Jordan Theriault is an Assistant Professor at Northeastern University, affiliated with both the Biology and Psychology departments. His research focuses on understanding the brain as a self-regulating system, particularly exploring brain-based metabolic costs of information encoding and their implications for mental health and neuroimaging interpretation. He employs advanced neuroimaging technologies like 7 Tesla MRI and simultaneous PET/MR imaging to study brain metabolism and functional changes in critical regions like the brainstem and hypothalamus. Dr. Theriault’s theoretical work emphasizes the brain’s role in predicting sensory input and regulating bodily states, proposing that predictable environments enhance metabolic efficiency. This framework has implications for mental/physical health and social dynamics, such as conformity and social pressure. His lab, the Interdisciplinary Affective Sciences Laboratory, integrates neuroscience, psychology, and philosophy to address complex questions about emotion, morality, and interoceptive control. Key technical innovations include high-resolution imaging techniques and methodological critiques of brain-behavior relationships. His work bridges basic research with applied insights into human behavior, leveraging interdisciplinary approaches to advance understanding of neural systems and their societal relevance.
Dr Paul C. Bell is a Research Fellow in Computer Science currently affiliated with Liverpool John Moores University since 2017, maintaining a continuing Visiting Fellow position at Loughborough University where he previously served as a lecturer from 2011 to 2017. His academic foundation includes postdoctoral research at Turku University (Finland), Universite catholique de Louvain (Belgium), and Liverpool University following doctoral studies. His educational background comprises: BSc, University of Liverpool PhD, University of Liverpool Dr Bell specializes in Theoretical Computer Science and Formal Language Theory, investigating the boundaries of tractability and computability for reachability problems across mathematical models including matrix semigroups, hybrid systems, and probabilistic/quantum automata. His interdisciplinary work bridges Computer Science, Physics, and Mathematics through encodings of optimization problems for adiabatic quantum computers, alongside contributions to energy-efficient computing via speed-scaling algorithms for multi-processor systems. With over twenty publications in leading international venues, he actively serves as a reviewer and program committee member for conferences such as Reachability Problems 2017.
Christian Timmerer is a Professor at the Institute of Information Technology, Alpen-Adria-Universität Klagenfurt. His research focuses on adaptive video streaming , energy efficiency , MPEG standardization , and quality of experience (QoE) , with significant contributions to HTTP Adaptive Streaming (HAS), multi-codec optimization, and immersive media systems. Email: christian.timmerer@aau.at Office Hours: Monday 3:00-4:00 PM (by appointment) Projects: CD-Labor ATHENA, GAIA, SPIRIT His research integrates machine learning and generative AI to enhance video encoding, super-resolution, and voice dubbing, while prioritizing sustainability through energy-aware algorithms and open-source tools like GREEM and VEED. Current work emphasizes latency reduction and dynamic bitrate adaptation in live streaming environments. Recent publications address VVC optimization , multi-resolution encoding , and perceptual quality modeling , reflecting interdisciplinary efforts in networking , computer vision , and human-computer interaction . Awards include leading funded projects on adaptive streaming and green video systems.
Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.
Ashish Khisti is an Associate Professor at the University of Toronto's Department of Electrical and Computer Engineering (ECE), where he directs the Signals, Multimedia and Algorithms Laboratory (SMA Lab). He holds the Canada Research Chair (Tier II) and maintains affiliations with the Vector Institute for Artificial Intelligence. His research bridges communication systems, information-theoretic security, and machine learning, with a focus on real-time streaming and privacy-preserving algorithms. Research Trends: Recent publications emphasize streaming codes for latency-sensitive networks , machine learning-driven compression , and privacy mechanisms in federated learning . Scientific Recognition: Canada Research Chair (Tier II), 2012 and 2017 renewal Cisco Research Center Award, 2017 Ontario Early Researcher Award, 2012 Best Paper at NeurIPS 2021 Deep Generative Models Workshop Academic Contributions: Supervised PhD students Ahmed Badr, Farrokh Etezadi, and Si-Hyeon Lee. Served as Associate Editor for IEEE Transactions on Communications (2012-2015) and IEEE Transactions on Information Theory (2015-2018). Labs & Collaborations: Leads the Signals, Multimedia and Algorithms Laboratory, collaborating with institutions like KAUST, Texas A&M University (Qatar), and the Vector Institute. Organized workshops at BIRS and IEEE conferences.
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.
Kia Bazargan is an Associate Professor and the Leroy and Ruth Fingerson Co-op Professor at the University of Minnesota, College of Science and Engineering. He currently serves as Director of the Co-op Program and focuses on VLSI-CAD, FPGA physical design, and hybrid binary-unary computing. University: University of Minnesota School: College of Science and Engineering Department: Electrical and Computer Engineering His research emphasizes stochastic computing and unary computing, where numbers are encoded as streams of bits. He explores techniques to reduce hardware costs while maintaining efficiency, particularly for edge computing and neural network applications. Recent publications highlight his work on hybrid binary-unary computing, FPGA-based inference acceleration, and lossless compression of lookup tables. Grants from Cisco Systems and the National Science Foundation support his projects. Scientific Awards: PFI-TT Grant (2020-2024): Commercializing hybrid computing for modern applications Uniqomp NSF Grant (2020-2021) EAGER Grant (2015): Studying complex dynamical systems His lab (4-162 EE/CSci) investigates scalable computing paradigms to bridge the gap between ASICs and FPGAs in performance and energy efficiency.
Robert Pollice is a Lecturer at the Faculty of Science and Engineering , University of Groningen , specializing in Homogeneous Catalysis . His research integrates computational chemistry , machine learning , and automated experimentation to accelerate molecular design and catalyst development . Research Interests focus on homogeneous catalysis , quantum chemistry , and machine learning applications. His work addresses challenges in reaction mechanism modeling , noncovalent interactions , and inverse molecular design , leveraging closed-loop optimization and large language models for chemical data analysis . Publications span quantum chemical simulations , solvation energy calculations , excited state engineering , and automated catalyst discovery . His recent studies explore inverted singlet-triplet gaps , machine learning for reaction modeling , and SELFIES for molecular string representations . Peer-review Contributions include evaluations for journals like Organic Process Research & Development , Materials Advances , and Chem , reflecting his expertise in catalysis , quantum chemistry , and AI-driven chemical discovery .
Prof. Floris de Lange is a Professor at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, and holds a part-time W3-Professorship in Cognitive Computational Neuroscience at the University of Bonn. His research focuses on understanding how top-down factors like goals, attention, expectations, and prior knowledge shape perception, cognition, and decision-making. He uses behavioral and neuroimaging techniques (MEG, fMRI, TMS) to study these processes in healthy and pathological brains. Key research themes include predictive perception, attention, and the neural mechanisms underlying decision-making. His work has been supported by prestigious grants such as the Vici and ERC Consolidator Grants. He teaches courses on Attention and Prediction, Cognitive Control, and Neurophysiology of Cognition and Behaviour. Education: Not explicitly stated in the provided text. Awards: Vici Grant (NWO), ERC Consolidator Grant, Ammodo Science Award, and others. Labs/Teams: Leads the Predictive Perception and Cognition group within the Donders Institute. His research highlights the brain’s predictive nature, demonstrating how expectations modulate sensory processing in early visual cortex and influence decision-making. He collaborates internationally, including an adversarial testing project on theories of consciousness.
Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.
Nuno Pereira Lopes is an Associate Professor at Instituto Superior Técnico , part of Universidade de Lisboa , and a researcher at INESC-ID . He also serves as an advisor at FuriosaAI , focusing on tensor contraction processors for AI workloads. Research Interests : Compilers, formal verification of LLVM optimizations, machine learning frameworks, undefined behavior exploitation, probabilistic model checking, blockchain security, and many-core code generation. Teaching : Compilers and Computer/Informatics Engineering projects. Funding : Supported by Google, Matter Labs, NLnet, Oracle, PRACE, RNCA, and Woven by Toyota. Recent Publications focus on LLVM backend validation , PyTorch pipeline parallelism , C++ dynamic cast optimization , undefined behavior in C/C++ , and AI tensor processors . His work bridges compiler design, formal methods, and AI hardware. Academic Service includes representing Portugal in ISO/IEC JTC 1/SC 22 (C++), organizing FLoC'26 , and serving on program committees for PLDI, EuroLLVM, and CGO.
Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.
Aleksandar Jevremović is a Full Professor at the Faculty of Informatics and Computing, Singidunum University (Belgrade, Serbia), and holds multiple academic and professional roles. He is the Serbian representative at the UNESCO IFIP Technical Committee on Human-Computer Interaction since 2018. He has served as Vice-Dean of his faculty (2015–2018) and held visiting professorships at institutions like Ss. Cyril and Methodius University (North Macedonia) and Tallinn University (Estonia). His research focuses on cybersecurity, IoT, AI, and e-learning innovation. Education and Affiliations: External Researcher at the Mathematical Institute of the Serbian Academy of Sciences and Arts Visiting Scholar at Cyprus Interaction Lab (Cyprus University of Technology) Alumni/Postdoc Researcher at Tallinn University's HCI Group Member of IEEE and the Informatics Association of Serbia Research Interests: Jevremović’s work spans cybersecurity (e.g., intrusion detection, secure IoT protocols), human-computer interaction (HCI), AI-driven education tools, and neurotechnological applications like EEG-based assessment systems. He emphasizes practical solutions for digital safety, such as children’s online protection and cryptographic key generation from biometric data. Grants and Projects: Member of the External Advisory Committee for the EU-funded ONTOCHAIN project (2022–2023) Mentor for training schools like AAPELE Training School and NET4Age-Friendly initiatives Trainer in IoT, cybersecurity, and health promotion programs across Europe Labs and Teams: He collaborates with interdisciplinary teams on projects like CASPER (Children Agents for Secure and Privacy Enhanced Reaction) and led the development of WIDE, a collaborative web development education platform.
Prof. Dr.-Ing. Martin Hoffmann is a Professor of Microsystems Technology at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum. His academic career began at the University of Dortmund, where he earned his doctorate in high-frequency technology and later habilitated in microsystems technology (2003). He held roles as a private lecturer and industry researcher before becoming a university professor at TU Ilmenau (2006). He joined Ruhr University in 2017, specializing in cutting-edge microsystems research. His research focuses on MEMS, THz technology, microactuators, and nanoimprint lithography. Key projects include cooperative microactuator systems, THz biosensors, and energy-autonomous sensors. He collaborates with institutions like TU Ilmenau, Purdue University, and Nagoya University through international programs like Double Degree and Erasmus. His work spans academic advising, grants, and industry partnerships (e.g., HL Planartechnik GmbH, Silicon Manufacturing Itzehoe GmbH). Notable contributions include silicon grass nanostructuring, palladium-based gas sensors, and wafer-scale MoS₂ deposition. His lab develops micromechanical systems for biomedical, environmental, and defense applications.
Dr. Yao Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University, New Brunswick, since Fall 2021. Previously, she held an Associate Professor (tenured) position at Binghamton University, SUNY. Her research focuses on immersive streaming technologies, including 360-degree and volumetric video delivery, edge/cloud computing, and distributed systems. She has led projects such as SGSS for 6-DoF navigation in 3DGS scenes and EVASR for edge-based video enhancement. Her work has been recognized with awards like the NSF CAREER Award and Best Paper Awards at MMSys (2017, 2020). Research interests include immersive video streaming, virtual/augmented reality, mobile systems, and network optimization. Notable contributions include the 👁️NavGS dataset for VR navigation and the Dynamic 6-DoF Volumetric Video toolkit. She advises PhD students like Mufeng Zhu and Na Li, with past advisees receiving accolades such as the Binghamton Distinguished Dissertation Award. Publications span conferences like ACM Multimedia Systems (MMSys), IEEE ICME, and AAAI. Her work emphasizes practical solutions for bandwidth efficiency, real-time streaming, and energy optimization in immersive media. Grants include NSF CAREER funding for immersive streaming research. Labs and collaborations involve open-source projects hosted on GitHub (e.g., symmru repositories), emphasizing reproducibility and accessibility.
