Alan Zaoxing Liu is an Assistant Professor of Computer Science at the University of Maryland, College Park , with appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) and Maryland Cybersecurity Center (MC2) . His research bridges systems, networking, and cybersecurity to design scalable, trustworthy approximate computing systems. Ph.D. in Computer Science from Johns Hopkins University (2018) Postdoctoral research at Carnegie Mellon CyLab (2018–2020) Research Interests : Networked and data-intensive systems Telemetry/analytics for heterogeneous networks Machine learning for network optimization Security in programmable networks Recent Publications include work on future-proof telemetry (PromSketch, VLDB’25), scalable caching (OctoCache, ASPLOS’25), and secure network analytics (TrustSketch, NDSS’24). His NSF-funded projects focus on optics-enabled DDoS defense and data-driven network management. Scientific Awards : USENIX FAST Best Paper (2019) USENIX ATC 'Best of Rest' (2021) Red Hat Collaboratory Research Awards (2022, 2023) Teaching : Leads Cloud Computing at Boston University , emphasizing agile development, open-source collaboration, and cloud infrastructure.
Azad J Naeemi is a Professor holding the Dean's Professorship in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He serves as Editor-in-Chief of the IEEE Journal on Exploratory Computational Devices and Circuits and Associate Director for Computation of the NSF-supported National Nanotechnology Coordinated Infrastructure (NNCI). His educational background includes a B.S. in Electrical Engineering from Sharif University (1994) and M.S./Ph.D. in Electrical and Computer Engineering from Georgia Tech (2001/2003). Prior to academia, he worked as a design engineer in Tehran (1994-1999) and as a research engineer at Georgia Tech's Microelectronics Research Center (2004-2008). Professor Naeemi's research spans nanotechnology with focus on emerging nanoelectronic devices, spintronics, ferroelectric devices, and design technology co-optimization for CMOS/beyond-CMOS technologies. His work bridges materials, devices, circuits, and systems, particularly investigating integrated circuits based on nanoscale devices and interconnects. Educational research includes experiential learning environments for engineering education. Recent publications (2024-2025) demonstrate strong emphasis on spin-orbit torque MRAM, ternary content addressable memories, ferroelectric/antiferroelectric devices, and plasmonic circuits. Key trends include energy-efficient hardware accelerators, neuromorphic computing applications, and compact modeling for advanced technology nodes. His scientific honors include: IEEE Solid-State Circuits Society James Meindl Innovators Award (2022) IEEE Electron Devices Society Paul Rappaport Award (2008) NSF CAREER Award (2013) SRC Inventor Recognition Award (2010) Multiple Georgia Tech teaching awards Professor Naeemi leads research supported by NSF (including NNCI infrastructure) and SRC. His editorial role with IEEE JXCDC positions him at the forefront of exploratory computational devices. He previously served as General Co-Chair for the IEEE International Interconnect Technology Conference (2013). His work connects with Georgia Tech's Microelectronics Research Center and national nanotechnology initiatives through the NNCI network, focusing on computational infrastructure for nanoscale device characterization and design.
Olivia Di Matteo serves as an Assistant Professor in the Department of Electrical and Computer Engineering within UBC's Faculty of Applied Science, leading the Quantum Software and Algorithms Research (QSAR) group since her January 2022 appointment. Her academic foundation includes a BSc from Lakehead University and MSc/PhD in Physics (Quantum Information) from the University of Waterloo, completed in 2019. Dr. Di Matteo's research centers on quantum software engineering , with pioneering work in quantum compilation , circuit optimization , and debugging tools . She champions open-source quantum frameworks and develops accessible educational resources to democratize quantum computing. Analysis of her 15 most recent publications (2021-2025) reveals dominant trends in quantum programming infrastructure, particularly circuit analysis (33%), bug classification (20%), and qubit network optimization (15%), with strong emphasis on practical software tooling over theoretical physics. No scientific awards were documented in the source materials. She advises graduate students in the QSAR group while contributing to open-source quantum ecosystems through projects like PennyLane and The Ionizer transpiler, and teaches courses including CPEN 400Q (Gate-model quantum computing) and ELEC 221 (Signals and Systems). The QSAR group operates at the intersection of quantum software development and education, focusing on making quantum programming accessible through visual tools, real-time debugging environments, and hardware-agnostic compilation techniques.
David Juncker is a Professor and Department Chair of the Department of Biomedical Engineering at McGill University. He serves as a Principal Investigator at the McGill University & Genome Quebec Innovation Centre and holds associate memberships in the Department of Neurology and Neurosurgery, Department of Electrical and Computer Engineering, Division of Experimental Medicine, Department of Surgery, and Goodman Cancer Research Centre. His research focuses on micro- and nano-bioengineering technologies for bioanalysis, precision medicine, and organs-on-chips. Key areas include microfluidics, lab-on-a-chip devices, biomedical sensors, medical diagnostics, biomaterials, tissue engineering, and cancer biomarker discovery. His lab develops scalable antibody microarrays, self-powered diagnostic platforms, microfluidic probes for brain tissue perfusion, and nanogradients for neuronal navigation, with applications in cancer diagnostics, global health, and neuroscience. Recent publications (2023-2025) reveal strong emphasis on extracellular vesicle analysis, single-cell proteomics, 3D-printed microfluidic/organ-on-a-chip systems, and capillary-driven circuits. Key trends include low-cost point-of-care diagnostics, advanced circulating tumor cell isolation methods, and biomimetic synthetic vesicles for drug delivery, demonstrating translational potential in early disease detection. Dr. Juncker leads a highly interdisciplinary team comprising undergraduate and graduate students, post-doctoral fellows, and staff from diverse scientific, engineering, and cultural backgrounds. His lab actively recruits Canadian/permanent resident graduate students for projects on single extracellular vesicle and protein detection in cancer and infectious diseases, leveraging microfluidics and wearables for biomarker discovery. The Juncker Lab operates from the McGill University & Genome Quebec Innovation Centre (740 Dr. Penfield Avenue, Room 6206). It maintains a collaborative, multicultural environment focused on developing transformative micro- and nano-bioengineering technologies with significant potential impact on human health diagnostics and treatment.
Gabriele Facciolo is a Professor at the Centre Borelli, ENS Paris-Saclay, France. He is a Senior Member of the Institut Universitaire de France (IUF) and holds an Innovation Chair (2025). His research focuses on image and video processing, remote sensing, and super-resolution techniques. Current affiliations: Centre Borelli (ENS Paris-Saclay), Institut Universitaire de France His research explores advanced algorithms for satellite stereo pipelines, real-time deblurring, denoising, and explainable AI systems for legal evidence enhancement. He coordinates projects like ANR SURECAVI (Super-resolution for visible camera systems) and ANR IMPROVED (video enhancement for judicial use), with recent work on Gaussian Splatting for Earth Observation and multi-date satellite super-resolution. Notable scientific achievements include the IGARSS 2025 Top 10 Student Paper Award and leadership in projects funded by ANR (€890k) and Prime Minister's entities (SGDSN/ANSSI). His work bridges computational imaging, defense applications, and digital forensics. Project leadership: SURECAVI, IMPROVED, BOFOR Key technologies: GPU acceleration, real-time processing, optical flow estimation, RPC refinement Gabriele actively contributes to open-source tools like S2P (Satellite Stereo Pipeline), MGM (MultiGlobal Matching), and OMNIflip. He teaches in the Master MVA program and collaborates across institutions (ENPC, UPF).
Laura Devendorf is an Associate Professor at the ATLAS Institute and Department of Information Science at the University of Colorado Boulder. As director of the Unstable Design Lab, she bridges human-computer interaction (HCI), computational design, and craft practices through smart textiles and collaborative innovation. BFA in Studio Art and BS in Computer Science from University of California Santa Barbara PhD in Information Science from UC Berkeley Research Interests focus on smart textiles as a medium to challenge human-machine relationships, with projects exploring: Computational design tools for weaving Gendered labor in technology Biodegradable materials for wearables Interdisciplinary collaboration with craftspeople Speculative design practices Human-fungi relationships Recent Research Trends demonstrate her leadership in: AdaCAD software for parametric weaving Desktop biofiber spinning systems Interactive hygromorphic textiles Material-led HCI frameworks Scientific Recognition : Best Pictorial Award (TEI '23) Best Paper Honorable Mention (DIS ’22) Honorable Mention (CHI EA ’20) Best Pictorial Honorable Mention (DIS ’22) Collaborations & Grants : NSF CAREER Grant (2020) for smart textiles innovation Extensive partnerships with Mirela Alistar, Kristina Andersen, and others Advancing open-source tools like AdaCAD and Desktop Bio-spinning
Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Renjie Zhao is an Assistant Professor in the Department of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. His research focuses on wireless networking and mobile computing, with significant contributions to millimeter-wave communications, software-defined radio, and IoT systems. Dr. Zhao received his B.E. in Electric Power Engineering and Automation from Shanghai Jiao Tong University in 2018, followed by an M.S. (2020) and Ph.D. (2023) in Electrical and Computer Engineering from the University of California San Diego, where he was advised by Professor Xinyu Zhang. His research centers around three main areas: next-generation wireless network architectures (5G millimeter wave, 6G joint communication and sensing, Internet of Things), novel radio hardware and software design (software-defined radio, wireless brain interfaces, low-power ultra-wide-band), and ubiquitous communication and sensing systems (smart homes, virtual/augmented reality, localization, ultra-reliable RFID for supply chains). His work bridges theoretical innovation with practical implementation, often resulting in open-source hardware and software platforms that advance the field. Dr. Zhao's research has been published in top conferences including ACM SIGCOMM, MobiCom, and NSDI. His work demonstrates a clear progression from foundational wireless communication systems to increasingly sophisticated sensing and localization applications, with consistent focus on practical deployment challenges and solutions. Scientific Awards: Best Paper Award at ACM MobiCom 2020 for work on massive MIMO millimeter-wave software radio Best Paper Award at ACM SenSys 2023 for NeuroRadar paper Hopkins AITC funding for AI technologies promoting healthy aging Dr. Zhao actively serves the research community as TPC member for major conferences including MobiCom'25, NSDI'25, and MobiSys'25, and as a reviewer for leading journals. He is involved in multiple NSF-funded projects, including an NSF CIRC project developing the next-stage M-Cube platform. His lab, focused on wireless systems, maintains strong industry connections with companies like Qualcomm and Samsung. Dr. Zhao leads the M-Cube project, an open-source millimeter-wave massive MIMO software radio platform that has been adopted by numerous research institutions worldwide. His team continues to develop innovative wireless technologies with practical applications in supply chain management, healthcare, and smart environments.
Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and Department of Computer Science at the University of California, Irvine. His academic journey includes a B.S. in Computer Science from the University of Massachusetts Amherst (1983), an M.S. in Computer Science from The George Washington University (1991), and a Ph.D. in Computational Sciences and Informatics from George Mason University (1997). Prior to UCI, he served as Assistant Professor at George Mason University (1997-1999) and Senior Fellow at The Neurosciences Institute (1999-2007). University of California, Irvine (2007-present) George Mason University (1997-1999) The Neurosciences Institute (1999-2007) His research focuses on neurorobotics , exploring how embodied cognition and biologically plausible neural models can enhance robotic systems. Key areas include spiking neural networks , neuromodulation , path planning , and interactive tactile robots for therapeutic applications. His work bridges neuroscience , robotics , and cognitive science , with applications in autonomous vehicles , neuroprosthetics , and AI explainability . Recent publications emphasize spiking neural networks for navigation , neuromodulated attention , and neuromorphic hardware integration. The development of CARLsim, a GPU-accelerated spiking neural network simulator now in version 6.0, represents a major technical contribution. His team's work on socially assistive robots like CARL-SJR targets therapeutic applications for autism and ADHD. Scientific Awards IJCNN 2020 Best Paper Award Finalist for Best Student Paper at IJCNN 2018 Best Paper Award at IEEE IJCNN 2009 Grants include National Science Foundation funding for neural models of decision-making (2009). His lab (Cognitive Anteater Robotics Laboratory) develops systems that use large-scale brain simulations for autonomous behavior , with applications in adaptive robotics , sensorimotor learning , and neuroethology . Current projects explore neuromodulatory influences on attention systems and cognitive flexibility .
Berit Greinke is an Assistant Professor of Wearable Computing at the Berlin University of the Arts (UdK) and the Einstein Center Digital Future (ECDF) since 2018, previously serving as a researcher at UdK's Design Research Lab and DFKI (2016-2018). Her academic foundation includes a PhD from Queen Mary University of London (2017), an MA from Central St Martins (2009), and a Diploma from Weissensee Academy of Art Berlin (2007). Her educational trajectory: PhD in Media and Arts Technology, Queen Mary University of London (2017) MA in Design for Textile Futures, Central St Martins College of Art and Design (2009) Diploma in Textile and Surface Design, Berlin Weissensee School of Art (2007) Greinke's research pioneers the convergence of craft, textile design, and digital technology, with core expertise in electronic textiles and smart materials. She investigates metamaterial-based 'metatextiles' for electromagnetic applications and explores transdisciplinary collaboration between designers and scientists, particularly regarding 'negative data' in creative and scientific workflows. Her current UdK work focuses on four interconnected domains: performing materials for expressive textile/fashion design; multi-modal sensing converting visual processes into haptic/audible experiences; micro-to-macro material design spanning nanostructures to final products; and transdisciplinary processes for technology-art-science collaboration. Analysis of her 2020-2025 publications reveals dominant trends in sustainable textile electronics, with emphasis on knitted/folded sensor structures, origami-inspired capacitive shape estimation, and social sustainability in e-textile communities. Her work uniquely bridges fundamental material science (e.g., textile metamaterials) with artistic applications (e.g., interactive orchestra garments) and industrial production challenges. Berit Greinke supervises PhD students including Giorgia Petri. Her junior professorship is co-financed by SAP under a public-private partnership model, supporting projects like WEAR (Wearable technologists engage with artists for responsible innovation) and STELEC (Sustainable Textile Electronics), which emphasize ethical co-design and industry-academia collaboration. She leads research within UdK's Institute for Product and Process Design and collaborates with the Design Research Lab (formerly part of Connected Textiles group), focusing on sustainable industrial production of electronic clothing and transdisciplinary innovation frameworks.
John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute, University of Amsterdam, working with the QuSoft research center. He co-authored the open-access book Picturing Quantum Software and developed the PyZX quantum compiler. His research spans quantum computation and quantum foundations, focusing on diagrammatic methods like the ZX-calculus and ZH-calculus. Quantum circuit optimization and verification Quantum foundations via algebraic/compositional methods Co-creator of PyZX His recent publications explore multi-qutrit systems, completeness of graphical calculi, and quantum state representations. Supervises students in quantum computing, including Lia Yeh and Sarah Li. Directs the new Master's program in Quantum Computer Science at UvA. Actively contributes to open-source projects and international conferences. Notable collaborations include Aleks Kissinger, Neil J. Ross, and QuSoft researchers. Uses GitHub for DiZX development (qudit extension of PyZX). No explicit scientific awards mentioned.
Prof. Dr. Harald Reiterer is a leading researcher in Human-Computer Interaction at the University of Konstanz, where he has served as Professor since 2009. His academic journey includes a Ph.D. (1991) and habilitation (1995) from the University of Vienna, followed by roles including Senior Researcher at Fraunhofer FIT and Associate Professor at Konstanz. He currently holds multiple leadership roles: Dean of the Faculty of Sciences , Senator of Section 1 , and Consulting Dean . Ph.D. in Computer Science (University of Vienna, 1991) Venia Legendi (Habilitation) in HCI (University of Vienna, 1995) His research focuses on: Interaction Design for mixed reality environments Information Visualization in immersive contexts Hybrid User Interfaces combining physical and virtual elements 3D Object Manipulation in handheld AR Behavioral Analytics through mHealth interventions Recent work explores: Avatar representation in Augmented Reality (2024) Node selection efficiency in Virtual Reality (2024) Peripheral vision toolkits for Head-Mounted Displays (2023) Hybrid interface optimization for Mixed Reality (2023) Smartphone AR extensions for Spatial Memory (2023) Key scientific contributions: Landeslehrpreis 2021 for interdisciplinary exhibition design Development of Colibri cross-reality toolkit (2023) Foundational work on Re-locations for remote collaboration (2022) He leads numerous projects including: SMARTACT (Smart Mobility, 2015-2023) SFB TRR 161 (2009-2027) on XR interface measurement Blended Library (2011-2015) for future library design
Dr. Hai Phan is an Associate Professor in Data Science at New Jersey Institute of Technology's Ying Wu College of Computing. He holds a Ph.D. in Computer Science and Engineering from CNRS, University Montpellier 2 (2013), an M.S. from Konkuk University (2010), and a B.S. from HCM City University of Technology (2008). His research explores privacy-preserving machine learning and computational health analytics: Federated learning systems and optimization Privacy-enhancing technologies (differential privacy) Health informatics and social media analysis Cybersecurity defenses and adversarial learning Fair and ethical AI systems Dr. Phan's publications demonstrate strong emphasis on federated learning architectures with privacy guarantees, defenses against emerging security threats, and analysis of health-related behaviors through social media. Recent work focuses on IoT applications, large language model security, and mobile federated learning ecosystems. His research integrates techniques from distributed systems, cryptography, and machine learning. No scientific awards are mentioned in available sources. Information regarding student advising, research grants, or laboratory affiliations is not provided in available documentation.
Jeff Zhang is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University. He joined ASU in January 2023 after completing a postdoctoral fellowship at Harvard University. His research spans deep learning, computer architecture, embedded systems, and EDA, with particular emphasis on energy-efficient and fault-tolerant design for AI/ML systems and hardware accelerators. Education: Ph.D., New York University M.Eng., B.Eng., Hunan University Dr. Zhang's research bridges theoretical machine learning with practical hardware implementation, developing novel architectures that optimize performance, power consumption, and reliability. He has pioneered approaches for hardware acceleration of large language models, efficient sparse matrix operations, and novel memory technologies for AI workloads. His work has received multiple awards including IEEE Top Picks in Test and Reliability (2023) and IEEE Micro Best Paper Award (2022). His recent publications demonstrate a strong trend toward heterogeneous computing, with significant work in chiplet-based AI accelerators, photonic computing for AI, and 2.5D/3D integration techniques. The research spans from high-level compiler frameworks to circuit-level innovations, with a consistent theme of co-designing algorithms and hardware for optimal AI performance. His work on the SODA toolchain has been particularly influential in bridging Python to silicon. Scientific Awards: IEEE Top Picks in Test and Reliability, IEEE ITC, 2023 Best Paper Award, IEEE Micro, 2022 Best Paper Award Candidate, IEEE DATE, 2022 Best Presentation Award Nomination, ACM SIGDA DATE PhD Forum, 2020 Best Paper Award Nomination, IEEE VLSI Test Symposium, 2018 Ernst Weber Ph.D. Fellowship, New York University, 2015, 2016 Dr. Zhang actively mentors a diverse group of graduate and undergraduate students, with several alumni now working at leading technology companies including Apple, TSMC, and Ansys. His research is supported by prestigious grants from NSF, Sandia National Labs, and industry partners. He serves on technical program committees of numerous top conferences and has organized special sessions on emerging topics like Gen AI for Chip Design and LLM-Aided Design. Dr. Zhang leads a vibrant research group that collaborates extensively with industry partners and national laboratories. Current projects focus on next-generation AI hardware, including chiplet-based systems, photonic accelerators, and novel memory technologies for large language models. His group has developed several open-source tools and frameworks, including the SODA toolchain for bridging Python to silicon.
