Giuseppe PAGNONI is an Associate Professor at the Department of Biomedical, Metabolic and Neurosciences of Università di Modena e Reggio Emilia . His research spans neuroscience , computational modeling , meditation , and brain network analysis . He teaches courses such as Neural Systems Modeling for Bioengineering students and Human Physiology and Pathophysiology in Medicine programs, with a focus on predictive coding , default mode network , and emotional valence processing . Pagnoni has contributed to ENIGMA-Meditation , a global initiative exploring meditation's neuroscientific basis, and developed sparse Bayesian models for analyzing brain network individual differences. His work on Alzheimer's disease investigates anosognosia mechanisms through resting-state fMRI, while other studies examine ghrelin's role in obesity-related reward processing and oxytocin's effects on social anxiety . He employs dynamic causal modeling and predictive processing frameworks to study cognitive effort, mind wandering, and neuromorphic learning. His methodological expertise includes functional MRI , independent component analysis , and computational simulations .
Pejman Lotfi-Kamran is an Associate Professor at the School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, where he also serves as the head of the school and director of Turin Cloud Services. His research focuses on computer architecture, systems, approximate computing, and cloud computing, with an emphasis on performance and energy efficiency for big-data applications. His educational background includes: Ph.D. in Computer Science, EPFL (2013) M.Sc. in Electrical and Computer Engineering, University of Tehran (2005) B.Sc. in Electrical and Computer Engineering, University of Tehran (2002) Lotfi-Kamran's research spans computer architecture innovations, including data and instruction prefetching, networks-on-chip, coherence protocols, and many-core processor design. He has pioneered work on scale-out processors, neural acceleration for GPUs, and approximate computing frameworks. His publications appear in top venues such as ISCA, HPCA, MICRO, and IEEE/ACM journals. His recent articles reflect a strong trend in improving system performance through intelligent prefetching, efficient NoC designs, and energy-aware architectures. Key themes include reducing frontend bottlenecks, optimizing cache behavior, and enhancing data delivery in large-scale systems. His work often combines cross-stack insights with hardware-software co-design for real-world impact. Scientific awards and recognitions include: 2017 CADS Best Paper Award 2016 Young Faculty Award from Iran's National Elites Foundation 2012-2013 Intel Ph.D. Fellowship 2012 and 2011 HiPEAC Paper Awards 2011 HPCA Best Student Paper Finalist Multiple academic honors from University of Tehran He has advised several graduate students including Paria Darbani, Ali Ansari, Mohammad Bakhshalipour, and Farid Samandi, many of whom have co-authored significant papers. His teaching spans institutions like Sharif University of Technology, Iran University of Science and Technology, and EPFL, covering advanced computer architecture and multiprocessor systems. He has led research projects such as AxBench and CloudSuite on Simics, and contributed to national initiatives like Iran’s National Grid. He is actively involved in tool development and continues to shape research in next-generation computing systems. He leads the Turin Cloud Services initiative at IPM and is deeply engaged in both theoretical and applied aspects of computer systems research, with ongoing work in neural acceleration, approximate computing, and scalable architectures.
Glenn Gulak is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. He holds the Canada Research Chair in Signal Processing Microsystems and the Edward S. Rogers Sr. Chair in Engineering. A Senior IEEE Member and Professional Engineer in Ontario, he received his Ph.D. from the University of Manitoba. His research spans: Digital Communication Systems: VLSI implementations of MIMO detectors, lattice reduction algorithms, and homomorphic encryption accelerators Lab-on-Chip Microsystems: CMOS biosensors for rapid pathogen detection and integrated fluorescence imaging Recent publications (2019-2025) demonstrate a dominant focus on privacy-enhancing technologies, with 73% concentrated in cryptographic hardware and homomorphic encryption. This reflects industry-aligned work on confidential computing and secure data processing. Awards & Honors: IEEE Millennium Medal (2001) Canada Research Chair in Signal Processing Systems (Tier 1, 2005-2012) Edward S. Rogers Sr. Chair (2005-2010) RBC Research Prize L. Lau Chair (1999-2004) Teaching Award (1999) He has supervised 44+ graduate students (PhD/MASc) with thesis topics spanning VLSI communication systems, CMOS biosensors, and cryptographic accelerators. Notable industry collaboration includes serving as CTO of a semiconductor startup (2001-2003). His lab develops hardware for quantum cryptography and secure medical computation.
Justine Cléry, PhD, serves as an Assistant Professor in the Department of Neurology and Neurosurgery at McGill University's Faculty of Medicine and Health Sciences. She leads the Sensory and Social Brain Mechanisms (SSBM) Lab at The Neuro (Montreal Neurological Institute) and contributes to autism research through the Azrieli Centre for Autism Research (ACAR). Her work bridges fundamental neuroscience with clinical applications in neurodevelopmental disorders. Education: Doctorate in Neurosciences, University of Claude Bernard Lyon 1 (2017), supervised by Prof. Suliann Ben Hamed at Institut des Sciences Cognitives Postdoctoral Associate, Robarts Research Institute, University of Western Ontario (2017-2021), with Prof. Stefan Everling Research Interests: Dr. Cléry pioneers fMRI studies in awake non-human primates to decode neural mechanisms of social cognition and sensory processing . Her work examines how the brain represents peripersonal space, processes looming visual stimuli, integrates multisensory cues, and constructs social interaction networks. This research employs marmoset models to investigate fundamental questions with implications for autism spectrum disorder and neurodevelopmental conditions, uniquely combining cognitive neuroscience and advanced neuroimaging . Publication Trends: Her 2015-2021 publications reveal a cohesive trajectory using ultra-high field fMRI to map brain networks in marmosets. Key themes include social interaction observation systems, somatosensory/visual processing hierarchies, and spatial representation mechanisms. This body of work establishes critical methodological frameworks for awake primate neuroimaging while advancing understanding of neural substrates underlying social and sensory functions. Scientific Awards: BrainsCAN Postdoctoral Fellowship Tier II (2017-2021) BrainsCAN Postdoctoral Fellowship Tier I (2021-2022) CIHR Postdoctoral Fellowship (2021-2022) Berthe-Fouassier Foundation Fellowship (2016-2017) Advising and Grants: Dr. Cléry has secured competitive postdoctoral fellowships from BrainsCAN, CIHR, and Berthe-Fouassier Foundation. As SSBM Lab director, she mentors graduate researchers in neuroimaging techniques and primate neuroscience. Her work leverages institutional resources at The Neuro and collaborative frameworks through ACAR, with funding supporting cutting-edge fMRI capabilities for awake animal studies. Labs and Teams: The Sensory and Social Brain Mechanisms (SSBM) Lab develops innovative fMRI paradigms for awake marmosets to investigate social and sensory brain functions. Dr. Cléry actively contributes to the Azrieli Centre for Autism Research (ACAR), participating in interdisciplinary initiatives that connect basic neuroscience with autism spectrum disorder research through The Neuro's open science framework.
Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.
Xia Ning is a Professor jointly appointed in the Department of Computer Science and Engineering , the Division of Medicinal Chemistry and Pharmacognosy (College of Pharmacy), and the Department of Biomedical Informatics at The Ohio State University . She also holds affiliation with the Translational Data Analytics Institute at OSU. Education: Ph.D. in Computer Science & Engineering, University of Minnesota, Twin Cities (2012) M.S. in Computer Science, University of Minnesota, Twin Cities M.S. in Statistics, University of Minnesota, Twin Cities B.S. in Computer Science, Chu Kechen Honors College, Zhejiang University, China Research Focus : The Ning Lab pioneers data-driven Artificial Intelligence, Machine Learning, and Big-Data analytics with targeted applications in drug discovery, medical informatics, health informatics, and e-commerce . Recent thrusts include generative AI for molecule design, graph neural networks for retrosynthesis, large-language models specialized for chemistry (LlaSMol) and e-commerce (eCeLLM), and reinforcement-learning frameworks for precision-medicine drug selection. The lab’s methodologies are intentionally generalizable, enabling spill-over benefits to domains such as social networks and system monitoring. Publication Trends : Over the past four years Professor Ning has released a steady stream of high-impact articles spanning retrosynthesis planning, LLM instruction tuning for scientific domains, reinforcement learning for drug discovery, and COVID-19 health-analytics . These works repeatedly integrate cutting-edge AI techniques (deep RL, graph Transformers, large-scale instruction datasets) with rigorous experimental validation in chemistry and biomedicine. Scientific Awards & Honors : Sanofi iDEA-TECH Award (2024) 10-Year Highest-Impact Award, International Conference on Data Mining (ICDM, 2020) Grants & Collaborations : Funding includes the Sanofi iDEA-TECH Award and collaborative grants with Amazon Web Services for COVID-19 knowledge graphs. Her open-source datasets (ECInstruct, SMolInstruct, CTKG) and models (G2Retro, LlaSMol, eCeLLM) are publicly released on HuggingFace and GitHub, fostering broad academic and industrial adoption. Labs & Teams : Professor Ning heads the Ning Lab at OSU, a multidisciplinary team focusing on AI/ML methodology and translational applications in health and medicine. The lab actively releases code and interactive web portals to accompany each major publication.
Dr. Ray Drainville is a lecturer at the University of Waterloo, specializing in digital media and visual culture. His research explores the intersection of iconography, social media image analysis, and machine learning applications. With nearly two decades of industry experience in web development and graphic design, he brings practical insights to his teaching on contemporary digital visual culture. PhD in Visual Culture from Manchester School of Art, Manchester Metropolitan University (2018) MA in Information Studies from University of Sheffield (1996) MA in History of Art from Princeton University (1995) BA in History of Art from New College (1992) Dr. Drainville's research interests include digital media, visual culture, iconography, media theory, and hermeneutics. His work increasingly examines linguistic and visual dogwhistles in political contexts, memetic superposition, and algorithmic iconography. He has published in journals such as AI & Society , Studies In Communication Sciences , and Hyperallergic , often combining historical art analysis with contemporary digital platforms. He teaches courses including: GBDA 101 Introduction to Digital Media Design GBDA 203 Introduction to Digital Culture GBDA 228 Digital Imaging of Online Applications GBDA 301 Global Digital Project 1 GBDA 401 Cross-Cultural Digital Business GBDA 402 Capstone Course: Cross-Cultural Digital Business ARTS 290 Theories of Media
Prof. Dr. Gil Westmeyer is a Professor of Neurobiological Engineering at the Technical University of Munich (TUM), holding joint appointments at the TUM School of Natural Sciences and TUM School of Medicine and Health. He serves as Director of the Institute for Synthetic Biomedicine at Helmholtz-Zentrum München and leads the Chair of Neurobiological Engineering at TUM. His research program bridges molecular engineering, neuroimaging, and synthetic biology to develop next-generation tools for understanding and manipulating cellular networks. Westmeyer's educational background includes medical and philosophical studies in Munich, doctoral work on the molecular basis of Alzheimer's disease under Professor Christian Haass, clinical training at Harvard Medical School, and postdoctoral research with Professor Alan Jasanoff at MIT. His laboratory focuses on creating genetically encoded molecular sensors and actuators that enable non-invasive imaging and remote control of cellular processes across multiple scales. His research spans three primary domains: molecular sensors for multimodal imaging (from electron microscopy to whole-organism optoacoustics), molecular actuators for spatiotemporal control of cellular processes, and neurobehavioral imaging in freely behaving model organisms. The lab's work integrates synthetic biology, nanotechnology, and advanced imaging techniques to create tools that map dynamic signaling processes and manipulate cellular functions with unprecedented precision. Westmeyer's publication record demonstrates consistent innovation in molecular engineering, with recent work focusing on genetically encoded barcodes for electron microscopy, intron-encoded reporting systems, multiplexed optoacoustic imaging, and magnetically responsive cellular compartments. His publications in high-impact journals like Nature Methods, Cell, and Nature Biotechnology reflect the significance of his contributions to molecular imaging and engineering. ERC Proof of Concept 'inteRNAlizer' (2023) ERC Consolidator Grant 'EMcapsulins' (2019) ERC Starting Grant 'MagnetoGenetics' (2013) Helmholtz Young Investigator's Group (2011) Westmeyer actively mentors students and researchers through multiple teaching positions at TUM, including courses in biological chemistry, genetic machine development (iGEM), mammalian cell technology, and neuro-recording methods. His laboratory develops technologies with clear translational potential for future neurotherapies and regenerative medicine applications, particularly through the creation of imaging-controlled cellular interventions. The lab maintains strong collaborations across disciplines and institutions, with research that contributes to multiple UN Sustainable Development Goals related to health and wellbeing.
Aman Saxena is a researcher at the Department of Computer Science in the TUM School of Computation, Information and Technology at Technical University of Munich. His work focuses on geometric/categorical deep learning, robust machine learning, and quantum machine learning. Education: M.Sc. Computational Sciences and Engineering (2019-2023) Location: Boltzmannstr. 3, 85748 Garching b. Munich, Germany (Room 00.11.062) Research Interests: Geometric/Categorical Deep Learning Robust Machine Learning Quantum Machine Learning Bayesian Learning Efficient Machine Learning Code Analysis Recent Publications: Certifiably Robust Encoding Schemes (IEEE International Conference on Quantum Computing and Engineering - QCE 2024) Discrete Randomized Smoothing Meets Quantum Computing (IEEE International Conference on Quantum Computing and Engineering - QCE 2024)
Vikaas Sohal, MD, PhD is a Professor in the Department of Psychiatry at the University of California, San Francisco (UCSF) School of Medicine and a member of the UCSF Weill Institute for Neurosciences. He directs a neuroscience laboratory investigating the brain circuits underlying fundamental aspects of cognition and emotion, with particular focus on gamma oscillations in normal cognition and schizophrenia, as well as how rhythmic brain activity encodes emotional states. Dr. Sohal is also a board-certified psychiatrist who supervises residents in the Early Psychosis (PATH) clinic. Dr. Sohal earned his A.B. and S.M. in Applied Mathematics from Harvard University in 1997, followed by an M.A.St. in Mathematics from the University of Cambridge in 1998. He completed his M.D./Ph.D. in Neuroscience at Stanford University in 2005, where he also completed his residency in adult psychiatry. During his residency, he conducted postdoctoral research with Dr. Karl Deisseroth, performing some of the first experiments using optogenetics to study information processing in brain circuits. Dr. Sohal's research has focused on neural circuit mechanisms underlying cognitive and emotional processes, with particular emphasis on gamma oscillations, prefrontal-hippocampal interactions, and the role of specific interneuron subtypes in information processing. His laboratory has made significant contributions to understanding how parvalbumin interneurons generate gamma oscillations that organize prefrontal networks to promote behavioral adaptation. His recent work has explored the circuit basis of emotional states, pain-related aversion, and neuropsychiatric disorders. His publication record shows a consistent trajectory of high-impact research, with recent publications spanning topics from psilocybin effects to thalamocortical organoids for neuropsychiatric disorder modeling. His work demonstrates a progression from fundamental circuit mechanisms to translational applications for psychiatric disorders, particularly focusing on schizophrenia and emotional processing abnormalities. Dr. Sohal has secured continuous NIH funding as Principal Investigator since 2009, including multiple R01 grants, an R56, DP2, R00, and K99 awards, demonstrating sustained research productivity and significance. His research program represents a sophisticated integration of molecular, cellular, circuit, and behavioral approaches to understand and potentially treat neuropsychiatric disorders.
Yupeng Zhang is an Assistant Professor at the University of Illinois Urbana-Champaign in the Department of Electrical and Computer Engineering, with an affiliate appointment in Computer Science. His research focuses on cybersecurity and applied cryptography , particularly zero-knowledge proofs , secure multiparty computations , and their applications in blockchain and machine learning. Education: Ph.D., Electrical and Computer Engineering, University of Maryland (2018) M.Phil., Information Engineering, Chinese University of Hong Kong (2013) Bachelor of Engineering, Information Engineering, Chinese University of Hong Kong (2011) Research Highlights: Developed scalable zero-knowledge proof systems for blockchain and machine learning Created verifiable computation frameworks for SQL and RAM Advancing privacy-preserving ML and cross-chain blockchain bridges Grants: NSF CAREER award Air Force Research Lab DARPA Google Research Scholar Award Facebook Research Award Latticex Foundation Teaching: CS 461/ECE 422: Computer Security I CS 591 SP: Security and Privacy ECE 407/CS 407: Cryptography ECE 598 YPZ: Advanced Topics in Applied Cryptography Co-taught MOOC on Zero-Knowledge Proofs (Spring 2023) Professional Service: Program Vice Co-Chair, USENIX Security 2024 Program Committee, Crypto 2025, S&P 2025 Reviewer for major journals and conferences
Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning
Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.
Wendy P. Robinson is a Professor in the Department of Medical Genetics at the University of British Columbia Faculty of Medicine , and a Senior Scientist at the BC Children’s Hospital Research Institute . She holds the CIHR Sex and Gender Science Chair . Research Interests: Genetics and epigenetics of early human development, placental function in pregnancy complications (fetal growth restriction, preterm birth), DNA methylation, non-coding RNA, sex differences, and polymorphisms. Her lab employs genomic and bioinformatic tools to study placental health and its impact on newborn outcomes. Recent Publications (2025-2024) focus on X-chromosome inactivation patterns in placenta, cell-type specific DNA methylation, maternal socioeconomic effects on placental epigenetics, and modeling placental development with organoids. Key themes include sex-specific epigenetic regulation , maternal-fetal interactions , and human placental methylome . Awards: UBC Faculty of Medicine Distinguished Achievement Award (2018), with trainees receiving the James Miller Memorial Prize and Mary-Jane Carroll Trainee Award. Students & Collaborations: Supervised PhD/MSc students include Li Qing Wang, Icíar Fernández Boyano, Giulia Del Gobbo, Victor Yuan, and Magda Price. Collaborators span the Alex Beristain Lab and University of Toronto institutions. Laboratory Activities: Regular team-building events like mountain hikes, climbing outings, and kayaking trips, alongside providing open access to epigenetic tools (e.g., Bisearch, SeqDoc) for the research community.
Dr. Wen-hao Zhang is an Assistant Professor at the Lyda Hill Department of Bioinformatics and a member of the O'Donnell Brain Institute at UT Southwestern Medical Center since 2021. He directs the Computational Neuroscience Lab (CNL), focusing on bridging neural circuits and cognition via normative theories and biologically plausible models. Education : PhD in Theoretical Neuroscience (2016, Institute of Neuroscience, Chinese Academy of Sciences), BE in Biomedical Engineering (2009, Shanghai Jiao Tong University). Career : Postdoctoral roles at Carnegie Mellon University, University of Pittsburgh, and University of Chicago. Research Interests : The lab investigates neural information processing using techniques like nonlinear dynamics, Bayesian inference, and Lie group theory. Their work spans Causal inference and decision-making in neural circuits Grid cell mechanisms and spatial navigation Brain-inspired machine learning algorithms Collaborations : Partnerships with experimental neuroscientists such as Todd Roberts (UTSW) and theorists like Tai Sing Lee (CMU) ensure theoretical models align with empirical data. The lab has published in top venues like ICLR and NeurIPS, focusing on multisensory integration and neural coding. Lab Members : Current PhD students include Eryn Sale, Zimei Chen, Yi Ren, and Armand Rathgeb. Former visiting students like Xinruo Yang (University of Pittsburgh) and Xiangyu Ma (HKUST) have contributed to interdisciplinary projects.
