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Anand RaghunathanView profile

Professor

• Brain-inspired Computing
• Machine Learning
• System-on-Chip Design
• +7 more

Anand Raghunathan is a Silicon Valley Professor and Chair of the VLSI area at Purdue University's Elmore Family School of Electrical and Computer Engineering. He serves as Associate Director of the $36M SRC/DARPA Center for Brain-inspired Computing (C-BRIC) and Co-Director of the Purdue/TSMC Center for a Secure Microelectronics Ecosystem (CSME). His research focuses on Brain-inspired computing Energy-efficient machine learning System-on-chip design Post-CMOS devices Heterogeneous computing Educated with a B.Tech from IIT Madras, and MA/PhD in Electrical Engineering from Princeton University, he directs Purdue's Integrated Systems Laboratory (ISL) which explores domain-specific architectures and compute-in-memory technologies. His work spans security for embedded systems, battery-efficient computing, and computational imaging commercialized through High Performance Imaging, Inc. Research keywords include: Neural network acceleration Hardware-software co-design Security vulnerabilities Post-CMOS devices Transformer optimization Scientific awards include: IEEE Fellow MIT TR35 innovator 9 Best Paper Awards 2 NEC Technology Commercialization Awards Purdue Faculty Excellence Award Advising and grants: Co-founded High Performance Imaging, Inc. Founded Purdue/TSMC Center for Secured Microelectronics Ecosystem Hold 29 U.S. and 16 international patents Chair of 5 IEEE/ACM conferences Editorial roles in ACM/IEEE journals Labs and teams: Integrated Systems Laboratory (ISL) Collaborations with NEC Labs, Princeton University, and IIT Madras Lead $36M SRC/DARPA C-BRIC Center

• Natural Language Processing
• Machine Learning
• Large Language Models
• +3 more

Hao Peng is an Assistant Professor at the Siebel School of Computing and Data Science, part of the Grainger College of Engineering at the University of Illinois Urbana-Champaign. He holds a B.S. from Peking University (2016) and a Ph.D. from the University of Washington’s Paul G. Allen School of Computer Science & Engineering (2022). His research focuses on Natural Language Processing (NLP) , Machine Learning , Large Language Models (LLMs) , and AI for Science , with particular emphasis on improving LLM efficiency, factuality, and interdisciplinary applications. Recent courses include 'CS 598 PEN - Efficiency in NLP' and 'CS 598 PEN - LLM Post-pretraining.' Hao’s work spans advancing LLM generalization capabilities, mitigating hallucinations, and addressing hardware constraints. In 2024, he co-authored an award-winning paper on 'LM-Infinite,' enabling zero-shot extreme length generalization in LLMs. He collaborates internationally, including with the Hebrew University of Jerusalem under a joint research grant since 2019. He also contributes to Argonne National Laboratory’s AI initiatives through invited lectures. Education : B.S., School of Electronics Engineering and Computer Science, Peking University, 2016 Ph.D., Paul G. Allen School of Computer Science & Engineering, University of Washington, 2022 Key Collaborations : Interdisciplinary research with Hebrew University of Jerusalem (2025) Joint seed grant program with HUJI since 2019 His advising includes graduate student Chi Han, who contributed to the NAACL award-winning work. Grants and seed funding focus on accelerating economic development through tech innovation. No specific lab affiliations are explicitly mentioned, but his research aligns with Argonne’s AI Distinguished Lecture series and open-source platforms like OpenDevin/OpenHands.

Chenyang LuView profile

Professor

• AI for Health
• Cyber-Physical Systems
• Internet of Things (IoT)
• +4 more

Chenyang Lu is the Fullgraf Professor of Computer Science & Engineering at Washington University in St. Louis, with joint appointments in Anesthesiology and Medicine. As the founding director of the AI for Health Institute, he leads interdisciplinary efforts to address healthcare challenges through AI and data-driven approaches. His research focuses on machine learning for health outcomes prediction, cyber-physical systems, and real-time embedded systems. Education: PhD (University of Virginia, 2001), MS (Chinese Academy of Sciences, 1997), BS (University of Science and Technology of China, 1995). Research interests include AI-driven healthcare, wearable sensor systems, and cyber-physical co-design. Notable projects include predictive models for postoperative risks, HIV treatment adherence, and phenotyping neurological disorders like Chiari malformation. His work has produced foundational AI tools for perioperative care, physician burnout prediction, and mental health monitoring. Awards include ACM Fellow (2020), IEEE Fellow (2016), and the 2022 IEEE TCRTS Outstanding Technical Achievement Award. He has published over 200 papers and serves as Editor-in-Chief of ACM Transactions on Cyber-Physical Systems . Led teams securing grants from NIH, NSF, and industry. Advises over 30 PhD students who now hold roles at top universities and tech companies like Google, Meta, and Amazon. Directs the Cyber-Physical Systems Laboratory and oversees the AI for Health Institute’s translational research initiatives.

Michael Anthony C. DizonView profile

Senior Lecturer

• Law and Information Technology
• Cybersecurity
• Cybercrime Law
• +3 more

Michael Anthony C. Dizon, PhD, is a Senior Lecturer and Co-Director of Research and Innovation at Curtin Law School, Curtin University. He holds academic roles in multiple countries, including New Zealand, the Netherlands, the UK, and the Philippines. His research focuses on law and information communication technology (ICT), cybersecurity, and the socio-legal study of technology. Dizon earned degrees from Ateneo de Manila (BA), University of the Philippines (LLB), Edinburgh (LLM with Distinction), and Tilburg (PhD). His research interests include encryption policy, cybercrime laws, and the norms/values shaping technology regulation. Notable projects include studies on AI cloning regulation (2024–present), cybersecurity and human rights (2023–present), and governance of remote court hearings in the Philippines (2022–2023). He contributed to the Jean Monnet Network on European Cyber Diplomacy (2020–2024) and led Technology in Legal Education for New Zealand (2019–2021). Recent publications (2021–2024) address encryption law frameworks, socio-legal norms in technology, and trust’s role in encryption policy. His work bridges legal theory, technology ethics, and regulatory practice, emphasizing interdisciplinary approaches to emerging digital challenges. Professional affiliations include roles as a tech/AV enthusiast (macdizon), with an active online presence (website, LinkedIn). He teaches law and technology courses, integrating practical and theoretical dimensions of digital-age legal challenges.

Ahmed SabirView profile

Researcher

• Natural Language Processing
• Computer Vision
• Machine Learning
• +8 more

Ahmed Sabir is a postdoctoral researcher at the Institute of Computer Science , University of Tartu , focusing on AI Ethics (biases, fairness, and explainability in Large Language Models). He earned his Ph.D. in Computer Science from Universitat Politècnica de Catalunya (BarcelonaTech) in 2020 and an MSc from Kanagawa Institute of Technology under the Masuda AI lab. Current Research : Biases in LLMs, Explainable AI, Multimodal Fact-Checking Past Work : Gesture recognition, OCR correction via visual semantics, Image captioning Research Interests span Natural Language Processing , Computer Vision , and their intersection in Vision-Language Models and AI Ethics . His work emphasizes Context-aware multimodal learning Bias measurement and mitigation Efficient post-processing techniques Scientific Contributions include 15+ peer-reviewed publications at venues like ACL, EMNLP, CVPRW, and COLING, with datasets and re-ranking frameworks for vision-language tasks. Notable achievements: Driven advancements in semantic relatedness for OCR correction Developed visual grounding techniques for captioning Created benchmark datasets for text spotting Explored gender bias in genderless languages Technical Expertise includes Measurement electronics for industrial tools LaTeX thesis template development Participated in 48-hour Mobility Hackathon (2017)

Ramtin ZandView profile

Assistant Professor

• Hardware Design for Machine Learning Systems
• Neuromorphic Computing
• Emerging Nanoscale Electronics
• +7 more

Ramtin Zand is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on hardware design for machine learning systems, neuromorphic computing, emerging nanoscale electronics (e.g., spintronic devices), and energy-efficient VLSI circuits. He leads the ICAS Lab, which explores reconfigurable architectures and low-power computing solutions. Education: Ph.D., Computer Engineering, University of Central Florida (2019) M.S., Electrical Engineering, Sharif University of Technology (2012) Research Interests: Dr. Zand’s work spans hardware-software co-design for AI, neuromorphic systems, and novel devices like MRAM and memristors. He emphasizes practical applications such as manufacturing anomaly detection, edge computing, and energy-efficient neural network deployment. Recent Trends in Articles: His publications emphasize hybrid PIM architectures, LLM-driven hardware design, and neuromorphic edge systems. Themes include optimizing communication efficiency, leveraging memristive crossbars, and bridging vision transformers with embedded platforms. Grants/Funding: Funded by AFRL for smart manufacturing projects ONR grants for perception systems research NSF support for in-memory computing Labs/Teams: The ICAS Lab collaborates on projects such as neuromorphic accelerators and low-power sensor systems. Recent milestones include DAC 2025 awards and CVPR 2025 demonstrations.

Sazadur RahmanView profile

Assistant Professor

• Hardware Security
• Supply Chain Security
• Electronic Design Automation
• +7 more

Sazadur Rahman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), affiliated with both ECE and Computer Science departments under the Cyber Security and Privacy Cluster. He holds a Ph.D. and M.Sc. from the University of Florida and a B.Sc. from Bangladesh University of Engineering and Technology. Previously, he worked as a Security Architecture Engineer at Intel Corporation, focusing on processor security hardening and threat modeling. His research interests include hardware security, semiconductor supply chain security, and AI-assisted chip design. He has published over 20 peer-reviewed papers and contributed to patents and textbooks. Education Ph.D. Electrical Engineering, University of Florida, 2022 M.Sc. Electrical and Computer Engineering, University of Florida B.Sc. Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology Research Interests His work centers on securing electronic design automation (EDA) tools, semiconductor supply chains, and hardware systems against reverse engineering, piracy, and attacks. Key areas include: Hardware obfuscation techniques (e.g., logic locking, watermarking) CAD tools for security assessment and mitigation Machine learning applications in secure chip design Secure heterogeneous integration and FHE acceleration Professional Activities Technical Program Committee Member: IEEE HOST Conference (2024) Reviewer: Top journals/conferences like IEEE Transactions on CAD, ACM TODAES, and DAC Awards 2022 IEEE/ACM DAC PhD Forum Finalist 2023 IEEE/ACM DATE Best Paper Nomination 2022 IEEE VTS TTTC Best Thesis Runner-Up Research Contributions His publications focus on innovative solutions like LLE for IC piracy mitigation, iPROBE for probing attack protection, and ReTrustFSM for RTL obfuscation. He has developed tools for threat modeling using NVD databases and evaluated cloud-based EDA platforms' security.

Shaoyi HuangView profile

Assistant Professor

• Efficient machine learning algorithms
• Algorithm-system co-design for AI acceleration
• Large-scale machine learning for chip design
• +3 more

Shaoyi Huang is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology. She holds a PhD in Computer Engineering from the University of Connecticut (2024), where she was advised by Professors Caiwen Ding and Omer Khan. Her research focuses on advancing AI systems through algorithm-hardware co-design, with emphasis on efficient machine learning algorithms, energy-efficient privacy-preserving techniques, and large-scale machine learning for chip design. She has received multiple accolades including the 2024 Machine Learning and Systems Rising Star award and the 2022 DAC Publicity Paper Award. Education: PhD in Computer Engineering, University of Connecticut (2024) Appointments: Assistant Professor, Stevens Institute of Technology (2024–present) Key Research Areas: AI acceleration, graph neural networks, privacy-preserving ML, and EDA applications Her work spans theoretical contributions to practical implementations, with publications in top-tier conferences like HPCA, ASPLOS, and NeurIPS. She actively contributes to the academic community through program committee roles (e.g., DAC 2025, IPDPS 2025) and serves as a Guest Editor for special issues on high-performance computing. Huang mentors PhD students and oversees a team advancing interdisciplinary AI research, including funded projects from NSF and industry partners like AWS and OpenAI. Awards highlight her impact: Marion & Frederick Buckman Engineering Fellowship (2024), multiple fellowships from UConn, and recognition at WISE conferences for promoting women in hardware security. Her lab emphasizes scalable solutions for real-world challenges in AI and hardware systems.

Shaowei WangView profile

Assistant Professor

• Software Engineering
• Data Mining
• Recommendation Systems
• +5 more

Shaowei Wang is an Assistant Professor in the Department of Computer Science at the University of Manitoba's Faculty of Science. His research focuses on software engineering and data mining, aiming to develop algorithms that leverage big software data (e.g., code repositories, developer social media) for efficient and effective software development. Key interests include recommendation systems, data-driven software engineering, software debugging, program comprehension, and secure software development. He leads the Mamba Lab, which explores these areas through empirical studies and tool development. His work spans topics such as large language model (LLM) applications in code analysis, vulnerability detection, graph fairness, and automated evaluation frameworks for API-oriented code generation. Recent studies address challenges like input order bias in LLMs, silent vulnerability fixes, and fair graph learning. He has contributed to over 50 peer-reviewed publications, emphasizing practical and theoretical advancements in software engineering and data science. Teaching interests include software engineering, data-driven software engineering, and data mining. His research has been recognized through collaborations with industry and academic partners, though no specific awards are listed. Advising and grant details are not explicitly mentioned in available texts.

Longfei ShangguanView profile

Assistant Professor

• Mobile and wireless systems
• IoT systems
• Edge computing
• +5 more

Longfei Shangguan is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He leads the Pittsburgh Experimental Research Group (PERG), focusing on hardware/software systems to advance computing, sensing, and communication in sensor/wireless technologies. Previously, he was a postdoc at Princeton and a senior researcher at Microsoft. He holds a PhD from HKUST (2015) under Professors Yunhao Liu and Ke Yi. Research Interests: Mobile/wireless systems, IoT, edge computing, and hardware/software co-design for sensor networks. Key areas include novel IoT applications, low-power hardware, and wireless protocol optimization. Key Projects Sensor-in-the-loop AI agents leveraging multimodal sensors Human-centric sensing using acoustic/vision modalities Wireless Digital Twin via neural ray tracing Professional Contributions Editor: IEEE Pervasive Computing, ACM GetMobile PC Member for top conferences including MobiCom, NSDI, SenSys Organized Dagstuhl Seminar on IoT Wireless Scenarios Awards NSF CAREER Award (2024) Google Research Scholar Award (2023) Best Paper Awards at MobiCom 2024 and MobiCom 2021 Advising & Grants Supervises 5 PhD students, 3 MS students, and undergraduate researchers Recipient of MassAITC Pilot Award (2025), NSF CAREER (2024) Labs/Teams: PERG lab focuses on experimental systems research with hands-on prototyping.

Sarath Chandar Anbil ParthipanView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Sarath Chandar Anbil Parthipan is an Associate Professor at Polytechnique Montréal and leads the Chandar Research Lab (CRL) . He holds the Canada Research Chair Tier 2 in Lifelong Machine Learning and the CIFAR Chair in Artificial Intelligence . As a core faculty member at Mila - the Quebec AI Institute , he also serves as an adjunct professor at the University of Montreal and IIT Madras . PhD in Computer Science (University of Montreal, 2015) MS by Research in Computer Science (IIT Madras, 2015) B.Eng in Computer Science (Anna University, 2009) His research focuses on lifelong/continual learning , deep learning , reinforcement learning , and natural language processing . He created the Conference on Lifelong Learning Agents (CoLLAs) in 2022 and serves as its organizer. Recent works explore molecular design with BindGPT , multi-agent RL coordination, and model interpretability. Scientific recognition includes: Canada Research Chair Tier 2 CIFAR Chair in AI He supervises students in topics spanning AI for science , medical image segmentation , and reset-free reinforcement learning . The CRL Annual Symposium 2023 featured collaborations with institutions like Princeton University and IBM.

Marco HutterView profile

Professor

• Robotics
• Intelligent Systems
• Autonomous Navigation
• +4 more

Prof. Marco Hutter is a Full Professor at the Department of Mechanical and Process Engineering at ETH Zürich, and Deputy Head of the Institute for Robotics and Intelligent Systems. His research focuses on advanced robotics, including autonomous legged systems, robotic perception, and control strategies for challenging environments. He leads projects in areas such as forest inventory using legged robots, space exploration robotics, and mobile manipulation systems. His work emphasizes real-world deployment, with contributions to robotic navigation, sensor integration, and policy optimization. Prof. Hutter collaborates on large-scale datasets and frameworks like ROS-LLM for embodied AI, and explores applications in logistics, construction, and planetary exploration. His team develops both hardware (e.g., LEVA logistic vehicles) and software solutions (e.g., risk-guided diffusion models for safety-critical systems). Key projects include ETHcavation (construction environment understanding) and TartanGround (robot perception datasets). He pioneers work in agile locomotion policies, magnetic climbing robots, and multi-agent robotic teams for parcel delivery. His research bridges theoretical advancements with practical implementations, addressing challenges in terrain adaptation, energy efficiency, and robustness in autonomous systems.

Guangdong BaiView profile

Associate Professor

• Trustworthy AI
• Cyber Security
• Software Engineering
• +5 more

Associate Professor Guangdong Bai is affiliated with The University of Queensland's School of Electrical Engineering and Computer Science, specifically within the Discipline of Cyber Security and Software Engineering. He holds the academic rank of Associate Professor and is currently on leave. His research focuses on Trustworthy AI, Cyber Security, Software Engineering, and Formal Methods, with notable contributions to privacy compliance, IoT/ICS security, and adversarial machine learning defense mechanisms. Education includes a PhD from the National University of Singapore and undergraduate/master's degrees from Peking University. His work spans theoretical advancements and practical applications, including blockchain-based systems for EV charging and vaccine tracking, adversarial attack frameworks (e.g., GenDroid), and federated learning security. Research interests emphasize AI ethics, privacy-preserving techniques, and securing emerging technologies like autonomous vehicles and smart agriculture networks. Notable projects include AuSSE (autonomous vehicle security framework), CoreLocker (neural network usage control), and PAODING (neural network pruning toolkit). Publications (15 most recent) reflect expertise in adversarial machine learning, federated learning, and IoT security. His work bridges theory and practice, addressing real-world challenges in critical infrastructure and decentralized systems. Labs/teams include collaborations on Cyber Security and Software Engineering at UQ, with supervision of PhD candidates such as Yanjun Zhang (Deakin University) and Kailong Wang (Huazhong University of Science and Technology).

Luca ArditoView profile

Associate Professor

• Software Engineering
• Mobile Computing
• Gamification in Education
• +3 more

Luca Ardito is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) of Politecnico di Torino. His research focuses on software engineering methodologies, mobile computing, and gamification strategies in education and testing. His work integrates technical domains such as Android development, GUI testing automation, energy efficiency in software systems, and IoT security. Ardito is a core member of the SoftEng research group, contributing to projects like SIFIS-home for privacy-preserving smart home systems. Research Interests: Advanced GUI testing techniques for Android and web applications Application of gamification in software education and quality assurance Energy-aware software development and green computing practices Certification frameworks for IoT device firmware and cybersecurity His recent work emphasizes empirical studies on gamification in UML modeling education, Kotlin adoption in Android ecosystems, and innovative approaches to firmware certification. Ardito has pioneered tools like Umlegend for gamified UML learning and Bipmin for BPMN education. Grants & Collaborations: Leading multidisciplinary projects combining computer science with agriculture (e.g., UAV-based hazelnut tree characterization) Active in industry partnerships for mobile testing frameworks (e.g., TOGGLE library) Labs & Teams: Core contributor to Politecnico's SoftEng Research Group , collaborating on projects like SIFIS-home (FP7/ERC-funded).

Fabiano DalpiazView profile

Professor

• Requirements Engineering
• Natural Language Processing
• Software Engineering
• +1 more

Prof. Fabiano Dalpiaz is Professor of Software Production at Utrecht University, where he leads the Requirements Engineering Lab. As Editor-in-Chief of Springer's Requirements Engineering Journal and Steering Committee chair of REFSQ, he blends AI with requirements engineering. His research develops tools for improving requirements elicitation and analysis. His work integrates natural language processing and visualization techniques to enhance requirements engineering processes. Recent publications focus on terminology standardization, automated analysis of software artifacts, and AI-assisted requirement extraction methods. Honors include the 2021 IEEE RE Best Paper Award. As program leader for the Computing Science master's program, he teaches model-driven engineering and requirements engineering courses.