Find Academic Supervisors

• Data Integration
• Data Quality
• Recommender Systems
• +5 more

Assoc Prof Henry Nguyen is an Associate Professor at Griffith University's School of Information and Communication Technology, with expertise in data integration, data quality, recommender systems, and big data visualization. He directs the Responsible Big Data Lab and has secured over $3.5M in funding since 2015 from ARC, DFAT, and industry partners. PhD & Master's from EPFL, Switzerland ARC DECRA Award (2020) His research focuses on privacy-preserving AI for social data , IoT , and satellite analytics , with over 200 publications in top venues like SIGMOD, KDD, and IEEE TKDE. Recent work spans federated learning , graph neural networks , and secure AI systems . Article trends highlight 2024-2025 publications on: Federated recommendation security On-device AI optimization Privacy-preserving explainable AI Graph condensation techniques LLM-powered risk analysis Cloud-edge collaboration Scientific contributions include ARC DECRA Award 2020 Multiple senior PC roles in A* conferences Citations in International AI Safety Report 2025 Henry Nguyen supervises 12 active PhD/MSc students and has directed 8 completed doctoral theses . His funded projects include collaborations with Ubitech , KARI , and CSIRO , focusing on Australia-Korea partnerships and responsible AI development.

Felix NaumannView profile

Professor

• Data Profiling
• Data Quality
• Data Integration
• +4 more

Professor Felix Naumann is Chair for Information Systems at the Hasso Plattner Institute (HPI) at the University of Potsdam in Germany, where he leads the Information Systems research group. He is also Coordinator for MSc. Data Engineering and for the Data and AI track for MSc. Computer Science, and Speaker of the Research School on Data Science and Engineering. His extensive academic career includes visiting positions at CIRES Centre in Brisbane (2024-2025), SAP's Innovation Center (2020), AT&T Research (2016), and QCRI (2012). Professor Naumann's research focuses on data profiling, data cleansing, data integration, and data quality assessment with over 200 scientific publications. His work spans theoretical foundations and practical applications, with significant contributions to data quality metrics, metadata extraction, and AI-driven data preparation techniques. His research group develops prominent systems like Metanome for data profiling and Metis for data quality assessment. His recent publications demonstrate continued innovation in data management, with a growing intersection between traditional database research and AI applications. The 15 most recent papers show increasing focus on data quality for AI applications (KITQAR), multimodal data analysis (MELArt), and practical data cleaning frameworks that bridge database systems with machine learning pipelines. GI Dissertationspreis 2000 for best computer science PhD thesis IBM Research Division Award, 2002 Distinguished ACM member since 2021 Distinguished Reviewer Award - SIGMOD 2023 Best paper award at EDBT 2024 for Tasheeh paper Professor Naumann has successfully advised over 30 PhD students who now hold prominent positions at institutions like MIT, Google, Snowflake, and universities worldwide. His research has been funded by major grants including DFG Nachwuchsforschergruppe (2003-2008), IBM SUR Grant (2007), and DFG Forschergruppe Stratosphere (2010-2016). He leads the Information Systems research group at HPI, which includes PostDocs, PhD students, and student assistants working on projects like Metanome, Metis, KITQAR, and Janus, focusing on data profiling, quality assessment, and change exploration in data systems.

Xiaoyang WangView profile

Senior Lecturer

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +10 more

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

Fendy SantosoView profile

Academic

• cyber-physical systems
• cybersecurity
• artificial intelligence
• +6 more

Dr. Fendy Santoso is a Visiting Fellow at UNSW Canberra's School of Engineering and Information Technology, where he conducts cutting-edge research at the intersection of cyber-physical systems, cybersecurity, and artificial intelligence. His work focuses on developing robust security mechanisms for autonomous systems, particularly UAVs and robotics operating in adversarial environments. His educational background includes a PhD in Electrical Engineering from UNSW Sydney and a Master of Engineering (Electrical and Computer Systems) from Monash University. Dr. Santoso's research interests span cyber-physical systems security, adversarial machine learning, trustworthy autonomy, and AI-driven cybersecurity for autonomous platforms. His work specifically targets penetration-resistant architectures and secure decision-making frameworks for UAVs and robotic systems operating in dynamic, threat-prone environments. His approach integrates advanced fuzzy logic systems with deep learning techniques to create resilient control mechanisms that can withstand cyberattacks and operational uncertainties. Analysis of his recent publications reveals a consistent focus on applying type-2 fuzzy systems, deep learning, and negative imaginary control theory to solve critical challenges in autonomous systems security and control. His work demonstrates strong interdisciplinary connections between cybersecurity, control theory, and artificial intelligence, with particular emphasis on real-world implementation and experimental validation. Distinguished Early Career Travel Fellowship 2019, University of Wollongong ARC Linkage Project: Robust Defenses Against Adversarial Machine Learning for UAV Systems (2023) CSIRO Next Generation Grant for AgriTwins: Bridging Cyber-Secure Emerging Technologies and Data-Centric Twin Tech for Resilient Agriculture of the Future (2024) Dr. Santoso has secured over AUD 3 million in competitive research funding from prestigious sources including the Australian Research Council, U.S. Army Ground Vehicle Systems Centre, and Defence Science and Technology Group. He leads multi-disciplinary research teams and maintains strategic partnerships with defense and government agencies to translate theoretical advances into practical cybersecurity frameworks. His laboratory work centers around the Autonomous System Laboratory at UNSW Canberra, where he conducts experimental validation of security protocols for military ground robots and UAVs under realistic cyberattack scenarios.

Sirojan TharmakulasingamView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Data Science
• +6 more

Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.

Mahsa SalehiView profile

Senior Lecturer

• Data Mining
• Machine Learning
• Time Series Analysis
• +5 more

Mahsa Salehi is a Senior Lecturer in the Department of Data Science & AI at Monash University’s Faculty of Information Technology. She holds a PhD in Computer Science from the University of Melbourne and previously served as a postdoctoral researcher at IBM Research Australia. Her research focuses on data mining, machine learning, and time series analysis, with applications in healthcare, cybersecurity, and smart grids. Education: PhD in Computer Science, University of Melbourne (2016) MSc in Software Engineering, Amirkabir University of Technology (2009) BSc in Information Technology & Computer Engineering, Amirkabir University of Technology (2008/2006) Her key research interests include multi-dimensional time series analysis, anomaly detection, brain-inspired machine learning, and non-stationary data learning. She has led or contributed to over 40 research outputs, including high-impact papers on anomaly detection frameworks (e.g., CARLA) and EEG representation learning (EEG2Rep). Her work bridges theoretical advancements with practical applications, such as detecting urinary anomalies in seniors and securing smart grid systems against cyberattacks. Dr. Salehi has secured significant grants, including AU$246K from ARENA (2019–2021) and AU$30K from Emotiv Research (2022–2024). She is an Associate Editor of the ACM Transactions on Knowledge Discovery from Data and has been recognized with awards like the ICDM 2022 Best Paper Runner-Up and IBM’s Manager’s Choice Award (2016). Grants & Projects: Privacy-Preserving Machine Learning (CSIRO Next Gen, 2023–2027) AI for Clean Energy & Sustainability (Monash, 2023–2027) Deep Learning for Brain EEG Analysis (PhD Top-Up, 2022–2025) Her contributions extend to editorial and patent activities, including roles at IBM Research and collaborative projects with industry partners like Emotiv.

Montserrat RosView profile

Associate Professor

• Embedded Systems
• Sensor Networks Data Fusion
• Cyber-Physical Systems
• +6 more

Montserrat Ros is an Associate Professor and Associate Dean (Education) at the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia. She has been with the university since 2006, initially joining as a Lecturer in Computer Engineering and progressing to her current senior academic and leadership roles. Her educational background includes: B.E.(Hons1)/B.Sc. double degree majoring in Computer Systems Engineering and Mathematics from the University of Queensland (2000) Ph.D. degree in Computer Engineering from the University of Queensland (2007) Professor Ros's research focuses on the intersection of embedded computing systems and practical engineering applications. Her work spans several key areas including embedded systems design, sensor network data fusion, cyber-physical systems development, and innovative approaches to engineering education. She has particular expertise in sensor-based localization techniques, computer architecture optimization, and code compression methodologies for resource-constrained environments. More recently, her research has expanded into machine learning applications for constrained systems and Internet of Things implementations. Analysis of her recent publication record reveals a strong emphasis on Internet of Things networks, UAV-based systems, and applications of artificial intelligence in both engineering education and manufacturing processes. Her work demonstrates a consistent pattern of bridging theoretical computer engineering concepts with practical real-world applications across diverse domains including healthcare, environmental monitoring, and industrial automation. Her significant contributions to academia have been recognized through numerous prestigious awards: 2019: AAUT Citation for Outstanding Contribution to Student Learning 2018: IEEE TALE 2018 Meritorious Service Award 2018: Featured in UOW Leadership in Education Booklet 2017: UOW Vice Chancellor's Award for Outstanding Contribution to Teaching and Learning 2016: UOW Women of Impact for inspiring young women in STEM 2015: UOW Vice Chancellor's Interdisciplinary Research Excellence Award 2012 & 2007: UOW Vice Chancellor's Awards for Teaching Excellence 2011: UOW Vice Chancellor's Award for Community Engagement Senior Fellow of WATTLE (Wollongong Academy for Tertiary Teaching & Learning Excellence) Professor Ros has secured substantial research funding across multiple projects spanning from 2006 to the present. Her grant portfolio demonstrates a consistent focus on engineering education innovation, sensor network development, and practical applications of embedded systems. Notable projects include "The AI Tutor: Enabling 24x7 student support across engineering" (2024), "AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods" (2023), and "Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane" (2022). She actively supervises HDR students and has completed multiple successful candidatures. Her leadership extends beyond research and teaching, as evidenced by her role as Associate Dean (Education) for the Faculty of Engineering and Information Sciences. She is also actively involved in community engagement through volunteering with the State Emergency Service (Wollongong SES) and Athletics Wollongong Club.

Jonathan KummerfeldView profile

Senior Lecturer

• Natural Language Processing
• Artificial Intelligence
• Human-Computer Interaction
• +4 more

Dr. Jonathan Kummerfeld is a Senior Lecturer at the University of Sydney's School of Computer Science, specializing in Natural Language Processing (NLP) and AI collaboration. He holds a PhD from UC Berkeley and a B.Sc. from the University of Sydney, with postdoctoral research at the University of Michigan. His work focuses on enabling computers to collaborate effectively with humans through NLP, including interactive systems, dialogue, and crowdsourcing. Education: PhD in Computer Science, UC Berkeley (advised by Dan Klein), 2016 B.Sc. in Computer Science, University of Sydney (advised by James Curran), 2010 Postdoctoral Researcher at University of Michigan (2016–2021) Research Interests: Interactive Executable Semantic Parsing NLP Data Science for Messy Real-World Data AI Collaboration in Games and Dialogue His recent work explores empathetic AI systems, alignment algorithms, and AI-resilient interfaces. Over 50 publications span venues like ACL, NAACL, and ICML. Grants & Advising: CHIRON (DARPA): AI for recognizing opponent narratives Google/Amazon grants for annotation tools and dialogue systems Advising PhD student Kent O'Sullivan His lab develops tools like SLATE and contributes to dialogue corpora. Labs/Teams: Leads the Sydney NLP Lab, focusing on human-AI collaboration and scalable NLP systems.

• Pervasive Computing
• Machine Learning
• Autonomous Systems (Drones)
• +5 more

Associate Professor Damith Ranasinghe holds a position at the University of Adelaide's School of Computer Science within the Faculty of Sciences, Engineering and Technology. His research focuses on Pervasive Computing, Machine Learning, Autonomous Systems (Drones), and Cybersecurity with applications in population ageing, conservation, and software security. He leads the Adelaide Auto-ID Lab, dedicated to developing innovative solutions for real-world challenges through multi-disciplinary approaches. Ranasinghe's work emphasizes embedded systems security and adversarial machine learning defenses. His lab explores autonomous UAV-based wildlife tracking systems and wearable sensor technologies for healthcare applications like fall prevention in geriatric care. He actively supervises postgraduate students in Masters and PhD programs and has contributed to projects such as the Ambient Intelligent Geriatric Management (AmbIGeM) system. His technical contributions span firmware fuzzing methodologies (e.g., ICICLE emulator), Bayesian learning defenses against adversarial attacks, and hardware security solutions using PUF-based authentication. These innovations address critical needs in both theoretical and applied domains of computer science and engineering.

• Computational Logic
• Knowledge Representation
• Artificial Intelligence
• +5 more

Professor Kewen Wang is a faculty member in the School of Information and Communication Technology at Griffith University , where he has been actively working in computational logic, knowledge representation, and their applications in artificial intelligence for over 30 years. His research has led to the development of novel logics, computer languages, and systems for knowledge representation and reasoning. Broad research areas: Computational Logic, Knowledge Representation, Artificial Intelligence, Programming, Knowledge Graphs, Explainable AI, Ontology Reasoning, Rule Learning Key contributions: RLvLR (scalable rule learner for KGs), TyRuLe (typed rule learning), Drewer (Datalog+- query engine), ALBERT+CCR (CommonsenseQA model), auction design algorithms His research has been widely cited in top venues like Artificial Intelligence , JAIR , ACM Transactions on Computational Logic , and conferences AAAI (Area Chair 2022-2025), IJCAI , KR . He has secured six ARC Grants (five Discovery, one Linkage) and smaller grants from NICTA, CSIRO, and Griffith University. Editorial roles: Area Chair for AAAI (2022-2025), Associate Editor for Journal of Web Semantics , TGDK Supervision: Has supervised over 15 PhD students including Hong Wu, Peng Xiao, and Pouya Omran Teaching: Courses in Intelligent Systems, Data Structures, Discrete Mathematics, and Robotics

• Cybersecurity
• Wireless Communication
• Network Security
• +7 more

Saeed Ur Rehman serves as a Senior Lecturer in Cybersecurity and Networking within the College of Science and Engineering at Flinders University. He holds leadership positions as Course Leader for BIT and MIT (Networks and Cybersecurity) programs and Deputy Research Section Lead for Data and Information Sciences. His institutional affiliations include membership in the Centre for Defence Engineering Research and Training and the South Australia Cyber Security Innovation Node (SA Node) Advisory Board. Dr. Rehman's educational background includes: PhD in Electrical and Electronic Engineering from the University of Auckland (2015) Graduate Diploma in Higher Education from Unitec Institute of Technology (2016) Master of Engineering from the University of Auckland (2009) BSc Engineering from Pakistan (2004) His research spans cybersecurity, wireless communication, and network security with specific expertise in physical layer security, satellite communication, visible light communication, and fog computing. Dr. Rehman has developed innovative approaches in RF fingerprinting for device authentication and has contributed to vital sign monitoring technologies using radar systems. His work bridges theoretical security frameworks with practical applications in digital agriculture, vehicular networks, and energy harvesting systems. Analysis of his recent publications reveals a strong focus on emerging security challenges in next-generation networks, particularly addressing vulnerabilities in digital agriculture systems, privacy preservation in fog computing environments, and physical layer security enhancements for 6G networks. His research demonstrates consistent interdisciplinary collaboration across computer science, electrical engineering, and healthcare technology domains. Dr. Rehman has received significant recognition including: IEEE Senior Member status (2016) Fellowship from The Higher Education Academy, UK (2019) Cisco Networking Academy instructor certifications (2019, 2022) PhD Completion Award from University of Auckland (2014) As an active researcher and supervisor, he has secured substantial research funding including AUD$810,000 for the SmartSat CRC project (2020), AUD$150,000 from Defence Innovation Partnership (2023), and multiple grants from Flinders University and New Zealand institutions. He supervises PhD and Master's students in cybersecurity, with completed projects focusing on IoT security and privacy-preserving data aggregation, and current projects exploring intelligent reflective surfaces, RF emitter geolocation, and vehicular data communication frameworks. Dr. Rehman contributes extensively to the academic community as an IEEE Senior Member serving multiple societies, conference chair, journal editorial board member, and peer reviewer for numerous publications in his field.

Kai Li LimView profile

Researcher

• Electric Vehicles
• Autonomous Vehicles
• Data Engineering
• +12 more

Dr Kai Li Lim is the St Baker Fellow in E-Mobility at the Dow Centre for Sustainable Engineering Innovation , part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland (UQ) . Specializing in data engineering , telematics , and autonomous vehicle systems , his research examines electric vehicle (EV) usage patterns , charging reliability , and spatiotemporal data analysis to inform policy and sustainable transport strategies. Education : BEng (Hons) in Electronic and Computer Engineering (University of Nottingham), MSc in Computer Science (Lancaster University), PhD in Artificial Intelligence and Robotics (The University of Western Australia, funded by Australian Government Research Training Programme). Research Interests include: Electric vehicle telematics and data analytics Computer vision and deep learning for autonomous navigation Policy development for sustainable transport Infrastructure planning for EV and hydrogen systems Integration of cloud computing and IoT in mobility solutions Publication Trends reveal a focus on EV charging behaviors , data platform architectures , and autonomous driving frameworks , with recent work addressing regional emission disparities , modular safety systems , and online assessment integrity . Scientific Awards : St Baker Fellow in E-Mobility, Visiting Fellowship at UC Davis Electric Vehicle Research Center, Australian Government Research Training Programme (PhD funding). Supervision and Grants : Available for supervision, Dr Lim has secured funding from Energy Consumers Australia , iMove CRC , and Australian Urban Research Infrastructure Network (AURIN) for projects on EV charging incentives, freight emissions modeling, and longitudinal vehicle data databases. Labs and Collaborations : Collaborates with UC Davis Electric Vehicle Research Center , UQ School of Civil Engineering , and UQ School of Electrical Engineering and Computer Science . Leads the UQ Teslascope Project and contributes to the Green Australian Vehicle Ownership (GreenAVO) Capability for AURIN .

Gea RahmanView profile

Lecturer

• Data Science
• Machine Learning
• Ensemble Learning
• +7 more

Dr. Gea Rahman is a Lecturer in Computing at Charles Sturt University (CSU), specializing in data science and machine learning. He holds a PhD in Computer Science from CSU, and MSc/BSc degrees from Rajshahi University, Bangladesh, where he was awarded a Gold Medal for academic excellence. With over 20 years of teaching experience, he previously served as Professor and Programme Director at Bangladesh Agricultural University. Education: PhD in Computer Science (Data Science/ML), Charles Sturt University (2011-2015) MSc in Computer Science & Engineering, Rajshahi University (2002-2003) BSc (Hons) in Computer Science & Technology, Rajshahi University (1998-2002) Research Interests: Data science applications in agriculture, healthcare, and environmental monitoring Machine learning techniques including ensemble/deep learning, transfer learning, and incremental learning Data preprocessing methods (missing value imputation, outlier analysis) AI ethics and healthcare consent strategies Awards: Global Research Impact Recognition Award 2020 Best Researcher Award Gold Medal for Academic Excellence (2003) Advising & Grants: Principal supervisor for multiple postgraduate students Recipient of grants including: Ai-Enabled Segmentation of Brain MRI (2024) Adaptive Federated Learning Framework (2024) Unusual Behaviour Detection in Aged Care (2023) He is part of the Data Mining Research Group (DaMRG) and actively contributes to conferences/journals as a reviewer and editorial board member.

Marwa KeshkView profile

Lecturer

• Cyber Security
• Artificial Intelligence
• Privacy Preservation
• +5 more

Dr. Marwa Keshk is a Lecturer in Cyber Security at the School of Professional Studies, University of New South Wales (UNSW) Canberra. Her academic career focuses on the intersection of cybersecurity and artificial intelligence, with particular expertise in privacy preservation, anomaly detection, and threat intelligence. Her educational background includes: PhD in Cyber Security and Privacy Preservation (2021) from UNSW Master's degree in Evolutionary Computation (2017) from UNSW Bachelor's degree in Computer Science (2012) from the Faculty of Computers and Information at Helwan University, Egypt Dr. Keshk's research interests primarily center around the application of artificial intelligence and machine learning techniques to enhance cybersecurity measures. Her work explores how computational intelligence, statistical methods, and emerging technologies can be leveraged for privacy preservation, anomaly detection, and threat intelligence. She has made significant contributions to the security of Internet of Things (IoT) networks, cyber-physical systems, and critical infrastructure protection. Her research often combines theoretical advancements with practical applications, addressing real-world security challenges in increasingly connected digital environments. Analysis of Dr. Keshk's publication history reveals a strong focus on applying AI and machine learning to cybersecurity challenges, particularly in IoT environments. Her work demonstrates a progression from foundational research in evolutionary computation to specialized applications in cybersecurity. Recent publications show increasing emphasis on explainable AI for security applications, reflecting the growing need for transparency in security decision-making processes. Her research spans multiple domains including smart cities, industrial control systems, and power networks, demonstrating the versatility and wide applicability of her security frameworks. Dr. Keshk has received notable recognition for her academic work: UNSW Tuition Fee Scholarship for PhD Research (2018-2021) Data61/CSIRO Scholarship (2019-2021) During her PhD studies, Dr. Keshk was a research candidate at Data61-CSIRO in Australia, providing her with valuable industry experience alongside her academic pursuits. Her collaborative approach to research is evident in her extensive co-authorship with colleagues across various institutions, particularly with researchers from UNSW and other Australian universities. Dr. Keshk's work contributes significantly to several research teams and initiatives focused on cybersecurity and AI at UNSW Canberra. Her expertise complements broader efforts in secure systems development, particularly in the context of increasingly connected environments where traditional security boundaries are challenged by new technologies.

• Embedded Systems
• Internet of Things
• Cyber-Physical Systems
• +5 more

Sudeep Pasricha is a Professor in the Department of Electrical and Computer Engineering at Colorado State University's Walter Scott Jr. College of Engineering, serving as Chair of Computer Engineering and Director of the Embedded, High Performance, and Intelligent Computing (EPIC) Laboratory. His educational background includes: B.E. in Electronics and Communication Engineering from Delhi Institute of Technology, India (2000) Ph.D. in Computer Science from the University of California, Irvine (2008) Prof. Pasricha's research centers on innovative software algorithms, hardware architectures, and hardware-software co-design techniques for energy-efficient, fault-tolerant, real-time, and secure computing. His work drives advancements in embedded systems, IoT, and cyber-physical systems through rigorous interdisciplinary approaches that bridge theoretical innovation with practical implementation challenges. His exceptional contributions have been honored with prestigious awards including: George T. Abell Outstanding Research Faculty Award IEEE-CS/TCVLSI Mid-Career Research Achievement Award IEEE/TCSC Award for Excellence for a Mid-Career Researcher AFOSR Young Investigator Award ACM Technical Leadership Award ACM SIGDA Distinguished Service Award With over 250 peer-reviewed publications yielding seven best paper awards and six nominations, Prof. Pasricha has filed multiple patents and co-authored influential books and book chapters. His academic leadership extends to editorial roles as Vice Chair of ACM SIGDA, Steering Committee Chair for IEEE Transactions on Sustainable Computing, and Senior Associate Editor for ACM Journal on Emerging Technologies in Computing Systems, alongside extensive conference organization spanning 150+ events. At CSU's EPIC Laboratory, he leads cutting-edge research on intelligent computing architectures that address critical challenges in energy efficiency, security, and real-time performance across emerging technology domains.