Supriyo Ghosh is a Senior Researcher at Microsoft Research, India. Prior to this role, he held positions at IBM Research AI Lab (2019–2021) and the Institute of Infocomm Research (I2R), A*STAR. He completed his PhD in Information Systems at Singapore Management University (2017) under Prof. Pradeep Varakantham and conducted postdoctoral research at MIT's SMART and LIDS centers (2016–2017). His research focuses on data-driven decision analytics, including algorithmic optimization, reinforcement learning, urban logistics, and network resilience in cyber-physical systems. His work has addressed cloud incident management, proactive decision-making under uncertainty, and applications of large language models (LLMs) in system reliability. Notable contributions include developing automated root-cause analysis frameworks and improving incident response strategies in large-scale cloud environments. He has also explored reinforcement learning applications in healthcare treatment optimization and air traffic control systems. Award-winning research includes the Best Paper Award at ACM SoCC'22 for an empirical study on high-severity cloud service incidents. He actively serves as a PC member for top conferences like AAAI, NeurIPS, and ICML, demonstrating his leadership in advancing AI and optimization fields. His academic background includes a graduate exchange at Carnegie Mellon University (CMU) and collaborations with MIT faculty like Prof. Patrick Jaillet. His work bridges theoretical foundations with real-world applications in transportation, cybersecurity, and enterprise systems.
Holly A. Yanco, Ph.D. , is a Professor of Computer Science at the University of Massachusetts Lowell (UML), where she also serves as the Distinguished University Professor and Director of the New England Robotics Validation and Experimentation (NERVE) Center . She founded the Human-Robot Interaction (HRI) Laboratory at UML in 2001, fostering interdisciplinary research in robotics, assistive technology, and artificial intelligence. Education: Dr. Yanco earned her B.A. in Computer Science from Wellesley College , followed by an M.S. and Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) . Research Interests: Her work spans human-robot interaction , robotics education , assistive technologies , urban search and rescue (USAR) , and multi-modal interfaces . She has led collaborative initiatives such as Artbotics , integrating art and robotics in K-12 and college curricula, and Pyro , a Python-based robotics platform recognized as Premier Courseware of 2005 . Grants & Funding: Her research has been supported by the National Science Foundation (NSF) , U.S. Army Research Office , Microsoft Research , and National Institute of Standards and Technology (NIST) . Notable grants include an NSF CAREER Award (2005) and leadership roles in DARPA’s Fast Lightweight Autonomy (FLA) program . Awards & Recognition: Dr. Yanco has received teaching awards from UMass Lowell and MIT , served as General Chair of the 2012 ACM/IEEE International Conference on Human-Robot Interaction , and held leadership roles in AAAI’s Executive Council (2006-2009) . Lab & Outreach: The HRI Lab engages students from high school to Ph.D. levels, emphasizing K-12 outreach through Botball tournaments , STREAM teacher workshops , and the Artbotics program in collaboration with The Revolving Museum .
Mayank Goel serves as an Assistant Professor in the Software and Societal Systems Department (S3D) at Carnegie Mellon University's School of Computer Science. His research bridges computer science and societal impact through practical sensing systems that leverage existing environmental devices for health monitoring and human-computer interaction without requiring hardware modifications. Dr. Goel specializes in mobile computing, signal processing, and machine learning to develop unobtrusive health technologies applicable to real-world scenarios. His core research areas include passive activity recognition for chronic disease management (particularly multiple sclerosis), privacy-preserving acoustic sensing, smartwatch-based clinical interventions for post-operative care, and equitable healthcare systems for global development contexts. He emphasizes end-to-end solutions through close collaboration with medical professionals and designers to ensure immediate deployability outside laboratory environments. Analysis of his 2024-2025 publications reveals a strong interdisciplinary focus spanning computer science, biomedical engineering, and clinical practice. Key trends include longitudinal digital phenotyping for neurological conditions, on-device privacy preservation in activity recognition, and multimodal procedural assistance systems. His work consistently addresses real-world challenges in sensor placement flexibility, user adoption barriers, and equitable access to medical technologies. No scientific awards were mentioned in the available documentation. Information regarding student advising, research grants, or laboratory affiliations was not specified in the provided materials, though his publication record indicates active collaboration with medical professionals and bio-engineers for clinical validation of health technologies.
Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.
David Ardia is a Full Professor in the Department of Decision Sciences at HEC Montréal, promoted to this position on June 1, 2025. Previously, he served as an Associate Professor from June 2020 to May 2025. He holds the Research Professorship in Sentometry and is a member of the Study and Research Group on Decision Analysis (GERAD) and the International Statistical Institute. Ardia is also an elected member of the ISI Louis Bachelier Fellow and serves as Associate Editor for both the International Journal of Forecasting and the Journal of Statistical Software. His educational background includes a Ph.D. in Financial Econometrics from the University of Fribourg, a Master of Applied Sciences in Quantitative Finance from the Swiss Federal Institute of Technology Zurich and University of Zurich, and a Master of Science in Financial Engineering from the University of Neuchâtel. Ardia's research focuses on the intersection of quantitative finance, machine learning, and natural language processing, with particular emphasis on sentometrics (textual sentiment analysis in finance), risk management, and climate finance. His work spans financial econometrics, volatility modeling, and the application of advanced statistical methods to asset allocation and economic forecasting. He has pioneered methods for analyzing climate change concerns in financial markets and has made significant contributions to understanding green versus brown stock performance. His publication record shows a strong trajectory in high-impact finance and statistics journals, with recent work examining Robinhood trading patterns, cryptocurrency markets, climate finance, and innovative methodological approaches to financial time series analysis. His research demonstrates increasing focus on sustainability applications within quantitative finance. Prix de la qualité des données ouvertes 2024 (Canadian Open Data Community) Prix de recherche pour les professeures et professeurs agrégés (HEC Montréal, 2024) Prix pour l'excellence en pédagogie (HEC Montréal, 2022) Best Paper Award at the 38th International Conference of the French Finance Association Best Paper Award 2018-2019 from International Journal of Forecasting eRum 2020 COVID19 contest winner for the COVID-19 Data Hub Ardia actively supervises numerous graduate students, with over 70 mentorship activities documented in the past five years, spanning both thesis supervision and supervised projects. His research is supported by collaborations with institutions including IVADO, the R Consortium, and the University of Lugano. He co-created the influential COVID-19 Data Hub platform, which integrates epidemiological data with policy measures and spatial databases to analyze pandemic impacts. His research group focuses on developing computational tools for financial analysis, particularly through R packages like MSGARCH for Markov-switching GARCH models and sentometrics for textual sentiment analysis. This work bridges academic research with practical applications in financial institutions and policy analysis.
Abhinav Sharma, MD, PhD is an Assistant Professor in the Department of Medicine, Division of Cardiology at McGill University and a Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC), where he is affiliated with the Centre for Outcomes Research and Evaluation (CORE) and the Cardiovascular Health Across the Lifespan program. Education & Training While specific degrees are not detailed in the supplied text, his dual MD and PhD credentials indicate combined clinical and research training, most likely in medicine and either epidemiology, health-informatics or cardiovascular sciences. Research Interests Dr Sharma’s work centres on digital health and precision cardiovascular medicine . His team investigates: Mobile-health and remote-monitoring technologies to drive behaviour change and improve clinical outcomes in heart failure and diabetes. Artificial-intelligence–driven digital biomarkers and voice-assistant systems for scalable clinical screening. Large-scale secondary analyses of landmark trials (CANVAS, CREDENCE, EMPA-REG, EXAMINE, TOPCAT) to dissect heterogeneous cardiovascular phenotypes, sex-specific responses and regulatory implications. Publication Trends Across >100 manuscripts since 2018, three thematic arcs emerge: Pharmacotherapy Trials & Meta-analyses evaluating SGLT2 inhibitors, GLP-1 receptor agonists and mineralocorticoid antagonists across heart-failure and diabetic cohorts. Digital-Health Intervention Studies including RCT protocols (TARGET-HF-DM, DECIDE-CV) and validation of AI voice-screening for COVID-19 exposure in cardiology clinics. Precision-Medicine Analytics employing unsupervised machine-learning to delineate phenotypic clusters predictive of cardiovascular and renal events. Scientific Awards & Recognition No specific honours or prizes are listed in the provided text. Grants & Clinical Trials Dr Sharma is principal architect of several multicentre programmes: TARGET-HF-DM – mobile-health intervention to enhance physical activity and medication adherence in heart failure with comorbid diabetes. DECIDE-CV – synchronous telehealth model for integrated cardiorenal care in type 2 diabetes. SOGALDI-PEF – factorial RCT of SGLT2 inhibition ± aldosterone antagonism in heart failure with preserved ejection fraction. Laboratory & Collaborations Operating within the RI-MUHC ecosystem, his research group leverages the institute’s Technology Platforms (Bioinformatics, Clinical Informatics, Proteomics, Small-Animal Imaging) and maintains partnerships across McGill’s Department of Epidemiology, the MUHC cardiac e-health clinic, and international consortia such as the Heart Failure Collaboratory (HFC) and the Academic Research Consortium (ARC).
Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.
Matthew Grilli is an Associate Professor in the Department of Psychology at the University of Arizona , where he directs the Clinical Program, Neuropsychology Minor, and Human Memory Lab. His research spans the neuropsychology and cognitive neuroscience of autobiographical memory, imagination, and cognitive aging, with a focus on disorders like amnesia and Alzheimer’s disease. His work investigates individual differences in autobiographical thought , the impact of aging on memory specificity, and innovative neuropsychological assessment methods. Recent publications highlight aging-related changes in memory dynamics, brain structure-function relationships, and real-world applications such as phishing vulnerability and bilingualism’s role in cognitive aging. Grilli’s research utilizes naturalistic observation and computational approaches (e.g., latent brain state analysis, large language models) to study memory coherence, emotional simulation, and intergenerational conversations. He is also involved in developing tools like the Phishing Email Suspicion Test (PEST) to assess cognitive mechanisms in aging populations. The Human Memory Lab under his direction explores memory’s intersection with well-being, identity, and brain health, employing multimodal methodologies to address both theoretical and applied questions in cognitive neuroscience.
Yan Cong is an Assistant Professor at Purdue University, focusing on Chinese linguistics and computational linguistics. Their work bridges natural language processing (NLP), semantics, and pragmatics, with applications in artificial intelligence (AI), language education, and healthcare. Research Focus: Developing text analysis models to quantify and improve language learning, assessing semantic/pragmatic competence in language models, and applying computational methods to speech and language fluency. Background: Former NLP researcher at the Feinstein Institutes, with a PhD in Linguistics from Michigan State University. Research Themes: Yan Cong integrates linguistic theory with AI to explore language understanding in humans and machines. Key areas include Computational modeling of semantics and pragmatics Application of NLP to second language acquisition Development of interpretable AI systems for education and healthcare Analysis of speech disturbances in clinical contexts (e.g., schizophrenia, aphasia) Awards: No specific honors mentioned in the provided text.
Dr Chris Carignan serves as Programme Director for the Language Science MSc at University College London's Division of Psychology and Language Sciences within the Faculty of Brain Sciences. His research focuses on the intricate mechanisms of human speech production, particularly through cross-linguistic phonetic studies and articulatory dynamics using advanced imaging technologies. PhD in Speech Science Specializes in the intersection of language heritage and phonetic research Develops innovative methodologies for speech data collection and analysis His work spans multiple areas including nasal coarticulation , real-time MRI of vocal tract movements , and cross-linguistic phonetic analysis . He has contributed significantly to understanding how speakers produce complex speech sounds across different languages through comparative studies. Dr Carignan's research has led to the development of open-source tools for speech production analysis and pioneered new approaches for ultrasound articulography . His work on vowel nasalization and sibilant contrasts has been particularly influential in speech science. As Programme Director, he oversees a comprehensive curriculum that provides students with methodological training , statistical expertise , and hands-on research experience while allowing specialization through optional modules. His teaching philosophy emphasizes the importance of curiosity-driven research and interdisciplinary approaches in advancing language sciences.
Dr. Irfan Ahmad serves as an Associate Professor in the Department of Information and Computer Science at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, where he teaches undergraduate and graduate courses in Computer Science and Software Engineering while conducting research and advising graduate students. His academic service includes committee roles on graduate studies, program development, and competitions. His research expertise centers on Pattern Recognition with specialized focus on Document Image Analysis , Handwriting Recognition , and Machine-Printed Text Recognition . He actively explores Machine Learning applications including Deep Learning and Natural Language Processing , with significant contributions across Artificial Intelligence, Computer Vision, Data Mining, Neural Networks, and Computational Linguistics as evidenced by his PeerJ subject area specializations. Recent publications reveal a strategic emphasis on adaptive deep learning architectures for document analysis, particularly generative methods for handwritten text recognition and knowledge distillation techniques. His editorial work on feature extraction and multilingual fake news detection further demonstrates applied research bridging theoretical machine learning with real-world language processing challenges. As an active Academic Editor for PeerJ Computer Science with 1,205 contribution points, Dr. Ahmad provides substantial service to the scholarly community through manuscript evaluation and editorial oversight across emerging technologies in data science and artificial intelligence.
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.
Wout Joseph is a Professor in the domain of Experimental Characterization of wireless communication systems at Ghent University (Belgium), where he has been working since October 2009. He is also an IMEC Principal Investigator since 2017. His research is conducted within the wireless, acoustics, environment & expert systems (WAVES) research unit at the Department of Information Technology (INTEC). Dr. Joseph was born in Ostend, Belgium on October 21, 1977. He received his M.Sc. degree in electrical engineering from Ghent University in July 2000. From September 2000 to March 2005 he was a research assistant at the Department of Information Technology (INTEC), where his scientific work focused on electromagnetic exposure assessment around base stations for mobile communications related to health effects. This work led to his Ph.D. degree in March 2005. Professor Joseph's research expertise spans multiple domains within wireless communications and bioelectromagnetics. His primary research interests include electromagnetic field exposure assessment, in-body electromagnetic field modeling, electromagnetic medical applications, propagation for wireless communication systems, IoT, antennas and calibration. He also specializes in wireless performance analysis, industry 4.0 applications, wireless localization, and Quality of Experience metrics. His work is particularly notable for its focus on dosimetric studies in the radiofrequency range, where his research is ranked first in number of peer-reviewed studies. His research has practical applications in wireless network planning, occupational safety, and public health policy related to electromagnetic fields. His extensive publication record (over 886 publications with an h-index of 45 in ISI Web of Science and 66 in Google Scholar) demonstrates a clear trajectory from fundamental electromagnetic field measurements to applied research in industrial wireless networks and bioelectromagnetic applications. Recent work shows a strong emphasis on 5G exposure assessment across multiple European countries, millimeter-wave channel modeling, and the application of machine learning techniques to exposure assessment and wireless localization. EBEA council board member (2015-2018) EBEA board member at large (2019) Bioelectromagnetics Society board member (2022) Bioelectromagnetics Society board member (2024) 24 research awards Professor Joseph leads significant research efforts in electromagnetic field exposure assessment, with particular emphasis on developing measurement methodologies and computational models for real-world exposure scenarios. His work bridges theoretical electromagnetic modeling with practical applications in wireless communications and bioelectromagnetics. His research group within the WAVES unit is highly active in both theoretical and experimental aspects of wireless communications and bioelectromagnetics, with current projects focusing on 5G exposure assessment across Europe, millimeter-wave channel modeling for data centers and industrial environments, and the development of novel exposure assessment methodologies using advanced signal processing and machine learning techniques.
Dr. Patrick W. C. Ho is a Lecturer in the Department of Electrical & Computer Systems Engineering (ECSE) at Monash University Malaysia School of Engineering. He holds a PhD in Electronics Engineering from the University of Nottingham Malaysia Campus (2016), with research focusing on non-volatile FPGA architectures using memristors. His academic journey includes roles as a Scholarly Teaching Fellow and unit coordinator for courses like ECE2131 Electrical Circuits and ECE4063 Large Scale Digital Design. He has industry experience with Intel Microelectronics and Altera Corporation, alongside teaching A-level Physics at Methodist College Kuala Lumpur. Education: BEng (First Class Honours) in Engineering (2009) MSc in Science (2012) PhD in Electronics Engineering (2016) Research Interests: Dr. Ho specializes in memristor-based non-volatile memory systems, VLSI design, and FPGA architectures. His work bridges hardware design with emerging materials, as seen in his Q1 journal article on memristive LUTs. Collaborations with CAD-IT expand his focus into AI, image processing, and object recognition. Recent projects include studies on memristor substrate performance (2023–2026) and UAV communication reliability (2021–2024). Teaching and Industry Engagement: As ECSE’s Industrial Training Advisor and IAP representative, he actively connects academic curricula with industry needs. His teaching spans foundational engineering courses and advanced digital design modules. Labs and Collaborations: Active in CAD-IT partnerships for student FYP co-sponsorship. Research groups focus on nanotechnology, machine learning integration in UAV systems, and memristor material analysis.
Xiao Han is a Senior Lecturer (Assistant Professor) in Finance at the Bayes Business School , part of the City, University of London . His research focuses on investor expectations, asset pricing, and the application of machine learning in finance. He holds a PhD in Finance from the University of Edinburgh and a Higher Education Fellowship in the UK. Education: PhD in Finance, University of Edinburgh (2017-2021) MSc Finance with Risk Management, University of Bath (2016-2017) B.A. in Accounting, Dongbei University of Finance and Economics & Curtin University (2012-2016) His research interests include subjective investor expectations , financial institutions and demand-based pricing , and machine learning applications in Fintech . He has held visiting positions at the Wharton School, Peking University, and Shanghai University of Finance and Economics. His recent work explores topics such as return decomposition in financial markets, machine learning-driven earnings analysis, and the impact of investor sentiment on mispricing. His research has been published in top journals like the Journal of Financial Economics and Review of Financial Studies . Awards: Best Paper Award in Investments (Eastern Finance Association) Jacobs Levy Center Research Best Paper Prize 2023 Marshall Blume Prize in Financial Research 2023 Xiao Han serves as a referee for journals including Journal of Financial Economics , Review of Financial Studies , and Management Science . His work bridges theoretical finance with practical applications in Fintech and behavioral economics.
