential project directions include:

Learning abstractions for robot planning. Abstractions allow robots to first focus on the high-level aspects of a task before getting bogged down in details. We would like a robot to automatically learn abstractions—state abstractions (predicates) and action abstractions (skills)—that are specialized for planning in its domain. We are especially interested in abstractions for task and motion planning.
Program synthesis for planning. We want robots to be like self-supervised software engineers, writing their own code and growing libraries that can be used to solve increasingly difficult decision-making problems. We use LLMs, Bayesian program learning, inductive logic programming, SAT solvers, and heuristic search to synthesize programs.
Learning to accelerate planning. Even with good abstractions, online planning can be slow, especially in high-dimensional environments with many objects. Robots should learn to plan better and faster over time. We can automatically accelerate planning by learning object-centric task abstractions, learning to self-impose constraints, or learning heuristics.
Planning to learn. Robots should plan to practice to get better at planning. They should rapidly learn to specialize to the objects, goals, preferences, and constraints that are unique to their deployment. We can plan to learn samplers, predicates, and operators for bilevel planning. Our ultimate goal is to create a virtuous cycle of learning and planning.

I am also open to discussing ideas initiated by students so long as they are related to planning, learning, and robotics.

Prof. Sturm will be on sabbatical in the fall, so he is not available to advise.

We work on nanophotonics, quantum optics and atomic physics, both with laser-cooled atoms and impurities in solids. Possible projects include:

• Design, fabrication and characterization of nanophotonic structures
• Design and construction of laser systems and associated electronics, in fiber or free space
• Quantum optics calculations, exploring new directions for quantum networks based on quantum repeaters
• Experimental control hardware and software (we use a lot of FPGAs, DDSs, various microwave electronics, etc.)
• Lastly, I am happy to discuss any crazy physics- or engineering-related idea that you might have, to see if we can turn it into a well-posed project that I could advise

We are looking for curious and motivated undergraduates to carry out research with our group on following projects. Please do not hesitate to contact me if you have questions. 

• Development of hybrid quantum-classical algorithms on cloud-based superconducting quantum computing platforms (e.g. IBM, Rigetti). Requires minimal exposure to quantum mechanics [a basic quantum mechanics/engineering course such as ELE342 or ELE396 is perfectly sufficient].
• Research into neuromorphic computing with physical systems — Hardware examples: Coupled microlasers, superconducting non-linear oscillators, optical parametric oscillators [requires only competency with basic engineering mathematics e.g. solving coupled ODEs in python; will employ in-house developed computational solvers for accurate modeling of the physical system]. 
• Hardware-based Reservoir Computing for forecasting — (you likely have never heard of it. If so, read this recent research from UMD researchers for fun: https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/) [basic engineering mathematics e.g. solving coupled ODEs in python; will employ in-house developed computational solvers for accurate modeling of the physical system]. 
• Several other projects on quantum information processing systems also are available for more advanced students [exposure to quantum master equations is helpful for these projects] 
• Theoretical and computational research into frequency comb generation with microlasers and superconducting oscillators —  dynamics and coherent-feedback based stabilization [will work with SALT (Steady-state Ab-inito Laser Theory), an in-house computational modeling tool for dynamics of complex lasers. If you are curious read this commentary on SALT: https://science.sciencemag.org/content/320/5876/623.summary]

I'm open to advising student-driven projects. My research interests are in the broad areas of hardware design, machine learning and pattern recognition, and their intersections.

The research in my group explores integrated circuits and systems for advanced sensing applications. Basically, we are interested in creating electronic systems that can perform extensive and sophisticated interactions with the real world. Since the signals presented by the real world are numerous and physically complex, electronic systems must overcome major challenges. Our research spans the areas of emerging electronic devices (flexible, large-area electronics), new circuit architectures, advance algorithms for signal analysis (machine learning, statistical signal processing), and full-system synthesis. The applications we focus on are medical sensors (for neurological and cardiovascular diseases), smart cities (infrastructure monitoring), smart homes (highly sensorized spaces), ubiquitous energy harvesting...

• System for electroencephalogram (EEG) replay: In my lab we build devices that perform functions on physiological signals (such as EEGs). To test these devices, we would like to perform digital-to-analog conversion on EEGs we have previously recorded from patients, and replay the signals so that they can be used to test our devices. This project involves the use of bench-top laboratory equipment, LabView software, and custom devices built in the lab.
• System for real-time EEG acquisition and streaming: We have a system for many-channel EEG acquisition in my lab. We would like to adapt this to stream EEG recordings in real time to the custom electronics we build in our lab. This project involves the use of a EEG-recording system, Mac drivers and system software, and FGPA platforms.
• Application design for specialized machine-learning processor: In my lab, we have build a custom microprocessor that includes a CPU (capable of executing software compiled from C) along with custom hardware accelerators for machine-learning functions. This project involves using this microprocessor to implement applications for advanced embedded sensing that were never possible before. Think about performing gesture recognition using a cell-phone camera for gaming applications, etc.
• Algorithms for computing using 'broken' hardware: As CMOS technology continues to scale, it is becoming impossible to ensure the correct operation of the transistors underlying our computing systems. The question is whether a system can 'learn' and compensate for its own faults, performing high-value computations despite highly-broken hardware. We have discovers some algorithms that can enable this. This project involves emulation of such systems and algorithms on FPGAs by injecting models for various physical faults.
• Audio processing using a large array of microphones: We are developing flexible sheets that integrate arrays of microphones with active electronics. Using microphone arrays, we can perform beam forming, where our array effectively 'listens' to specific points in a room. This project will involve using the acquired audio data to perform speech recognition.

1. Bandit and reinforcement learning theory (mathematical proofs)

2. Machine learning for drug discovery

• Tracking and photographing near earth objects and in-orbit objects such as artificial satellites has remained a challenge. This is primarily driven by the problem that these objects move quite fast across the sky and are typically not very bright. To solve this challenge, we propose to construct a computer controlled camera mount to track and photograph interesting objects which have otherwise been impossible to photograph. A successful student or team of students is sought to create such a camera mount to photograph objects such as satellites whose configuration is currently unknown.
• Construct and modify a open source 3D printer which is then used to print biodegradable motherboards and computer cases.
• In our research group, we are building a new manycore microprocessor, we are looking for an undergraduate to study whether it is possible to use Crowd-sourcing to test the functionality of this new microprocessor. This would involve working with Amazon's Mechanical Turk along with setting up experiments to see if Crowd-sourcing can be effective at finding bugs. This would involve using untrained labor to to write test cases for a processor. Finally, we would like a student to use crowd-sourced computing to help verify the chip by using altruistic peoples' computer power. Think of SETI-at-Home for chip verification.

Here is a non-exhaustive list of example projects: 

• Drone-assisted 3D tomographic remote sensing of atmospheric chemicals using quantum cascade laser dual comb spectrometer 
• Characterization and control of quantum- and interband-cascade laser frequency combs
• High-resolution photothermal hyperspectral microscopy of solid- and biological-samples
• Characterization and testing of THz frequency combs
• Non-linear generation of offset-free frequency combs in the THz regime
• Application of machine learning (ML) to stabilization and control of tunable lasers and optical frequency combs

Our lab creates, manipulates, and characterizes atomically thin materials for applications in electronics and optoelectronics. The work involves semiconductors, optical, electrical, and mechanical characterizations of thin film materials.

• We study how the electronic properties of atomically thin materials can be controlled by mechanical deformation via strain engineering and their morphology.
• We study how epitaxial growth happens at confined two dimension, for creating electronic materials and devices with higher performance.  
• We work on developing techniques for imaging thin film materials and analyzing hyperspectral data to understand their properties with spatial and spectroscopic resolution.
• Feel free to contact me if you have ideas related to the mechanical, optical, and electronic properties of thin film materials and devices.