Summer Research Opportunities

Below is a listing of research projects that are being offered by professors in the EE department. Please refer to the respective websites for project descriptions and requirements. Academic credit may be given at the discretion of the faculty advisor. Depending on the project and individual faculty, registration options for summer research may be: (a) Research for Credit with paid tuition: course ELEN E6001 or 6002; (b) ENGI 4900 (0 credit, no tuition) - Summer Research Projects, or (c)  Non-credit-bearing research experience (no tuition).

Professor Kostic

1. 2023 Spring/Summer/Fall Project : Real-time AI-based multi-sensor multi-object object tracking and classification to support streetscape digital twins

2. 2023 Spring/Summer/Fall Project: Smart Intersections, Cloud-Connected Vehicles - Applications of Deep Learning

3. 2022/2023 Spring/Summer/Fall Project: Using speech and language to identify patients at risk for hospitalizations and emergency department visits in homecare

4. 2022/2023 Spring, Summer/Fall Project: Applications of Deep Learning in Surgery Using Videos

5. 2022/2023 Spring/Summer/Fall Project: AI on the EDGE - Deep Learning with Low-Power GPU and IoT Devices

6. 2023 Summer Project: Lidars and multi-mode sensors for smart city intersections

7. 2023 Summer Project: Radar-map and alerting via a feedback channel in smart city intersections

 

Professor Zussman

1. Title: Evaluating 5G/6G Wireless (Collaboration with Nokia Bell Labs)

5G-and-beyond networks will utilize data transmissions at millimeter-wave (mmWave) and terahertz (THz) frequencies to improve data throughput and wireless spectrum utilization. In collaboration with Nokia Bell Labs, we evaluate 5G/6G wireless at a variety of locations within the Columbia campus using a channel sounder. Students working on this project will be responsible for collecting data using the channel sounder and potentially helping analyze the results.

Lab: Wireless and Mobile Networking Lab

Qualifications:

  • Must be able to access the Columbia Morningside Heights campus
  • Some background in Wireless Communications is preferred

Contact: Abhishek Adhikari <[email protected]>

2. Title: Full duplex wireless

Full-Duplex (FD) wireless technology allows for simultaneous transmission and reception on the same frequency channel, a more spectrum-efficient communication paradigm than the current half-duplex architecture used in all modern wireless systems. The FlexICoN interdisciplinary project directly addresses important cross-layer challenges stemming from novel small-form-factor FD transceiver implementations. In this project, students will explore FD transceiver and algorithm development, familiarizing themselves with the fundamentals of FD operation at the node, link, and network levels. 

Lab: Wireless and Mobile Networking Lab

Qualifications: 

  • Experience with C/C++ and MATLAB required.
  • Background in signal processing (digital and analog) and electrical/electronic lab work preferred. 
  • Some understanding of wireless communication systems, or an interest in learning, is preferred.

Eligibility: Junior, Senior, Master's

Contact: Alon S. Levin <[email protected]>

3. Title: Opportunistic Weather Sensing via ML and data analysis, using Link Measurements from a NYC-Wide Wireless Network

5G-and-beyond networks will use high-frequency millimeter-wave (mmWave) links to transmit and receive information with high throughput. A particularity of mmWave links is that they can be severely affected by diverse weather conditions such as rain, snow, fog, and even humidity. In this project, our goal is to leverage measurements of weather-induced link attenuation to infer the current weather conditions and to predict link attenuation in the near future. Students working on this project will have access to a unique set of measurements of link attenuation from a city-wide wireless network in NYC. The project entails: (i) developing a pipeline that continuously collects the relevant data from our partner’s database, (ii) ensuring the quality of the dataset and maintaining an easy-to-access dataset, (iii) correlating the link attenuation data with weather monitoring (and perhaps pollution) information, (iv) developing a machine learning architecture that infers the current weather and its impact on links based on the sequence of past attenuation values, and (v) validating the accuracy of the proposed architecture. The student will have the opportunity to work with a team of experienced researchers from Columbia and Tel Aviv Universities and gain valuable experience in machine learning, data science, and weather analysis.

Lab: Wireless and Mobile Networking Lab

Qualifications: 

  • Skill sets: Must have experience with Python. Must have experience with machine learning. Familiarity with time-series prediction is preferred.

Contact: Shuyue Yu <[email protected]>

4. Title: Intelligent Spectrum Coordination for Future Wireless Networks

The wireless revolution is fueling the demand for access to the radio frequency spectrum. Smartphones, wearables, modern cars, and smart homes are all competing for spectrum resources. Managing this increasing demand is an important and timely research challenge. Dynamic Spectrum Allocation (DSA) methods allow multiple wireless networks to collaboratively adapt in real-time to dynamic RF frequency environments. In this project, we consider intelligent wireless networks that exchange Spectrum Consumption Models (SCMs) in order to dynamically coordinate the spectrum usage aiming to avoid harmful interference. Students working on this project will construct SCMs based on real measurements of wireless signals, develop novel frequency coordination protocols based on SCMs, and implement the protocols in a custom-built python simulator and/or in a Software-Defined Radio testbed.

Lab: Wireless and Mobile Networking Lab

Qualifications: Some experience with Python and MATLAB is required. Some background in Wireless Communications is preferred. 

Contact: Dr. Igor Kadota <[email protected]>
 

Professor Ethan Katz-Bassett

Tom is a senior PhD student in Ethan Katz-Bassett’s Systems and Networking Lab.

1. Title: Optimizing Internet Paths to Cloud Deployments

Cloud providers like Google, Amazon, and Microsoft are at the forefront of serving emerging applications such as virtual reality and self-driving cars that demand high network performance and availability, and rely heavily on the Internet to do so. The Internet and its defining protocols, however, are being pushed to their limits to provide the performance we see today. We aim to invent new ways of using the network to deliver next-generation performant applications to the world. This project explores how we can combine methods in optimization, stochastic modeling, and Internet measurement to help clouds use old Internet protocols in new ways, so that we’re better equipped to handle the needs of tomorrow’s users. 

Lab: Systems and Networking Lab

Period: Spring/Summer/Fall 2023

Qualifications: Background in Python

2. Title: Learning Low-Footprint Forwarding Policies

There are billions of Internet-connected devices and that number seems to be growing exponentially. Devices have unique addresses, called Internet Protocol (IP) addresses, so that the Internet knows how to get packets where they need to go. In the middle of the network are packet forwarding elements, called routers, that forward trillions of packets each day (hundreds of millions per second!) to their proper destination. However, routers are inherently limited by very expensive hardware that enables routers to forward packets so quickly. A fundamental limitation is that there are too many addresses to store, and routers are running out of space. This project uses machine learning to explore ways of compressing information routers need to store so that the Internet can continue to expand and grow, paving the road for new applications, better services, and more users on the Internet.

Lab: Systems and Networking Lab

Period: Spring/Summer/Fall 2023

Qualifications: Background in Python, experience with machine learning preferred.

 

Professor Xiaofan (Fred) Jiang 

Intelligent and Connected Systems Lab (ICSL) 

Time: Summer/Fall 2023

Contact: Jingping Nie ([email protected])

Note for interested students:

  • Please use the subject line: [ICSL Summer Intern Applications].
  • In the email body, please identify the project name(s) you are interested in and what are the qualification(s) you meet.
  • For those who have already sent Jingping email after the career coffee chat, no need to send your application again.
  • Make sure to include your CV/resume and your transcripts.

 

1. Digital Health / AI for Health

Multiple positions involving sensing and data analytics of health-related signals. 

Qualifications (one or more):

  • Signal processing (analysis in the time and frequency domain)
  • Programming experiences in Python and MATLAB
  • Machine learning techniques in data analytics
  • Experiences in iOS/AndroidOS application development
  • UI design

2. Mobile and wearable sensing in fitness

This project aims to use multimodal sensors worn on bodies or attached to other equipment to monitor fitness performance. 

Qualifications (one or more):

  • Programming experiences in Python, Flutter, Javascript, or C.
  • Experience with digital signal processing.
  • Familiarity with audio processing is preferred.
  • Embedded Machine learning experience. 
  • Mobile app development experience is preferred. 
  • Web services and cloud computing.

3. Flutter App Developer (Machine Learning)

    The successful candidate will be responsible for developing mobile applications using Flutter and integrating machine learning algorithms into the app.

    Qualifications (one or more):

    • Develop mobile applications using Flutter
    • Integrate mobile machine learning algorithms into the app
    • Knowledge of RESTful APIs and JSON

    4. Modular Drone Platform

    This project aims to use multimodal sensors worn on bodies or attached to other equipment to monitor fitness performance. 

    Qualifications (one or more):

    • Programming experiences in Python, Flutter, Javascript, or C.
    • Experience with digital signal processing.
    • Experiences with CAD/PCB/mechanical design.
    • Embedded platform experience. 
    • Experience with mmWave/LiDAR/Depth camera.

      Disruption of long-term memory by interictal epileptiform activity

      • Background: IEDs have been shown to disrupt memory, but the mechanisms by which they do so are not clear. Hippocampal ripples order and replay neural firing, facilitating physiologic memory storage.
      • Hypothesis: During learning, hippocampal ripples are decreased, or potentially replaced by IEDs, in epileptic animals, contributing to poor long-term memory.
      • Methods: Analysis of rat hippocampal neurophysiologic data, detection of IEDs and ripples, analysis of rat behavior and memory during performance of a cheeseboard maze task (video).
      • Goal: Decode neural signals that predict deterioration of memory.
      • Contact: Dion Khodagholy ([email protected])

      Responsive electrical stimulation to improve memory in epilepsy

      • Background: Coupling of hippocampal IEDs to spindles in the medial prefrontal cortex (mPFC) is correlated with poor long-term memory.  Certain forms of electrical stimulation applied to the mPFC can abolish spindle oscillations.
      • Hypothesis: Application of closed loop electrical stimulation in response to IED occurrence can prevent spindle oscillations and improve long-term memory.
      • Methods: Analysis of rat in vivo neurophysiology and behavior for rats undergoing responsive electrical stimulation compared to control rats.
      • Goal: Determine how responsive electrical stimulation affects neural networks and memory.  
      • Contact: Dion Khodagholy ([email protected])

      Characterizing neural networks in mouse models of pediatric epilepsy and evaluating response to targeted genetic interventions 

      • Background: Although rodent models of pediatric epilepsies exist, analysis of in vivo neurophysiologic data during ages corresponding to the human neonate, infant, and child is lacking. This limits our ability to evaluate precision medicine therapies. 
      • Hypothesis: Rodent models of pediatric epilepsies will exhibit altered oscillatory patterns and maturation compared to wildtype rodents that will permit longitudinal study and better understanding of epileptogenesis in these disorders. These parameters can be tracked to evaluate response to genetic therapy.
      • Methods: Analysis of mouse developmental in vivo neurophysiology.
      • Goal: Decode characteristic neural network features corresponding to emergence of epileptic activity across mouse models of pediatric epilepsy. Predict and track response to genetic therapies.
      • Contact: Dion Khodagholy ([email protected])

      Intelligent Spectrum Coordination for Future Wireless Networks

      Students working on this project will construct SCMs based on real measurements of wireless signals, develop novel frequency coordination protocols based on SCMs, and implement the protocols in a custom-built python simulator and/or in a Software-Defined Radio testbed.

      • Requirements: 
        • Some experience with Python and MATLAB is required. Some background in Wireless Communications is preferred. 
      • Website
      • Mentor: Gil Zussman
      • Contact: Dr. Igor Kadota <[email protected]>

      Weather Effects on High Frequency Communication Links

      In this project, students are expected to learn how the mmWave radar works, design experiments to obtain labeled data, perform measurements, and develop the classifier.

      • Requirements: 
        • Some background in Digital Signal Processing such as FFT is required.
          Some experience with Python is required.
        • Some experience with classification predictive modeling is preferred.
        • Some understanding of wireless networks, or interest to learn is preferred.
        • Must be able to access the Columbia Morningside Heights campus
      • Website
      • Mentor: Gil Zussman
      • Contact: Dr. Igor Kadota <[email protected]>

      Evaluating 5G mmWave Outdoors-to-Indoors

      Students working on this project will be responsible for extensive data collection and analysis. The results can guide the design and deployment of next generation wireless networks.

      • Requirements: Must be able to access the Columbia Morningside Heights campus. Some background in Wireless Communications is preferred.
      • Website
      • Mentor: Gill Zussman
      • Contact: Abhishek Adhikari <[email protected]> 

      Video analytics over edge-cloud networks 

      Video analytics can be used in different applications, including traffic control, security surveillance, and factory floor monitoring. A typical video analytics application consists of a pipeline of video processing modules including an NN-based object detector. The pipeline has several knobs such as frame resolution, frame sampling rate, bitrate, and detector model. The choice of configuration impacts resource consumption, latency, bandwidth requirements, and accuracy of the video application. The best configuration for a video analytics pipeline also varies over time, often at a timescale of minutes or even seconds. In many cases, the policy that reduces the frame rate and lowers the resolution can save resources without impacting the accuracy. In this project, we design and evaluate a controller that dynamically picks the best configurations for existing NN-based video analytics pipelines.

      • Requirements: 
        • Programming languages: C/C++, Python, Shell scripting
        • Computer vision: previous experience in working with  Deep learning models for object detection/tracking e.g. YOLOv4/Nvidia DCF tracker
        • Preferred: Familiarity with the GStreamer library, DeepStream SDK.  
      • Website
      • Mentor: Gill Zussman
      • Position Dates: Spring May 31-Aug. 19
      • Hours per Week: 35
      • Position type: Hybrid (both remote and on site)
      • Contact: Mahshid Ghasemi <[email protected]>

      Projects in the Analog and RF Integrated Circuits Design Research Group

      Analog vs Digital Feature Extraction for On-Device Keyword Spotting

      The advent of ubiquitous computing calls for intelligent human-machine speech interfaces, in particular on-device keyword spotting (KWS)--think Amazon Alexa and Google Home. However, the ultimate goal is a KWS system that can survive on a coin cell battery for several years, which demands ultra-low power (nanowatt levels). Contemporary KWS systems consist of a frontend feature extractor and backend neural network, where the feature extractor is the power dissipation bottleneck. An open research question is whether feature extraction is more power-efficient using digital approaches fueled by Moore's Law or analog approaches inspired by history. Tackling this open-ended research question will entail traversing the entire stack of circuit design research skills: literature review, pencil-and-paper analysis, exploration in MATLAB, and analog & digital design in Cadence. Further, this work can potentially lead to co-authorship on a publication.

      Phased Array Antenna Design for RF Sensing Systems

      Emerging communication systems are relying on the use of multiple antenna systems and intelligent ways of allocating resources to increase channel capacity. Phased array antenna systems are being used in unique ways to solve new challenges in next-generation communication systems. In this project, you will work on the design, simulation, and testing of a phased array antenna that will be used to perform various sensing and communication tasks. The work will consist of literature review, MATLAB simulations, EM simulations, prototyping, PCB design, and antenna testing.

      High Performance Electronics Under Cryogenic Conditions

      The push towards quantum computing and deep-space exploration motivates the need for scalable electronics that can tolerate a wide temperature range, from near super-conducting (~4 K) up to 400 K.  The current focus is in understanding how CMOS circuits, particularly analog-to-digital converters done in a conventional 65nm process, respond to these temperature changes and what can be manipulated to compensate for any unwanted behavior.  However, before components can be tested at the target temperatures, a number of infrastructural tasks must be done to understand the experimental setup.  These tasks include but are not limited to (1) RTL coding, verification, and implementation for ADC data acquisition on an FPGA that can communicate data to a computer running a GUI.  (2) Setup and validation of a temperature-stable cryostat for liquid nitrogen and a hot-box, and (3) testing of COTS parts, such as RF balun transformers and passive components, at these temperatures. (4) Design of a temperature-stable switched-capacitor closed-loop amplifier for use in a pipelined SAR ADC.   

      AI on the EDGE - Deep Learning with Low-Power GPU and IoT Devices 

      Research with low power portable computing devices for data acquisition and AI-based processing. Applications in smart cities and healthcare. Hands-on experiments with NVIDIA Jetson Nano, Google Coral, Intel Neural Compute Stick. Sharing computational load with edge-cloud compute and data servers. Measurement of latencies and exploration of real time capabilities and constraints.

      • Qualifications/skill-set: Signal Processing, Video Processing, Data Analysis, Software Development, some Machine Learning and Deep Learning.
      • Hours per Week: 20-35 hours 
      • Direct Supervisor: Zoran Kostic
      • Paid Position: limited
      • Credit: Can be up to 3 credit hours
      • Number of positions: multiple

      COSMOS Smart Intersections, Cloud-Connected Vehicles - Applications of Deep Learning

      Applications of deep learning to project COSMOS.

      COSMOS - advanced wireless research platform -> link, link, link, link.

      Applications of Deep Learning - Research on: (i) COSMOS cloud connected vehicles, (ii) Monitoring of traffic intersections, using bird’s eye cameras, supported by ultra-low latency computational/communications hubs; (iii) Simultaneous video-based tracking of cars and pedestrians, and prediction of movement based on long-term observations of the intersection; (iv) Real-time computational processing, using deep learning, utilizing GPUs, in support of COSMOS applications; (v) Sub-100ms latency communication between all vehicles and the edge cloud computational/communication hub, to be used in support of autonomous vehicle navigation. The research is performed using the pilot node of the project COSMOS infrastructure https://cosmos-lab.org/.

      COSMOS applications - short description: smart cities, smart intersections, cloud connected vehicles, applications of artificial intelligence, real time edge computing, https://cosmos-lab.org/experimentation/smart-city-intersections/.

      • Direct Supervisor: Zoran Kostic
      • Hours per Week: 20-35 hours Paid Position: limited
      • Credit: Can be up to 3 credit hours
      • Number of positions: multiple
      • Qualifications/skill-set: Machine Learning, Deep Learning, Signal Processing, Video Processing, Data Analysis, Software Development, Cloud Computing, GPUs.

      Using speech and language to identify patients at risk for hospitalizations and emergency department visits in homecare

      This study is the first step in exploring an emerging and previously understudied data stream - verbal communication between healthcare providers and patients. In partnership between Columbia Engineering, School of Nursing, Amazon, and the largest home healthcare agency in the US, the study will investigate how to use audio-recorded routine communications between patients and nurses to help identify patients at risk of hospitalization or emergency department visits. The study will combine speech recognition, machine learning and natural language processing to achieve its goals. 

      • Direct Supervisor: Zoran Kostic
      • Hours per Week: 20-35 hours
      • Paid Position: limited
      • Credit: Can be up to 3 credit hours
      • Number of positions: multiple
      • Qualifications/skill-set: Signal Processing, Speech Processing, Data Analysis, Software Development, some background and interest in Cloud Computing, GPUs, Machine Learning, Deep Learning.

      SensorHub - A Modular Platform for Rapidly-Deployable Sensing Systems

      Researchers in various fields frequently find them spending too much time and budget in engineering a sensor system to collect the data they need. Tech-savvy homeowners dream to customize their smart home but are set back by its engineering complexity. The goal of this project is to create a Raspberry Pi based system with a modular interface to allow users from different engineering backgrounds to mix and match different sensors within seconds. We also plan to develop a customized indoor drone system, which, after integrating with this project, can load / unload sensors automatically and carry a specific sensor to a specific indoor location, with the control of a program.

      • Requirements: 
        • Experience in Python programming, UNIX-based systems, PCB Design and embedded coding. 
        • Experience in designing mechanical systems and / or designing customized drones is preferred.
      • Website
      • Mentor: Prof. Xiaofan (Fred) Jianc
      • Contact: Scott Zhao <[email protected]>

      Data-driven At-home Mental Health Assessment and Intervention

      The growth of IoT devices and sensors enables over 70% of homes in the United States to have at least one smart home device. Reports show that with the impacts of the pandemic, more than half of employed adults are currently working from home. The Census Bureau reports that there were 37 million one-person households in 2021. In addition, the pandemic makes the in-person therapist/doctor visit constraint and challenging. Thus leveraging the existing smart home devices to perceive the physical and mental status of the occupants would be a desirable solution to take care of the well-being of people who live alone or in the early phase of particular mental diseases. Besides, some smart home devices could also be utilized to provide appropriate interventions.

      In collaboration with therapists, this project takes advantage of the prevalence of low-cost smart home sensors to bring mental health services and support to everyone’s home. This environment does not require patients to wear anything to avoid discomfort and won’t affect the patient’s regular life routine. The project aims to use smart home sensors devices such as the proximity sensor in Roomba and machine learning methods to predict mental health problems, to use devices such as Alexa to chat with the users to provide mental health support and to use actuators to provide intervention in case of an emergency. We want to provide objective evaluations to the patients for self-assessment or support materials for doctor visits.

      • Requirements: 
        • Experience in Python programming and data analysis.
        • Experience in machine learning would be preferred.
      • Website
      • Mentor: Prof. Xiaofan (Fred) Jiang
      • Contact: Jingping Nie <[email protected]>

      Applications of Deep Learning and Internet of Things
      Description/website: Summer research experience 2021
      Mentor: Prof. Zoran Kostic
      Contact: [email protected] 

      Summer research at the Columbia Intelligent and Connected Systems Lab (ICSL)
      Description/website: summer research positions at ICSL
      Mentor: Prof. Xiaofan (Fred) Jiang
      Contact: [email protected]

      Summer Research in Photonic Systems
      Description/website: Participate in experimental and modeling work related to silicon photonic interconnect systems. Photonic Systems
      Mentor: Prof. Keren Bergman
      Contact: [email protected] 

      Summer Research at the Wireless and Mobile Networking (WiMNet) Lab
      Description/website: Edge/cloud Video Analytics | mmWave Wireless Channel Measurements in the COSMOS testbed | Streaming video analysis and optimization during Work-from-Home period | Weather Effects on High Frequency Communication Links
      Mentor: Prof. Gil Zussman
      Contact: [email protected] 

      Algorithm and Hardware Co-Design for Artificial Intelligence and Machine-Learning Chips
      Description/website: http://www.ee.columbia.edu/~mgseok/research.html 
      Mentor: Prof. Mingoo Seok
      Contact: [email protected] 

      Characterizing network behavior in phishing emails
      Description/website: Columbia Systems/Networks Lab summer research projects 
      Mentor: Professors Asaf Cidon and Ethan Katz-Bassett
      Contact: See website for instructions

      Applications of Deep Learning and Internet of Things
      Description/website: Summer research experience (option to register for academic credit)
      Mentor: Prof. Zoran Kostic
      Contact: [email protected]

      Summer research at the Columbia Intelligent and Connected Systems Lab
      Description/website: http://icsl.ee.columbia.edu/
      Mentor: Prof. Xiaofan (Fred) Jiang
      Contact: [email protected]

      Building Interactive Computing Tools for the Fruit Fly Brain Observatory
      Description/website: http://www.bionet.ee.columbia.edu/projects/fbl
      Mentor: Mehmet Kerem Turkcan, PhD student (Bionet Group)
      Contact: [email protected] (to apply, send CV/resume along with code samples, Github repositories of related previous work)

      Characterizing network behavior in phishing emails
      Description/website: Columbia Systems/Networks Lab summer 2020 research projects (option to register for academic credit)
      Mentor: Professors Asaf Cidon and Ethan Katz-Bassett
      Contact: See website for instructions

      The Internet under widespread shelter-in-place: Resilience, response, and lessons for the future
      Description/website: Columbia Systems/Networks Lab summer 2020 research projects (option to register for academic credit)
      Mentor: Prof. Ethan Katz-Bassett
      Contact: See website for instructions

      Understanding and optimizing Internet video delivery with self-driving networks
      Description/website:  Columbia Systems/Networks Lab summer 2020 research projects (option to register for academic credit)
      Mentor: Prof. Ethan Katz-Bassett
      Contact: See website for instructions

      Algorithm and Hardware Co-Design for an Intelligent Internet-of-Things Device
      Description/website: http://www.ee.columbia.edu/~mgseok/research.html
      Mentor: Prof. Mingoo Seok
      Contact: [email protected]

      Summer research at the Columbia Translational Neuro-Electronics lab
      Description/website: https://www.dion.ee.columbia.edu/
      Mentor: Prof. Dion Khodagholy
      Contact: [email protected]

      Developing a Python sum-of-squares toolbox for polynomial optimization
      Description/website: http://www.columbia.edu/~ja3451/positions.html 
      Mentor: Prof. James Anderson
      Contact: [email protected]