Find Academic Supervisors

• Complex Systems
• Computational Modeling
• Network Science
• +7 more

Jorge Louçã is a Full Professor in the Department of Information Science and Technology at ISCTE-IUL, where he has been a faculty member since 2000. He is also an Integrated Researcher at ISTAR-Iscte, the Research Center in Information Sciences, Technologies and Architecture, and leads the research group The Observatorium . He holds a PhD in Computer Science and Artificial Intelligence from Université Paris Dauphine and the University of Lisbon, and completed his Aggregation in Complexity Sciences in 2019. PhD in Computing – University of Lisbon & Université Paris-Dauphine (2000) Master’s in Informatique: Intelligent Systems – Université Paris-Dauphine (1995) Aggregation in Complexity Sciences – ISCTE-IUL (2019) His research centers on computational modeling of social systems, focusing on data-intensive analysis of human communication, knowledge generation in large networks, and the dynamics of complex systems. He founded the Doctoral Program in Complexity Sciences and has been instrumental in advancing the field through international collaborations such as the UNESCO Unitwin network for the Complex Systems Digital Campus and participation in the Conference on Complex Systems (CCS/ECCS). The recent publications highlight a strong interdisciplinary focus, combining network science, data analysis, and social theory. Key themes include the modeling of malaria transmission, information diffusion in social media, structural inequality in education, and the dynamics of opinion and popularity. His work often employs agent-based models, temporal network analysis, and entropy-based measures, reflecting a deep integration of computational and theoretical approaches. Research Methods for Doctorate in Complexity Sciences Advanced Topics in Complexity Sciences Data Science Fundamentals Development for the Internet and Mobile Applications Web Interfaces for Data Management Advanced Network Analysis Jorge Louçã has supervised over a dozen doctoral and master’s students, with completed theses on topics such as malaria modeling, information diffusion, temporal networks, and social inequality. His research has been supported by projects like NESS (Non-Equilibrium Social Science in ICT and Economics), reflecting his leadership in interdisciplinary science. He has held significant academic management roles, including Director of the Department of Information Science and Technology and head of multiple degree programs. His work continues to bridge computer science, social science, and policy, positioning him as a key figure in the global complexity science community.

Christina Gertrude YapView profile

Senior Lecturer

• Diabetic Nephropathy
• Metabolic Syndrome
• Obesity
• +11 more

Christina Gertrude Yap is a Senior Lecturer in Metabolic Medicine at the Jeffrey Cheah School of Medicine and Health Sciences (JCSMHS), Monash University Malaysia. She is actively involved in research, teaching, and academic leadership, with a focus on diabetic nephropathy, metabolic disorders, and medical education. She supervises PhD students and leads multiple research projects in diabetes and obesity. PhD in Biomedical Sciences, Monash University (2012) Graduate Certificate in Higher Education, Monash University (2015) Dr. Yap's research centers on identifying early biomarkers for metabolic syndromes, particularly diabetic nephropathy, with a focus on CYP2E1 and other endogenous molecules. Her work integrates proteomics, metabolomics, and animal models to explore pharmacokinetics, xenobiotic interactions, and translational applications. She also investigates teaching and learning methodologies in medical education. Her recent publications (2021–2025) span diabetes, neuroprotection, obesity, and public health, with a strong emphasis on mechanistic studies and clinical translation. Articles explore hippocampal neuroprotection in diabetic rats, social determinants of obesity, and vaccine mechanisms, reflecting a multidisciplinary approach bridging basic science and public health. Scientific awards include: Best Mention Award, 14th Biological Science Congress (2009) Best Presentation Award, 5th World Congress on Diabetes and Metabolism (2014) First Prize and Best Presentation Award, Monash CSJB Research Week and World Congress on Clinical Trials in Diabetes (2017) Seed Grant Award, Monash University (2024) Dr. Yap has been a PhD supervisor since 2011 and is currently accepting new students. She has secured multiple research grants, including active projects on renal protection in type 2 diabetes and anti-obesity effects of natural compounds. Her leadership roles include coordinating physiology and hematology in the MBBS program and serving on the Student Support Committee. She is a member of the Malaysian Scientific Association and the Malaysian Society of Pharmacology & Physiology. She leads a research team focused on metabolic medicine, utilizing advanced analytical techniques and collaborative networks to advance understanding of diabetes and obesity. Her lab integrates molecular biology, animal models, and clinical data to develop early diagnostic tools and therapeutic strategies.

Shuangping LiView profile

Assistant Professor

• Probability Theory
• High-Dimensional Statistics
• Theoretical Machine Learning
• +5 more

Shuangping Li is an Assistant Professor in the Department of Statistics and Data Science at Yale University. She was previously a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). Her research lies at the intersection of probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms. Ph.D. in Applied and Computational Mathematics, Princeton University (2022) B.Sc. in Mathematics, University of Hong Kong Her research interests include probability theory , high-dimensional statistics , theoretical machine learning , and theory of algorithms . She investigates foundational aspects of random constraint satisfaction problems, neural networks, spectral methods, and phase transitions in high-dimensional models. Her work often draws from statistical physics and combinatorics to explain algorithmic behavior. The recent articles highlight a strong focus on binary perceptrons , clustering in network models , and algorithmic phase transitions . Keywords across publications include probability, theoretical computer science, machine learning, and statistical inference. Subfields reveal deep engagement with spin glass theory, discrepancy minimization, spectral embedding, and information-computation gaps. Scientific awards include: Stein Fellow, Department of Statistics, Stanford University (2022–2025) She has advised and taught at both Stanford and Yale, including courses such as Advanced Probability , Theory of Probability , and Stochastic Processes . She has organized seminars at Stanford and has delivered invited talks at institutions including Cornell, Duke, UC Berkeley, and Princeton. Her collaborative research involves prominent scholars such as Allan Sly, Emmanuel Abbe, and Tselil Schramm. There is no mention of external grants, but her postdoctoral fellowship suggests research funding support. She is involved in academic service through organizing the Stanford Statistics and Probability Seminars. She maintains an active research presence with publications in top venues like STOC, FOCS, COLT, ICLR, and journals such as Annals of Probability and Annals of Statistics .

John BickfordView profile

Senior Lecturer

• Personality and Social Psychology
• Gay and Lesbian Psychology
• Social Cognition
• +6 more

John Bickford is a Senior Lecturer II in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst, within the College of Natural Science. He is actively involved in undergraduate education, teaching foundational and specialized psychology courses, and serves as the Coordinator of Online Instruction, mentoring graduate students in online teaching methodologies. Education: PhD, 2003 – University of Massachusetts Amherst MS, 1999 – University of Massachusetts Amherst BA, 1996 – Salem State College Dr. Bickford's research interests lie primarily in personality and social psychology, with a strong focus on gay and lesbian psychology, social cognition, identity formation, and social stigma. His earlier scholarly work developed a multidimensional model of sexual orientation distinct from sexual identity. However, he is no longer active in research and now dedicates his efforts entirely to teaching and pedagogical innovation, especially in online learning environments. His teaching philosophy is deeply rooted in social justice, self-acceptance, and advocacy for marginalized communities. Although no recent publications are listed, his academic contributions have been primarily educational, reflected in his extensive teaching portfolio and leadership in online instruction. His influence extends through curriculum development and mentorship in digital pedagogy. Scientific Awards and Recognitions: 2021 Winner – UMass College of Natural Science Teaching Award Multiple-time nominee for the UMass Distinguished Teaching Award (2022, 2018, 2017, 2007, 2000) Recipient of the UMass Residential First-Year Experience Student-Choice Award (2014, 2011, 2010, 2009) 2000 Award for Demonstrated Effectiveness in Teaching, Division of Continuing Education Nominee for McKeachie Early Career Teaching Award (2002, 2001) Dr. Bickford advises undergraduate students informally through teaching and mentoring, particularly graduate students teaching online courses. While he does not supervise formal thesis or dissertation research, his role as a mentor in pedagogy is significant. He has contributed to educational grants or programs through the Division of Continuing and Professional Education, though specific grant details are not listed. He is actively involved in shaping the online psychology curriculum and fostering inclusive classroom environments. He is part of the core instructional team in the Department of Psychological and Brain Sciences and plays a key role in the online teaching initiative, supporting both course delivery and instructor development. His work helps expand access to high-quality psychology education beyond the traditional classroom.

Alvaro Fernandez QuilezView profile

Associate Professor

• Artificial Intelligence
• Responsible AI
• AI Ethics
• +13 more

Alvaro Fernandez Quilez is an Associate Professor in Artificial Intelligence at the Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, University of Stavanger. He leads the Stavanger AI Laboratory (SAIL), fostering interdisciplinary AI research with a focus on healthcare and education applications. Research Interests: His work centers on responsible AI, emphasizing ethics, fairness, transparency, and uncertainty in AI systems. He applies deep learning and machine learning techniques to medical imaging, particularly in prostate cancer and neurodegenerative diseases like Alzheimer’s and Parkinson’s. His research integrates algorithmic innovation with clinical relevance, addressing challenges in data scarcity, bias, and model interpretability. The recent publications highlight a strong trend in developing and evaluating AI models for diagnostic support in radiology and neurology. Key themes include uncertainty quantification, self-supervised learning, synthetic data generation via GANs, and fairness analysis across gender and centers. The work spans from foundational AI methods to their clinical translation in multi-center studies. Teaching and Academic Leadership: He coordinates the course DAT105 - AI for everyone and has contributed as a guest lecturer in bioinformatics, technological foundations, and PhD ethics, particularly on AI and ethics. He is also enrolled in a PhD supervisory qualification program, underscoring his growing role in graduate education. Advising and Grants: While specific students and grants are not listed in the text, his leadership of SAIL and active publication record suggest involvement in research supervision and project funding. His collaborations span multiple institutions and disciplines, indicating strong team-based research efforts. Laboratories and Teams: He leads the Stavanger AI Laboratory (SAIL), which serves as the central hub for AI research at the University of Stavanger, promoting collaboration across departments and with external partners in healthcare and technology.

Kaiyi JiView profile

Assistant Professor

• Large-scale optimization
• Machine learning
• Multi-task learning
• +6 more

Kaiyi Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He earned his PhD in Electrical and Computer Engineering from Ohio State University in 2021 and completed a postdoctoral fellowship at the University of Michigan. His research focuses on large-scale optimization, machine learning, and foundation models. PhD: Electrical and Computer Engineering, Ohio State University (2021) Postdoc: University of Michigan (2022) BSc: University of Science and Technology of China (2016) Research interests include bilevel optimization, multi-task learning, continual learning, and AI4Science, with applications in robotics and crystal property prediction. Recent work explores efficient algorithms for LLM training and optimization. His publications span top venues like ICLR, ICML, NeurIPS, and IEEE Transactions on Information Theory. He received the CSE Junior Faculty Research Award (2023) and NSF CAREER Award (2025). He advises PhD students and actively participates in departmental service as Associate Chair for Graduate Student Admissions and organizer of academic workshops.

Jordan BateView profile

Associate Professor

• Clinical Psychology
• Attachment Theory
• Mentalization
• +7 more

Jordan Bate is an Associate Professor in the Department of Psychology at the Gordon F. Derner School of Psychology, Adelphi University. He is a licensed clinical psychologist in New York and maintains active research and clinical affiliations, including with Lenox Hill Hospital and Northwell Health. His work integrates psychodynamic, attachment, and mentalization-based approaches across child, adolescent, and family therapy contexts. Education: PhD, The New School for Social Research (2017) MA, The New School for Social Research (2012) BA, Williams College (2006) His research focuses on the psychotherapy process, particularly how attachment and mentalization shape therapeutic outcomes. Key areas include parent involvement in child treatment, therapist facilitative interpersonal skills (FIS), mentalization-based interventions for youth and families in the child welfare system, and adolescent personality development. He leads the Attachment and Psychotherapy Process Lab and employs mixed-methods designs, including observational coding and self-report measures. His recent publications explore therapist training, teletherapy efficacy, epistemic trust, and the psychological impact of the pandemic on families. The work spans journals in clinical psychology, psychotherapy research, and child mental health, reflecting a strong interdisciplinary and translational focus. Scientific Awards and Honors: President, Section II (Children and Adolescents), APA Division 39 (2021–2023) Chair, Child, Adolescent and Family Therapy Research (CAFTR) SIG, Society for Psychotherapy Research (2023–Present) Steering Committee, Psychodynamic Research Mentorship Program (PRMP), Austen Riggs Center & Yale Child Study Center (2024–Present) Committee Member, Mentorship and Progression Committee, Academics for the Advancement of Psychodynamic Psychology (A2P2) (2024–Present) Voluntary Faculty, Department of Psychiatry, Lenox Hill Hospital, Northwell Health (2018–Present) Bate has been instrumental in advancing training models such as Facilitative Interpersonal Skills Training (FIRST) and has contributed to major publications on deliberate practice and mentalization-based parent work. He has received no explicitly listed awards, but his leadership roles and publication record reflect significant professional recognition. He is deeply involved in mentoring students and supporting research in attachment and psychotherapy science. He is also engaged with multiple collaborative research teams, particularly around mentalization-based treatment implementation and therapist responsiveness. His lab collaborates with institutions including Lenox Hill Hospital, The New School, and international partners, supporting a broad network of clinical and academic inquiry.

Stavros DemetriadisView profile

Professor

• Learning Technologies
• Educational Technology
• ICT in Education
• +7 more

Stavros Demetriadis is a Full Professor at the School of Informatics, Aristotle University of Thessaloniki, Greece. His research focuses on Learning Technologies, including Conversational Agents in Education, Learning Analytics, Computer-Supported Collaborative Learning (CSCL), Computational Thinking, and Massive Open Online Courses (MOOCs). He has led EU-funded projects like colMOOC and developed educational tools such as 'pytolearn' for Python instruction and 'Cubes Coding' (winner of Open Education Challenge 2014 and NUMA Competition 2014). He has supervised 5 completed PhD theses, 4 ongoing PhDs, and over 60 Master’s theses. Academic Appointments: Full Professor (2020–present), Associate Professor (2015–2020), Assistant Professor (2012–2015), Lecturer (2002–2008), Informatics Teacher (1989–2002) Education: PhD in Multimedia Technology in Education (2000), MSc in Electronic Physics (1986), BSc in Physics (1983) His work bridges AI and education, with over 161 publications and an h-index of 27. Recent research explores ChatGPT integration, ethics in Learning Analytics, and AI-driven assessment tools. He has delivered invited talks at institutions like the University of Valladolid (2024) and coordinates the 'Teachers' Fast-paced Distance Training on Tele-education' project. Awards include three international best paper awards and recognition for his 'Cubes Coding' project. Key Research Contributions: Developed frameworks for Conversational Agents in CSCL Innovated Computational Thinking pedagogy through robotics Explored ethics and culture in Learning Analytics adoption Created Python-based MOOCs for non-programmers He has taught courses like Human-Computer Interaction and Learning Analytics, and led short programs on Conversational AI. His collaborations span institutions in Spain, Denmark, and Greece. ORCID: 0000-0002-1561-6372; Google Scholar, Semantic Scholar, and Scopus profiles list his extensive output.

• Microfinance
• Informal Savings Groups
• Sustainable Finance
• +3 more

Bert D'Espallier is a Professor at KU Leuven's Faculty of Economics and Business (FEB), based in the Finance Research Group at the Brussels campus. He serves on the FEB Council and Campus Council FEB Brussels, contributing to institutional governance. His research centers on microfinance and informal savings groups , examining financial/social performance, ethical dilemmas in hybrid organizations, and gender dynamics. Key areas include sustainable finance, donor impacts on social enterprises, and development finance, with emphasis on financial inclusion and poverty alleviation through community-based finance mechanisms. Recent publications (2024-2025) reveal a cohesive research program using advanced methodologies (survival analysis, machine learning, longitudinal studies) to analyze savings groups globally. Dominant themes include autonomy-performance relationships, sustainability determinants, and social potential of informal finance, bridging finance, development economics, and social impact assessment. No scientific awards were documented in the provided sources. D'Espallier supervises 5 PhD students (2 at KU Leuven, 3 at University of Agder) and leads multiple funded projects including 'Contextual performance of informal financial associations' (2020-2025) and 'Sabbatical for informal savings groups research' (SAB/22/036), supported by FWO grants and development industry partnerships. He collaborates with the CERCEM research group at UiA (Norway) and leverages the SAVIX-VSLA database for large-scale analysis of savings groups, actively engaging with development organizations to translate research into policy.

Michał Paweł MichalakView profile

Assistant Professor

• computational geometry
• machine learning in Earth sciences
• statistical methods in Earth sciences
• +2 more

Michał Paweł Michalak is an Assistant Professor at AGH University of Science and Technology in Kraków, affiliated with the Faculty of Geology, Geophysics and Environmental Protection and Department of Geoinformatics and Applied Informatics. His research spans computational geometry in geological modeling, machine learning applications in Earth sciences, statistical epidemiology, and ecclesiological dynamics. Doctoral degree from University of Silesia in Katowice Developed GeoAnomalia software suite combining C++, R, and ParaView Created unbiased risk metrics (WCSIR) for infectious disease surveillance Research focuses on: Subsurface geological modeling using combinatorial algorithms and machine learning; Spatial epidemiology emphasizing testing heterogeneity bias; Ecclesiological dynamics analyzing factional theological interactions. His 2020/37/N/ST10/02504 grant project received 'very good' evaluation for developing angular distance metrics in geological contacts. Scientific contributions include: 2025 Solid Earth paper on fault-related structure detection 2025 Frontiers in Public Health work on global pandemic risk evaluation 2023 AGH Rector award recipient Developer of open-source geological analysis tools Collaboration network includes Harvard University, Technische Universität Freiberg, and University of Texas at Austin. Currently teaching GIS modeling, optimization methods, and geoinformatics systems. Maintains a personal website with open data access.

• Image/Video Processing
• Computer Vision
• Remote Sensing
• +3 more

Prof. Hasan F. Ateş is a Professor at Özyeğin University's Faculty of Engineering, Department of Computer Engineering, specializing in Artificial Intelligence. He joined Özyeğin University as a full-time Professor in 2022 after holding positions at Sabancı University (2004-2005), Işık University (2005-2018), and İstanbul Medipol University (2018-2022). His academic journey began with a B.S. in Electrical and Electronics Engineering from Bilkent University in 1998, followed by M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2000 and 2004, respectively. Prof. Ateş's educational background includes: Doctorate in Electrical Engineering, Princeton University, 2004 Master's in Electrical Engineering, Princeton University, 2000 Bachelor's in Electrical and Electronics Engineering, Bilkent University, 1998 His research focuses on the intersection of deep learning and visual information processing, with particular emphasis on image/video processing, computer vision, remote sensing, autonomous systems, and artificial intelligence. Prof. Ateş leads the Deep-VIP Laboratory, which applies deep learning to solve diverse problems in computer vision and image processing. The lab's work spans multiple sectors including defense, telecommunications, medicine, retail, and consumer electronics. Analysis of Prof. Ateş's recent publications reveals a strong focus on advanced deep learning techniques for visual information processing. His work prominently features state-space models, transformer architectures, and hybrid neural approaches applied to problems like super-resolution, 3D reconstruction, object detection, and medical imaging. Notably, there's a growing emphasis on domain adaptation techniques and cross-spectral registration methods, reflecting the increasing complexity of real-world vision applications. Prof. Ateş's contributions have been recognized through several prestigious awards: First place in the 24th Alper Atalay Best Student Paper Competition (2025) Third place in the Master's Thesis category of the 5DT Thesis Competition (2025) As a dedicated mentor, Prof. Ateş has supervised numerous graduate students through their research journeys. His lab, Deep-VIP, has successfully guided students like İ. Can Yağmur (MS thesis on "Self-Supervised Deep Learning for Multispectral Image Matching") and Gökçe Güven (PhD thesis on "DEEP LEARNING TECHNIQUES FOR 3D VOLUME RECONSTRUCTION FROM PLANAR X-RAY IMAGES"). The lab maintains active collaborations with industry partners including Vestel, Tübitak Bilgem, and Obase, securing research funding for cutting-edge projects in computer vision and deep learning. The Deep-VIP Laboratory is at the forefront of deep learning based computer vision research, with current projects focusing on Image Registration, Tiny Object Detection in Aerial Imagery, Building Height Estimation from Satellite Images, 3D Reconstruction, and Super-Resolution Imaging. The lab's recent work has been presented at major conferences including SIU, IGARSS, and ECAI, demonstrating their commitment to advancing both theoretical understanding and practical applications of visual intelligence systems.

Thomas EbelView profile

Professor

• Power Electronics
• High-Voltage Electrical Engineering
• Dielectric Materials
• +3 more

Thomas Ebel is Professor and Head of the Centre for Industrial Electronics at the University of Southern Denmark (SDU) , Institute of Mechanical and Electrical Engineering. A leading expert in power electronics, high-voltage engineering and capacitor technology, he directs large, multi-partner research projects and teaches/supervises at both graduate and PhD levels. Education & Career Path Prof. Ebel holds the academic title Dr. rer. nat. and has been appointed full Professor at SDU. He concurrently serves as Head of Section at the Centre for Industrial Electronics, orchestrating cross-disciplinary research teams and infrastructure. Research Interests Power Electronics & Power Conversion: advanced converter topologies, WBG devices (GaN, SiC), high-frequency magnetics, grid-forming control. Dielectric Materials & Capacitors: polymer and hybrid nanocomposite dielectrics, self-healing metallized film capacitors, aluminium electrolytic capacitors, lifetime modelling and reliability. High-Voltage Engineering & Breakdown Physics: breakdown mechanisms in nanocomposites, corona and partial discharge, insulation coordination. IoT & Data-Driven Monitoring: real-time condition monitoring, digital twins, data-driven RUL estimation for power components. Publication Trends Across 133 research outputs (2018-2025) the dominant themes are (i) construction and reliability of 700 V-class aluminium polymer electrolytic capacitors, (ii) GaN-based power converter optimisation, (iii) hybrid AC/DC microgrid control and harmonic mitigation, and (iv) nanocomposite dielectrics for next-generation capacitors. The 15 most recent articles (2025) reinforce these directions while adding socio-technical energy analytics and green-vehicle powertrains. Scientific Awards Tek Innovation Prize 2023 – awarded for outstanding contributions to power electronics research and industrial innovation. Advising & Funding Prof. Ebel currently supervises ~10 PhD candidates and post-docs including L. Tavares, M. A. Khan, R. Maheshwari, S. Mateen, A. N. Pinky and others. He is Principal Investigator or Head Coordinator of six active projects (2024-2027) valued at >€8 M, spanning ultra-high-efficiency drives, hydrogen-PtX converters, self-healing capacitors and hybrid power-plant concepts. Laboratory & Teams He heads the High-Voltage Power Electronics Laboratory at SDU, equipped with 700 V/200 A capacitor test rigs, GaN/SiC converter prototyping benches, and environmental chambers for accelerated ageing studies. The centre collaborates with 20+ industrial partners and coordinates the international IEA Wind Task 50 on hybrid power plants.

• Software Engineering
• Model-Based Software Engineering
• Automated Reasoning
• +5 more

Sebastián Uchitel is a Professor at the Department of Computing, Imperial College London, UK. His research focuses on foundational aspects of Software Engineering, particularly in modeling and analysis for automated reasoning, verification of probabilistic systems, controller synthesis, and adaptive systems. He has led major research projects, including the ERC-funded IDEAS StG project on Partial Behaviour Modelling and the European FP6 SENSORIA project. Research Interests: Model-Based Software Engineering, Controller Synthesis, Probabilistic Systems, Adaptive Systems, Requirements Engineering External Roles: General Chair, International Conference on Software Engineering (2017); Associate Editor, Elsevier Science of Computer Programming; Steering Committee, International Conference on Software Engineering His recent work explores intersections between Software Engineering and AI, including assured adaptive systems and logic-based learning. A Senior Member of IEEE and Distinguished Scientist of ACM , he has received awards like the Houssay Prize (2015) and Philip Leverhulme Prize (2005). Collaborators include institutions in Argentina, Canada, and the UK. Selected Publications: 150+ peer-reviewed works spanning controller synthesis, requirements engineering, and formal methods Students: Supervised 12 PhD students since 2003 Grants: Principal Investigator for 3 major grants (2005-2016) totaling over $3.7M USD, including: 2013-2016: Technology Platform in Software Engineering (ANPCYT, $1.6M) 2009-2014: ERC IDEAS StG on Partial Behaviour Modelling (€1.4M) 2005-2008: FP6 SENSORIA Project (€0.7M)

• AI Hardware
• Digital Chip Design
• Efficient AI Algorithms
• +6 more

Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.

• Data Science and Data Analytics
• Machine Learning
• Recommender Systems
• +7 more

Guandong Xu is a Professor in the School of Computer Science at the University of Technology Sydney (UTS), where he has been employed since 2012. He also serves as the Director of the UTS-Providence Smart Future Research Centre, which focuses on disruptive technology for sustainability, and leads the Data Science and Machine Intelligence Lab dedicated to research excellence and industry innovation in data science and artificial intelligence. Dr. Xu holds a PhD in Computer Science from Victoria University, Australia, along with MSc and BSc degrees in Computer Science and Engineering. After holding various research positions at European and Australian universities, he joined UTS in 2012 and was promoted to Associate Professor in January 2017, then to Professor in January 2019. His research spans data mining, machine learning, social computing, recommender systems, text mining, predictive analytics, and user behavior modeling. He has published over 240 papers in these areas with increasing citations from academia. His recent work demonstrates a strong focus on integrating large language models with recommendation systems, causal inference in recommendation, multimodal learning, and fairness in AI systems. His publications reveal sophisticated graph-based approaches and addressing challenges in dynamic recommendation scenarios, particularly through temporal modeling and hypergraph structures. Dr. Xu has received numerous prestigious awards including the Digital Disruptors Winner for ICT Research Project of the Year (2021), eBay's Leaders' Choice Award (2021), and was elected Fellow of Institution of Engineering and Technology (IET), UK (2021) and Fellow of Australian Computer Society (ACS) (2022). He has shown strong academic leadership as founding Editor-in-Chief of Human-centric Intelligent Systems Journal, Assistant Editor-in-Chief of World Wide Web Journal, and founding Steering Committee Chair of the International Conference of Behavioural and Social Computing Conference. He has supervised over 25 high degree research students and secured over $8 million in research funding from ARC, government, and industry sources, including projects like 'Smart Personalized Privacy Preserved Information Sharing in Social Networks' and 'A Secured Smart Sensing and Industry Analytics Facility for Industry 4.0.' Dr. Xu directs the Data Science and Machine Intelligence Lab at UTS, which aligns with UTS research priority areas in data science and artificial intelligence. The lab focuses on research excellence and industry innovation across academia and industry, with particular emphasis on developing advanced techniques for recommendation systems, knowledge graphs, and multimodal learning applications.