جستجوی استاد راهنمای دانشگاهی

Sungmee Parkمشاهده پروفایل

پژوهشگر

• Electronic Textiles
• Smart Textiles
• Wearable Biomedical Systems
• +۵ مورد دیگر

Sungmee Park is a Principal Research Scientist at the School of Materials Science and Engineering at Georgia Institute of Technology. Her work bridges academia and industry, with groundbreaking contributions to smart textiles and wearable technologies. She co-invented the world’s first Wearable Motherboard (Smart Shirt) in 1996, foundational to modern wearables. At Kolon Glotech in South Korea, she developed Printronix printing technology and heating textiles (HeaTex), applied in sports, military, and automotive sectors. She also served as Vice President and Head of Future Strategy at Kolon Corporation, driving innovation in strategic partnerships and new ventures. Her research focuses on electronic textiles, smart fabrics, and wearable biomedical systems, addressing health monitoring, security, and human welfare. With over 30 patents and numerous publications, her work emphasizes translating research into practical applications like low-cost reusable masks and respiratory protection devices. Key collaborations include ongoing projects with Professor Jayaraman on wearable data platforms and Big Data analysis. Dr. Park’s articles highlight advancements in textile-based electronics, wearable healthcare systems, and smart textile manufacturing. She has pioneered methodologies for integrating sensors and communication systems into fabrics, enabling applications from athlete monitoring to SIDS prevention. Her contributions reflect a vision of technology enhancing quality of life through interdisciplinary innovation at the intersection of art, science, and engineering. Notable projects include the Inspiring Journey Exhibit showcasing her work in art-science fusion and strategic leadership in identifying new growth areas for the Kolon Group. Her research continues to drive global impact through sustainable, human-centered technological solutions.

Eduardo Gildinمشاهده پروفایل

استاد

• Reservoir Modeling and Simulation
• Control and Optimization of Dynamical Systems
• Model Reduction Techniques
• +۵ مورد دیگر

Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.

Peter Chinمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Differential Geometry
• +۸ مورد دیگر

Professor Peter Chin is a Professor of Engineering at Dartmouth College and Director of the Learning, Intelligence + Signal Processing (LISP) Lab. He holds affiliations with the Thayer School of Engineering and serves as Associate Editor of IEEE Transactions on Computational Social Systems. His research bridges signal processing, machine learning, game theory, and differential geometry, with applications in cybersecurity, healthcare, and network analysis. Education: Bachelor of Science in Electrical Engineering, Computer Science, and Mathematics from Duke University (1993) Doctor of Philosophy in Mathematics from MIT (1998) Research Interests: Chin’s work focuses on fundamental questions at the intersection of machine learning, game theory, and signal processing. His lab explores topics like adversarial defense mechanisms, topological machine learning, and computational neuroscience. Recent projects include cybersecurity resilience modeling, medical imaging enhancements via GANs, and multi-agent reinforcement learning frameworks. Publications Trends: His most recent articles address cutting-edge challenges in cybersecurity (e.g., autonomous defense systems), medical AI (e.g., Alzheimer’s classification), and adversarial robustness. A notable 2025 focus is on quantitative resilience modeling for cyber defense, reflecting growing demand for AI-driven security solutions. Awards: Faculty Scholar Award, Duke University George Sherred III Award, Duke University Julia Dale Memorial Award, Duke University Grants & Leadership: Recipient of DARPA cybersecurity research grants Co-chair for SPIE/DSS Cyber Sensing Conference (2013–2020) Developed novel compressive sensing microscope for biological imaging LISP Lab: This interdisciplinary lab pioneers projects like nFlip (multiplayer security game models) and topological machine learning frameworks, emphasizing practical applications of theoretical advancements.

Kanika Goelمشاهده پروفایل

مدرس

• Process-Oriented Data Analytics
• Data Governance
• Data Quality
• +۳ مورد دیگر

Dr. Kanika Goel is a Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in Business Process Management (BPM), Data Governance, and Process Analytics. She holds a PhD from QUT and has over 9 years of teaching experience, coordinating programs such as BIT Honours (IN10) and Masters of Philosophy (IN80). Her research focuses on process-oriented data analytics, data quality, and process mining, with industry collaborations spanning health, retail, and asset management sectors. She is a Lean Six Sigma Green Belt certified trainer and a Fellow of the Higher Education Academy (FHEA). Dr. Goel has led several industry-funded projects, emphasizing applied research in data governance, process mining, and process improvement. Notably, she received the Vice-Chancellor's Award for Excellence (2019) for innovative BPM integration in research management systems. Her work bridges academic research and real-world applications, contributing to journals like Business and Information Systems Engineering and IEEE Access . She teaches courses on Business Process Technologies, Modern Data Management, and BPM units in QUT's continuing professional education programs. Her articles explore topics like data imperfections in healthcare systems, process standardization strategies, and privacy risks in NoSQL databases. She advocates for digital literacy and has published on initiatives to build tech-savvy communities. Dr. Goel is also involved in supervising research topics such as prescriptive process analytics and process-data governance patterns.

• Criminology
• Urban Space and Social Control
• Deviant and Criminal Subcultures
• +۸ مورد دیگر

Theo Kindynis is a Senior Lecturer in Criminology at the Department of Sociology and Criminology, City St George's, University of London, where he joined in March 2024. Previously, he taught at Goldsmiths, University of London, and the University of Roehampton. His academic affiliations reflect a strong commitment to critical, ethnographic, and spatially informed criminological inquiry. PhD in Criminology, University of Greenwich, United Kingdom MPhil in Criminological Research, University of Cambridge, United Kingdom BA Hons in Sociology, University of Kent, United Kingdom Fellowship of the Higher Education Academy, Higher Education Academy, United Kingdom Theo Kindynis’s research centers on the intersections of urban space, lawbreaking, and social control. He is a leading expert on deviant subcultures, particularly graffiti writing and urban exploration, having conducted long-term ethnographic fieldwork in London. His work bridges empirical investigation with theoretical innovation, most notably through co-pioneering the concept of "ghost criminology" —a framework that examines the haunting aftereffects of crime, violence, and punishment. His methodological contributions include publishing legal, ethical, and practical guidance for criminological ethnographers researching criminalized groups, especially in digital and high-surveillance environments. His recent publications reveal a sustained engagement with spatial criminology, subcultural practices, and the ethics of ethnographic research. Themes across his work include the policing of urban aesthetics, the affective engineering of consumer spaces, the spectral presence of past violence, and the information security challenges faced by researchers in sensitive fields. His scholarly output combines theoretical rigor with grounded empirical insight, frequently published in top journals such as The British Journal of Criminology and Crime, Media, Culture . Theo Kindynis has contributed to public discourse as an expert commentator on graffiti and urban exploration, appearing in media outlets including the BBC Evening News and The Guardian. His work on information security for ethnographers addresses a critical gap in methodological literature, drawing from journalism, activism, and digital rights advocacy. Co-authored the foundational book Ghost Criminology: The Afterlife of Crime and Punishment (NYU Press, 2022) Pioneered methodological guidance on protecting ethnographic data from state and non-state threats Developed innovative approaches to anonymization and data protection in high-risk research Advocates for ethical rigor in researching criminalized populations As a supervisor, Theo welcomes doctoral proposals on urban space, deviant subcultures, and ethnographic methods. He is actively involved in shaping the future of criminological theory and methodology, particularly in relation to digital surveillance, researcher safety, and the cultural afterlives of crime. Theo Kindynis is affiliated with research teams exploring the intersections of space, crime, and memory. His ongoing projects continue to develop the framework of ghost criminology, while expanding into issues of algorithmic surveillance, border control, and the digital footprints of academic research. His work is increasingly relevant in an era of automated state profiling and data-driven social control.

Jianqiang Chengمشاهده پروفایل

دانشیار

• Stochastic Programming
• Robust Optimization
• Distributionally Robust Optimization
• +۷ مورد دیگر

Dr. Jianqiang Cheng is an Associate Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He is also a member of the Graduate Faculty and affiliated with the Applied Mathematics and Statistics Graduate Interdisciplinary Programs. His research is centered on optimization under uncertainty with applications in energy systems and logistics. Research Interests: His primary research areas include stochastic programming, robust optimization, distributionally robust optimization, semidefinite programming, and chance-constrained optimization. He applies these methodologies to challenges in power systems, renewable energy integration, microgrid design, and resilient supply chains. The recent publications (2020–2022) reflect a strong trend toward data-driven and computationally efficient methods in optimization. Key themes include distributionally robust optimization under moment and Wasserstein ambiguity, chance-constrained AC optimal power flow, and resilient supply chain modeling under disruptions such as the COVID-19 pandemic. His work frequently appears in top journals like INFORMS Journal on Computing , IEEE Transactions on Power Systems , and European Journal of Operational Research . Scientific Awards: Best Short Paper Award, INFORMS Workshop on Data Science (Fall 2022) NSF CAREER Award, National Science Foundation (Spring 2022) Science Foundation Arizona's 2017 Bisgrove Scholar (Spring 2017) Dr. Cheng has secured significant research funding, including the NSF CAREER Award, supporting his work in data-driven optimization. He collaborates extensively with researchers in energy systems and operations research, including K. Pan, M. Cheramin, A. M. Fathabad, and A. Lisser. While specific advisees are not listed, his role as a member of the Graduate Faculty indicates active supervision of graduate students in systems engineering, applied mathematics, and statistics. His research contributes to the development of advanced optimization models for real-world systems affected by uncertainty, particularly in energy and logistics. Though no specific lab is mentioned, his work implies involvement in computational optimization and energy systems modeling research groups within the College of Engineering.

Carsten Rudolphمشاهده پروفایل

استاد

• Cyber Security
• Formal Methods
• Cryptographic Protocols
• +۵ مورد دیگر

Professor Carsten Rudolph serves as Deputy Dean at Monash University's Faculty of Information Technology and directs the Oceania Cyber Security Centre (OCSC). He holds a PhD in Information Security from Queensland University of Technology (2002) and a Diplom in Computer Science from Goethe University Frankfurt (1997). His interdisciplinary research focuses on cybersecurity foundations, including cryptographic protocols, AI-driven security, human factors, and national cybersecurity policy. Key areas include securing smart grids, digital health systems, and transnational energy networks. Notable contributions include establishing the OCSC, leading Pacific region cybersecurity maturity reviews with Oxford University, and advancing frameworks for firmware security in virtual power plants. He chairs major projects like RAI4IoE (Responsible AI for Energy) and Post-Quantum Cryptography initiatives. Teaching responsibilities include cybersecurity modules like FIT3173 and FIT3168. Rudolph's research outputs (137+ publications) emphasize phishing detection via AI, blockchain-based energy trading, and resilient smart grid systems. He collaborates internationally on policy development and has advised 12 major research projects funded by agencies like the U.S. Bureau of East Asia and Pacific Affairs.

Funlade Sunmolaمشاهده پروفایل

مدرس ارشد

• Applied Artificial Intelligence
• Sustainable and Smart Industries
• Manufacturing Engineering
• +۵ مورد دیگر

Funlade Sunmola is a Principal Lecturer in Manufacturing and Industrial Engineering at the University of Hertfordshire , affiliated with the School of Engineering and Computer Science and the Department of Engineering and Technology. He holds a PhD in Computer Science (Artificial Intelligence and Robotics) from the University of Birmingham and has nearly 40 years of professional experience across civil engineering, manufacturing, healthcare, and academia. Education: BEng (Hons) in Civil Engineering, Ahmadu Bello University MSc in Industrial Engineering, University of Ibadan MA in Accounting and Finance, Birmingham City University MPhil in Manufacturing Engineering, University of Birmingham PhD in Computer Science, University of Birmingham Research Interests: Focuses on Applied Artificial Intelligence , Sustainable and Smart Industries , and Industry 4.0 . Key areas include supply chain visibility, blockchain integration, machine learning applications in manufacturing, and virtual engineering. Leads the Duncan Calder Virtual Engineering Lab and oversees MSc Online Engineering Programmes. Grants & Projects: PI of LINK: Digital Direct Connection for Salvage Construction Materials (Circular Economy) PI of N-BICC: Cassava Innovation Deployment Co-I in Solar Cool System (So-Cool) for Smallholder Farmers Labs/Teams: Heads the Duncan Calder Virtual Engineering Lab , focusing on immersive technologies and virtual product design.

Omar Hegazyمشاهده پروفایل

استاد

• Power Electronics Systems
• Electric Vehicles
• Battery Management Systems
• +۷ مورد دیگر

Omar Hegazy is a Professor in Electrical Engineering and Power Electronics at Vrije Universiteit Brussel (VUB), affiliated with the MOBI - Electromobility Research Centre. He leads research in power electronics systems, electric vehicle drivetrains, and energy management. His work focuses on reliability, WBG semiconductors, and sustainable transportation systems. Education details are not explicitly provided, but his extensive publication record and project leadership imply advanced academic qualifications. Research interests include power electronics, battery management systems, hybrid/fuel cell vehicles, and V2X technologies. His projects address challenges in electric vehicle infrastructure, grid integration, and renewable energy systems. Key trends in his articles include digital twin development for electric trucks, advanced thermal management of SiC devices, and optimization of DC charging systems. His work emphasizes practical applications like modular converters, fault-tolerant drives, and interoperable charging solutions. Awarded Best Master Thesis (2019), Best Paper (2024), and Optimal Design Recognition (2016) Supervised over 49 theses, including master's and doctoral studies in power electronics and EV systems Secured funding for projects like HiPower 5.0, HARPOONERS, and FLEXMCS Labs/Teams: Active in MOBI's Electromobility Research Centre, collaborating on advanced power electronics and e-mobility solutions. Involved in interdisciplinary teams for microgrid design and DC charging infrastructure.

Michael Möhringمشاهده پروفایل

استاد

• Data Science
• Big Data Analytics
• Industry 4.0
• +۴ مورد دیگر

Prof. Dr.-Ing. Michael Möhring is a Professor of Data Science at Reutlingen University's Faculty of Informatics. He serves as Prodekan for the Herman Hollerith Zentrum (HHZ) and leads research in data analytics, Industry 4.0, and process mining. Previously, he held roles as an IT consultant, project manager at Bosch Group/BSH, and academic researcher. Education: Dr.-Ing. (PhD) in Business Informatics M.Sc. in Business Informatics B.Sc. in Business Informatics Research Interests: Focuses on leveraging structured/unstructured data for industrial applications, enterprise architecture management, digital twins integration, and AI-driven decision support. Specializes in bridging technical systems with organizational processes in manufacturing and service industries. Lab Affiliations: AI-Real Lab AIDA Future Mobility Lab Internet of Things Lab Virtual Reality Lab Articles Trends: Recent work emphasizes practical implementations of AI in production failure analysis (language models), energy optimization systems (HollerithEnergyML), and technical debt management in SMEs. Consistently explores data integration challenges across manufacturing, service ecosystems, and digital twin frameworks. Grants & Collaborations: Active in EU-funded projects like 5G-PreCiSe and bwHealthApp. Collaborates with industry partners on digital transformation initiatives through HHZ's applied research programs.

Stavros Vougioukasمشاهده پروفایل

استاد

• Agricultural Robotics
• Mechanization of Specialty Crops
• Automation in Agriculture
• +۷ مورد دیگر

Stavros Vougioukas is a Professor and Vice Chair in the Department of Biological and Agricultural Engineering at the University of California, Davis, within the College of Engineering. He is actively involved in research and graduate mentorship, focusing on agricultural robotics and automation for specialty crops. His work integrates engineering solutions to improve efficiency and sustainability in farming systems. His research interests include agricultural robotics , automation of harvesting processes , sensors and control systems , precision agriculture , and wireless sensor networks for orchard environments . He develops technologies for robotic and robot-aided harvesting, particularly in strawberries and orchard crops, emphasizing optimal management of inputs and yield monitoring. The recent publications reflect a strong trend in robotics integration , real-time sensing , and data-driven decision-making in agriculture. His work spans mechanical design, signal processing, path planning, and structural durability, indicating a multidisciplinary approach to solving agricultural challenges through engineering innovation. Scientific Awards and Recognition: $1.6M grant (2021) to develop innovative fruit-picking machines CITRIS Seed Award (2023) for engineering solutions in agriculture Professor Vougioukas mentors graduate students and leads funded research projects focused on automation and robotics in agriculture. He has secured significant grants, including a $1.6M award for fruit-picking robotics, demonstrating strong research leadership. His collaborations span institutions and disciplines, particularly in agricultural machinery design and sensor network deployment. He leads research efforts in agricultural automation, particularly through projects involving robot-aided harvesting , orchard navigation systems , and wearable worker tracking devices . His lab contributes to the development of intelligent systems for sustainable farming, integrating mechanical, electronic, and computational components.

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۴ مورد دیگر

Özlem Özgöbek is an Associate Professor at the Department of Computer Technology and Informatics, Norwegian University of Science and Technology (NTNU). Her research spans artificial intelligence, machine learning, and recommender systems with a focus on privacy, fake news detection, and educational technology. NTNU - Department of Computer Technology and Informatics Her work explores multimodal fake news detection, privacy implications in recommender systems, and technology-enhanced classroom interaction. Recent publications analyze digital education trends and classroom tools. Özgöbek collaborates with international researchers and contributes to news recommendation workshops. Her projects address ethical AI, environmental sustainability, and real-time information processing.

Scott T. Doyleمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Digital Pathology
• Bioinformatics
• +۷ مورد دیگر

Scott T. Doyle is an Associate Professor in the Department of Pathology and Anatomical Sciences at the Jacobs School of Medicine & Biomedical Sciences, University at Buffalo. His research integrates biomedical imaging, artificial intelligence, and computational pathology to develop quantitative tools for clinical diagnostics and anatomical modeling. Education: PhD in Biomedical Engineering, Rutgers, The State University of New Jersey (2011) BS in Biomedical Engineering, Rutgers, The State University of New Jersey (2006) Optical Microscopy & Imaging in the Biomedical Sciences, Marine Biological Laboratory (2014) hES Stem Cell Culture Training, WNYSTEM (2014) R Bioconductor Training, Roswell Park Cancer Institute (2016) Dr. Doyle’s research focuses on developing AI-driven algorithms for biomedical image analysis, particularly in digital pathology and 3D anatomical modeling. His work spans tumor segmentation, risk prediction in oral and thyroid cancers, and integration of virtual and physical anatomy in medical education. He applies machine learning, deep learning, and computational modeling to enhance diagnostic accuracy and patient outcomes. His recent publications reflect a strong trend in applying artificial intelligence to histopathology, with emphasis on active learning, 3D reconstruction, and multi-institutional data fusion. Key areas include oral cavity cancer recurrence prediction, thyroid cancer subtyping, and computational modeling of surgical margins and anatomical structures. Scientific Service and Recognition: Reviewer for NIH SPORE grants Peer reviewer for journals including Medical Image Analysis , BMC Bioinformatics , IEEE Transactions on Biomedical Engineering Program Committee and Session Chair, SPIE Medical Imaging: Digital Pathology (2016–present) Member, Graduate Program Steering Committee, Pathology & Anatomical Sciences Mentor, McNair Scholarship and CSTEP programs for underrepresented students Dr. Doyle has secured significant research funding as Principal Investigator on NIH and CTSI grants, including a $2M+ NIH grant for predicting oral cancer recurrence. He has also contributed to educational innovation through hybrid anatomy curriculum development and AI training for pathologists. He leads the 'Atoms to Anatomy' research initiative and is active in strategic planning at the Jacobs School. Laboratories and Collaborative Teams: Dr. Doyle collaborates with the Center for Computational Research (CCR) and is involved in the Structural Sciences Learning Center (SSLC). He has led projects with teams at Ibris, Inc., Veterans Affairs Hospital, and Mount Sinai School of Medicine.

Qiteng Hongمشاهده پروفایل

دانشگاهی

• Power System Protection
• Renewable Energy Integration
• Wide Area Monitoring and Control
• +۷ مورد دیگر

Dr. Qiteng Hong is a Reader in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. He holds a BEng (Hons) and PhD from the same institution and is a leading researcher in power system protection and control for renewable-dominated grids. He is Deputy Director of the MSc in Electrical Power and Energy Systems and a member of the Steering Committee for the Joint MSc with Hong Kong University of Science and Technology (HKUST). BEng (Hons), Electronic and Electrical Engineering, University of Strathclyde, 2011 (Top Graduate of the Year) PhD, Electrical Engineering, University of Strathclyde, 2015 (fully funded by National Grid) His research focuses on novel solutions for monitoring, protection, and control of future power systems, particularly those with high renewable penetration. Key areas include wide-area monitoring using synchronized measurements, protection of converter-dominated systems, fast frequency response in low-inertia networks, and digital twin-based real-time control. His work contributes to UN Sustainable Development Goals in clean energy and climate action. Dr. Hong has published over 110 research outputs, including 59 journal articles. His recent publications (2025) emphasize fault detection and arc suppression in active distribution networks using advanced converter topologies and signal processing techniques. Themes include traveling wave analysis, Hough transform, synthetic zero-sequence signals, and machine learning for frequency prediction, reflecting a strong trend toward intelligent, data-driven power system protection. Gold Medal, 49th International Exhibition of Inventions Geneva (2024) IET Best Paper Award (DPSP APAC 2025) Best Paper Award, IEEE APAP (2019) Principal’s Award Runner Up, University of Strathclyde (2024) Students' Choice Award (2021) British Renewable Energy Awards – 'Highly commended' (2018) IET Prize for Academic Excellence (2011) John Moyes Lessells Scholarship (2013) Shortlisted for Best Innovation Award, Scottish Renewables (2018) Dr. Hong has led or participated in over 50 research and KE projects, securing £11M in funding (PI on £2.26M). He leads a team of 10 researchers, including 5 PhD students, and has developed the LGMVP platform—the UK’s first online tool of its kind. He serves on the University Senate, is a guest editor for 5 journal special issues (Co-Guest Editor-in-Chief for a special issue on zero-carbon power systems), and has delivered teaching across 9 modules. He has been PI or Co-I on major projects such as SETTLE-INSIGHT (NIA), Shell-iCase, and NGET SIF ALPHA. He leads an active research group focused on smart grid protection and digital twin technologies. He is the main developer of four prototype software tools and mentors a team of PhD students and research associates. His lab collaborates with industry partners like SSE, National Grid, and Shell, and he is a key figure in international initiatives through IEEE and CIGRE.

Ashish Khistiمشاهده پروفایل

دانشیار

• Communication Systems
• Information Theoretic Security
• Machine Learning
• +۴ مورد دیگر

Ashish Khisti is an Associate Professor at the University of Toronto's Department of Electrical and Computer Engineering (ECE), where he directs the Signals, Multimedia and Algorithms Laboratory (SMA Lab). He holds the Canada Research Chair (Tier II) and maintains affiliations with the Vector Institute for Artificial Intelligence. His research bridges communication systems, information-theoretic security, and machine learning, with a focus on real-time streaming and privacy-preserving algorithms. Research Trends: Recent publications emphasize streaming codes for latency-sensitive networks , machine learning-driven compression , and privacy mechanisms in federated learning . Scientific Recognition: Canada Research Chair (Tier II), 2012 and 2017 renewal Cisco Research Center Award, 2017 Ontario Early Researcher Award, 2012 Best Paper at NeurIPS 2021 Deep Generative Models Workshop Academic Contributions: Supervised PhD students Ahmed Badr, Farrokh Etezadi, and Si-Hyeon Lee. Served as Associate Editor for IEEE Transactions on Communications (2012-2015) and IEEE Transactions on Information Theory (2015-2018). Labs & Collaborations: Leads the Signals, Multimedia and Algorithms Laboratory, collaborating with institutions like KAUST, Texas A&M University (Qatar), and the Vector Institute. Organized workshops at BIRS and IEEE conferences.