Find Academic Supervisors

• Storage Systems
• Persistent Memory
• Key-Value Stores
• +4 more

Oana Balmau is an Assistant Professor in the School of Computer Science at McGill University, where she leads the Data-Intensive Storage and Computer Systems Laboratory (DISCS Lab). She also holds a status-only appointment at the University of Toronto and serves as a working group chair for MLPerf Storage. Her research focuses on creating storage infrastructure that enables fast and energy-conscious insights from data, with particular emphasis on storage and persistent memory technologies for machine learning, data science, and edge computing workloads. Dr. Balmau's research interests span computer systems, with specific focus on: Design and implementation of efficient key-value stores Storage systems for machine learning workloads Edge computing infrastructure Persistent memory technologies Performance optimization of data-intensive systems Her recent work has led to significant contributions in storage benchmarking through the MLPerf Storage benchmark and in edge computing frameworks. The MLPerf Storage benchmark has become an industry standard for evaluating storage performance in machine learning environments, while her work on hierarchical edge computing addresses security and performance challenges in distributed edge environments. Her publications show consistent high-impact contributions to top systems venues, with recent work focusing on processing-in-memory virtualization, stream processing reconfiguration, and efficient data preprocessing pipelines. Dr. Balmau has received numerous awards for her research, including: SEC 2024 Best Paper Award for "Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge" MLCommons Hero Award 2023 for leadership as MLPerf Storage working group chair ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award 2021 Honorable Mention CORE John Makepeace Bennett Award 2021 for the best Computer Science doctoral dissertation in Australia and New Zealand USENIX ATC 2019 Best Paper Award for "SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores" As an educator, Dr. Balmau teaches courses on advanced computer systems, operating systems, and principles of computer systems design at McGill University. She has served on program committees for top systems conferences including SOSP, SIGMOD, FAST, and EuroSys, and has co-organized workshops on resource-efficient machine learning and edge computing. She leads the DISCS Lab, which focuses on two main research directions: Systems for ML (including the MLPerf Storage benchmark) and Edge computing (including frameworks for fast and secure edge computing in hierarchical edge environments).

Xilin LiuView profile

Assistant Professor

• Integrated Circuits
• Biomedical Engineering
• Brain-Machine Interfaces
• +5 more

Xilin Liu is an Assistant Professor at the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (University of Toronto) and the Center for Advancing Neurotechnological Innovation to Application (CRANIA) . He obtained his PhD from the University of Pennsylvania and previously worked at Qualcomm Inc. in California. Expertise in integrated circuits and systems for brain-machine interfaces , neuromodulation , and edge AI Published in top venues including Nature Electronics , IEEE JSSC , and ISSCC Recipient of multiple best paper awards and IEEE Senior Member His research spans three main themes: High-speed data converters for wireless/wireline communication IC design for neural interfacing Accelerating machine learning via hardware Recent publications focus on closed-loop neuromodulation , ultra-wideband transceivers , and flexible biomedical sensors . These works integrate analog IC design , edge AI , and real-time neural interfacing across medical rehabilitation , parkinson's monitoring , and memory research . Awards include: IEEE Solid-State Circuits Society Predoctoral Achievement Award (2016) Best Paper Award at BioCAS (2015) ECE Department Teaching Award (2022) Multiple conference best paper finalists His lab collaborates with UHN , EMBS , and global institutions while maintaining strong commitments to equity, diversity, and inclusion (EDI) in research practices.

• Integrated Circuits
• Mixed-Signal Computing
• Neural Recording Systems
• +1 more

Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.

Michael EconomoView profile

Assistant Professor

• Systems Neuroscience
• Motor Control
• Neural Circuits
• +3 more

Michael N. Economo, PhD, is an Assistant Professor in the Department of Biomedical Engineering at Boston University. His research focuses on neural circuits controlling movement, leveraging cutting-edge optical, electrophysiological, and genetic tools. He holds affiliations with Neuroscience & Neuroengineering and Photonics & Optical Systems programs. Education: PhD in Biomedical Engineering from Boston University, B.S. Biomedical Engineering and B.A. Mathematics from Duke University. Research Interests: Systems neuroscience, motor control, long-range neural circuits, computational neuroscience, neurotechnology. His lab investigates how neural circuits across brain regions coordinate movement using advanced techniques like optogenetics, in vivo imaging, and transcriptomics. Key Projects: Orofacial motor control, voltage imaging with TICO microscopy, neural circuit dissection. Technologies: Neuropixels probes, fluorescent voltage indicators, single-cell RNA sequencing. Notable Awards: NSF CAREER (2023), Scialog Fellow (2023), Whitehall Foundation Young Investigator Award (2021). Advising & Grants: Supervises graduate students (e.g., Munib Hasnain, Jackie Birnbaum) and postdocs (e.g., Vicky Moya, Yujin Han). Lab members focus on motor planning, neuromodulation, and neural dynamics. Labs/Teams: The Economo Lab collaborates on neurotechnology development and integrates interdisciplinary approaches from engineering and biology. Affiliated with BU’s Graduate Program in Neuroscience.

Hernan G. ReyView profile

Assistant Professor

• Anterior Temporal Lobectomy
• Drug Resistant Epilepsy
• Electroencephalography
• +6 more

Hernan G. Rey, PhD, is an Assistant Professor in the Department of Neurosurgery at the Medical College of Wisconsin (MCW) and the Marquette-MCW Joint Department of Biomedical Engineering. He previously held an Assistant Professor position at Baylor College of Medicine until July 2022. His research focuses on understanding human episodic memory, improving epilepsy diagnosis and treatment, and developing tools for electrophysiological data analysis. Rey's lab records single-neuron activity and intracranial EEG from epilepsy patients to investigate brain mechanisms underlying memory and neurophysiological processes. Education: PhD in Engineering, University of Buenos Aires (2009) Postdoctoral Fellowship in Biomedical Informatics, University of Leicester (2012–2015) Bachelor's in Electronics Engineering, University of Buenos Aires (2002) Research Interests: Dr. Rey explores anterior temporal lobectomy, drug-resistant epilepsy, electrophysiology, hippocampal function, machine learning applications in neuroscience, and signal processing. His work bridges clinical neurosurgery, biomedical engineering, and cognitive neuroscience to advance both fundamental understanding and clinical interventions. Publications: His recent work highlights studies on parietal cortex function in action monitoring, single-neuron responses in memory encoding, and neurophysiological correlates of depression. These reflect a focus on translational neuroscience and interdisciplinary collaboration. Awards: EPSRC Rising Star Award (2014) Labs/Teams: The ReyLab drives innovation in electrophysiological data acquisition and analysis, emphasizing clinical application for epilepsy and memory disorders.

• Medicinal Chemistry
• Chemical Biology
• Organic Synthesis
• +3 more

Wei Shi is an Associate Professor of Chemistry (Organic) at Ball State University’s College of Sciences and Humanities, Department of Chemistry. He joined the institution in 2019, specializing in teaching Organic Chemistry and conducting research in Medicinal Chemistry, Chemical Biology, and Organic Synthesis. His research focuses on designing chemical probes derived from natural products to understand small molecule regulation of macro-biomolecules for disease treatment. Education: B.Sc. in Electrochemistry from Shanghai Jiao Tong University (China), M.Sc. in Bioorganic Chemistry from East China University of Science & Technology (China), Ph.D. in Chemistry from the University of Alberta (Canada), and postdoctoral training at Johns Hopkins School of Medicine (USA). Research Interests: Explores the pharmaceutical potential of bioactive natural products, particularly glycoconjugates, aiming to design novel drug candidates with enhanced therapeutic profiles. Key projects include studying ipomoeassin F’s mechanism of action as an ER translocon inhibitor and developing itraconazole analogs for antiangiogenic and anticancer applications. Selected Publications: Over 20 peer-reviewed articles, including landmark studies on ipomoeassin F’s Sec61α binding, itraconazole’s dual targeting of NPC1/VDAC1, and structural analog design. Recent work highlights Sec61 translocon inhibition mechanisms in Mycobacterium ulcerans pathogenesis and ER stress response modulation in cancer cells. Labs/Teams: Director of the Shi Research Group , focusing on natural product-inspired drug discovery and chemical biology.

Dejan MarkovicView profile

Professor

• Implantable neuromodulation systems
• Domain-specific compute architectures
• Design methodologies
• +3 more

Dejan Markovic is a Professor of Electrical and Computer Engineering at the University of California, Los Angeles (UCLA), holding the Mukund Padmanabhan Term Chair in Electrical Engineering and serving as Area Director for Circuits and Embedded Systems. His research spans implantable neuromodulation systems, domain-specific compute architectures, and energy-efficient design methodologies for biomedical and embedded applications. Dr. Markovic earned his PhD (2006) and MS (2000) from UC Berkeley and BS (1998) from the University of Belgrade, Serbia. His research focuses on ultra-low-power integrated circuits for neural interfaces, including artifact-free stimulation/sensing platforms and neural recording front-ends, alongside domain-specific architectures for sparse linear algebra in IoT and mobile computing. Recent work demonstrates strong trends in medical neuromodulation and energy-efficient VLSI for biomedical signal processing and wireless communications. His scientific contributions have been recognized with prestigious awards including IEEE Fellow (2021), ISSCC Lewis Award (2014), ISSCC Jack Raper Award (2010), and NSF CAREER Award (2009). Additional honors include UC Berkeley's David J. Sakrison Memorial Prize (2007) and multiple best paper awards. Dr. Markovic co-founded semiconductor IP startup Flex Logix Technologies (2014) and leads the DMGroup research lab at UCLA. He teaches core courses including ECE 115C (Digital Integrated Circuits), ECE M216A (VLSI Design), and BME M260 (Neuroengineering), mentoring students in cutting-edge VLSI design for medical and communication applications. The DMGroup lab specializes in developing implantable neuromodulation systems, domain-specific compute engines, and novel design methodologies, with recent projects featured in UCLA's coverage of brain-computer interface technology and Anari AI's $2M funding for personalized AI hardware.

Valerie VenturaView profile

Professor

• Computational Neuroscience
• Time Series Forecasting
• Graphical Models
• +6 more

Valerie Ventura is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She serves as Co-Director of the Ph.D. Program and maintains dual affiliations as affiliated faculty in the Machine Learning Department and the Center for the Neural Basis of Cognition (CNBC). As a graduate advisor for the Program in Neural Computation (PNC), she bridges statistical methodology with neuroscience applications. Her research focuses on computational neuroscience, particularly time series forecasting, networks, algorithms, and nonparametric methods. Key research areas include computational neuroscience, biostatistics & epidemiology, graphical models & networks, and DELPHI lab projects. Her work integrates advanced statistical techniques to solve complex problems in neural data analysis, motor control, and neural decoding. Analysis of her publication record reveals consistent contributions to neural decoding methodologies, spike train analysis, and cortical covariance modeling. Her recent work emphasizes practical applications in brain-machine interfaces and neural prosthetics, with a strong focus on improving decoding accuracy through novel statistical approaches to spike sorting and waveform analysis. The integration of machine learning with traditional statistical methods characterizes her interdisciplinary approach. Scientific Awards: 2006 Canadian Journal of Statistics Best Paper Award As an educator, Ventura serves as Co-Director of the Ph.D. Program in Statistics and Data Science while advising students through the CNBC's Program in Neural Computation. Her collaborations span multiple departments and research centers, particularly through the DELPHI Lab Group which focuses on computational neuroscience applications. Current research directions suggest continued innovation in neural decoding algorithms and applications to motor control systems.

Tim C. LeiView profile

Associate Professor

• Biomedical Engineering
• Neuroscience
• Electronic Systems
• +3 more

Tim C. Lei is an Associate Professor of Electrical Engineering at the College of Engineering, Design and Computing, University of Colorado Denver. He serves as the Principal Investigator of the Laboratory for Electronic and Neuromorphic Systems (LENS), focusing on developing novel electronic and optical systems for neuroscience research and neural disorder treatments. Research Interests: Closed-loop neural control system development and miniaturization Real-time spike sorting algorithm development Brain stereotaxic system for small animals Neural circuit modeling using spiking neural networks Optogenetic and electrical neural stimulation Brain-machine interface His lab is equipped for neural surgeries, behavioral studies, and development of biomedical instrumentation. Past work includes nonlinear microscopy techniques for kidney cell studies and non-invasive optical imaging for eye disease detection.

• Biomedical Engineering
• Nanotechnology
• Microelectronics
• +5 more

Professor Omid Kavehei is a Professor of Intelligent Microsystems in the Faculty of Engineering at the University of Sydney, serving as Deputy Head of the School of Biomedical Engineering. Previously, he held roles as a Research Fellow at the University of Melbourne and Lecturer at RMIT University. His research focuses on biomedical microsystems, nanotechnology, and brain-inspired hardware for healthcare applications, particularly epilepsy monitoring and seizure prediction. Education: PhD (specific institution not explicitly stated) His work bridges nanoelectronics and healthcare, aiming to develop low-power, brain-inspired devices for sensory perception and medical diagnostics. Key interests include neuromorphic engineering, wearable sensors, and AI-driven medical systems. Awards include the Ramaciotti Biomedical Research Award (2021), Microsoft AI for Accessibility Grant (2019), and multiple teaching/research excellence awards from the University of Sydney. His recent work explores neuromorphic cytometry for cell analysis, edge AI for ECG/EEG diagnostics, and closed-loop neurostimulation systems. Collaborations include the Sydney Nano Institute, Brain and Mind Centre, and industry partners like Microsoft. Current research students are advancing topics like bio-inspired algorithms for seizure detection, hardware-friendly machine learning models, and flexible sensor systems for aquatic environments. Grants include funding for neurophysiology platforms, quantum sensors, and semiconductor design. Labs/teams: Involved in the Centre for Drug Discovery Innovation and collaborations across engineering, neuroscience, and clinical domains.

Lauren O'ConnellView profile

Associate Professor

• Evolutionary Neuroscience
• Physiology
• Chemical Ecology
• +4 more

Lauren O'Connell is an Associate Professor in the Department of Biology at Stanford University, where she investigates how genetic and environmental factors contribute to biological diversity and adaptation. Her research focuses on the neural basis of parent-offspring communication, spatial cognition evolution, and toxin sequestration mechanisms in amphibians. She earned her Ph.D. in Cellular and Molecular Biology from the University of Texas at Austin (2011) B.S. in Biological Sciences from Cornell University (2006) A.A. in Natural Sciences from Tarrant County Community College (2004) Research interests span evolutionary neuroscience and physiology, particularly exploring: Neural mechanisms of parent-offspring communication in poison frogs Spatial cognition evolution in amphibians Physiological adaptations for chemical defense acquisition Vertebrate pair bonding mechanisms Her work integrates field studies in Ecuador, Madagascar, and Australia with laboratory experiments on brain function and toxin metabolism. Recent publications highlight innovations in Amphibian electrophysiology methods Microbiome interactions with alkaloids Neural correlates of social behavior Chemical defense ecology Scientific honors include 2023 Camille Dreyfus Teacher-Scholar Award 2018 Frank A. Beach Award 2015 For Women in Science Fellowship 2013 Capranica Prize 2012 Young Investigator Award 2011 Early Career Award She mentors a diverse team across multiple projects, emphasizing inclusivity and equity in science training programs. Her lab supports community college students through NSF-funded initiatives and engages in international collaborations with institutions like Flinders University and Centro Jambatu.

Yan QinView profile

Associate Professor

• Neuroscience
• Cell Biology
• Biophysics
• +3 more

Yan Qin is an Associate Professor in the Department of Biological Sciences at the University of Denver. She holds a Ph.D. in Neuroscience from Ohio University (2008) and completed postdoctoral research at the University of Colorado Boulder (2009–2015), specializing in bio-imaging. Her research focuses on the role of zinc in the brain, particularly its effects on axonal transport, neurodegenerative mechanisms, and neuronal signaling pathways. She develops innovative optical sensors and genetic tools to study zinc dynamics in cellular compartments like mitochondria, lysosomes, and the cytosol. Dr. Qin’s work intersects biophysics, molecular biology, and neuroscience, with contributions to understanding zinc’s role in neurodegenerative diseases such as Alzheimer’s and Parkinson’s. Her methodologies include advanced microscopy, microfluidic systems, and genetically encoded biosensors. Key areas of exploration include zinc’s effects on microtubules, synaptic transmission, and mitochondrial function. Her professional affiliations include the American Chemical Society. She has published extensively on topics like zinc-induced axonal transport inhibition, TRPML1-mediated signals, and α-synuclein aggregation. While no awards are explicitly listed, her research has addressed critical gaps in understanding metal ion dynamics in neurological contexts.

Majid ZamaniView profile

Lecturer

• Biomedical Engineering
• Artificial Intelligence
• Neuroscience
• +3 more

Dr Majid Zamani is a Lecturer in the School of Electronics & Computer Science at the University of Southampton. His research focuses on implantable and wearable biomedical devices, applied AI in biomedical engineering, and hardware-efficient processing frameworks. Member of: Digital Health and Biomedical Engineering Institute for Life Sciences Member of: Centre for Internet of Things and Pervasive Systems Member of: Centre for Health Technologies Member of: UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) Current research addresses key challenges in scalability, signal processing, sensing, energy efficiency, and miniaturization for next-generation implantable brain-machine interfaces (iBMI). He explores hardware-efficient computational platforms for biomedical applications, including AI-driven algorithms for neural signal/image processing, augmented navigation in constrained anatomical spaces, and low-power real-time processors in 180/90/45 nm CMOS technologies. Recent publications focus on deep learning for spike sorting, binarized neural networks, noise-aware speech enhancement, and AI integration in surgical navigation. His work bridges biomedical engineering with AI and hardware optimization. Teaching: Digital system design (ELEC6236) 42+ publications in journals like IEEE Transactions on Medical Imaging, Journal of Neural Engineering, and IEEE Access

Francesca LanzariniView profile

Research Fellow

• Neuroethology
• Neural Decoding
• Motor Control
• +3 more

Francesca Lanzarini is a Research Fellow at Goethe University Frankfurt, affiliated with the Laurens Lab. Her work focuses on neuroethology and neural mechanisms underlying motor control and decision-making in primates. She investigates natural behaviors such as foraging and reach-and-grasp movements, leveraging computational methods like clustering algorithms and unsupervised learning to decode neural activity patterns. Her research integrates experimental neuroscience with machine learning to understand how premotor cortex activity translates into complex actions. She has contributed to studies on aging effects in decision-making processes in primates and developed methodologies for behavioral analysis. Education details are not explicitly provided in the text. Her affiliations include the Laurens Lab, located at Deutschordenstr. 46, Frankfurt am Main, Germany. She can be reached at francesca.lanzarini@esi-frankfurt.de . Research interests span neuroethology, neural decoding, motor control, and primate cognition. Her recent work (2023-2025) emphasizes real-time decoding of premotor activity and the dynamics of naturalistic behaviors in freely moving monkeys. She has also explored methodological tools for online research and generative vocabulary testing. No scientific awards or grants are mentioned in the provided text. Francesca collaborates within the Laurens Lab team, which includes researchers like Jean Laurens, Farzad Ziaie Nezhad, and Deepak Surendran, and is managed by Sogand Ghiasi.

Vincent HokView profile

Lecturer

• Neuroscience
• Cognitive Science
• Spatial Cognition
• +5 more

Vincent Hok is a Lecturer at the Laboratoire de Neurosciences Cognitives (LNC) at Aix Marseille Université, where he conducts research in cognitive neuroscience with a focus on spatial cognition and hippocampal function. His work primarily investigates how neural systems support spatial navigation, memory formation, and cognitive mapping in rodent models. Dr. Hok's research interests center on spatial cognition, hippocampal function, and neural coding mechanisms. His work explores how place cells and head-direction cells in the hippocampus and related brain structures represent spatial information, how these representations change during learning and memory tasks, and how stress and aging affect these neural systems. He employs sophisticated electrophysiological recording techniques to examine neural activity patterns during spatial navigation tasks, contributing significantly to our understanding of how the brain creates cognitive maps of the environment. His publication record shows consistent research output since 2004, with a focus on spatial navigation systems, hippocampal place cells, and the neural basis of memory. Dr. Hok's collaborative work spans multiple institutions and demonstrates expertise in both experimental neuroscience and computational analysis of neural data. His research has appeared in high-impact journals including Journal of Neuroscience, European Journal of Neuroscience, and Progress in Neurobiology. Dr. Hok has maintained a productive research program through collaborations with prominent researchers in the field including Bruno Poucet, Etienne Save, and Shane O'Mara. His work bridges experimental neuroscience with computational approaches to understand fundamental mechanisms of spatial cognition and memory.