computer science phd university of toronto

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Computer science, computer science: introduction, faculty affiliation.

Arts and Science

Degree Programs

Applied computing.

Applied Mathematics;
Artificial Intelligence;
Artificial Intelligence in Healthcare;
Data Science;
Data Science for Biology;
Quantum Computing

MSc and PhD

Collaborative Specializations

The following collaborative specializations are available to students in participating degree programs as listed below:

Computer Science, PhD
Computer Science, MSc, PhD

Graduate faculty in the Department of Computer Science are interested in a wide range of subjects related to computing, including programming languages and methodology, software engineering, operating systems, compilers, distributed computation, networks, numerical analysis and scientific computing, data structures, algorithm design and analysis, computational complexity, cryptography, combinatorics, graph theory, artificial intelligence, neural networks, knowledge representation, computational linguistics and natural language processing, computer vision, robotics, database systems, graphics, animation, interactive computing, and human-computer interaction.

For further details, consult the graduate student handbook prepared by the department and available online.

Contact and Address

Msc and phd programs.

Web: cs.toronto.edu Email: [email protected] Telephone: (416) 978-8762

Department of Computer Science Graduate Office University of Toronto Bahen Centre for Information Technology 40 St. George Street Toronto, Ontario M5S 2E4 Canada

MScAC Program

Web: mscac.utoronto.ca Email: [email protected] Telephone: (416) 946-8440

University of Toronto 700 University Avenue, 9th Floor Toronto, ON M5G 1Z5 Canada

Computer Science: Graduate Faculty

Full members, members emeriti, associate members, computer science: applied computing mscac, master of science in applied computing, program description.

The Master of Science in Applied Computing (MScAC) program is offered as

a general Computer Science program ( no concentration ) or as

a concentration in :

Applied Mathematics, offered jointly by the Department of Computer Science and the Department of Mathematics;

Artificial Intelligence, offered jointly by the Department of Computer Science, the Department of Statistical Sciences, and the Faculty of Applied Science and Engineering;

Artificial Intelligence in Healthcare, offered jointly by the Department of Computer Science and the Temerty Faculty of Medicine;

Data Science, offered jointly by the Department of Computer Science and the Department of Statistical Sciences;

Data Science for Biology, offered jointly by the Department of Computer Science and the Department of Cell and Systems Biology;

Quantum Computing, offered jointly by the Department of Computer Science and the Department of Physics.

There is no thesis requirement.

Computer Science: Applied Computing MScAC General Program (No Concentration)

Mscac general program (no concentration), minimum admission requirements.

Applicants are admitted under the General Regulations of the School of Graduate Studies. Applicants must also satisfy the Department of Computer Science's additional admission requirements stated below.

An appropriate bachelor's degree from a recognized university in computer science or a related discipline.

A standing equivalent to at least B+ in the final year of undergraduate studies.

Applicants whose primary language is not English and who have graduated from a university where the primary language of instruction is not English must submit results of the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) with the following minimum scores:

Internet-based TOEFL: 93/120 and 22/30 on the writing and speaking sections.

IELTS: an overall score of 7.0, with at least 6.5 for each component.

If students complete a portion of their degree in English, or part of their degree at another university where English is the language of instruction, applicants must still provide proof of English-language proficiency.

Three letters of support from faculty and/or employers.

Applicants will be asked to respond to program-specific questions addressing their interest in the concentration and objectives for the program.

Program Requirements

Coursework. Students must successfully complete a total of 3.0 full-course equivalents (FCEs) including:

1.0 FCE in required courses: technical communications (CSC2701H) and technical entrepreneurship (CSC2702H).

An eight-month industrial internship , CSC2703H (3.5 FCEs). The internship is coordinated by the department and evaluated on a pass/fail basis.

Program Length

4 sessions full-time (typical registration sequence: F/W/S/F)

3 years full-time

Computer Science: Applied Computing MScAC (Applied Mathematics Concentration)

Mscac program (applied mathematics concentration).

An appropriate bachelor’s degree from a recognized university in a related area such as applied mathematics, computational mathematics, computer science, mathematics, physics, statistics, or any discipline where there is a significant mathematical component. The completed bachelor’s degree must include coursework in advanced and multivariate calculus (preferably analysis), linear algebra, and probability. In addition, there should be some depth in at least two of the following six areas:

analysis (for example, measure and integration, harmonic analysis, functional analysis);

discrete math (for example, algebra, combinatorics, graph theory);

foundations (for example, complexity theory, set theory, logic, model theory);

geometry and topology;

numerical analysis; and

ordinary and partial differential equations.

There should also be a demonstrated capacity at programming and algorithms.

Applicants must satisfy the admissions committee of their ability to be successful in graduate courses in computer science and mathematics, and in an industrial internship in applied mathematics. Applicants should be able to demonstrate a potential to conduct and communicate applied research at the intersection of computer science, mathematics, and a domain area. Applicants may be asked to do a technical interview as part of the application process.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Mathematics or Applied Mathematics.

Applicants must indicate a preference for the concentration in Applied Mathematics in their application. Admission is competitive, and students who are admitted to the MScAC program are not automatically admitted to this concentration upon request.

Coursework. Students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

1.0 FCE chosen from the MAT1000-level courses or higher.

1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings.

1.0 FCE in required courses:

CSC2701H Communication for Computer Scientists (0.5 FCE) and

CSC2702H Technical Entrepreneurship (0.5 FCE).

Course selections should be made in consultation with the Program Director.

Computer Science: Applied Computing MScAC (Artificial Intelligence Concentration)

Mscac program (artificial intelligence concentration).

An appropriate bachelor’s degree from a recognized university in a related area such as physics, computer science, mathematics, statistics, engineering, or any discipline where there is a significant quantitative component. The completed bachelor’s degree must include significant exposure to computer science or statistics or engineering including coursework in advanced and multivariate calculus (preferably analysis), linear algebra, probability and statistics, programming languages, and general computational methods.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Artificial Intelligence (AI).

Applicants must indicate a preference for the concentration in AI in their application. Admission to the AI concentration is competitive. Students who are admitted to the MScAC program are not automatically admitted to the AI concentration upon request.

1.5 FCEs of coursework in the area of AI:

1.0 FCE selected from the core list of AI courses (see list below) from at least two different research areas

0.5 FCE selected from additional AI courses outside the core list

CSC2701H Communication for Computer Scientists (0.5 FCE)

CSC2702H Technical Entrepreneurship (0.5 FCE)

Remaining 0.5 FCE of coursework will be chosen from outside of AI:

Course selections should be made in consultation with and approved by the Program Director. Appropriate substitutions may be possible with approval.

A maximum of 1.0 FCE may be chosen from outside the Computer Science (CSC course designator) graduate course listing.

Artificial Intelligence Core Courses

*different courses with the same title, offered by different Faculties. **different courses with similar titles, offered by different Faculties.

Computer Science: Applied Computing MScAC (Artificial Intelligence in Healthcare Concentration)

Mscac program (artificial intelligence in healthcare concentration).

An appropriate bachelor’s degree from a recognized university in an area such as life sciences, biochemistry, medical sciences, computer science, biotechnology, biostatistics, engineering, or a related discipline.

Applicants should have sufficient academic undergraduate background in programming (ability to program and basic software engineering skills), calculus, statistics, a first- or second-year undergraduate course in statistics, linear algebra, and an undergraduate course that introduces concepts of healthcare and/or molecular biology. If courses were not taken prior to application to the program, please note that equivalent experience will be considered.

Applicants must satisfy the admissions committee of their ability to be successful in graduate courses in artificial intelligence (AI) and an industrial internship in healthcare. Applicants may be asked to do a technical interview as part of the application process.

The program will consider admitting candidates without an undergraduate degree in computer science, statistics, or a life sciences field, but who show a demonstrated aptitude to be an excellent candidate for this concentration. Applicants should be able to demonstrate a potential to conduct and communicate applied research at the intersection of computer science and a healthcare domain area. Background academic preparation to be successful in graduate-level computer science and medical sciences courses typically, though not always, includes intermediate or advanced undergraduate courses in the following topics:

Programming, software engineering, algorithms.

Statistical theory and/or mathematical statistics and linear algebra.

Students who are otherwise qualified but lack the appropriate background may be granted conditional admission, pending successful completion of additional background material as judged by the admissions committee.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in computer science, biology, or data science.

Applicants must indicate a preference for the concentration in AI in Healthcare in their application. Admission to the AI in Healthcare concentration is competitive. Students who are admitted to the MScAC program are not automatically admitted to the AI in Healthcare concentration upon request.

0.5 FCE in approved data science courses

0.5 FCE in approved AI courses

0.5 FCE in approved visualization/systems/software engineering courses

0.5 FCE in approved Laboratory Medicine and Pathobiology (LMP) or Master of Health Informatics (MHI) courses

A maximum of 1.0 FCE may be taken from outside the Department of Computer Science.

Students who lack the academic background in AI and/or statistics may be required to take additional courses in these areas.

Approved Data Science Courses

Approved artificial intelligence courses, approved visualization/systems/engineering courses, approved lmp and mhi courses, computer science: applied computing mscac (data science concentration), mscac program (data science concentration).

An appropriate bachelor’s degree from a recognized university in a related area such as statistics, computer science, mathematics, or any discipline where there is a significant quantitative component.

Applicants must satisfy the admissions committee of their ability to be successful in graduate courses in computer science, statistics, and an industrial internship in data science. Applicants may be asked to do a technical interview as part of the application process.

The program will consider admitting candidates without an undergraduate degree in computer science, statistics, or a related field, but who show a demonstrated aptitude to be an excellent data scientist. Applicants should be able to demonstrate a potential to conduct and communicate applied research at the intersection of computer science, statistics, and a domain area. Background academic preparation to be successful in graduate-level computer science and statistics courses typically, though not always, includes intermediate or advanced undergraduate courses in the following topics:

Algorithms and Complexity, Database Systems, or Operating Systems.

Statistical Theory/Mathematical Statistics, Probability Theory, or Regression Analysis.

Applicants must indicate a preference for the concentration in Data Science in their application. Admission is competitive, and students who are admitted to the MScAC program are not automatically admitted to this concentration upon request.

1.0 FCE chosen from the STA2000-level courses or higher. This may include a maximum of 0.5 FCE chosen from the STA4500-level of six-week modular courses (0.25 FCE each).

Computer Science: Applied Computing MScAC (Data Science for Biology Concentration)

Mscac program (data science for biology concentration).

Applicants must satisfy the admissions committee of their ability to be successful in graduate courses in computer science, statistics, cell and systems biology, ecology and evolutionary biology, molecular genetics, and an industrial internship in biological data science. Applicants may be asked to do a technical interview as part of the application process.

The program will consider admitting candidates without an undergraduate degree in computer science, statistics, or a related field, but who show a demonstrated aptitude to excel in this concentration. Applicants should demonstrate a potential to conduct and communicate applied research at the intersection of computer science, statistics, and cell biology. Students who are otherwise qualified but lack the appropriate background may be granted conditional admission, pending successful completion of additional background material as judged by the admissions committee.

Three letters of support from faculty and/or employers, with preference for at least one such letter from a faculty member in biology or data science.

Applicants must indicate a preference for the concentration in Data Science for Biology in their application. Admission is competitive, and students who are admitted to the MScAC program are not automatically admitted to this concentration upon request.

1.0 FCE chosen from Cell and Systems Biology (CSB), Ecology and Evolutionary Biology (EEB), Molecular Genetics (MMG), or Statistical Sciences (STA) 1000-level or higher courses from the approved list below. A maximum of 0.5 FCE may be selected from EEB, MMG, and STA courses.

1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings from the approved list below and in two different research areas.

Course selections should be made in consultation with the Program Director. Appropriate substitutions may be possible with approval.

Approved CSB, EEB, MMG, and STA Courses

Approved computer science courses, computer science: applied computing mscac (quantum computing concentration), mscac program (quantum computing concentration).

An appropriate bachelor’s degree from a recognized university in a related area such as physics, computer science, mathematics, or any discipline where there is a significant quantitative component. The completed bachelor’s degree must include significant exposure to physics, computer science, and mathematics, including coursework in advanced quantum mechanics, multivariate calculus, linear algebra, probability and statistics, programming languages, and computational methods.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Physics.

Applicants must indicate a preference for the concentration in Quantum Computing in their application. Admission is competitive, and students who are admitted to the MScAC program are not automatically admitted to this concentration upon request.

1.0 FCE chosen from the Physics (PHY course designator) graduate course listings. Of eligible courses, the following are examples that are particularly relevant to the Quantum Computing concentration:

PHY1500H Statistical Mechanics (0.5 FCE)

PHY1520H Quantum Mechanics (0.5 FCE)

PHY1610H Scientific Computing for Physicists (0.5 FCE)

PHY2203H Quantum Optics I (0.5 FCE)

PHY2204H Quantum Optics II (0.5 FCE)

PHY2212H Entanglement Physics (0.5 FCE)

1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings. Of eligible courses, the following are examples that are particularly relevant to the Quantum Computing concentration:

CSC2305H Numerical Methods for Optimization Problems (0.5 FCE)

CSC2421H Topics in Algorithms (0.5 FCE)

CSC2451H Quantum Computing, Foundations to Frontier (0.5 FCE)

Computer Science: Computer Science MSc

Master of science.

The MSc degree program is designed for students seeking to be trained as a researcher capable of creating original, internationally recognized research in computer science.

The MSc program can be taken on a full-time or part-time basis.

An appropriate bachelor's degree with a standing equivalent to at least a University of Toronto B+. Preference is given to applicants who have studied computer science or a closely related discipline.

Applicants whose primary language is not English and who graduated from a university where the language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 580 on the paper-based test and 4 on the Test of Written English (TWE); 93/120 on the Internet-based test and 22/30 on the writing and speaking sections.

Coursework. Completion of 2.0 graduate full-course equivalents (FCEs) in computer science. The courses must satisfy breadth in three of the four different Methodologies of Computer Science to ensure that MSc graduates have a breadth of skills for research and problem solving throughout their careers.

A major research paper (CSC4000Y; 1.0 FCE) demonstrating the student's ability to do independent work in organizing existing concepts and in suggesting and developing new approaches to solving problems in a research area. The standard for this paper is that it could reasonably be submitted for peer-reviewed publication.

4 sessions full-time (typical registration sequence: F/W/S/F); 8 sessions part-time

3 years full-time; 6 years part-time

Computer Science: Computer Science PhD

Doctor of philosophy.

The PhD degree program is designed for students seeking to be trained as a researcher capable of creating original, internationally recognized research in computer science. Research conducted under the supervision of a faculty member will constitute a significant and original contribution to computer science.

Applicants may enter the PhD program via one of two routes: 1) following completion of an appropriate master’s degree or 2) direct entry following completion of a bachelor’s degree.

PhD Program

Successful completion of an appropriate master's degree with a standing equivalent to at least a University of Toronto B+. Preference is given to applicants who have studied computer science or a closely related discipline.

Applicants whose primary language is not English and who graduated from a university where the language of instruction is not English must achieve a Test of English as a Foreign Language (TOEFL) score of at least 580 on the paper-based test and 4 on the Test of Written English (TWE); or 93/120 on the Internet-based test and 22/30 on the writing and speaking sections.

Students must successfully complete a total of 2.0 full-course equivalents (FCEs) and a thesis .

The courses must satisfy breadth in four different research areas of computer science to ensure a broad and well-balanced knowledge of computer science.

Students must meet the department's timeline for satisfactory progress as outlined in the PhD handbook .

A meeting of the PhD supervisory committee must be held by the 16th month of the PhD program. This is typically the initial meeting with the supervisory committee and is referred to as the qualifying oral examination. After the qualifying oral, the student's PhD supervisory committee must meet at least once annually. The student must have their thesis topic approved at a PhD supervisory committee meeting within the time frame for achieving candidacy. The departmental thesis examination must be passed before the SGS Final Oral Examination can be scheduled.

PhD Program (Direct-Entry)

Applicants may be admitted to this program directly from a bachelor's degree with a standing equivalent to at least a University of Toronto A–. Preference is given to applicants who have studied computer science or a closely related discipline.

Students must successfully complete a total of 4.0 full-course equivalents (FCEs) and a thesis .

The courses must satisfy breadth in four different research areas and three different methodologies of computer science to ensure a broad and well-balanced knowledge of computer science.

Computer Science: Computer Science MScAC, MSc, PhD Courses

Not all courses are offered every year. Please consult the department for course offerings .

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Doctor of Philosophy (PhD)

Degree requirements:.

Between three and eight approved technical graduate courses
JDE1000H ‘Ethics in Research Seminar’
Attendance at the DLS is mandatory for all PhD students.
PhD Background Statement
PhD Qualifying Examination
PhD Thesis Proposal
Annual Meetings of PhD Supervisory Committee
Department Oral Examination (DOE) of PhD Thesis
Final Oral Examination (FOE) of PhD Thesis

Schedule for Timely Completion

ECE’s expectations for the timely completion of the PhD degree requirements are outlined below. Timely completion is a condition of financial support and continued registration.

SGS policy requires that the supervisory committee be formed and meet within the first 16 months of registration; in ECE this requirement is met through the thesis proposal (i.e. the thesis proposal presentation is the 1st annual supervisory committee meeting).

Thereafter, the student must meet with their supervisory committee at least once per year. As per Section 7.5.2 of the SGS General Regulations :

“A student is expected to meet with this committee at least once a year, and more often if the committee so requires. At each meeting, the supervisory committee will assess the student’s progress in the program and provide advice on future work.”
“A student who, through their own neglect, fails to meet with the supervisory committee in a given year will be considered to have received an unsatisfactory progress report from the committee.”

The Department Oral Examination (DOE) is the student’s final annual supervisory committee meeting. The DOE can replace the requirement of a supervisory committee meeting in the student’s final year if the DOE takes place within 12 months of the student’s previous supervisory committee meeting.

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I am a faculty member at the department of computer science at the university of Toronto. I conduct research in the areas of data systems, analysis of big data, data science and applied machine learning. My main interest is to develop algorithms and build systems capable to analyze and process massive and diverse data sets. Of particular interest in our group is the incorporation of machine learning techniques as primitives in scalable data systems capable of processing and analyzing vast data collections.

I received a Bachelors Degree from the University of Patras in Greece, a MSc from the University of Maryland at College Park and a PhD degree from the University of Toronto .

Our work has received various best paper awards at international forums. I was named the 2011 inventor of the year by the University of Toronto (1st prize).

My interests in data analytics expand beyond academic activities. In the past, I have co-founded Sysomos (part of Marketwire/NASDAQ) and Aislelabs (part of Trapeze Group/Constellation Software). I am an advisor at Round 13 Capital .

You can find a list of my recent publications here

computer science phd university of toronto

SVQ: Streaming Video Queries

We are building a system to execute interactive queries on streaming video. We are investigating query semantics, formulation and interactive query execution utilizing video content and image analysis primitives.

Exploring query processing with a deep learning lens. Revisiting query processing, query execution and estimation utilizing deep learning primitives.

Automating the discovery and labelling of training data for any data type

Here is a list of courses regularly offered

Review of Relational and noSQL/newSQL systems and an understanding their strengths and limitations. Exploration of new trends in data management fueld by deep learning and application needs, such as support for advanced analytics and neural information retrieval and LLMs for data processing.

Introduction to database design. Topics covered: Entity relationship models, relational algebra, normalization theory, SQL and embedded SQL, implementation of relational database operators.

Introduction to the technolgy behind database management systems. The topics covered include: storage systems, buffer management, physical database design, indexing and searching in one and more dimensions, query processing, query optimization, transaction management.

Here is a list of current and past PhD students. I am actively recruiting new PhD students! Some advice if you are or plan to be a PhD student.

Akshay Bapat
Kaiwen Chen
Naiqing Guan
Yannis Xarchakos
Xiaohui Yu (York University)
Chaitanya Mishra (Facebook)
Nikolaos Sarkas (Tower Research Capital)
Dimitrios Tsirogannis (Microsoft Research)
Albert Angel (Google)
Michail Mathioudakis (Aalto University)
Nilesh Bansal (serial enterpreneur)
Manos Papagelis (UC Berkeley)
Milad Eftekhar (Google)

Get in touch

The best way to contact me is via email.

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Computer Science at the University of Toronto Scarborough

U of T’s computer science programs rank among the best in the world — and U of T Scarborough is where you can combine a world-class education with paid work experience through our co-op program. From artificial intelligence to social networks, we focus on equipping you with the skills to solve the most pressing problems in the field.

Our program is offered as a specialist and major, both with co-op options that have you complete three work terms, giving you 12 total months of real-world experience before you graduate. The major program is designed to be combined with another program, typically another major, giving you the freedom to combine a thorough foundation in computer science with other disciplines that interest you. Our specialist program has four streams that let you further customize your learning:

Comprehensive stream:  This stream gives you a broad and balanced education in computer science and lays a solid foundation for both graduate studies in computer science and a range of career paths.
Entrepreneurship stream:  You’ll gain a comprehensive skillset in computer science and software engineering, and apply your learning to the process of turning innovative technologies into business opportunities.
Software Engineering stream : Alongside your core computer science knowledge, you’ll develop in-demand software engineering skills, including advanced abilities in computer systems and core applications.
Information Systems stream:  Develop strong software engineering skills along with essential knowledge in business management. If you want to pursue a career in technical management and have a deep interest in technology, this stream is for you.

If you’re interested in both computer science and business, check out our Management and Information Technology program . This four-year BBA degree program also has a co-op option, and gives you a wide-ranging skillset in computer science, management, economics, mathematics and statistics. It’s designed to make sure you graduate with an expertise in information technology and the in-demand skills to use them in modern management environments.

Design and Analysis of Data Structures
Algorithm Design and Analysis
Introduction to Machine Learning and Data Mining

Contact Admissions & Student Recruitment

Alan Aspuru-Guzik

Department of Chemistry and Department of Computer Science, University of Toronto

CIFAR AI Chair, Vector Institute for Artificial Intelligence

Director, Acceleration Consortium ( accelerationconsortium.ai )

Co-founder, Kebotix, Inc.

Co-founder, Zapata AI

Alán Aspuru-Guzik is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute . He is a CIFAR Lebovic Fellow in the Biologically Inspired Solar Energy program . Alán also holds a Google Industrial Research Chair in Quantum Computing. Alán is the director of the Acceleration Consortium , a University of Toronto-based strategic initiative that aims to gather researchers from industry, government and academia around pre-competitive research topics related to the lab of the future.

Alán began his independent career at Harvard University in 2006 and was a Full Professor at Harvard University from 2013-2018. He received his B.Sc. from the National Autonomous University of Mexico (UNAM) in 1999 and obtained a PhD from the University of California, Berkeley in 2004, where he was also a postdoctoral fellow from 2005-2006.

Alán conducts research in the interfaces of quantum information, machine learning and chemistry. He was a pioneer in the development of algorithms and experimental implementations of quantum computers and quantum simulators dedicated to chemical systems. He has studied the role of quantum coherence in the transfer of excitonic energy in photosynthetic complexes and has accelerated the discovery by calculating organic semiconductors, organic photovoltaic energy, organic batteries and organic light-emitting diodes. He has worked on molecular representations and generative models for the automatic learning of molecular properties. Currently, Alán is interested in automation and "autonomous" chemical laboratories for accelerating scientific discovery.

Among other recognitions, he received the Google Focused Award for Quantum Computing, the Sloan Research Fellowship, The Camille and Henry Dreyfus Teacher-Scholar award, the Chemical Engineering Medal, ETH Zürich, and was selected as one of the best innovators under the age of 35 by the MIT Technology Review. He is an elected fellow of the American Physical Society and an elected fellow of the American Association for the Advancement of Science (AAAS) and received the Early Career Award in Theoretical Chemistry from the American Chemical Society.

Alán is editor-in-chief of the journal Digital Discovery as well as co-founder of Zapata AI and Kebotix .

To request a Resume or Curriculum Vitae of Alán please contact Melissa Szopa( [email protected] ). Alan's Twitter , Substack and Mastodon profile.

University of Toronto | Department of Chemistry 80 St. George Street Toronto, ON, M5S 3H6

let’s connect

Our concentrations, artificial intelligence, what is artificial intelligence.

While much of AI is centered in Computer Science, the field draws on tools, techniques, and expertise from many disciplines including statistics, mathematics, and engineering. Today, AI includes intellectual focus such as knowledge representation, probabilistic and statistical theory, machine learning (deep learning), computational linguistics and natural language processing, computer vision, and robotics. The efficacy of AI is embraced in industry as we witness rapid adoption from recommenders in e-commerce to self-driving vehicles in transportation.

Endless Career Opportunities

Discover the endless possibilities to accelerate your career as a world-class innovator.

Career Opportunities in Artificial Intelligence

Applied Research Scientist

Artificial Intelligence Engineer

AI Research Scientist

Computer Vision Researcher

Deep Learning Research Scientist

Director, AI Model Risk

Machine Learning Engineer

Research Engineer

Senior 3D Computer Vision Engineer

Senior Machine Learning Applied Researcher

Senior Machine Learning Research Scientist

Program Requirements

Two courses (1.0 FCEs) must be selected from the core list of AI courses (see list below) and cover two different research areas.
One course (0.5 FCE) must be chosen from additional AI courses outside the core list.
One course (0.5 FCE) must be chosen from courses outside of AI
Two required courses (1.0 FCEs): Communication for Computer Scientists ( CSC 2701H ) and Technical Entrepreneurship ( CSC 2702H ).

Students are permitted to choose a maximum of two courses (1.0 FCEs) from outside the Computer Science graduate course listing.

An eight-month industrial internship , CSC 2703H (3.5 FCEs). The internship is coordinated by the department and evaluated on a pass/fail basis. ‘Pass’ grades are awarded based on evaluations received from the industry/academic supervisors of the internship project and submission of an appropriately written final report, documenting the applied research internship.
Students in the artificial intelligence concentration are permitted to choose a maximum of two courses (1.0 FCEs) from outside the Computer Science graduate course listing.
Students in the artificial intelligence concentration may also request a waiver of an AI core course requirement by demonstrating mastery of equivalent material. All waivers are subject to approval of the Academic Director, Professional Programs. Note that such a waiver would allow students to take additional AI courses from outside the core list. In all cases, students must complete 1.5 FCEs in AI courses.

All course selections should be made in consultation with and approved by the Program Director. Appropriate substitutions may be possible with approval.

AER1513H – State Estimation for Aerospace Vehicles
AER1517H – Control for Robotics
CSC2501H – Computational Linguistics
CSC2502H – Knowledge Representation and Reasoning
CSC2503H – Foundations of Computer Vision
CSC2511H – Natural Language Computing
CSC2515H – Introduction to Machine Learning (exclusion: ECE1513H)
CSC2516H – Neural Networks and Deep Learning (exclusion: MIE1517H)
CSC2529H – Computational Imaging
CSC2533H – Foundations of Knowledge Representation
CSC2630H – Introduction to Mobile Robotics
ECE1512H – Digital Image Processing and Applications
ECE1513H – Introduction to Machine Learning (exclusion: CSC2515H)
MAT1510H – Deep Learning: Theory and Data Science
MIE1517H – Introduction to Deep Learning (exclusion: CSC2516H)

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Graduate Program in

Computer science.

M.Sc. in Computer Science

Fall Entry Available

Find out more about Lassonde’s Computer Science Graduate Program

About the Program

The Department of Electrical Engineering and Computer Science (EECS) is one of the leading academic and research departments in Canada. Our graduate programs are research based, thriving on the broad range of active research conducted by faculty members in the Department. Specializations include: Artificial Intelligence, Big Data, Biomedical Engineering, Computer Graphics, Computer Security, Computer Vision, Data Science, Human-Centered Computing, Information Systems, Integrated Circuits, Micro/Nanoelectronics, Networks, Power and Renewable Energy Systems, Robotics, Software Engineering, Theory of Computation and Virtual Reality. With 58 research faculty and over 150 graduate students and research staff our graduate programs foster a dynamic research environment.

The Graduate Program in EECS offers a research intensive, well supported, congenial environment for a select group of graduate students. We offer studies leading to the degrees of Master of Science (MSc) in Computer Science (including a non-thesis, project-based specialization in Artificial Intelligence ), Doctor of Philosophy (PhD) in Electrical Engineering and Computer Science as well as Master of Applied Science (MASc) in Electrical and Computer Engineering. These degree programs consist of courses and research conducted under faculty member supervision. Our internship program offers a combination of industrial and academic experience for student.

A podcast series with students, alumni, and faculty members from Lassonde.

Admissions requirements and deadlines, msc in computer science, admission deadline for eecs graduate applications, meet the eecs faculty, contact the eecs graduate department.

Graduates with an honours degree in Computer Science or equivalent, with at least a B+ average in the last two years of study, may be admitted as Candidates for the Masters program in Computer Science. In addition, those admitted must have completed the equivalent of a senior-level course in the area of theoretical computer science.

Applicants must also satisfy specified requirements set out by the Faculty of Graduate Studies (FGS), such as demonstrating competence in the English language. The following are the minimum English Language test scores (if required): TOEFL: 90(iBT), IELTS 7 or YELT 4.

The application deadline is available on the Future Students website .

Learn more about our talented team of professors!

Email: [email protected]

Location: 1st Floor, Lassonde Building

Shivani Sheth

MSc Candidate, Computer Science

“I have always been fascinated by technology, whether it was computer games, apps, websites, chat boxes or speech systems. All this really sparked my passion for computer science. In addition, my love for mathematics supported my interests and made learning fun.

For me, becoming a computer scientist means I will have skills that I can use to create a better world.

My focus of study is Artificial Intelligence. AI not only has the ability to automate tools but it can help us perform tasks that are beyond human capabilities. This power can be utilized in creating a positive change in the world, such as smart irrigation systems, combating human trafficking and much more.”

RESEARCH AREAS/STRENGTHS

Artificial Intelligence
Biomedical Engineering
Computer Graphics
Computer Vision
Human-centred Computing
Information Systems
Integrated Circuits
Micro/nanoelectronics
Power and Renewable Energy
Software Engineering
Theory of Computation
Virtual Reality

GRADUATE FUNDING

The Lassonde School of Engineering provides a competitive, guaranteed funding package to all qualified graduate students pursuing a degree by thesis or dissertation.

This funding package can come in the form of teaching assistantships, fellowships, research assistantships, and/or scholarships. On average, newly admitted domestic graduate students received a graduate funding package of $25,000 and $37,000 for international students.

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Zheng Ming (Jack) Jiang

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Undergraduate Programs

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Computer Science

St. George Campus, Faculty of Arts & Science

Program Information

Degree(s): Honours Bachelor of Science

Program(s): Computer Science (Major, Minor, Specialist)

OUAC Admission Code: TAD (Computer Science)

Academic Requirements

Ontario Secondary School Diploma Six 4U/M courses, including:

Calculus & Vectors (MCV4U)
English (ENG4U)
Supplemental Application Required

Find equivalent requirements for Canadian high school systems , US high school system , International Baccalaureate , British-Patterned Education , French-Patterned Education , CAPE , and other international high school systems .

Mississauga Campus

OUAC Admission Code: TMZ (Computer Science, Mathematics & Statistics)

Advanced Functions (MHF4U)
Mathematics of Data Management (MDM4U) is recommended

Scarborough Campus

Program(s): Computer Science (Co-op, Major, Minor) Comprehensive Stream (Co-op, Specialist) Entrepreneurship Stream (Co-op, Specialist) Information Systems Stream (Co-op, Specialist) Software Engineering Stream (Co-op, Specialist)

OUAC Admission Code: TXC (Computer Science)

Computer Science (MSc, PhD)

Part of the Faculty of Science

Dalia Hanna, Computer Science PhD student

Program Overview

Format : Full-time (MSc, PhD)

Degree Earned : Master of Science or PhD

Computer science is an exciting, rapidly evolving discipline that impacts our everyday lives in innumerable ways. Graduate degree-holders in computer science are in high demand. Graduates from our programs have a wide range of exciting career options in industry and academia.

Careers include but are not limited to:

software developer
data scientist
database analyst
computer vision scientist
information technologist

Our faculty actively collaborate with industrial partners, which makes Toronto Metropolitan University’s central downtown location advantageous. It provides walking distance access to Toronto’s vibrant and rapidly growing start-up community, major companies, financial institutions and research hospitals.

The program provides funding to each domestic thesis student. Typical funding packages are outlined below.

At a Glance

Admissions information.

Completion of a four-year undergraduate in computer science (or equivalent degree) from an accredited institution
Minimum grade point average (GPA) or equivalent of 3.00/4.33 (B) in the last two years of study
Statement of intent
Transcripts
Two letters of recommendation
English language proficiency requirement
Completion of a master’s degree in computer science or a closely related discipline from an accredited institution
Minimum grade point average (GPA) or equivalent of 3.33/4.33 (B+)
Three letters of recommendation

More information on admission requirements . Due to the competitive nature of our programs, it is not possible to offer admission to everyone who applies that meets the minimum entrance requirements for the program.

Program-specific requirements

Check Application Deadline

Students are encouraged to submit applications prior to the first consideration date to increase their chances of securing financial support for their graduate studies. Applications received after the first consideration date will be accepted and reviewed based on spaces remaining in the program.

See application dates .

Financing Your Studies

For detailed graduate tuition and fees information please visit Fees by Program .

For information on scholarships, awards and financing your graduate studies visit Financing Your Studies.

Research Areas

The Computer Science Graduate faculty conduct research in a wide range of subjects, including:

Artificial Intelligence
Augmented and Virtual Reality
Computer Graphics
Computer Vision
Cyber-security
Data Mining
Data Science
Machine Learning
Software Engineering

Computer Science (MSc, PhD) graduate program calendar

Graduate Admissions

Admissions information and how to apply

Graduate Studies Admissions Office 11th Floor, 1 Dundas Street West Toronto, ON Telephone: 416-979-5150 Email: [email protected]

For information specific to programs, please see the program contact information below.

Program Contacts

Dr. Alex Ferworn Graduate Program Director PhD Research areas: Computational public safety: Urban Search and Rescue (USAR) and Chemical, Biological, Radiological and Nuclear explosives (CBRNe) applications, serious gaming, mobile/autonomous/teleoperated robotics, cyber operations, network applications, entrepreneurship and innovation, physical computation, digital media, and algorithms. Telephone: 416-979-5000 , ext. 556968 Email: [email protected]

Norm Pinder Graduate Program Administrator Telephone: 416-979-5000 , ext. 552656 Email: [email protected]

Student Profile: VICE Canada feature (external link)

Jimmy Tran (computer science PhD student) designed and built a robot used by archaeologists to explore dangerous tombs in el-Hibeh, Egypt.

Find curriculum, course descriptions and important dates for Computer Science (MSc, PhD).

Once you’ve made an informed choice about which program(s) you are going to apply to, preparing your application requires careful research and planning.

At Toronto Metropolitan University, we understand that pursuing graduate studies is a significant financial investment. Funding comes from a combination of employment contracts (as a teaching assistant), scholarships, awards and stipends. There are a number of additional funding sources – internal and external – available to graduate students that can increase these funding levels.

As an urban innovation university, Toronto Metropolitan University offers 60+ cutting-edge, career-oriented graduate programs, as well as 125+ research centres, institutes and labs, in a wide range of disciplines. Our close connections with industry, government and community partners provide opportunities to apply your knowledge to real-world challenges and make a difference.

CPI Talk • Characterizing Machine Unlearning through Definitions and Implementations

Please note: this cpi talk will take place in east campus hall, room 1111..

Nicolas Papernot, Assistant Professor Computer Engineering and Computer Science, University of Toronto

The first part of the talk discusses approaches that provide exact unlearning; these approaches output the same distribution of models as would have been obtained by training without the subset of data to be unlearned in the first place. While such approaches can be computationally expensive, we discuss why it is difficult to relax the guarantee they provide to pave the way for more efficient approaches. The second part of the talk asks if we can verify unlearning. Here we show how an entity can claim plausible deniability when challenged about an unlearning request that was claimed to be processed, and conclude that at the level of model weights, being unlearnt is not always a well-defined property. Instead, unlearning is an algorithmic property.

Bio : Nicolas Papernot is an Assistant Professor of Computer Engineering and Computer Science at the University of Toronto. He also holds a Canada CIFAR AI Chair at the Vector Institute, and is a faculty affiliate at the Schwartz Reisman Institute. His research interests span the security and privacy of machine learning.

Some of his group’s recent projects include generative model collapse, cryptographic auditing of ML, private learning, proof-of-learning, and machine unlearning. Nicolas is an Alfred P. Sloan Research Fellow in Computer Science and a Member of the Royal Society of Canada’s College of New Scholars. His work on differentially private machine learning was awarded an outstanding paper at ICLR 2022 and a best paper at ICLR 2017. He co-created the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) and is co-chairing its first two editions in 2023 and 2024. He previously served as an associate chair of the IEEE Symposium on Security and Privacy (Oakland), and an area chair of NeurIPS.

Nicolas earned his Ph.D. at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, he spent a year at Google Brain where he still spends some of his time.

Everyone is welcome to attend this free CPI talk, but please register .

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David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario Canada N2L 3G1 Phone: 519-888-4567 ext. 33293 Fax: 519-885-1208

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The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations .

School of Graduate Studies

Applied computing, program overview.

The University of Toronto’s Master of Science in Applied Computing (MScAC) Program is committed to educating the next generation of world-class innovators.

With concentrations in Artificial Intelligence, Applied Mathematics, Computer Science, Data Science, and Quantum Computing , our program will provide students with a truly unparalleled academic experience. Not only will students learn from world-class faculty about the latest developments in cutting edge technologies, they also have the opportunity to apply that knowledge through a practical applied-research internship at one of our partner companies.

Quick Facts

Master of science in applied computing, program description.

The Master of Science in Applied Computing (MScAC) program is offered as

a general Computer Science program ( no concentration ) or as

a concentration in :

Applied Mathematics, offered jointly by the Department of Computer Science and the Department of Mathematics;

Artificial Intelligence, offered jointly by the Department of Computer Science, the Department of Statistical Sciences, and the Faculty of Applied Science and Engineering;

Artificial Intelligence in Healthcare, offered jointly by the Department of Computer Science and the Temerty Faculty of Medicine;

Data Science, offered jointly by the Department of Computer Science and the Department of Statistical Sciences;

Data Science for Biology, offered jointly by the Department of Computer Science and the Department of Cell and Systems Biology;

Quantum Computing, offered jointly by the Department of Computer Science and the Department of Physics.

There is no thesis requirement.

MScAC General Program (No Concentration)

Minimum admission requirements.

An appropriate bachelor's degree from a recognized university in computer science or a related discipline.

A standing equivalent to at least B+ in the final year of undergraduate studies.

Internet-based TOEFL: 93/120 and 22/30 on the writing and speaking sections.

IELTS: an overall score of 7.0, with at least 6.5 for each component.

Three letters of support from faculty and/or employers.

Applicants will be asked to respond to program-specific questions addressing their interest in the concentration and objectives for the program.

Program Requirements

Coursework. Students must successfully complete a total of 3.0 full-course equivalents (FCEs) including:

1.0 FCE in required courses: technical communications (CSC2701H) and technical entrepreneurship (CSC2702H).

An eight-month industrial internship , CSC2703H (3.5 FCEs). The internship is coordinated by the department and evaluated on a pass/fail basis.

Program Length

4 sessions full-time (typical registration sequence: F/W/S/F)

3 years full-time

MScAC Program (Applied Mathematics Concentration)

analysis (for example, measure and integration, harmonic analysis, functional analysis);

discrete math (for example, algebra, combinatorics, graph theory);

foundations (for example, complexity theory, set theory, logic, model theory);

geometry and topology;

numerical analysis; and

ordinary and partial differential equations.

There should also be a demonstrated capacity at programming and algorithms.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Mathematics or Applied Mathematics.

Coursework. Students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

1.0 FCE chosen from the MAT1000-level courses or higher.

1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings.

1.0 FCE in required courses:

CSC2701H Communication for Computer Scientists (0.5 FCE) and

CSC2702H Technical Entrepreneurship (0.5 FCE).

Course selections should be made in consultation with the Program Director.

MScAC Program (Artificial Intelligence Concentration)

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Artificial Intelligence (AI).

1.5 FCEs of coursework in the area of AI:

1.0 FCE selected from the core list of AI courses (see list below) from at least two different research areas

0.5 FCE selected from additional AI courses outside the core list

CSC2701H Communication for Computer Scientists (0.5 FCE)

CSC2702H Technical Entrepreneurship (0.5 FCE)

Remaining 0.5 FCE of coursework will be chosen from outside of AI:

Course selections should be made in consultation with and approved by the Program Director. Appropriate substitutions may be possible with approval.

A maximum of 1.0 FCE may be chosen from outside the Computer Science (CSC course designator) graduate course listing.

Artificial Intelligence Core Courses

*different courses with the same title, offered by different Faculties. **different courses with similar titles, offered by different Faculties.

MScAC Program (Artificial Intelligence in Healthcare Concentration)

Programming, software engineering, algorithms.

Statistical theory and/or mathematical statistics and linear algebra.

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in computer science, biology, or data science.

0.5 FCE in approved data science courses

0.5 FCE in approved AI courses

0.5 FCE in approved visualization/systems/software engineering courses

0.5 FCE in approved Laboratory Medicine and Pathobiology (LMP) or Master of Health Informatics (MHI) courses

A maximum of 1.0 FCE may be taken from outside the Department of Computer Science.

Students who lack the academic background in AI and/or statistics may be required to take additional courses in these areas.

Approved Data Science Courses

Approved artificial intelligence courses, approved visualization/systems/engineering courses, approved lmp and mhi courses, mscac program (data science concentration).

Algorithms and Complexity, Database Systems, or Operating Systems.

Statistical Theory/Mathematical Statistics, Probability Theory, or Regression Analysis.

1.0 FCE chosen from the STA2000-level courses or higher. This may include a maximum of 0.5 FCE chosen from the STA4500-level of six-week modular courses (0.25 FCE each).

MScAC Program (Data Science for Biology Concentration)

Three letters of support from faculty and/or employers, with preference for at least one such letter from a faculty member in biology or data science.

1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings from the approved list below and in two different research areas.

Course selections should be made in consultation with the Program Director. Appropriate substitutions may be possible with approval.

Approved CSB, EEB, MMG, and STA Courses

Approved computer science courses, mscac program (quantum computing concentration).

Three letters of reference from faculty and/or employers, with preference for at least one such letter from a faculty member in Physics.

PHY1500H Statistical Mechanics (0.5 FCE)

PHY1520H Quantum Mechanics (0.5 FCE)

PHY1610H Scientific Computing for Physicists (0.5 FCE)

PHY2203H Quantum Optics I (0.5 FCE)

PHY2204H Quantum Optics II (0.5 FCE)

PHY2212H Entanglement Physics (0.5 FCE)

CSC2305H Numerical Methods for Optimization Problems (0.5 FCE)

CSC2421H Topics in Algorithms (0.5 FCE)

CSC2451H Quantum Computing, Foundations to Frontier (0.5 FCE)

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Computer Science PhD Student First to Receive Competitive Award

BATON ROUGE, LA – LSU Computer Science Ph.D. student Ruxin Wang is the recipient of the highly competitive 2024 Cyber-Physical Systems (CPS) Rising Stars Award, making her the first-ever LSU student to receive the honor. She is also the first female LSU student to publish a first-author security paper at one of the Big 4 Security conferences.

Wang will next attend the 2024 CPS Rising Stars Workshop in May at the University of Virginia. There, she will receive the award, which aims to identify and mentor outstanding Ph.D. and post-doctoral students who are interested in pursuing academic careers in CPS core research areas.

“This award recognizes my research in CPS security and using CPS to improve traffic safety and public health,” Wang said. “In addition to my research, this award also recognizes my efforts in engaging women scholars in STEM research. I greatly appreciate the help and guidance of my Ph.D. advisor, Dr. Chen Wang, who consistently encouraged me to apply for this national award and be brave enough to compete with other top-ranking university peers. I am now more determined to pursue a faculty position after graduation, and I am now more confident that I can make significant contributions to CPS research and education.”

Last May, Wang became the first female LSU student to publish a first-author security paper at a Big 4 Security conference when her paper on developing a low-effort authentication method for VR headset users was published at the 44th IEEE Symposium on Security and Privacy. Existing VR authentications require users to enter a PIN number or draw graphical passwords, which can be observed by others in proximity to the user and create security issues. Wang’s proposed method would be based on the unique skull-reverberated sounds, which can be acquired when the user wears the VR device.

“It is very challenging to publish a first-author paper at a Big 4 Security conference, such as IEEE S&P, which has a low acceptance rate (e.g., 13%),” Wang said. “The review process is double-blind, and reviewers don’t know who you are…[they only have] your submitted paper to make decisions. I worked with my advisor, Dr. Wang, and my colleague, Mr. Long Huang, for over two years on the VR authentication topic, and our submission has been rejected many times. We kept researching on this area, solving challenges, and improving our paper to finally make it to the level of the Big 4 Security conferences.

“To me, it is a high recognition of my research achievements in ‘Cyber-Physical Systems for Security, Safety, and Healthcare’ at LSU and a milestone in my academic career. ‘First’ means there will be a second and a third. I am now working towards publishing the second Big 4 paper.

Like us on Facebook (@lsuengineering) or follow us on X (formerly Twitter) and Instagram (@lsuengineering).

Contact: Joshua Duplechain Director of Communications 225-578-5706 [email protected]

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PhD candidates and faculty selected as winning team for the 2024 Siemens Tech Sustainabilitiy through Cybersecurity Challenge

Professor and students gather for group photo.

On March 14, 2024, research from our Network Security Lab received the winning team award for the 2024 Siemen’s Tech Sustainability through Cybersecurity Challenge . PhD candidates, Amal Alshehri and Burak Tufekci, as well as Assistant Professor, Dr. Cihan Tunc , were one of the finalist research groups (out of 22 projects) and their project “OTZET: Operational Technology Zero Trust Engine for Trustworthiness” has been selected as the winning project during Siemens’ international competition.

The group’s project underwent 4 stages of review consisting of a 4-month timeframe. The first stage, Ideation, occurred during the month of December 2023, saw a large pool of research ideas submitted by universities and companies around the world. Then, the selected projects ideas were implemented under Siemens’ guidance. And the final review was made in March 2024 for the selected finalists, which allowed our team to present and win the competition under the sustainability through cybersecurity track during the final Hackathon & Live Pitches stage last month. The audience consisted of respected professionals in cybersecurity, as well as those in academia, with potential of future collaboration or sponsorship.

Here’s what Siemens outlines as their goals for their Cybersecurity challenge in Sustainability: At Siemens, we consider cybersecurity as one of the company’s sustainability goals. We live in an ever-evolving environment filled with digital threats and risks. Only through robust cybersecurity can we effectively address those risks. This includes safeguarding information and intellectual property by preventing digital attacks from materializing in the real world, or by supporting businesses and production sites to operate without disruptions.

The OTZET project continues on as the Network Security Lab crew are moving forward with more paper submissions and more advances in their work. Dr. Cihan Tunc states, “We’re looking to build this research for further progression. [Amal Alshehri] is doing this for her final research.”

The Department of Computer Science and Engineering extends a huge congratulations to Amal Alshehri, Burak Tufekci, and Dr. Cihan Tunc on a job well done!

For more information on the Network Security Lab and the Smart CyberSpace group, check out their website links here:

Home | Network Security Lab (NSL) (unt.edu )

Welcome to Smart CyberSpace (SCS) at UNT | Smart Cyber Space (SCS)

For more information on Siemens, click here to access their website: Sustainability through cybersecurity | Siemens Innovation Ecosystem

News & Events

Automating Algae: Computer science capstone melds digital and physical skills

photo: Algohelix members Ibrahim and Pooja Natarajan with automated algae growth system

A team of four computer science graduate students are working with the Yogh Group , an energy technology company, to build a fully automated algae growth system called Algohelix.

Ibrahim, who prefers to be referred to by his last name, said that at scale, Algohelix could provide biofuel, food, air-purification and even T-shirts through the production and processing of these tiny life-forms.

The four master’s students: Ibrahim, Sudarshan Sridhar, Paul Hoffmann and Pooja Natarajan, who prefers to use her full name, will graduate after having worked on the project for an entire academic year.

"It's like growing a bonsai plant inside of a house without human intervention," said capstone team lead Pooja Natarajan.

An intensive problem

Yogh Group CEO Levar Jackson said growing algae is currently an intensive process.

"It's prohibitive to the locales and communities that would benefit from algae-derived products such as food and biofuel," he said.

To solve this problem, the graduate students have worked since October to build a prototype of a system to manage and monitor oxygen, temperature, pH, water density and nutrition for spirulina, a form of algae that is commonly used as a source of plant protein.

"Algae will easily die if there's some kind of contamination in the environment, so we want it to be a fully automated, enclosed system," Ibrahim said.

The team's largest challenges have been in building the bridge between hardware and software. Two members of the team, Hoffmann and Pooja Natarajan, have undergraduate experience with electrical engineering, which helped, but the complexity of automation led to additional difficulties.

Smooth solutions

One of the primary elements of their project has been in trying to build a proportional-integral-derivative (PID) controller for the temperature in the tank.

A PID uses feedback from sensors to determine how far away the conditions in a system are from optimal and adjust, in this case, how powerful the heating element is.

It can be easy to overshoot the correction if the controller is not properly calibrated, which requires thoughtful coding and a deep understanding of how the hardware works.

"We want to control the temperature as precisely as we can," Ibrahim said. "We don't want there to be any fluctuations, but to make it settle to a target smoothly, like when you take a deep breath or do yoga, with no hiccups or unnatural movements. Building that has been a challenge."

A green future

Pooja Natarajan said that working with living, growing organisms like algae is fascinating and that she hopes to continue working on growing other algae for use in biofuels or textiles.

Ibrahim said that Jackson's vision is to grow algae at scale to get all of the benefits of the microorganisms through an integrated system of green energy, food, temperature regulation and more for hotels.

"For now," Ibrahim said, "this is a proof of concept. Everything about it, and the future, excites me."

Sudarshan Sridhar with the electronics of the automated algae growth system

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Onrí Jay Benally receives 2024 NSF Graduate Research Fellowship

Onri in a pale shirt and green jacket standing in a hallway outside the nano lab

Doctoral student Onrí Jay Benally is a 2024 recipient of the prestigious National Science Foundation Graduate Research Fellowship. Benally is currently pursuing his doctoral research under the guidance of Distinguished McKnight Professor and Robert F. Hartmann chair Jian-Ping Wang exploring the world of quantum computing and spintronic devices.

A Navaho (Diné) tribesman and carpenter, Benally comes to us from the mountains of Red Valley and Oak Springs, Arizona. After graduating from tribal high school, he found himself building off-road electric vehicles at a Utah State University lab led by Professors Curtiz Frazier and Jared Barrett. Two years later, in 2017, he transferred to the University of Minnesota and accepted a Research Experiences for Undergraduates (REU) through the NSF-funded Materials Research Science and Engineering Center (MRSEC) at the University. During this time, he worked with Professor Vlad Pribiag (School of Physics and Astronomy) building nanoelectronic devices in the cleanroom for Majorana fermion research. The REU was Benally’s first brush with quantum technology exploration. He returned to the MRSEC REU in summer 2018 and this time he worked with Wang on micro and nanoscale magnetic tunnel junctions for classical computer memory and logic applications. He earned his bachelor’s degree in multidisciplinary studies from the University in 2021.

While Benally was working on his undergraduate degree, he earned an IBM certificate in quantum computation using Qiskit, and began hypothesizing how metallic-based spintronics and new architectures could be used to support the expansion of quantum supercomputing worldwide. The initial hypothesis motivated him to enter ECE’s doctoral program in fall 2022.

Reflecting on his interest in quantum technology and his skills as a carpenter, Benally says, "Carpentry was my livelihood on the tribe before completing my undergraduate degree. It is a big part of who I am and has indirectly led to my success as a nanofabricator of spintronics and quantum chips." Benally shares that one of his first toys as a kid was a toy hammer.

Benally’s research interests revolve around the engineering of quantum computing hardware and spintronic devices. An interdisciplinary area, his research involves the nanofabrication of ultrafast nanoscale magnetic tunnel junctions, cryogenic magnetic random-access memory (cryo-MRAM), and hybrid spintronic quantum processing units (QPUs), systems that can form scalable, sustainable quantum hardware architectures. Under the guidance of Wang, Benally designs and fabricates these systems at the Minnesota Nano Center at the University. Benally addressed these new developments in his keynote speech at the Arizona State University-led Quantum Collaborative Summit this past fall in San Antonio, Texas. Over the upcoming summer, Benally will be a graduate intern with IBM Research in Yorktown Heights, New York. As a quantum hardware engineer, he will be working on cutting edge cryogenic electronics for large-scale superconducting quantum computers.

Benally has accepted the NSF Graduate Research Fellowship and feels honored to start delivering on his proposed ideas on supporting quantum supercomputing through spintronics and new architectures.

The NSF Graduate Research Fellowship Program helps “ensure the quality, vitality, and diversity of the scientific and engineering workforce of the United States.” Learn about the program and eligibility requirements.

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