Find Academic Supervisors

• Natural Language Processing
• Dialogue Systems
• Machine Translation
• +2 more

Luísa Coheur is an Associate Professor at the Department of Computer Science , Instituto Superior Técnico (University of Lisbon), and a researcher at INESC-ID 's Human Language Technologies group. She served on the Management Committee of IST-Taguspark (2020-2023), overseeing pedagogical organization and library services. Education: Ph.D. in Natural Language Processing (IST/Université Blaise-Pascal) Postgraduate in Higher Education Pedagogy (University of Lisbon, 2023) Degree in Applied Mathematics and Computer Science (IST) Research Interests: Specializes in NLP with focus areas in: Dialogue systems and conversational AI Machine translation (including Portuguese Sign Language) Question answering architectures Educational technology and accessibility Cyberbullying detection in social media Her work integrates linguistic theory with machine learning for real-world applications. Publication Trends: Over 120 publications emphasizing machine translation evaluation (e.g., fine-grained error detection), NLP for social good (cyberbullying datasets), sign language processing, and educational tools. Recent works leverage active learning and transformer models for low-resource scenarios. Awards: IST Outstanding Teaching Award (2023) Recognized as 'Excellent Professor' >20 times via IST QUC Advising & Projects: Supervised 10+ PhD and 80+ Master's students. Secured participation in 17 national/international projects (e.g., EU-funded initiatives in NLP). Leads pedagogical innovation like educational escape games for STEM courses. Labs/Teams: Core member of INESC-ID's Human Language Technologies group , developing resources for Portuguese NLP. Collaborates with clinicians on assistive tech (e.g., VITHEA-Kids for autism language skills).

Subhajit ChakrabartyView profile

Associate Professor

• Data science
• Machine learning
• Deep learning
• +8 more

Subhajit Chakrabarty serves as Associate Professor and Director of the Master of Science in Computer Systems Technology program within the Department of Computer Science at Louisiana State University Shreveport's College of Arts & Sciences. He joined LSUS in 2020 after relocating to the USA in 2016 to pursue research in data science and machine learning, bringing three decades of professional experience spanning IT management, government service, and corporate leadership. His educational credentials include: PhD in Computer Science from University of Massachusetts Lowell (2020) PhD in International Business from Indian Institute of Foreign Trade, New Delhi Alumnus of INSEAD (France/Singapore) Chakrabarty's research spans data science, machine learning, deep learning, bioinformatics, cybersecurity, and econometrics, with recent emphasis on biomedical applications of deep learning. His interdisciplinary approach integrates computer science with business analytics, neuroscience, and educational technology, reflecting his dual expertise in technical and business domains developed through extensive industry experience. His publication record from 2016-2022 demonstrates consistent innovation across multiple domains, with significant contributions to ensemble learning for financial forecasting, independent component analysis for high-dimensional data processing, and educational assessment tools for computer science pedagogy. Key trends include the adaptation of transformer networks for stock volatility prediction, novel denoising techniques for sensor data, and evidence-based studies on programming skill development. No scientific awards were documented in the source material. As an educator, Chakrabarty directs the MS in Computer Systems Technology program while teaching core courses including Introduction to Programming, Database Implementation, Machine Learning, and Deep Learning. His prior industry roles as Deputy Commandant in India's Border Security Force and National Security Guard, plus Director of IT & IS in corporate settings, inform his practical teaching methodology and student mentorship approach.

Fiona GilbertView profile

Professor

• Breast Imaging
• Medical Imaging
• Artificial Intelligence in Radiology
• +3 more

Fiona Gilbert holds the Chair of Radiology at the University of Cambridge since 2011 and serves as an Honorary Consultant Radiologist at Addenbrooke's Hospital. She currently leads as President of the European Society of Breast Imaging (EUSOBI) and chairs the breast subcommittee of the Radiological Society of North America. Her extensive leadership history includes Chair of the Academic Committee of the Royal College of Radiologists, Chair of the NCRI Imaging Advisory Group, and Chair of the Royal College of Radiologists Breast Group, with significant contributions to NICE and SIGN breast imaging guidelines. Her research centers on advancing breast imaging for early cancer detection, with core interests in breast Tomosynthesis, non-FDG radiotracers, and breast MRI for tumor physiology analysis. She pioneers risk-adapted screening protocols for women with dense breasts using multimodal imaging and variable mammography frequency. A major focus involves integrating Artificial Intelligence into radiology workflows to enhance diagnostic accuracy, reduce errors, and optimize resource allocation in cancer screening programs. Analysis of her recent publications reveals dominant themes in AI-driven screening optimization, supplemental imaging for dense breasts (BRAID trial), and preoperative MRI impact on surgical outcomes (MIPA study). She leads international initiatives like MyPeBS for personalized risk-stratified screening while addressing critical ethical and equity challenges in AI implementation. Her work consistently bridges technical innovation with clinical guideline development across European radiology practice. Professor Gilbert is embedded in the Cambridge Mathematics of Information in Healthcare (CMIH) Hub at the Centre for Mathematical Sciences, fostering interdisciplinary collaboration between mathematicians, computer scientists, and clinicians to develop computational solutions for healthcare challenges, particularly in oncology imaging and AI validation frameworks.

• Computer Architecture
• Edge AI Systems
• Memory Systems
• +6 more

Rafael Medina Morillas is a researcher at the Embedded Systems Laboratory (ESL) at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he focuses on computer architecture and hardware acceleration for edge AI systems. His research addresses the memory wall problem through innovative architectural designs that improve energy efficiency and performance in data-intensive applications. His primary research interests include: Compute-near-Memory architectures Hardware acceleration for machine learning Edge AI systems Chiplet architectures and interconnects Wireless communication for computing systems Medina Morillas' publication record demonstrates significant advancements in memory systems and hardware acceleration. His work on SideDRAM shows up to 83% EDAP reduction compared to state-of-the-art designs, while his research on wireless communication achieves up to 2.64x speedup for deep neural networks. His recent publications focus on structured pruning techniques for transformers, co-design frameworks for edge AI, and thermal management solutions for heterogeneous systems. His research is supported by collaborations with IMEC, Université de Bordeaux, and HEIG-VD, as well as funding from EC H2020 projects and the ACCESS-AI Chip Center. These partnerships enable comprehensive exploration of architectural innovations across different technology domains. As evidenced by his doctoral thesis 'System-aware Architectural Co-design to Tackle the Memory Wall,' Medina Morillas takes a cross-layer approach to system design, integrating hardware and software optimizations to address fundamental bottlenecks in modern computing systems. His work demonstrates how system-aware architectural design can achieve improvements in runtime, energy consumption, and thermal behavior for data-intensive applications.

Nicolaj Haarhøj MalleView profile

Assistant Professor

• Aerial Robotics
• Unmanned Aerial Vehicles (UAVs)
• Power-line Maintenance and Inspection
• +7 more

Nicolaj Haarhøj Malle is an Assistant Professor at the University of Southern Denmark’s Institute of Mechanical and Electrical Engineering and a member of the SDU Digital and High-Frequency Electronics group. He earned his PhD in Robotics in November 2023 with a dissertation on autonomous power-line perception and continues as a post-doctoral researcher until July 2025. Education PhD in Robotics, University of Southern Denmark (2020–2023) Post-doctoral Researcher in Aerial Robotics for Power-line Maintenance, University of Southern Denmark (2023–2025) Research Interests Malle’s work lies at the intersection of aerial robotics, power-line engineering, and embedded intelligence. He develops fully autonomous drones capable of landing on live overhead cables to recharge, inspect, and maintain critical infrastructure. His research integrates mm-wave radar sensing , computer vision , FPGA-accelerated perception , and machine learning to achieve robust navigation in GPS-denied, high-voltage environments. He also pioneers open-source hardware-software architectures for rapid prototyping of safety-critical drone swarms. Scientific Awards & Recognition Although no formal awards are listed, Malle’s work has received extensive national press coverage, including features in Information , DR Forskerfesten PhD Cup , and international tech media highlighting his “vampire drone” concept. Grants & Projects EU H2020 Drones4Safety (2020-2023) – PhD student participant EU H2020 Aerial Core (2019-2023) – PhD student participant InnoExplorer AIR-Ops (2023-2024) – Project participant Spin-outs Denmark OnGrid Aerial Systems (2024-2025) – Project participant Laboratories & Collaborations Malle collaborates closely with the SDU UAS Center and conducts experimental flights at dedicated drone test sites. He maintains an international network via visiting research stays (University of Zürich, Oct–Dec 2022) and active IEEE membership.

• Neurogenetics of behaviour
• Sensory neuroscience
• Neurophysiology
• +8 more

Ilona Grunwald-Kadow is Professor of Physiology and Head of the Institute of Physiology II at the University of Bonn ’s Faculty of Medicine, and concurrently serves as Vice Rector for Research and Young Scientists . An elected member of EMBO , she combines neurogenetics, physiology, and behaviour to decipher how neural circuits allow organisms to adapt to ever-changing environments. Education & Career: Professor of Physiology, University of Bonn (2022–present) Vice Rector for Research and Young Scientists, University of Bonn (2022–present) Head, Institute of Physiology II, Faculty of Medicine, University of Bonn (2022–present) Research Interests: Using Drosophila melanogaster and mouse models, her laboratory investigates how sensory inputs, internal states, and genetic programs interact to produce flexible, adaptive behaviour. Core themes include: Neurogenetic bases of state-dependent behaviour Dopaminergic modulation of sensory processing Mechanisms linking metabolic state to decision-making Hormonal modulation of olfaction and taste Neural circuits for motivation, risk-assessment, and memory Scientific Awards: Elected Member, European Molecular Biology Organization (EMBO) EMBO Young Investigator Award ERC Starting Grant Human Frontiers Science Program Career Development Award Otto Hahn Medal, Max Planck Society Research Environment & Funding: Her lab is embedded in the iBehave Network and NeurotechEU consortiums, fostering interdisciplinary collaboration across Europe. Funding sources include the European Research Council, German Research Foundation, and Human Frontiers Science Program. Lab Team & Mentoring: The Grunwald-Kadow group comprises post-doctoral researchers, doctoral students, and technical/administrative staff working at the intersection of genetics, physiology, imaging, and behaviour. The lab actively trains the next generation of neuroscientists through hands-on mentoring and cross-disciplinary projects.

• Embedded Systems
• Real-Time Systems
• System-on-Chip Design
• +7 more

Adriano José Conceição Tavares is an Associate Professor at the School of Engineering, University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI, where he is affiliated with both the IE R&D Group and the ESRG R&D Lab. His academic credentials include a PhD in Industrial Electronics from the University of Minho, a Master of Science in Information Technology from the University of Coimbra, and an undergraduate degree in Informatics from the University of Coimbra. Professor Tavares specializes in embedded systems with particular expertise in: Embedded systems modeling and design System software design System-on-chip design Real-time operating systems Hardware acceleration and FPGA design Virtualization for embedded systems IoT frameworks and protocols His publication record demonstrates a strong research trajectory in hardware-software co-design, with recent work showing an increasing integration of machine learning techniques into embedded systems. His publications span theoretical frameworks to practical implementations addressing real-time performance, resource constraints, and security challenges. Among his scholarly metrics: h-index of 18 126 publications with 1148 citations 20 publications in Q1/Q2 journals Author of a book on microcontroller programming Professor Tavares has supervised students including Miguel Ângelo Fernandes Silva and has established international collaborations through the Erasmus Program with institutions in China, Iran, Thailand, Jordan, and Cambodia. He teaches advanced courses on embedded and real-time systems modeling, compiler design, system-on-chip design, real-time operating system design, and advanced computer architectures at University of Minho.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

Andrew NelsonView profile

Assistant Professor

• Embedded Systems
• Real-Time Control
• Multi-Sensor Fusion
• +4 more

Andrew Nelson is a part-time Assistant Professor at the Electronic Systems department, College of Engineering , Eindhoven University of Technology . He also serves as a founder and R&D lead at Verintec Solutions B.V. . Research Focus : Predictable and composable embedded systems, real-time robotics, multi-sensor fusion for industrial positioning, multi-core processor optimization. Key Contributions : Development of the CompSOC platform, CompROS architecture for ROS2, and novel multi-rate control strategies. Recent Article Trends : His work emphasizes predictable execution on multi-core platforms, sensor fusion techniques (linear encoders + vision systems), and real-time robotics. Articles span 2015–2025, highlighting collaborations with institutions like TU/e and ECSEL JU grant projects IMOCO4.E (2021–2023) and COMP4DRONES (2021).

• Artificial Intelligence
• Edge AI
• Sensors
• +7 more

Amirreza Yousefzadeh is an Assistant Professor specializing in computer architecture design for embedded systems. His research focuses on hardware acceleration for artificial intelligence, particularly in energy-efficient neuromorphic computing and edge AI applications. Research Interests Neuromorphic computing architectures Event-driven AI hardware Sparsity exploitation in neural networks Embedded vision systems Digital circuit design for AI Research Trends Recent work (2024-2025) demonstrates expertise in spiking neural networks (SNNs), activation sparsity, and hardware-software co-design for neuromorphic processors. Key areas include object detection, energy efficiency optimization, and digital implementations of synaptic delays. Technical Contributions Developed SENMap for multi-objective data-flow mapping Created SENSIM simulator for multi-core neuromorphic systems Investigated 3D stacking for memory-dominated architectures Explored temporal sparsity in event-based processing

• 3D bioprinting
• Artificial intelligence
• Bioinformatics
• +7 more

Stefano Di Carlo is a Full Professor at the Department of Control and Computer Science (DAUIN) at Politecnico di Torino. He is the Coordinator of the Doctoral School in Artificial Intelligence and a member of the PolitoBIOMed Lab and the Doctoral School Council. His research spans Artificial Intelligence , Computer Architecture , Bioinformatics , and Cybersecurity , with a focus on 3D bioprinting , hardware security , and reliability analysis . Research Interests : Approximate computing systems Cybersecurity for connected vehicles Spiking neural networks and neuromorphic hardware Multicellular synthetic biological systems Hardware-based malware detection Biomedical simulation tools Article Trends : His recent publications address approximate computing (7/15), cybersecurity (6/15), and bioinformatics (4/15). Key subtopics include RISC-V security , gradient inversion attacks , photonic computing , and real-time fault injection . Scientific Awards : Best Paper Awards at IEEE AQTR (2010, 2012), IEEE DDECS (2013), and BIOINFORMATICS/BIOSTEC (2014) IEEE Computer Society Golden Core Award (2006), Meritorious Service Award (2010) IEEE Fellow (2011-) and Senior Member Advising & Grants : He supervises 14 PhD students and leads projects like Vitamin-V (RISC-V cloud services), APROPOS (approximate computing), and SERICS (cybersecurity). His lab, SMILIES, focuses on resilient computer architectures and bioinformatics . Labs & Teams : Lab 6 - Research Laboratory (DAUIN) SMILIES - Resilient computer architectures and life sciences PolitoBIOMed Lab - Biomedical engineering

• Genitourinary Pathology
• Cancer Biomarkers
• Molecular Carcinogenesis
• +4 more

Dr. Rajiv Dhir is Professor of Pathology at the University of Pittsburgh and holds multiple leadership roles at UPMC, including Executive Vice Chairman of Anatomic Pathology, Chief of Pathology at UPMC Shadyside, Director of the Genitourinary Pathology Center of Excellence, and Medical Director of the Pitt Biospecimen Core. He oversees tissue banking operations supporting translational research across UPMC hospitals and international consortia. Education: MBBS, New Delhi, India (1983) MBA, University of Pittsburgh (2012) Research Focus: Dr. Dhir investigates molecular alterations in genitourinary cancers (prostate, bladder, renal) using multi-omics approaches. His work bridges biomarker discovery, AI-enhanced diagnostics, and clinical validation, with emphasis on proteomic perturbations and therapeutic target identification. Recent Publications: His 180+ publications demonstrate leadership in cancer genomics (TCGA), artificial intelligence applications in pathology, and biobanking science, reflecting cross-disciplinary collaboration and clinical translation. Grants & Infrastructure: As PI for CPTAC and CHTN initiatives, he secures NIH/NCI funding for large-scale projects. He directs the Pitt Biospecimen Core—a critical resource for tissue-based research—and developed institutional honest broker systems for data integration.

Johannes PfauView profile

Researcher

• FPGA Architecture
• Reconfigurable Computing
• Digital Beamforming
• +3 more

Dr. Johannes Pfau serves as a Scientific Assistant at the Institute for Information Processing Technology (ITIV) within Karlsruhe Institute of Technology (KIT), working in Prof. Becker's research group. His position combines postdoctoral research in advanced FPGA architectures with teaching responsibilities including System-on-Chip internships and academic advising for specialized engineering tracks. Education: Doctorate (Dissertation) in Electrical Engineering and Information Technology, Karlsruhe Institute of Technology, 2024 Research Interests: Pfau's work centers on reconfigurable computing with three interconnected pillars: (1) Next-generation FPGA architectures using emerging technologies like RFETs that require fundamental toolchain redesigns; (2) Digital beamforming systems for satellite Earth observation that replace analog processing with FPGA-based solutions to enable on-orbit data compression; and (3) High-throughput data acquisition systems for 6G prototyping handling multi-100Gbps streams through RFSoC platforms. His research bridges semiconductor physics, hardware architecture, and practical applications in communications and remote sensing. Publication Trends: Pfau's 15 most recent publications (2021-2024) reveal a strong focus on practical FPGA implementations addressing real-world constraints. His work increasingly integrates power management (7 papers), 6G infrastructure (5 papers), and novel semiconductor technologies (4 papers), with a clear trajectory toward hardware solutions for satellite communications and next-generation wireless systems. The research demonstrates consistent progression from architectural innovations (RFET, V-FPGAs) to applied systems (beamforming, 6G testbeds). Advising and Mentorship: Pfau maintains an active student supervision portfolio with documented guidance of 7+ Bachelor's and Master's theses since 2021. His projects emphasize hands-on hardware development, spanning power management techniques, beamforming filter design, and prosthetic control systems. The academic advising role for specializations 13 and 21 positions him at the intersection of computer science and electrical engineering education. Research Context: As a core member of Prof. Becker's group at ITIV, Pfau contributes to KIT's leadership in reconfigurable systems research. The group maintains strong industry connections through 6G initiatives and satellite technology development, with Pfau's work directly supporting German and European efforts in secure communications infrastructure and Earth observation systems.

William J. BowmanView profile

Assistant Professor

• Secure and Verified Compilation
• Dependently Typed Programming
• Verification
• +4 more

William J. Bowman is an Assistant Professor in the Department of Computer Science at the University of British Columbia. He focuses on secure and verified compilation, dependently typed programming, and meta-programming systems. His work bridges high-level language design with low-level code generation to maintain correctness and security invariants throughout compilation. Education: PhD in Computer Science from Northeastern University Research spans type-preserving compilation, including: Typed closure conversion for dependently typed languages Allocation-aware type universes Hybrid embedding techniques Secure interoperability via multi-language semantics Recent publications examine: Heap allocation modeling through type universes Flat closure representations WebAssembly extensions with indexed types Control-effect semantics

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +6 more

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.