Dr. Ghazal Bargshady is a Lecturer at the University of Canberra , with expertise in Affective Computing , Artificial Intelligence , and Healthcare Technology . Her roles include teaching units such as Computer Vision, Data Analytics, and Soft Computing, as well as supervising PhD and Master by Research students in AI-driven projects for healthcare and road safety. Education: She earned her PhD in Artificial Intelligence and Computer Vision from the University of Southern Queensland in 2020. Research Interests: Dr. Bargshady specializes in Computer Vision Deep Learning Biosignal Processing Facial Expression Analysis Human Factors in AI Wearable Sensors Multimodal Data Fusion Brain–Computer Interfaces Her work addresses real-world challenges in pain assessment, depression recognition, and driver safety using cutting-edge AI models. Article Trends: Her recent publications focus on Transformer architectures , fNIRS signal analysis , multimodal pain detection , and depression severity estimation via facial video data. These studies highlight her contributions to AI in healthcare , transportation safety , and biomedical signal processing . Teaching Activities: Dr. Bargshady has lectured units including Programming for Data Science , Computer Vision , and Soft Computing , emphasizing practical AI applications.
Siddharth Garg is the Institute Associate Professor of Electrical and Computer Engineering at NYU Tandon School of Engineering, leading the EnSuRe Research Group. He holds a Ph.D. from Carnegie Mellon University (2009) and a B.Tech. from IIT Madras. His research focuses on secure and energy-efficient computing systems, integrating machine learning, cybersecurity, and hardware design. He previously held roles as Assistant Professor at NYU Tandon (2014-2020) and the University of Waterloo (2010-2014). Key affiliations include NYU Center for Cybersecurity (CCS), NYU Wireless, and the Center for Advanced Technology in Telecommunications. His work has been recognized with prestigious awards like the NSF CAREER Award (2015) and inclusion in Popular Science’s 'Brilliant 10' (2016). Notable research includes private inference optimization, secure hardware IP protection, and adversarial machine learning defenses. Publications highlight advancements in zero-knowledge proofs, AI-driven chip design, and mitigating backdoor attacks in neural networks. His grants include funding from NYU Wireless and NSF initiatives like the Chips4All project. The EnSuRe group emphasizes bridging software and hardware design gaps using AI and fostering cybersecurity education.
Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.
Prof. Ilia Polian serves as Head of the Institute of Computer Engineering and Chair of the Hardware-Oriented Computer Science (HOCOS) department at the University of Stuttgart. His leadership spans research, teaching, and institutional coordination across multiple high-impact projects. Prof. Polian's research focuses on developing circuit and system architectures based on both traditional and novel principles, including neuromorphic, stochastic, and approximate architectures. His second major research focus is systematic design methodology and design automation, with particular emphasis on safety and reliability properties of developed systems. Current research directions include quantum computing engineering, secure mixed-signal neural networks, and resource-efficient stochastic circuits for near-sensor computing applications. His recent publications demonstrate strong trends in quantum computing (particularly circuit partitioning and compilation for multi-QPU architectures), hardware security (including memristive cryptographic implementations), and AI-driven approaches to hardware testing and reliability. These works bridge fundamental computer architecture research with practical industrial applications. University of Stuttgart's Publication Prize for Paper on Partitioning of Quantum Circuits Prof. Polian actively supervises doctoral students including Devanshi Upadhyaya, and leads significant research grants such as the DFG Priority Program Nano Security which he coordinates. His department offers numerous thesis and research opportunities for students interested in cutting-edge hardware research. The Hardware-Oriented Computer Science department maintains strong collaborations with industry partners including IBM, Infineon Technologies, and Advantest, as well as academic institutions through the IQST Graduate School and QuantumBW initiatives.
Luca Sterpone is a Full Professor at the Department of Control and Computer Science (DAUIN), Politecnico di Torino. He serves as Head of the Control and Computer Engineering Department (2023-2027), coordinates the Aerospace and Safety Computing Lab, and is a member of the Academic Senate and Power Electronics Innovation Center (PEIC). His research spans reconfigurable computing, fault tolerance, and radiation effects analysis in electronic systems. Professor since 2021 Department Head (DAUIN) since 2023 Coordinates international collaborations with ESA, AMD Xilinx, NVIDIA, and Thales Alenia Space Develops radiation-hardened FPGA tools (SETA, VERI-Place, PyXEL) 2007 EDAA Outstanding Dissertation Award and 2005 IEEE Best Paper Award Research Focus : Designing radiation-tolerant systems for aerospace, including fault-tolerant AI accelerators, FPGA reliability, and software-based error mitigation. He investigates soft error propagation in nanoscale circuits and develops tools for radiation sensitivity analysis in VLSI. His work integrates hardware-software co-design for mission-critical applications. Awards : EDAA Outstanding Dissertation Award (2007) IEEE European Test Symposium Best Paper (2005) SMACD Best EDA Tool Award (2018) ARC Best Paper candidate (2018) Teaching : He leads courses in Reconfigurable Computing (PhD level), GPU Programming , and Operating Systems . He has formal responsibility for teaching roles across 9 bachelor's and 7 master's years, and mentors multiple PhD students. Collaborations : Coordinates with the European Space Agency (ESA), University of Bielefeld, Universidad de Sevilla, and industrial partners like AMD Xilinx, NVIDIA, and General Motors. He leads projects such as RESCHIP4EU, VEGAS, and TERRAC for radiation-hardened computing solutions.
Fatemeh Ganji is an Assistant Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), with an affiliation to the Cybersecurity program. She holds a Ph.D. in Electrical Engineering from the Technical University of Berlin (2017), where she received the BIMoS Ph.D. Award and was nominated for the ACM Dissertation Award. Prior to WPI, she served as a Post Doctoral Associate at the University of Florida (2018–2020) and at Telecom Innovation Laboratories/Technical University of Berlin (2017–2020). Her research focuses on interdisciplinary approaches in hardware security, combining machine learning and cryptography to design and evaluate security-critical hardware systems. Key areas include physically unclonable functions (PUFs), side-channel analysis, and countermeasures against tampering and counterfeiting. Her work is funded by the European Union (Horizon 2020, FP7), German BMBF, NSF, and NIST. Ganji actively contributes to the academic community as a reviewer for IEEE and ACM journals and serves on technical program committees for CHES, FPL, DATE, and SPACE conferences. Her recent projects include developing AI-driven forensic analysis for PCB tamper detection, secure multiparty computation frameworks for chiplet systems, and open-source tools for implementation security testing. Her awards include the BIMoS Ph.D. Award 2018 and recognition from the Technical University of Berlin for her doctoral work on PUF learnability. She has also pioneered methods to detect recycled integrated circuits and enhance hardware trust through reverse engineering and machine learning.
Daniel Müller-Gritschneder is an Adjunct Teaching Professor (Privatdozent) at the Technical University of Munich (TUM), affiliated with the Chair of Electronic Design Automation. He leads the 'Electronic System Level' research group, focusing on embedded systems, TinyML, virtual prototyping, and hardware resilience. He temporarily served as head of the Chair of Real-Time Systems (2019–2020) and holds a senior membership in IEEE. His research spans: TinyML : Optimizing neural network inference for microcontrollers. Virtual Prototyping : Fast simulation for embedded software development (e.g., ETISS simulator). Runtime Verification : Hardware monitoring for safety-critical systems. Fault Tolerance : Cross-layer resilience against soft errors. Design Automation : NoC synthesis and RISC-V toolchain optimization. His publications emphasize RISC-V-based systems, TinyML deployment, fault injection, and embedded AI. Recent works show trends toward compiler-assisted security, thermal management, and automated design-space exploration for edge devices. Awards: Best Paper Award (SiPS 2019) Habilitation Award (Bund der Freunde der TUM, 2019) 2nd Best Paper (SMACD'15) Best Paper nominations at DAC'07, DATE'10, Analog'10, NOCS'13 He advises researchers in the Electronic System Level group and contributes to EU projects (e.g., Scale4Edge). His lab develops tools like ETISS, MLonMCU, and Seal5 for RISC-V and TinyML ecosystems.
Vidya A. Chhabria is an Assistant Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. She holds a Ph.D. and M.Sc. in Electrical and Computer Engineering from the University of Minnesota (2022, 2018) and a B.E. in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology (2016). Education Ph.D. Electrical and Computer Engineering, University of Minnesota (2022) M.S. Electrical Engineering, University of Minnesota (2018) B.E. Electronics and Communication Engineering, M.S. Ramaiah Institute of Technology (2016) Her research focuses on computer-aided design (CAD) for VLSI systems, particularly addressing physical design challenges through optimization and analysis algorithms. She also explores intersections between machine learning (ML) and electronic design automation (EDA), with emphasis on sustainable computing solutions. Scientific recognition includes the ICCAD Best Paper Award (2021), the University of Minnesota Graduate School's Best Dissertation Award (2024), and a Doctoral Dissertation Fellowship (2021). She mentors students through honors directed study, thesis supervision, and doctoral research courses (EEE 525 VLSI Design, EEE 598 Special Topics). Scientific Awards ICCAD Best Paper Award (2021) University of Minnesota Graduate School Best Dissertation Award (2024) Doctoral Dissertation Fellowship (2021) Her industry experience includes internships at Qualcomm (2017) and NVIDIA Research's ASIC VLSI Research Group (2020-2021). She maintains active research through the VLSI Design and Automation (VDA) Lab at ASU.
Martin D. F. Wong is the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean of the College of Engineering at the University of Illinois. A pioneer in Electronic Design Automation (EDA) and VLSI circuit design, his work has significantly advanced chip design methodologies through algorithmic innovations. He holds over 450 publications and has been recognized with prestigious awards, including the ASP-DAC Most Frequent Author Award and the inaugural EDA Research Award from Synopsys. Wong’s research focuses on EDA, computational lithography, and 3D integrated circuits. He has mentored 48 PhD students, many of whom have excelled in academia and industry. His contributions include foundational frameworks like OpenILT (Inverse Lithography Technique) and Xplace (global placement). He is an IEEE Fellow and has served as a Distinguished Lecturer for the IEEE Circuits and Systems Society. Key Achievements: Recipient of six best-paper awards in chip design and routing optimization Developed GPU-accelerated tools for static timing analysis and global routing Advances in machine learning applications for EDA, including congestion prediction and hotspot detection Wong’s legacy combines technical innovation with mentorship, shaping the future of semiconductor design and manufacturing.
Azadeh Davoodi is a Vilas Distinguished Achievement Professor and Associate Chair of Undergraduate Studies in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. Her research focuses on Electronic Design Automation (EDA), integrated circuit debug, and machine learning applications in VLSI design. She holds editorial roles in journals like IEEE TCAD and ACM TRETS, and has chaired major conferences such as ISPD 2015 and served on technical program committees for DAC, ICCAD, and others. Education: PhD in Electrical Engineering, University of Maryland-College Park (2006) Research Interests: Machine learning for VLSI chip design VLSI design automation for machine learning IC-CAD for emerging nanotechnologies Hardware security Recent Research Trends: Her work bridges machine learning and hardware design, with publications on neural network optimization, distributed inference, and explainable AI for circuit design. She emphasizes energy-efficient CNNs, latency reduction in edge computing, and security in split manufacturing. Awards: 2025 DATE Best Paper Candidate 2024 Vilas Distinguished Achievement Professor 2015 ACM Best Paper Award 2011 NSF CAREER Award Service and Grants: Leads NSF-funded projects on explainable ML for CAD and holds grants for distributed neural network synthesis. Her service includes roles as IEEE HKN member and editorial board positions. Labs/Teams: Engages in interdisciplinary research teams at UW-Madison, focusing on EDA innovation and hardware-software co-design.
Pierre-Emmanuel Gaillardon is a Professor in the Department of Electrical & Computer Engineering and Adjunct Professor in the School of Computing at the University of Utah. He holds a joint appointment since July 2024, having previously served as Assistant Professor (2016–2019) and Adjunct Assistant Professor in Computing (2016–2019). His research focuses on FPGA design, VLSI systems, nanoelectronics, and hardware security. He leads projects in emerging devices like TIGFETs, compute-in-memory architectures, and radiation-hardened FPGA fabrics. Teaching includes courses on Digital VLSI Design, Embedded Systems Design, and thesis supervision. He has secured grants from NSF, DARPA, and industry partners totaling over $10M, addressing topics like FPGA redaction, neuromorphic systems, and environmental sensors. Notable awards include the NSF CAREER Award (2018) and IEEE Senior Member elevation (2016). He actively serves on IEEE committees for nanoelectronics and EDA tools, contributing to standards like OpenFPGA.
Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.
Girija Chetty is a Full Professor in Computing and Information Technology at the University of Canberra's School of Information Technology and Systems. She holds a PhD in Information Sciences and Engineering and has over 35 years of experience in academia and research leadership roles, including Head of Software Engineering and Program Director of ITS courses. Her research focuses on multimodal systems, medical image computing, AI, and data science. She leads a dynamic research group comprising PhD students, postdocs, and international collaborators. Education: PhD in Information Sciences (Australia, 2007), MSc and BSc in Electrical Engineering/Computer Science (India). She has held visiting roles at Deakin University and CSIRO. Research interests span computer vision, pattern recognition, and medical diagnostics, with 200+ publications in top journals/conferences. Her work addresses global challenges via AI-driven solutions in healthcare (e.g., pain assessment systems, malaria diagnostics) and sustainability (SDG impact frameworks). Projects include AI for remote ultrasound imaging and smart farming systems. She actively collaborates with industry and global research institutions. Grants/Projects: 12 funded initiatives including AI for extreme environment healthcare, malaria pathogen detection, and big data-driven population health. Awards: Senior IEEE/Australian Computer Society membership, editorial roles in IEEE/Elsevier journals. Labs/Teams: Leads a multidisciplinary research group focused on medical AI and multimodal systems.
Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.
Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.
