Find Academic Supervisors

Kei ItoView profile

Professor

• Neuroscience
• Drosophila Brain Architecture
• Glial Cell Biology
• +4 more

Prof. Dr. Kei Ito is a leading neuroscientist at the University of Cologne , specializing in Drosophila melanogaster brain architecture. His research focuses on mapping " terra incognita " regions of the brain, analyzing glial cell functions across development, and establishing standardized insect brain nomenclature through international collaboration. As AXA Chairholder and former HHMI Senior Fellow , he combines molecular genetics with 3D virtual reality visualization to unravel neuronal network organization . Academic Affiliation : University of Cologne (2016–present) Prior Positions : The University of Tokyo (2002–2017), National Institute for Basic Biology (1998–2002) Research Interests span multiple domains: "Terra Incognita" Analysis : Systematic mapping of previously uncharacterized brain regions (70% of brain volume) Glial Cell Biology : Classification and functional analysis from embryo to adult Connectome Research : Integration of electron microscopy data with genetic tools Neuronal Lineage Studies : 3D reconstruction of neuroblast progeny patterns Scientific Contributions include: Creation of 4,500 NP Gal4 driver lines (Japan consortium, 1998) Pioneering Split-Gal4 systems for precise neuron labeling Establishment of 6 distinct neuron naming schemes for EM data Leadership in Insect Brain Name Working Group (2007–2014) Advising Legacy features direct mentorship of 6 doctoral students and collaborative analysis of 3,777 neuron types in EM datasets. His lab maintains 1,000+ specific Split-Gal4 lines for terra incognita research.

Robert NowakView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Robert Nowak holds dual distinguished professorships as the Keith and Jane Morgan Nosbusch Professor in Electrical and Computer Engineering and the Grace Wahba Professor of Data Science at the University of Wisconsin–Madison. Based at the Discovery Building (330 N Orchard Street), he leads interdisciplinary research at the Wisconsin Institute for Discovery, bridging engineering with data science applications. His academic foundation includes: BS, MS, and PhD from the University of Wisconsin–Madison Post-doctoral Fellowship at Rice University Nowak's research program spans artificial intelligence, machine learning, and optimization with dual emphases on AI-driven health applications and systems optimization. His work integrates theoretical rigor with practical implementations, particularly in large language model fine-tuning, active learning frameworks, and neural network theory. Recent publications demonstrate strong focus on improving model efficiency, humor comprehension in AI systems, and theoretical bounds for retrieval-augmented generation. Analysis of his 15 most recent publications reveals dominant trends in large language model advancement (particularly humor understanding and task diversity), theoretical neural network analysis (including sparse architectures and multi-task learning), and novel active learning methodologies for open-world scenarios. His work consistently bridges theoretical machine learning with real-world applications in health and recommendation systems. While specific named awards aren't documented in the source material, his appointment to two endowed chairs (Nosbusch and Wahba professorships) represents exceptional institutional recognition of his scholarly impact. Nowak advises graduate students in the Electrical and Computer Engineering department and secures significant research funding, including NSF grants such as CIF: Small: Advanced Understanding and Applications of Deep Learning. His group operates within the collaborative ecosystem of the Wisconsin Institute for Discovery, fostering cross-disciplinary projects that integrate AI with health sciences and engineering systems. Current projects indicate strong momentum in human-AI collaboration frameworks and optimization of language model training pipelines.

Kristine Beate WalhovdView profile

Professor

• Cognitive Neuroscience
• Neuropsychology
• Brain Aging
• +5 more

Kristine Beate Walhovd is a Professor at the Department of Psychology , University of Oslo, and co-leader of the Center for Lifespan Changes in Brain and Cognition (LCBC) . She is affiliated with the UiO:Life Science initiative and has led major projects like Lifebrain (€2.5M) and Neurocognitive Plasticity (ERC Starting Grant, €1.5M). Current roles: Professor (since 2016), Co-leader of LCBC Key affiliations: University of Oslo, Lifebrain Consortium, UiO:Life Science Her research focuses on lifespan brain and cognitive changes , examining both positive/negative developmental trajectories from age 4 to 90. She studies interactions between biomedical risks (e.g., Alzheimer's, fetal drug exposure) and cognitive outcomes, employing MRI , DTI , and ERP methodologies. Recent publications highlight work on Alzheimer's biomarkers , sleep-brain interactions , memory consolidation , and fetal brain influences . Her 2025 papers in Scientific Reports and Neurobiology of Aging examine hippocampal stability and brain network segregation. Awards include: 2015 University of Oslo Research Prize (shared with Anders Fjell) 2011 Member, Norwegian Academy of Sciences and Letters 2006 His Majesty the King's Gold Medal for best doctoral thesis She supervises doctoral candidates and teaches cognitive neuroscience , experimental methods , and thesis writing .

• Digital Signal Processing
• Communication Systems
• Biomedical Signal Processing
• +6 more

Dr. Barry Cardiff is an Assistant Professor in the School of Electrical and Electronic Engineering at University College Dublin (UCD), where he has been a member of academic staff since September 2013. His career spans both industry and academia, with significant experience at Nokia Mobile Phone (UK) Ltd and Silicon & Software Systems (S3 group) before returning to complete his PhD at UCD. Education: B.Eng (1992), M.Eng.Sc. (1995), PhD (2011) from University College Dublin Professional Experience: Design Engineer at Nokia (1993-2001), Systems Architect at S3 group (2001-2007, 2011-2013) Current Position: Assistant Professor at UCD School of Electrical and Electronic Engineering Dr. Cardiff's research focuses on Digital Signal Processing applications in communication systems, with particular emphasis on theoretical analysis and practical implementation. His work bridges traditional communication theory with emerging biomedical applications, especially in wearable IoT sensors. He has made significant contributions to power/complexity reduction techniques in circuit design, specifically DSP algorithms for digitally assisted analog circuits. His research program addresses critical challenges in biomedical signal processing, sensor fusion, and efficient data transmission for healthcare applications. His recent publications demonstrate a strong trend toward biomedical applications of signal processing techniques, with a focus on ECG analysis, atrial fibrillation detection, and respiratory rate estimation using multimodal sensor fusion. The research shows a clear progression from traditional communication systems toward healthcare applications, with an emphasis on edge computing solutions that reduce power consumption in wearable devices. IEEE BioCas best paper award (2024) IEEE senior member since 2019 Active reviewer for multiple IEEE journals including Transactions on Biomedical Circuits and Systems, Circuits and Systems, and VLSI Systems Dr. Cardiff has supervised numerous research projects and has been instrumental in developing curriculum for digital communications, signal processing, and wireless systems. His teaching philosophy emphasizes open, friendly, and hands-on approaches that encourage independent thinking. He coordinates multiple modules including Communication Theory, Digital Electronics, DSP Technology, and Wireless Systems, demonstrating his commitment to both theoretical foundations and practical applications of electrical engineering principles. His research group works at the intersection of signal processing, machine learning, and biomedical engineering, developing innovative solutions for wearable healthcare monitoring. Current projects focus on event-driven processing architectures, decentralized classification systems, and signal quality-aware fusion techniques that enable robust performance in noisy real-world environments.

Silverio Juan Martinez FernandezView profile

Professor

• Empirical Software Engineering
• Environmental Sustainability of AI
• Green AI
• +3 more

Silverio Juan Martinez Fernandez is a Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Barcelona School of Informatics (FIB) and the Department of Service and Information Systems Engineering . He is a core member of the inSSIDE and GESSI research groups. His expertise spans Empirical Software Engineering , Green AI , MLOps , and Software Analytics . Education: Bachelor's in Computer Engineering PhD from UPC in Software Engineering Master's in Computing Research Interests: Focuses on sustainable AI practices, energy-efficient ML systems, and MLOps education. He investigates architectural design for green AI, energy labeling tools for ML models, and agile software development methodologies. His work bridges theoretical research and industrial applications, emphasizing data-driven decision-making. Grants & Collaborations: Leads projects like Green AI-Based Systems Architecture and Q-Rapids , funded by national and EU programs. Collaborates with institutions like Softeam and industry partners to apply software analytics in real-world scenarios. Labs & Teams: Coordinates the inSSIDE group, focusing on integrated software and data engineering. Active in organizing conferences like GREENS and ESEM , and co-develops tools like Skuld for technical debt management.

Damiano PiovesanView profile

Associate Professor

• Protein function prediction
• Intrinsically disordered proteins (IDPs)
• Machine learning in bioinformatics
• +5 more

Damiano Piovesan is Associate Professor in Bioinformatics (SSD BIO/10) at the Department of Biomedical Sciences , University of Padua , Italy. Since March 2022 he has held this rank, having previously served as Assistant Professor (2022) and PostDoc researcher (2019) in the same department. Education 2013 – PhD in Biotechnology, Pharmacology and Toxicology, University of Bologna 2009 – MSc in Bioinformatics, University of Bologna 2007 – BSc in Biotechnology, University of Bologna Research Focus Piovesan’s research integrates machine-learning approaches with structural bioinformatics to advance understanding of intrinsically disordered proteins (IDPs) and protein function prediction . He develops widely used resources such as MobiDB for disorder annotation, DisProt for functional curation of disordered regions, and RING for residue interaction networks. Additional interests include tandem repeat proteins , cancer-related IDP targets , and community benchmarking initiatives (CAFA, CAID, CAGI). Publication Trends His 2024–2025 output is dominated by updates to flagship databases ( InterPro , DisProt , MobiDB ), next-generation disorder predictors leveraging deep learning ( PredIDR , MobiDB-lite 4.0 ), and large-scale genomics challenges ( CAGI6 ). Across the decade, recurring themes include methodological advances in disorder prediction, creation of interoperable bioinformatics platforms, and rigorous benchmarking to ensure community-wide reliability. Scientific Awards No specific awards are listed in the provided materials. Advising & Grants No individual students or grant details are explicitly supplied; however, his leadership in multi-institutional consortia (e.g., InterPro, DisProt, CAFA) implies substantial supervisory and funding coordination roles. Labs & Teams Piovesan is affiliated with the BioComputingUP Lab ( https://biocomputingup.it/ ) at the University of Padua, a hub for computational biology and bioinformatics tool development.

• Safe Reinforcement Learning
• Data-Driven Model Predictive Control
• Artificial Intelligence
• +7 more

Sebastien Nicolas Gros is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on safe reinforcement learning (RL) and data-driven model predictive control (MPC), with applications in energy systems, biomedical engineering, and autonomous vehicles. Institution: Norwegian University of Science and Technology Department: Engineering Cybernetics His work emphasizes AI-driven optimization for domestic energy storage, battery integration, and smart building management. Collaborations include Equinor, DNV, Kongsberg, Volvo, and CorPower Ocean. Key themes in his publications include: Control theory for renewable energy systems (wave energy converters, buildings) Biomedical applications (artificial pancreas, glucose monitoring) Transportation systems (electric vehicles, autonomous ships) Machine learning integration with physical models He supervises 6 PhD students and co-supervises projects on multi-rotor wind turbines and industrial PhD collaborations. The articles demonstrate a convergence of RL, MPC, and uncertainty quantification across energy, biomedical, and transportation domains.

Xiaobo LiView profile

Professor

• Neuroscience
• Medical Imaging
• Machine Learning Applications in Neurology
• +3 more

Xiaobo Li is a Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology. Holding a Ph.D. in Computer Aided Geometric Design from the University of Birmingham and a B.S. in Automation from Nanjing University of Aeronautics, their research bridges computational methods with neuroimaging and psychiatric disorder analysis. Ph.D., University of Birmingham (Computer Aided Geometric Design, 2004) B.S., Nanjing University of Aeronautics (Automation, 1999) Dr. Li’s work focuses on applying machine learning and graph theory to understand brain network abnormalities in conditions like ADHD , schizophrenia , and traumatic brain injury . Their studies analyze structural-functional connectivity , reward processing , and gut-brain axis interactions using fMRI , fNIRS , and diffusion tensor imaging . Recent publications highlight their development of tools like the GAT-FD MATLAB toolbox for brain network analysis and their exploration of multimodal MRI in schizophrenia diagnosis. They also investigate the neurobiological effects of photobiomodulation and vision therapy interventions.

Bihuan ChenView profile

Associate Professor

• Software Engineering
• Software Supply Chain Security
• Program Analysis
• +4 more

Bihuan Chen is an Associate Professor at the College of Computer Science and Artificial Intelligence, Fudan University, specializing in software engineering with focus on software supply chain security and trustworthy AI systems. His research spans multiple programming languages including JavaScript, Python, Java, and C/C++ across application and AI domains. Dr. Chen earned his B.Sc. and Ph.D. in Computer Science from Fudan University in 2009 and 2014 respectively, followed by postdoctoral research at Nanyang Technological University (2014-2017). His research interests include software supply chain risk assessment, trustworthy AI systems, and program analysis. His recent publications demonstrate strong focus on malicious package detection in NPM/PyPI ecosystems, vulnerability patch porting using LLMs, and safety verification for autonomous driving systems. The work shows increasing integration of machine learning techniques with traditional program analysis approaches, particularly evident in the 2024-2025 publications that leverage LLMs for vulnerability detection and code refinement. ACM SIGSOFT Distinguished Paper Award (FSE 2016, ASE 2018, ASE 2022, FSE 2025) IEEE TCSE Distinguished Paper Award (ICSME 2020, SANER 2023) CCF Prototype Competition Awards (2nd and 3rd Prizes) Dr. Chen has advised over 50 students including current PhD candidates and notable alumni now at Huawei, ByteDance, and other leading tech firms. His fuxi platform assesses security, legal, and maintenance risks across the software engineering lifecycle. He serves on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, and as Associate Editor for the Journal of Software: Evolution and Process.

Gabriella CasalinoView profile

Assistant Professor

• Computational Intelligence
• eHealth
• Data Stream Mining
• +5 more

Gabriella Casalino is an Assistant Professor at the University of Bari Aldo Moro, Department of Computer Science, and a key researcher at CILAB - Computational Intelligence Lab. Her work focuses on Computational Intelligence methods for interpretable data analysis, particularly in eHealth, Data Stream Mining, and eXplainable Artificial Intelligence (XAI) within medical and educational domains. She has contributed to innovative approaches in smartphone-based health monitoring, fuzzy logic applications, and remote vital sign detection via photoplethysmography. Education : Ph.D. in Computer Science, with advanced training at institutions like Universitat de Girona and Université de Mons. Research Trends : Recent publications highlight applications of evolving granular computing, neuro-fuzzy systems, and explainable AI in hypertension prediction, bipolar disorder monitoring, and educational data analysis. Key subfields include remote health monitoring, medical data streams, and hybrid AI models. Grants : Research funded by AIRC (Italian Cancer Research Foundation), focusing on computational methods for healthcare challenges. Labs & Collaborations : Active in CILAB, collaborating on projects involving mHealth solutions, cardiovascular risk assessment, and intelligent educational systems.

Katrina ChoeView profile

Assistant Professor

• Social Neuroscience
• Autism Spectrum Disorders
• Oxytocin Signaling
• +4 more

Katrina Choe serves as Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University and holds a Tier 2 Canada Research Chair in Neurobiology of Social Behaviour. Her research program investigates the multi-level neurobiological mechanisms underlying psychiatric disorders, with primary focus on autism spectrum disorders (ASD) and oxytocin signaling pathways. Her academic training includes: PhD in Neuroscience from McGill University (2013) Honours BSc in Zoology from University of Toronto (2002-2006) Postdoctoral Fellowship at UCLA (2013-2020) Dr. Choe's research employs an integrative approach spanning molecular, cellular, circuit, and network levels to examine how ASD-associated gene mutations disrupt social behavior. Current work centers on oxytocin signaling mechanisms in ASD, convergent neurobiological pathways across psychiatric disorders, and the role of glial cells in neural circuit function. Her lab utilizes advanced techniques including optogenetic fMRI, single-cell RNA sequencing, and multi-level behavioral assays in genetic mouse models. Analysis of her 15 most recent publications reveals a clear research trajectory: early work (2015-2020) established foundational knowledge in vasopressin neuron regulation and salt homeostasis, while recent publications (2022-2025) demonstrate a focused shift toward ASD mechanisms, oxytocin signaling, and social circuit dysfunction using the Cntnap2 knockout model. This evolution reflects her transition from postdoctoral training to independent research leadership. Her scientific recognition includes: Tier 2 Canada Research Chair in Neurobiology of Social Behaviour (2022) NIMH K99/R00 Award CIHR Postdoctoral Fellowship Dr. Choe actively mentors six graduate students across PhD and MSc programs while leading a dynamic research team comprising postdoctoral fellows, laboratory technicians, and undergraduate researchers. Her program receives substantial support from major grants including a 5-year CIHR Project Grant and NSERC Discovery Grant focused on 'The role of CASPR2 in central oxytocin system development.' The Choe Lab maintains active collaborations with leading neuroscience groups including the Bourque, Prager-Khoutorsky, and Cunningham labs, as evidenced by participation in the 4th 1000 Islands/Gananoque Meeting on Hypothalamic Mechanisms. Her laboratory, established in 2020, operates as a multidisciplinary hub utilizing molecular biology (qPCR, RNA-seq), advanced imaging (lightsheet, confocal), electrophysiology (in vitro and in vivo), and behavioral neuroscience approaches to investigate social behavior mechanisms. Current projects examine microglia-astrocyte-neuron interactions in social circuit function and the therapeutic potential of oxytocin for ASD-related social deficits.

Jonathan Ragan-KelleyView profile

Assistant Professor

• High-Efficiency Visual Computing
• Compilers
• Computer Architecture
• +3 more

Jonathan Ragan-Kelley is the Esther and Harold E. Edgerton Assistant Professor of Electrical Engineering & Computer Science at MIT and an Assistant Professor of EECS at UC Berkeley. He leads the Visual Computing group at CSAIL, focusing on high-efficiency visual computing, compilers, and architectures for image processing, machine learning, and 3D rendering. His research bridges systems, compilers, and hardware design, emphasizing scalable solutions for computational challenges. Education: PhD in Computer Science from MIT (2014), postdoc at Stanford University, and visiting researcher at Google. He co-created the Halide language and has developed multiple domain-specific languages (DSLs) and compiler systems. Research interests include compiler optimization, scheduling languages (e.g., Exo), and efficient computing frameworks. He has received awards such as the NSF CAREER Award and ACM SIGGRAPH’s Significant New Researcher Award. Awards: ACM SIGGRAPH Award, NSF CAREER, Intel Outstanding Researcher Award Key Contributions: Halide compiler framework, Exo scheduling language, machine learning acceleration techniques Labs/Teams: Visual Computing at MIT CSAIL

Dong WangView profile

Assistant Professor

• Neural ensemble dynamics
• Learning and memory consolidation
• Emotion processing
• +5 more

Dong Wang, PhD, is an Assistant Professor in the Department of Neurobiology & Anatomy at Drexel University College of Medicine. His research focuses on neural circuit mechanisms underlying learning, memory consolidation, and emotional processing. Using multidisciplinary approaches including in vivo electrophysiology, optogenetics, and calcium imaging, Dr. Wang investigates hippocampal-prefrontal interactions and amygdala circuitry related to fear/anxiety. His work aims to develop therapies for memory disorders and pathological anxiety. Education includes a PhD from East China Normal University with research at Boston University and Augusta University (2007-2011), and a Bachelor's degree from East China Normal University (2000-2004). Research interests center on neural ensemble dynamics during memory consolidation, fear/anxiety circuitry in the amygdala, hippocampal-prefrontal communication, sleep neurobiology, and dopamine-mediated motivational processes. Current projects examine retrosplenial-hippocampal coordination during sleep and basolateral amygdala encoding of emotional states. Publication analysis reveals strong focus on hippocampal ripple dynamics (37%), amygdala function (24%), dopaminergic regulation (19%), and prefrontal-thalamic interactions (20%). Recent work demonstrates increasing use of calcium imaging and circuit manipulation techniques to study memory consolidation mechanisms. Awards include multiple NIH Fellows Awards for Research Excellence (2013-2014), travel grants, and mentoring recognition. Current funding includes NIH R01 MH119102 (2019-2023) and Pennsylvania CURE grants. Dr. Wang leads a neurophysiology laboratory employing electrophysiology, optogenetics, and viral tracing techniques. The team includes one postdoctoral fellow and three PhD students investigating neural circuit dynamics. Current projects focus on memory consolidation circuits and fear/anxiety neurobiology.

• Cryptography
• Theoretical Computer Science
• Machine Learning
• +3 more

Mohammad Mahmoody is an associate professor in the Computer Science Department at the University of Virginia. His research focuses on theoretical aspects of cryptography, computational complexity, and machine learning, particularly on understanding barriers such as lower bounds and impossibility results. He has taught courses including Algorithms, Cryptography, and Theory of Computation. His work explores foundational questions in cryptography, adversarial robustness in machine learning, and computational assumptions underlying cryptographic primitives. Education: Ph.D. in Computer Science from Princeton University (2010) Affiliations: University of Virginia, Department of Computer Science Research Interests: His interests span cryptography (e.g., encryption schemes, coin-tossing, and registration-based systems), theoretical computer science (computational complexity, algorithms), and machine learning (adversarial robustness, data poisoning). He emphasizes formal proofs and rigorous analysis in security and learning frameworks. Publications: Recent work includes studies on quantum-resistant cryptography, adversarial machine learning, and cryptographic protocol design. Key themes involve impossibility results, lower bounds for cryptographic assumptions, and the interplay between computational constraints and learning theory. Service & Grants: - Served on program committees for ICML, NeurIPS, CRYPTO, and TCC - Organized workshops on cryptography and lower bounds - Research funded by grants exploring adversarial robustness and cryptographic foundations Labs/Teams: - Collaborations with researchers in cryptography, machine learning, and theoretical computer science - Advises a team of graduate students and postdocs in security and learning theory.

• Partial Differential Equations
• Shape Optimization
• Numerical Analysis
• +3 more

Arian Novruzi is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, Faculty of Science. His expertise lies in partial differential equations (PDEs), shape optimization, numerical analysis, and mathematical modeling. He holds an MSc from the University of Tirana and a PhD from the University of Nancy. His research integrates theoretical and applied mathematics, with a focus on fluid dynamics, biomedical applications, and engineering challenges. Education: MSc in Mathematics, University of Tirana PhD in Mathematics, University of Nancy Dr. Novruzi’s research interests include the analysis and numerical solutions of PDEs, optimization of geometric shapes for engineering systems, and modeling of complex physical phenomena such as blood flow and tumor radiation therapy. His work bridges pure mathematics with practical applications, addressing problems in fluid mechanics, materials science, and biomedical engineering. His recent publications highlight advancements in non-diffusive neural network methods for hyperbolic conservation laws, blood flow modeling using Navier-Stokes equations, and the optimization of convex domains for energy maximization. These studies emphasize both theoretical rigor and computational innovation. Awards: No scientific awards explicitly mentioned in the provided texts. Dr. Novruzi has supervised students such as Terence C. Ngouoko. His grants and collaborations are not detailed here, but his research has implications for energy-efficient engineering designs and medical treatments. He has authored a Springer textbook on PDEs, reflecting his commitment to educational resources in mathematical sciences.