Find Academic Supervisors

Hamid KarimiView profile

Assistant Professor

• Social Media Mining
• Educational Data Mining
• Machine Learning
• +3 more

Hamid Karimi is an Assistant Professor of Computer Science at Utah State University (USU), where he leads the Data Science and Applications (DSA) lab. His research focuses on using AI and data mining for social good, including social media mining, educational data mining, and machine learning. He earned his Ph.D. in Computer Science from Michigan State University (MSU) in 2021, with a thesis on AI for social good. His interdisciplinary work includes the Teachers in Social Media project, which developed algorithms to improve PK-12 education quality. Dr. Karimi has received several awards, including the Best Paper Award at ASONAM 2018 and the International Faculty Recognition Award at USU in 2022. His research spans social media behavior analysis, misinformation detection, and fairness in machine learning. The DSA lab prioritizes practical solutions for socially impactful data science applications, such as cross-disciplinary projects in science and engineering. Education: Ph.D. in Computer Science, Michigan State University, 2021 Research Interests: Social Media Mining Educational Data Mining Graph Mining AI for Social Good Lab: Data Science and Applications (DSA) Lab, USU His work bridges theoretical data science with real-world applications, such as analyzing teacher behavior on Pinterest and leveraging GPT for scalable education tools. Dr. Karimi’s research emphasizes ethical AI practices and interpretable machine learning models.

Daphne YaoView profile

Professor

• Cybersecurity
• Program Analysis
• Machine Learning Algorithms
• +3 more

Daphne Yao is a Professor in the Department of Computer Science at Virginia Tech , part of the College of Engineering. She holds the Elizabeth and James E. Turner Jr. '56 Faculty Fellowship and CACI Faculty Fellowship. Her research focuses on cybersecurity, program analysis, machine learning algorithms, and applied cryptography. Yao has contributed to advancing secure coding practices, vulnerability detection in software, and privacy-preserving technologies. Education includes a Ph.D. in Computer Science from Brown University (2007), an M.S. in Computer Science from Indiana University (2002), and earlier degrees in Chemistry from Princeton University (M.A., 2000) and Peking University (B.S., 1998). Her work bridges theoretical computer science with practical applications, emphasizing real-world cybersecurity challenges. Recent research trends include combating ransomware via API profiling, improving privacy in voice assistants, and developing resilient network routing systems. Yao collaborates across disciplines, integrating machine learning into healthcare cybersecurity and epigenomic analysis. Her publications reflect a commitment to measurable, deployable security solutions and ethical AI practices. Key contributions include the CryptoGuard tool for detecting cryptographic vulnerabilities and frameworks like Madeline for low-cost cyber threat monitoring. She has also explored challenges in deploying intrusion detection systems and mitigating data-oriented attacks in cyber-physical systems.

Niyousha HosseinichimehView profile

Associate Professor

• Health Systems Engineering
• Public Health Policy
• System Dynamics Modeling
• +4 more

Niyousha Hosseinichimeh is an Associate Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech's College of Engineering. Her research focuses on improving health and healthcare systems through system dynamics modeling and simulation. She holds a Ph.D. in Public Policy from SUNY Albany (2012) and a B.S. in Mechanical Engineering from Sharif University of Technology (2001). Her work addresses complex issues like adolescent drinking/driving behaviors, major depressive disorder, and infant mortality. Methodologically, she advances calibration techniques for dynamic models and group model-building approaches. Her research has been funded by NIH, NSF, and the Ohio Department of Health. Notable contributions include modeling the hypothalamus-pituitary-adrenal axis and developing tools for rapid parameter estimation in system dynamics. She advises students such as Alba Rojas-Cordova (winner of multiple awards) and Arash Baghaei Lakeh. Her grants include projects on depression dynamics and emergency department utilization. Her work intersects systems engineering, public health, and computational methods, emphasizing practical policy and clinical applications.

Mohammad T. KhasawnehView profile

Professor

• Healthcare Systems Engineering
• Operations Management
• Data Science
• +3 more

Mohammad T. Khasawneh is a SUNY Distinguished Professor and Director of the School of Systems Science and Industrial Engineering at Binghamton University. He leads the Watson Institute for Systems Excellence (WISE) and the Healthcare Systems Engineering Center. His roles include Director of the Manhattan Graduate Program in Health Systems. Education: BS and MS in Mechanical Engineering from Jordan University of Science and Technology (1998, 2000), PhD in Industrial Engineering from Clemson University (2003). Research focuses on healthcare systems engineering, operations management, and data science. His work optimizes healthcare systems for improved patient outcomes and cost efficiency. Key areas include predictive analytics, hospital resource utilization, and clinical performance improvement. He has generated over $15M in external funding and led projects with U.S. hospital systems. Notable achievements include developing the Executive Master of Science in Health Systems and an MS in Healthcare Systems Engineering. His research has produced 60+ journal articles and 120+ conference papers. Awards include SUNY Chancellor’s Awards for Teaching (2011) and Scholarship (2021), University Awards for Graduate Director (2015) and International Education (2016). He is an IISE Fellow and holds honorary visiting professorships at Hebei University of Technology (China) and Vellore Institute of Technology (India). Grants and funding: $2.5-3M annually via WISE, $39M in software/equipment grants. Lab/Initiatives: Healthcare Systems Engineering Center, WISE, and multiple hospital partnerships.

• Generative Programming
• Model-Based Development
• Software System Families
• +7 more

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

Benjamin C. M. FungView profile

Professor

• Data Mining
• Artificial Intelligence
• Cybersecurity
• +6 more

Benjamin C. M. Fung is a Full Professor at McGill University's School of Information Studies and holds the Canada Research Chair in Data Mining for Cybersecurity. He is also an Associate Member at the School of Computer Science and affiliated with MILA and the McGill Digital Humanities Initiatives. His research focuses on data mining, AI, cybersecurity, privacy protection, and their applications in crime investigation, health informatics, and smart cities. Education: Ph.D. in Computing Science, Simon Fraser University (2007) M.Sc. and B.Sc. in Computing Science, Simon Fraser University Professional Engineer (P.Eng.) in Software Engineering, Ontario Research Interests: Data mining techniques for cybersecurity, AI ethics, privacy-preserving technologies, malware analysis, and foreign interference detection. His work has been supported by NSERC, SSHRC, DRDC, and FRQNT. Key Contributions: Developed the Kam1n0 tool for malware analysis, featured in Montreal's technological innovations. Co-founded the McGill Data Mining and Security (DMaS) Lab. Authored over 180 refereed publications with an h-index of 62. Awards & Recognition: 2022 IEEE CSR Best Research Paper Award ACM & IEEE Senior Member (2013) Top 2% Global Scientists (2021–present) Media & Public Engagement: Frequently comments on cybersecurity, disinformation, and foreign interference for CBC, CBC Radio, OMNI TV, and international outlets. His op-eds address topics like AI ethics and national security.

John McNamaraView profile

Research Fellow

• Artificial Intelligence
• Cybersecurity
• Transportation Systems
• +3 more

John McNamara is an IBM Master Inventor and Advanced Visiting Research Fellow at the University of Sheffield, holding Honorary Professorships at University College London (UCL) and Sheffield Hallam University. He specializes in interdisciplinary innovation across cybersecurity, transportation systems, and artificial intelligence, leveraging technologies like Cloud and Watson for Tech for Good initiatives. His roles include IBM Technical Specialist, IBM Thought Leader, and Open Group Distinguished Technical Specialist, with industry expertise spanning defense, finance, and healthcare sectors. Education: BSc (Hons) in Information Systems from the University of Hull. Research focuses on patentable inventions addressing complex event processing, automated systems optimization, and regulatory compliance. Key innovations include intelligent fuel management systems, automated style-checking frameworks, and goal-directed simulation tools for business processes. His work bridges academic research with real-world industry applications. Awards: IBM Master Inventor IBM Thought Leader Technical Specialist Open Group Distinguished Technical Specialist Collaborates with IBM UK University Programs to develop socially impactful technologies. Active in creating solutions for disease control, banking systems, and defense applications through cross-disciplinary partnerships.

Armando FoxView profile

Professor

• Software as a Service
• Cloud Computing
• Parallel Programming
• +3 more

Armando Fox is a Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he co-leads the ASPIRE Lab and directs the Berkeley MOOCLab. His work focuses on integrating online learning research into educational frameworks. He holds a PhD, MS and BS from Berkeley, Illinois, and MIT respectively. Fox's research spans applied statistical machine learning, Software as a Service (SaaS), cloud computing, parallel programming, and innovative online education methods. He pioneered Berkeley's first MOOC on software engineering and co-authored the textbook Engineering Software as a Service . His recent publications demonstrate strong focus on: AI-enhanced education tools and assessment systems Cloud-based learning management architectures Generative AI for collaborative programming education Automated testing frameworks for computer science Significant awards include: NSF CAREER Award ACM Distinguished Member Scientific American 50 top researcher recognition Fox previously contributed to Intel Pentium Pro microprocessor design and founded a mobile computing company based on his dissertation research.

Hoda MalekiView profile

Assistant Professor

• Cybersecurity
• Blockchain Technology
• Digital Forensics
• +4 more

Hoda Maleki is an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. She holds a Ph.D. (2019) and an M.S. in Computer and Information Sciences from the University of Connecticut. Her research focuses on cybersecurity, blockchain technology, digital forensics, and secure access control systems. She has contributed to frameworks like ForensiBlock, exploring blockchain for data provenance and auditability, and has published extensively on topics including machine learning in healthcare cybersecurity and multi-dimensional encryption techniques. Dr. Maleki serves on Augusta University committees including the Faculty Affair Committee and the MS Cybersecurity degree development workgroup. She advises the Women in Cybersecurity Club and the VIVID team. Her professional service includes roles as a grant proposal reviewer for NSF and CICI-NSF, and committee membership for conferences like CCSW'24 and IPDPS. Her recent work emphasizes integrating blockchain with AI for secure systems, such as DLACB (Deep Learning-Based Access Control using Blockchain) and ForensiBlock. Awards include the 2023 Faculty Inclusive Excellence Initiative and 2022 Faculty Recruitment and Retention honors. She teaches advanced courses on cybersecurity topics like secure pollbook systems and cryptographic protocols.

• Healthcare Informatics
• Artificial Intelligence
• Machine Learning
• +3 more

Maqbool Hussain is a Senior Lecturer in Computer Science at the College of Science and Engineering. His research focuses on healthcare informatics, artificial intelligence (AI) applications in medical decision-making, and the development of knowledge-based systems for aerospace and healthcare sectors. He has contributed significantly to areas such as digital twin frameworks for critical care workflows, clinical decision support systems, and semantic interoperability in healthcare. Research Interests Hussain’s work spans multiple disciplines, including: Healthcare Informatics and AI-driven medical solutions Knowledge Graphs and Large Language Models (LLMs) in healthcare and aerospace Digital twins for optimizing critical care and aerospace maintenance Data-driven clinical decision support systems Medical data security and interoperability standards Machine learning for text annotation and classification Recent Research Trends Recent publications highlight his focus on integrating advanced technologies like LLMs and digital twins into healthcare and aerospace contexts. His work addresses challenges in critical care workflow optimization, kidney disease diagnosis, and aerospace product maintenance through innovative knowledge graph approaches. He also explores the application of transformer-based models for text analysis and the secure exchange of medical data using biometric methods. Labs and Teams Hussain is affiliated with the College of Science and Engineering, contributing to interdisciplinary research teams focused on healthcare technology, aerospace systems, and AI applications. His collaborations span academic and industry partners, emphasizing practical solutions for healthcare informatics and aerospace engineering challenges.

Anika GroßView profile

Professor

• Database Systems
• Knowledge Graphs
• Ontologies
• +7 more

Prof. Anika Groß is a Professor for Database Systems and Programming at the Department of Computer Science and Languages at Anhalt University of Applied Sciences in Köthen, Germany. She holds a PhD from the University of Leipzig (2014) and has held roles as a PostDoc in strategic research at Daimler AG and as a research assistant at the Interdisciplinary Centre for Bioinformatics (IZBI). She is actively involved in academic governance, serving on the faculty council, board of examiners, and various sustainability and research data management committees. Her research focuses on database systems, knowledge graphs, and ontology engineering, with applications in environmental science, medicine, and material sciences. Education - PhD (Dr. rer. nat.) in Computer Science, University of Leipzig (2014) - Diploma in Bioinformatics, MLU Halle-Wittenberg (2007) - Postdoctoral research at University of Leipzig's Database Group (2008–2013) Research Interests Her work emphasizes temporal graphs, ontology evolution, and semantic data integration. She develops tools like Region Evolution eXplorer (REX) and contributes to projects such as AgriRestore (ecosystem restoration) and MeineWaldKI (AI-based forest monitoring). Her recent focus includes wearable device applications in healthcare and digital twins for lithium value chains. Recent Research Trends Recent publications highlight interdisciplinary applications of knowledge graphs in agriculture and environmental science, leveraging temporal analysis and machine learning for tasks like chemical transformation prediction and bladder monitoring. She also explores cross-lingual semantic annotations in medical forms and holistic clustering of linked data. Professional Roles - Executive Committee Member, GI Database Systems Group - Fellow, Institute for Technologies and Economics of Lithium (ITEL) - Co-Chair of multiple workshops including FGDB-Workshop @ LWDA 2018 and BigDS@BTW 2017 Labs & Collaborations She leads research teams in projects funded by DFG, European Union (EFRE), and ITEL, collaborating with organizations like Daimler AG and ANU. Her lab focuses on advancing database systems and interdisciplinary data science applications.

Ashish GuptaView profile

Professor

• Deep Learning
• Machine Learning
• AI
• +2 more

Ashish Gupta holds the Globe Life Professor title in the Department of Business Analytics & Information Systems at Auburn University and serves as an Adjunct Professor in the Department of Computer Science and Software Engineering. His research focuses on Deep Learning, Machine Learning, AI, Natural Language Processing, and Social Network Analytics. Education: Ph.D. in Management Science and Information Systems from Oklahoma State University. Research trends in his articles emphasize AI-driven solutions for misinformation detection, healthcare analytics, smart grids, and educational pedagogy. Notable themes include real-time fake news detection, pandemic impact studies, and predictive modeling in healthcare and agriculture. No scientific awards or grants are explicitly listed. He advises no formally listed students. His work integrates computational methods with societal challenges, particularly in healthcare, education, and agriculture. Collaborations span interdisciplinary domains, though specific lab affiliations are not mentioned.

Jorge Bernal BernabeView profile

Associate Professor

• Cybersecurity
• Internet of Things (IoT) Security
• Network Security
• +7 more

Jorge Bernal Bernabe is an Associate Professor at the University of Murcia , affiliated with the Faculty of Informatics and the Department of Information and Communication Engineering . He holds a PhD in Computer Science from the University of Murcia (2015), focusing on 'Authorization and Trust Management in Distributed Systems Based on the Semantic Web.' His research group, Intelligent Systems and Telematics , explores cutting-edge cybersecurity solutions for IoT, 5G/6G networks, and privacy-preserving technologies. He is a key contributor to frameworks like OLYMPUS (privacy-aware identity management) and ARIES (European identity ecosystem). Education : PhD in Computer Science, University of Murcia (2015) Research Interests : His work centers on securing next-generation networks (e.g., 5G/6G), IoT privacy, federated learning applications for intrusion detection, blockchain-based identity systems, and policy-driven security enforcement. He emphasizes privacy-by-design principles and explores challenges in digital sovereignty and decentralized governance. Notable Projects : OLYMPUS: Oblivious identity management system ARIES: Pan-European identity framework ANASTACIA: Security agents for CPS/IoT INSPIRE-5Gplus: Cognitive security for 5G networks Labs/Teams : Active in the Intelligent Systems and Telematics Group , collaborating with industry partners on secure IoT deployments and 5G cybersecurity solutions.

Faezeh EnsanView profile

Assistant Professor

• Information Retrieval
• Knowledge Engineering
• Semantics
• +3 more

Faezeh Ensan is an Assistant Professor at Toronto Metropolitan University, specializing in Information Retrieval, knowledge engineering, and data science applications in software engineering. She holds a B.Sc. from the University of Tehran (2004), M.Sc. from Ferdowsi University of Mashhad (2006), and Ph.D. from the University of New Brunswick (2011). Her research focuses on semantic technologies, ad hoc retrieval systems, and ontology evaluation. B.Sc., University of Tehran, 2004 M.Sc., Ferdowsi University of Mashhad, 2006 Ph.D., University of New Brunswick, 2011 Her work bridges semantic web, machine learning, and information systems, with notable contributions to entity-based retrieval and modular ontology evaluation. She has been funded by NSERC, Mitacs, and ACOA, and has held prestigious fellowships including the NSERC Industrial Research Fellowship (2014-2016). Recipient of NeOn Student Prize for Best Paper at EKAW 2008 Editor of Canadian Semantic Web: Technologies and Applications (2010) Her advising and grants include collaborations with industrial partners through NSERC and Mitacs projects. She teaches courses like COE528 and COE848, emphasizing object-oriented engineering and data engineering fundamentals.

Darshika G. PereraView profile

Assistant Professor

• Computer Engineering
• FPGA-Based Hardware Acceleration
• Neuromorphic Computing
• +5 more

Darshika G. Perera is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Colorado Colorado Springs (UCCS). Her research focuses on FPGA-based hardware acceleration, neuromorphic computing, and embedded systems optimization with applications in machine learning, cryptography, and battery management systems. She holds a PhD and has extensive experience in designing reconfigurable architectures for compute-intensive tasks. Her work bridges theoretical algorithms with practical hardware implementations, emphasizing efficiency and real-time performance. Key research areas include FPGA design methodologies, neuromorphic hardware for AI applications, and embedded systems security. She has published widely on topics such as hardware-software co-design for edge computing, optimization algorithms for genomics, and blockchain applications in healthcare IoT. Her contributions also span data mining hardware accelerators and dynamic reconfiguration techniques for cryptographic systems. Dr. Perera’s work is characterized by interdisciplinary collaborations, combining principles from electrical engineering, computer science, and applied mathematics. She is committed to advancing next-generation edge-computing platforms through innovative architectures and methodologies.