GENOV LAB OPENINGS
FOR UNDERGRADUATE and MEng STUDENTS
(ECE2500 MEng Projects, Research Volunteer Positions, USRA/UTEA/ESROP Positions, Eng Sci and ECE Thesis Projects, Senior Design Projects)
Please follow application instructions for each project posted at the above link. In the subject line of your email message, please include keywords: “MEng Project Application” or “Undergraduate Research Project Application”. Please include your resume and transcript (the minimum GPA is 3.4).
FOR PhD / Direct-Entry-PhD STUDENTS AND POST-DOCTORAL FELLOWS
2024: Several positions are available for applicants with experience in analog, mixed-signal, RF or digital IC design (industrial experience is welcome). Applicants with experience/interest in image sensors design, computer vision, neural interfacing ICs and digital systems design are currently of particular interest to us. More information on available projects is given below.
Please attach your CV/resume (WITH GPA STATED), your transcript (in pdf format) and list of the projects below (with project number and title included) in the order of your preference (highest first).
Starting dates are May or September of 2023-24, or on a rolling basis any time of the year. Most of the openings listed below are available immediately but not all may be filled. If you are looking to apply for a PhD degree or post-doctoral studies please contact me by email directly. The general application process is outlined at https://www.ece.utoronto.ca/graduates/admission/ Our positions are very competitively funded.
1. (NEW!) Transport-aware 3D Image Sensors and Cameras for Next-generation Smart Phones, Autonomous Vehicles, Machine Vision and Artificial Vision (several positions, in collaboration with Prof. Kyros Kutulakos in the Computer Vision Group)
The main goal will be to study, design, and deploy a new class of computational cameras whose key property is that they are "transport-aware." Unlike conventional cameras which record all incident light, transport-aware cameras can be programmed to block some of that light, based on the actual 3D paths it followed through a scene. Transport-aware cameras use a programmable light source for illumination and a programmable sensor mask for imaging, and are a pioneering breakthrough in the fields of computational photography and computer vision, with diverse novel applications, as unimaginable as 3D imaging outdoors, seeing against the sun, around the corner and seeing through skin!
Live video from a transport-aware camera can offer a very unconventional view of our everyday world in which refraction and scattering can be selectively blocked or enhanced, visual structures too subtle to notice with the naked eye can become apparent, and object surfaces can be reconstructed in 3D under challenging conditions well beyond the state of the art.
These capabilities will find new uses in many industrial, scientific and commercial applications such as: augmented and virtual reality (for gesture recognition and object recognition), self-driving cars, 3D printers and scanners, video games, biomedical imaging systems (such as endoscopy), material analysis, drones, robots, and industrial machine vision.
The applicants should have interest and ideally previous experience in some of the following areas (position 1 covers 1.1, 1.2, 1.3, 1.6 and position 2 covers 1.3, 1.4, 1.5, 1.6)
1.1 CMOS analog and mixed-signal integrated circuits/systems design (e.g., pixel design, CMOS imager circuits such as column-parallel readout amplifiers and analog-to-digital converters (ADCs)),
1.2 Semiconductor device physics: understanding of device physics of photodetectors as well as their 3D structure photo-generated charge simulation and layout (e.g., pinned photodiode, photonic mixer device);
1.3 CMOS image sensor experimental characterization using our state-of-the art IC testing facilities both in Prof. Genov’s and Prof. Kutulakos’ labs.
1.4 Digital systems for computer vision: computer architecture and microprocessor IP on-chip instantiation and programming (e.g., using open-core microprocessor IP such as TI MSP430 or ARM);
1.5 Embedded systems for computer vision: solid knowledge of Verilog and/or other hardware description languages for designing top-level camera architectures (e.g., design and programming of high-date-rate I/O interfaces such as memory IP interfaces such as DDR2/3/4, USB3 interface, Xilinx Microblaze Embedded Processor, Xilinx SPI interface protocol, FPGA programming tools such as by Xilinx or Altera);
1.6 Computational vision systems design and deployment through collaborative projects with world’s top computer vision centers (such as collaborators’ sites at Carnegie Mellon University, Madison and Stanford).
This is a highly collaborative project with several research groups around the world (USA, Italy), including those specializing in photodetector design, time-of-flight imaging, computer vision, and robotics. There may be opportunities to travel internationally to top research centers in North America and Europe.
Artificially-intelligent Implantable Neural Interfaces and Neurostimulators for
Diagnostics and Treatment of Neurological Disorders (collaborations with Profs. Taufik Valiante, Jose Zariffa, Paul Yoo).
The implantation targets include both the central nervous system (CNS) such as the brain and the peripheral nervous system such as the vagus nerve. This thrust involves the design of implantable and wearable CMOS analog and mixed-signal integrated circuits/systems for neuro-electrical signal acquisition, filtering and amplification (electrophysiology), ADC/DAC design, on-chip signal processing, RF communication (transceivers), inductive powering, electrical neurostimulation, interfacing with brain-implanted high-count microelectrodes, integration/interfacing with on-chip and off-chip microelectrodes, and in vivo experimentation with animals.
a. Embedded systems design for wearable brain and PNS interfaces (using commercially available IC components)
b. Implantable wireless integrated circuits for brain and PNS neural activity monitoring and modulation
c. Wireless brain and PNS implants with on-chip artificial intelligence (including studying machine learning algorithms and mapping them onto integrated circuits for on-line inference in neural recordings – this is collaborative with our AI-oriented team members)
d. Brain implants for optogenetic stimulation (using hybrid integration of electronics and photonic lasers, etc)
e. Selected topics in neuroscience, neurology, neurosurgery, electrophysiology, animal neurosurgery, epileptology (experience or interest in electrical engineering and electronics is of benefit, but is not a must)
3. E-CHEM: Miniature Electronic Chips for Nuclear Magnetic Resonance (NMR) Spectroscopy and Electrochemical Analysis of Biochemical Composition of Living Organisms (collaborations with Profs. Taufik Valiante, Peter Carlen, and Mike Thompson).
This thrust involves the design of implantable and disposable CMOS analog and mixed-signal integrated circuits/systems for bio-chemical analysis of living organisms. The integrated circuit design includes base-band and RF signal filtering and amplification, ADC / DAC design, RF communication (transceivers), E&M coil and antenna design, and in vivo experimentation with animals and human brain slices.
3.1 Nuclear magnetic resonance (NMR) spectrometry. Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical method for analyzing chemical compounds by sensing local magnetic fields around atomic nuclei. It finds broad applications in analytical chemistry and biochemistry. Conventional NMR spectrometers are expensive and bulky, and require a large sample size. Centimeter-scale NMR spectrometer system-on-a-chip (SoC) takes advantage of the low cost, small size and versatile sensory and computational capabilities of modern electronic chips.
3.2 Electrochemical sensing employs methods such as voltammetry, amperometry and impedance spectroscopy. Integrated circuits can be implanted in the brain to perform electrochemical sensing, imaging and modulation of neurochemicals in the brain (please see our 2016 MDPI Sensors paper and ISSCC 2018 paper for more details). Integrated circuits can also be implanted into peripheral nerves for closed-loop organ-specific neuromodulation (e.g., electroceuticals).
3.3 Gas sensors and olfactory sensors for volatile organic compounds detection in human breath in order to perform early medical diagnostics
3.4 High-throughput drug screening chips - integrated circuits for patch-clamp electrophysiology
4. Machine Learning ALGORITHMS for Big Data: Brain Signals Analysis for Therapeutic Adaptive Brain Stimulation (collaboration with Profs. Taufik Valiante and Stark Draper)
Responsive electrical neurostimulation is an emerging technology for the treatment of many brain disorders. In the Intelligent Sensory Microsystems Lab, we have been developing technologies to prevent seizures by means of responsive neurostimulation (i.e., stimulating the brain before an upcoming seizure in order to prevent the seizure from occurring). As part of this project, we have been collecting multi-terabyte datasets of clinical intracranial EEG recordings from patients being evaluated for epilepsy surgery. We are looking for talented, highly motivated individuals to help us analyze these data. Specifically, the candidate will develop machine learning algorithms for predicting the onset of seizures, detecting epileptogenic brain regions and for identifying optimum brain stimulation strategies. The following qualifications are required / preferable:
- Experience with artificial intelligence / machine learning algorithms (e.g., support vector machines, RNNs, deep learning, boosting, ensemble methods)
- Strong programming skills in MATLAB or Python
- Experience with analyzing time series data
- Experience with streaming data analysis
- (Preferably) experience with electrophysiology data
- (Preferably) experience with C and parallel programming (e.g., MPI)
5. At-the-edge Digital Machine Learning VLSI ACCELERATORS for Energy-efficient Brain State Classification and Responsive Stimulation (collaboration with Prof. Taufik Valiante and Prof. Andreas Moshovos)
Implementation of energy-efficient machine learning algorithms for both: (a) accurate prediction/detection of pathological brain states such as epileptic seizures; and (2) patient-tailored lifelong adaptive neurostimulation. The algorithms are currently support vector machines and will likely also include reinforcement learning / RNNs / deep learning etc in the future. These would be initially implemented on an FPGA connected in a closed loop to a human patient brain, with the constraints of a digital ASIC implementation in mind. Next these would be synthesized on a low-power implantable ASIC. We currently use a RISC-V microprocessor combined with accelerator co-processors both for feature extraction and inference. Please see our recent ISSCC papers for more details. The project involves fully-digital computing architectures (on-chip microprocessors such as RISC-V combined with accelerator co-processors, highly parallel accelerators, bit-level processing, asynchronous processors, etc) - first in Verilog/FPGA then fabricated in digital CMOS; novel ways of implementing both feature extraction (spatiotemporal filtering, PCA, ICA, etc) and data classification in VLSI; resources balancing between feature extraction, data classification and wireless communication.
6. (NEW!) Computing in Memory: Kerneltron-2 - Next-generation Charge-Domain Mixed-Signal Machine Learning Accelerator with Computing in DRAM (collaboration with Prof. Gert Cauwenberghs at UCSD, San Diego).
This project builds on our past success with Kerneltron processor which set a record for energy efficiency in computing high-dimensional linear transforms. Kerneltron performs computing directly in memory, so energy or time are not wasted on auxiliary memory storage, memory access and data communication. The next-generation processor will utilize a new massively-parallel architecture, an advance technology node (22nm or better) and a new embedded memory technology (DRAM, NVRAM), all well suited for accelerating deep neural networks (DNNs), convolutional neural networks (CNNs) and other network types at the edge. Visits to San Diego are possible/likely. The following qualifications are preferred:
- Experience in the mixed-signal circuit design (such as ADC design)
- Understanding of memory circuits (such as embedded NVRAM / DRAM / SRAM, sense amplifiers, SERDES, high-speed I/O etc)
- Interest in working in advanced CMOS nodes (such as 22nm CMOS or SOI process)
- Knowledge of machine learning algorithms (DNN, CNN, backpropagation, etc)
- Availability to travel to San Diego
7. At-the-edge VLSI ACCELERATORS Machine Learning near the sensor (e.g., within an implant, within an image sensor, etc; collaboration with Prof. Andreas Moshovos)
This project involves digital ASIC design for implementing high-throughput processor architectures for deep learning acceleration developed in Prof. Andreas Moshovos group. We aim to implement his group’s recent advances in processor architecture in custom silicon. One example of such a processor architecture innovation can be found in:
J. Albericio, A. Delmás, P. Judd, S. Sharify, G. O’Leary, R. Genov, A. Moshovos, “Bit-pragmatic Deep Neural Network Computing,” 50th Annual IEEE/ACM International Symposium on Microarchitecture, Boston, Oct. 2017.
The project includes feasibility study, architecture-to-ASIC translation/mapping, synthesis and place and route including memory compiler, IC fabrication and testing. The aim is to submit a manuscript to ISSCC.
8. Implantable Electrophysiology: Deployment of State-of-the-Art Intracranial EEG Recording and Neruostimulation Systems in Rodent Models of Neurological Disorders (in collaboration with Profs. Taufik Valiante, Paul Yoo and Jose Zariffa at BME / School or Medicine / Toronto Western Hospital)
We are looking for a graduate student who is interested in a project involving custom design and neurosurgical implantation of novel intracranial EEG microelectrodes (from Genov) into animal models of disorders of the central (Valiante) and peripheral (Yoo, Zariffa) nervous system. The student may also perform signal processing of the EEG signals to study the signal characteristics of the EEG from an animal model of a neurological disorder and the effects of treatment. The student would be expected to become skilled and independent for electrophysiological experimentation, and to interface between several collaborating labs.
OTHER ONGOING PROJECTS WITH PERIODIC VACANCIES:
9. Analog and Mixed-Signal Integrated Circuit Design for Next-Generation Brain-Chip, Skin-Chip and Other Organ-Chip Interfaces
a. Oversampling and Nyquist-rate analog-to-digital converters (please see our recent ISSCC papers for examples of previous designs)
b. Analog-to-digital converter arrays for sensory applications
c. Computational analog-to-digital converters / information-to-digital converters
d. Analog-to-digital converters for non-uniform / compressive data sensing
e. Analog signal processing, including digitally-assisted analog design
f. Analog-digital co-design / mixed-signal systems-on-chip
10. Opto-electronic Implantable, Wearable and Disposable Integrated Circuits (CMOS Imagers) for Bio-sensing/Neuro-imaging
a. Implantable wireless CMOS contact imagers for optical monitoring of the brain activity
b. Coded-exposure and coded-aperture imaging of the brain neural activity
c. Optical biosensors – fluorescence, bio-luminescence CMOS contact imagers
11. Wireless RF Integrated Circuit Design for Implantable, Injectable, and Wearable-patch Health Monitors and Neurostimulators
a. Low-power transmitter and receiver design (e.g., UWB / pulse radio design)
b. Implantable / wearable (eg. body-area) / disposable transceivers design
c. RFIC, RFID and antenna design
d. Antenna design (including on-chip antennas and coils) and high-frequency PCB design
Most positions are in ECE, ECE-BME Collaborative Program, or BME. If you have received/applied for a NSERC or OGS graduate scholarship or have another funding source, please inform me of this.
Most of the projects are collaborative, with participants from multiple other disciplines, mainly in Medicine, Neuroscience, Chemistry, Molecular Biology, Cell Biology, Computer Vision and Robotics. Unique opportunities exist for joint student supervision with other faculty members in Electrical and Computer Engineering (Electromagnetics – with Prof. George Eleftheriades, Photonics – with Prof. Joyce Poon), in BME (Neural Engineering – with Prof. Paul Yoo, Neurosurgery/Neuroscience – Prof. Taufik Valiante, and others) and those in other departments (Computer Science/Vision – Profs. Kyros Kutulakos and David Lindell, Chemistry/Sensors – Prof. Andre Simpson, Medicine, Collaborative Program in Neurosciences, Institute of Medical Science, etc).
Please note that the training focus in our lab is on academic careers (future professorships), entrepreneurship (future co-founders and CTOs/CEOs of companies) and senior-level R&D positions in industry worldwide. For most of the listed positions we prefer applicants with excellent communication skills who take initiative, and who are proven leaders, problem-solvers, self-starters, and team players.
Qualified students interested in joining our lab are encouraged to apply for admission into our Ph.D. or M.A.Sc. degree programs as well as the post-doctoral stream. Applicants with a Bachelor degree can enroll directly into the Ph.D. program immediately or upon successful completion of the first two semesters of studies. Admitted students generally receive full financial support for the duration of their studies. The general application process is outlined at https://www.ece.utoronto.ca/graduates/admission/ You can also contact me, Prof. Roman Genov, by email at roman[AT]eecg.utoronto.ca. Please attach your CV/resume in pdf format (with GPA clearly stated) and your transcript. Often, I am not able to answer all email inquiries but will keep them on file until the graduate office or our team has received all of your application materials.