Education

Ph.D. in Electrical & Computer Eng.

Dalhousie University (2018–2021)

NSERC CGS D • Killam Scholar

M.A.Sc. in Electrical & Computer Eng.

Dalhousie University (2009–2010)

Hardware Implementations of AES

B.Sc. in Computer Engineering

Princess Sumaya Univ. for Technology (2004–2008)

Graduated with Distinction

Senior Member, IEEE (SMIEEE) P.Eng. (Engineers Nova Scotia)

Dr. Issam Hammad, P.Eng.

Assistant Professor & Graduate Coordinator Department of Engineering Mathematics and Internetworking | Dalhousie University | Halifax, NS, Canada

Dr. Issam Hammad, P.Eng., SMIEEE, is an Assistant Professor and Graduate Coordinator at Dalhousie University, where he directs an NSERC-funded laboratory specializing in AI, Software Engineering, and Cybersecurity for Critical Infrastructure (Nuclear Energy Focused) and approximate computing for hardware acceleration.

Backed by over 15 years of industry leadership, he completed his Ph.D. concurrently while directing software engineering and AI deployments for nuclear operators in Ontario, Canada. His research program bridges foundational computing with operational deployment, spanning approximate computing architectures for efficient hardware, automated inspection of engineering drawings and infrastructure, domain-adapted LLMs, edge cyber-physical defense, and software qualification under CSA and IEEE standards.

At Dalhousie, he has trained and mentored 15 Highly Qualified Personnel across doctoral, master’s, and undergraduate tiers, and leads the Faculty of Engineering's centralized machine learning and Python programming curriculum. In industry, he serves as President of Tensorware Inc., an engineering consulting firm specializing in AI engineering design.

Contact Email Google Scholar LinkedIn Tensorware Inc.

Research Areas

Core Research Programs

Research initiatives span two core programs: industry-anchored applied machine learning and cybersecurity for critical energy infrastructure (partnered with OPG and CNSC), alongside foundational hardware research in approximate computing and efficient arithmetic architectures supported by NSERC.

AI & Cybersecurity for Critical Infrastructure (Nuclear Energy Focused)

Developing practical, verifiable AI models, cyber-physical defense pipelines, and modern regulatory qualification pathways for nuclear energy and high-assurance operational assets:

LLMs & Inspection Automation: Domain-adapted LLMs for nuclear safety reasoning, event classification, and knowledge retrieval, alongside computer-vision pipelines for fuel channel UT scans and engineering drawings.
Cyber-Physical Defense & IIoT: Multi-stage attention for operational anomaly detection, masked state-space models for intrusion discovery, and differential encryption for remote telemetry.
Nuclear AI Qualification & Regulation: Modernizing regulatory standards (CSA N286.7 and CSA N290.14) to establish risk-informed, auditable pathways for qualifying non-deterministic AI in CANDU and SMR deployments.

Approximate Multipliers & Energy-Efficient Computing Architectures

Pioneering mathematical optimizations and circuit co-design to overcome the energy, memory bandwidth, and computational bottlenecks of modern deep learning:

Approximate Multipliers & Arithmetic Logic: Designing novel approximate multipliers, dual-segmentation topologies, and reduced-precision arithmetic to optimize the energy–accuracy trade-off.
Generative AI Acceleration: Co-designing dynamic precision controllers and low-latency approximate inference operators for large-scale transformer and foundation models.
Quantization & Model Robustness: Information-theoretic bounding (KL divergence) to assess model fidelity and noise resilience in reduced-precision neural networks.
Hardware Cryptography & Efficient Architectures: FPGA implementations of the Advanced Encryption Standard (AES) algorithm and low-latency cryptographic engines.

Funded Initiatives

Mentorship

Highly Qualified Personnel (HQP)

Mentoring graduate researchers in hardware acceleration, cyber-physical safety, IIoT security, and enterprise AI systems.

Current Doctoral & Master's Researchers

Karthik Thiyagarajan Ph.D. Candidate (Completion: Feb 2027)

Focus: Multi-stage attention for IIoT anomaly detection, cyber-physical state-space tracking, and differential encryption for remote surveillance.

Muhammed Anwar, M.Sc. M.Sc. Alum & Ph.D. Student

Ph.D. (Jan 2026 – Dec 2029)

Ph.D. Focus: Human-in-the-loop controllable LLM architectures and safety-verifier pipelines for nuclear plant workflows.

M.Sc. Thesis: LLM-Driven Diagnostic Frameworks for Industrial Safety

AI Lead, Ontario Power Generation (OPG)

Mishca DeCosta, M.Sc. M.Sc. Alum & Ph.D. Student

Ph.D. (Jan 2026 – Dec 2029)

Ph.D. Focus: Secure sovereign hosting, model distillation, and system hardening for localized nuclear LLMs.

M.Sc. Thesis: Classification of Safety Events at Nuclear Sites using Large Language Models (LLMs)

AI Manager, Ontario Power Generation (OPG)

David Mercier M.Sc. Candidate (2025–2027)

Focus: AI-assisted hybrid semantic retrieval for legacy enterprise asset management systems.

Daniel Lau M.Sc. Candidate (2023–2027)

Focus: AI regulatory standards and safety governance for critical energy infrastructure.

Alumni & Placements

Samer Dahaweer, M.Sc. (ENGM, 2024) OPG

Thesis: Regulating Artificial Intelligence for Nuclear Software Qualifications

Current Role: Senior Engineer, Ontario Power Generation

Jack Langille, M.Sc. (ENGM, 2024) RBC

Thesis: On the Robustness of Quantized Convolutional Neural Networks

Current Role: Senior Data Scientist (AI Model Risk), Royal Bank of Canada

Victor Gao, M.A.Sc. (ECED, 2022) JTEKT NA

Thesis: A Subtractor-Based Convolutional Neural Network (CNN) Inference Accelerator

Current Role: Associate Engineer, JTEKT North America

Sheila Huang, M.Eng. (ECED, 2025) Toradex

Project: Implementation of Approximate Multipliers for LSTM Architectures

Current Role: System Designer, Toradex

B.Eng. Senior Capstone Team (2023) Best Paper Award

E. Chiasson, M. Kaniecki, J. Koechling, N. Uppal

Project: Automating Real Estate Appraisal with Machine Learning Models

Scholarly Output

Featured Publications

Full Bibliography on Google Scholar

IEEE Internet of Things Journal (IF: 8.7) Accepted 2026

EdgeCrypt tracker: Object tracking with differential encryption for IoAAV surveillance

K. Thiyagarajan, I. Hammad, R. Lu

IEEE Internet of Things Journal (IF: 8.7) 2024

EdgeAtten: Multi-stage Attention for IIoT Anomaly Detection, Correction and Diagnosis

K. Thiyagarajan, I. Hammad, R. Lu

Nuclear Engineering and Design 2025

Regulating Artificial Intelligence for CANDU Software Qualifications

S. Dahaweer, I. Hammad

Nuclear Engineering and Design 2025

Automating equipment identification in nuclear engineering drawings

I. Hammad, M. Anwar, M. DeCosta

Selected Keynotes & Plenary Addresses

ACEC Atlantic CEO & Leadership Forum

April 2024 • Halifax, NS

Vision address on the strategic impact of AI for 25 engineering executives and CEOs across Atlantic Canada.

3rd Canadian Nuclear Society DIET

Nov 2022 • Keynote & Plenary Panel

Plenary panel with Rumina Velshi (President & CEO, CNSC) and Todd Warnell (CIO, Bruce Power).

IEEE Canadian Atlantic Section

Dec 2025 • Invited Speaker

Emerging trends in artificial intelligence, approximate hardware, and critical energy infrastructure security.

Pedagogy

Curriculum Development & Teaching

Leading the centralized Machine Learning and computational programming pathways for Dalhousie Engineering.

Course	Title	Level & Scope	Role & Terms
ENGM 1081	Computer Programming	Core 1st Year (Entire Engineering Cohort)	Course Leader • Fall 2025
ENGM 2620	Python Programming	Core 2nd Year (Industrial Engineering)	Developed & Taught • Winter 2026
ENGM 4676 / 6676	Machine Learning for Engineers	Technical Elective (Senior UG & Graduate)	Developed & Taught • 2023, 2024, 2026
ENGM 4620	Python for Engineers	Elective 4th Year (UG)	Developed & Taught • 2023, 2024
ENGM 6657	Numerical Linear Algebra	Graduate Core (M.Sc. / Ph.D.)	Redeveloped Curriculum • Fall 2026

Contact & Inquiries

For academic inquiries, research collaborations in critical energy infrastructure, or engineering consulting:

O'Brien Hall, Sexton Campus, Dalhousie University, Halifax, NS

Contact via Email

Tensorware Inc. (President) — AI Engineering Consulting

Prospective Graduate Students

Graduate supervision in our laboratory is focused on candidates with active industry engagement. I actively consider part-time graduate arrangements for practicing engineers and data scientists whose operational work aligns with my core research themes.

When inquiring, please provide a brief summary of your professional background, current industry affiliation, and specific research alignment with our publications.