Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.
Marc Plantevit is a Full Professor at EPITA, member of the Laboratoire LRDE (LRDE). Previously, he served as an Associate Professor at University Claude Bernard Lyon 1 (2010–2021), leading the Data Mining & Machine Learning group at LIRIS lab. He holds a PhD in Computer Science from the University of Montpellier (2008), supervised by Maguelonne Teisseire and Anne Laurent at LIRMM Lab. His research focuses on foundational data mining, graph mining, subgroup discovery, and explainable AI. He is an editorial board member of Data Mining and Knowledge Discovery Journal and has held roles such as CAPES NSI jury member and former head of the Data Mining & Machine Learning group at LIRIS. Research Interests : Data Mining, Machine Learning, Explainable AI, Graph Mining, Subgroup Discovery, Exceptional Model Mining, Constraint-based Pattern Mining, and applications in neuroscience and energy systems. His work explores interpretable AI, GNN explainability, and interdisciplinary applications like odor perception modeling and electricity price forecasting. Key Contributions : Best Paper Award at EGC'22 for work on GNN representations. Active in program committees for ECMLPKDD, IJCAI, and IEEE ICDM . Supervised PhD students working on GNN explainability, electricity forecasting, and machine learning in exposome studies. Labs & Teams : LRDE (EPITA), previously involved with LIRIS (UMR CNRS 5205) and collaborative projects with institutions like INSA Lyon and ISGlobal (Barcelona).
Dr. Abubakar Bello is a Senior Lecturer in Criminal Justice and Program Leader at Edge Hill University's School of Law, Policing, and Criminal Justice. Previously, he held roles at Western Sydney University, including Academic Program Advisor and Lecturer in Cyber Security and Behaviour. He holds a PhD in Cyber Criminology, an MBA in Business Law and Technology, and degrees in Computer Science. His research focuses on interdisciplinary approaches to cyber security risks, threat intelligence models, and behavioral aspects of cyber crime. Education: PhD (Cyber Criminology, Murdoch University), MBA (Business Law & Tech, Western Sydney University), MSc & BSc (Computer Science, University of Wolverhampton). Research Interests: Combating cyber crime through AI and machine learning, secure systems design, and behavioral cybersecurity. Key areas include ransomware defenses, social engineering, and cybersecurity frameworks for diverse populations. Grants & Projects: Awarded funding for initiatives such as 'Social Engineered Payment Diversion Fraud' (NSW Cyber Security Network), 'Brain-Inspired Algorithm for Network Anomaly Detection' (DST Group), and 'Cyber Security Awareness Framework' (ECR Grant). Awards: 'Award for Teaching and Learning Contributing to Public Good.' Active in professional networks like the International Centre on Racism and Centre for Applied Criminal Justice Research. Labs & Collaboration: Engages in cyber investigations, forensics, and community outreach through initiatives like Western Cyber Aid. Serves as a consultant for corporate espionage cases and a speaker on ransomware and AI in law enforcement.
Suzy Anger serves as an Associate Professor and MA Advisor in the Department of English Language and Literatures within the Faculty of Arts at the University of British Columbia. Her academic work bridges literary studies with the history of science, focusing on how Victorian literature engaged with contemporary scientific thought. Dr. Anger received her BA from the University of California, Berkeley, followed by her MA and PhD from the University of Washington. Her educational background established the foundation for her interdisciplinary approach that connects literary analysis with scientific and philosophical inquiry. Her research primarily explores Victorian literature in relation to nineteenth-century science, psychology, and philosophy. Currently, she investigates Victorian fiction and nineteenth-century theories of consciousness, examining novels by Thomas Hardy, Charles Dickens, George Gissing, and Sarah Grand across genres including ghost fiction and scientific romance. Her scholarly work demonstrates how literary texts engaged with emerging scientific concepts of the period, particularly in the areas of mind sciences, hermeneutics, and technological understanding. She has particular expertise in how literary works processed scientific developments related to consciousness, automatism, and the relationship between humans and machines. Her publication record reveals a consistent trajectory of scholarship examining the intersection of Victorian literature and scientific thought, with recent work increasingly focused on theories of consciousness, literary automatism, and the philosophical implications of scientific developments in the nineteenth century. Her research demonstrates how literary works served as laboratories for exploring complex scientific concepts before they were fully developed in scientific discourse. Sonya Rudikoff Prize for the best first book in Victorian Studies (2006) Killam Teaching Prize, University of British Columbia (2018) Newhouse Center Faculty Fellowship, Wellesley College (2006-2007) SSHRC Faculty Research Grant (2005-2008) American Council of Learned Societies Fellowship (2000-2001) National Endowment for the Humanities Stipend (1999) As an academic leader, Dr. Anger has served as president of the Northeast Victorian Studies Association and was former co-chair of UBC's interdisciplinary graduate program in Science and Technology Studies. Her research has been supported by multiple prestigious fellowships and grants from major funding bodies including the American Council of Learned Societies, the National Endowment for the Humanities, and the Social Sciences and Humanities Research Council of Canada. Her editorial work on collections like 'Victorian Science as Cultural Authority' and 'Victorian Automata' has helped shape contemporary scholarship in the field. Through her editorial projects and interdisciplinary collaborations, Dr. Anger has been instrumental in developing the field of Science and Technology Studies within literary scholarship. Her work with colleagues across disciplines has created important spaces for examining how literature processes scientific and technological change, particularly in historical contexts where such changes were rapidly transforming society's understanding of itself.
Rebecca Nugent is the Stephen E. and Joyce Fienberg Professor of Statistics & Data Science and Department Head at Carnegie Mellon University. She holds a PhD in Statistics from the University of Washington (2006), an MS in Statistics from Stanford (2006), and a BA in Mathematics, Statistics, and Spanish from Rice University (2002). Her research spans clustering methodology , record linkage , educational data mining , public health , and semantic organization , with a focus on high-dimensional data and adaptive learning environments. She leads the Integrated Statistics Learning Environment (ISLE) and Corporate Capstone programs, emphasizing low-barrier data platforms for education and industry collaboration. Academic Roles : Department Head, Carnegie Mellon; Affiliated Faculty, Block Center for Technology and Society Research Grants : NSF (2017-2019), NIH (2018), Carnegie Mellon ProSEED/Simon Initiative (2020, 2018), Berkman Fund (2014) Her 15 most recent publications focus on data science pedagogy, clustering algorithms, record linkage applications in historical and medical data, educational data mining, and semantic organization studies. Awards include the ASA Waller Education Award (2015) and the William H. and Frances S. Ryan Award (2015) . She mentors a diverse group of PhD, Master's, and undergraduate students, with alumni pursuing careers in academia, industry, and sports analytics.
Dr. Sie Teng Soh is an Associate Professor at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences. With qualifications including a PhD from Louisiana State University, he specializes in computer networks, wireless systems, and algorithm design. Research focuses on: Network topology optimization for UAV systems Energy-efficient IoT task scheduling Reliable wireless communication protocols Game-theoretic network management Green computing in software-defined networks Publication trends show advancing work in UAV network optimization, with recent articles addressing max-min rate optimization, energy harvesting in IIoT, and machine learning approaches for coverage prediction. His research consistently addresses practical challenges in wireless network deployment under real-world constraints. Teaching areas include advanced courses in network reliability and traffic engineering. Professional service includes editorial roles for IEEE Transactions on Parallel and Distributed Systems and program committee memberships for major conferences including FAST and EuroSys.
Prof. Dr. Anne Lauscher is an Associate Professor of Data Science at the University of Hamburg Business School, specializing in fair, inclusive, and sustainable conversational AI systems. Her research focuses on improving algorithmic fairness through demographic factors in NLP systems and exploring ethical implications of large language models. She holds a PhD from the University of Mannheim, where her work on computational argumentation was awarded summa cum laude, and has conducted research at Grammarly and the Allen Institute for AI. Key contributions include gender-fair machine translation datasets (e.g., Building Bridges), bias detection frameworks (e.g., SHADES), and multilingual benchmarking tools like MultiQ. Her work has been recognized with the Maria Gräfin von Linden-Award and inclusion in the '100 Brilliant Women in AI Ethics' list. Research spans ethical NLP, multilingual AI, and societal impacts of AI technologies. Education: PhD in Data and Web Science (University of Mannheim, 2021), Postdoc at Bocconi University's NLP group (2021-2022). Academic roles include adjunct positions and international collaborations across Europe and the US. Research Interests: Conversational AI fairness, multilingual NLP systems, ethical AI evaluation, bias mitigation in LLMs, and interdisciplinary applications of machine learning in scientific discovery. Publications (select highlights): Over 55 peer-reviewed works in top-tier venues like ACL, EMNLP, and AAAI. Recent focus on LLM hallucination analysis, cross-cultural NLP benchmarks, and gender-neutral language resources. Awards: 2021 Maria Gräfin von Linden-Award (Baden-Württemberg), 2023 '100 Brilliant Women in AI Ethics', 2022 Dissertation Award Nominee (GI). Labs/Teams: Leads the UHH Data Science Research Group, collaborating with industry partners like Grammarly and academic institutions worldwide. Active in initiatives promoting gender equity in STEM and sustainable AI development.
Dr. M.Z. Naser is an Assistant Professor in the Glenn Department of Civil Engineering at Clemson University. His research focuses on causal and explainable machine learning methodologies applied to structural engineering, materials science, and fire safety. He holds a PhD from Michigan State University and an M.S. from the American University of Sharjah. Naser teaches courses such as Machine Learning for Civil Engineers and Structural Fire Engineering, emphasizing interdisciplinary innovation. His work bridges data-driven analysis with domain-specific knowledge to address challenges in resilient infrastructure design, including fire-resistant materials, structural retrofits, and AI-driven decision-making. Education: PhD, Michigan State University; M.S., American University of Sharjah Research Themes: Explainable AI, Fire Engineering, Structural Materials, Causal Inference Key Projects: Developing SPINEX framework, wildfire classification models, and cognitive infrastructure systems Recent publications analyze over 1000 fire tests to uncover spalling mechanisms, explore synthetic fire tests via GANs, and benchmark automated ML platforms. His work on causal diagrams for civil engineers and firefighter algorithms highlights contributions to both theory and practical applications. Naser also advocates for integrating AI into engineering education, emphasizing ethical and transparent model deployment.
Laura Sanchez is an Associate Professor in the Department of Chemistry and Biochemistry at the University of California, Santa Cruz (UCSC). She leads a research lab focused on imaging mass spectrometry and natural products discovery, with a particular emphasis on microbial interactions, metabolomics, and applications in women’s health. Previously, she was at the University of Illinois at Chicago (UIC) before relocating to UCSC in 2021. Education: B.A. in Chemistry from Whitman College (Walla Walla, WA) followed by a Ph.D. in Chemistry at UCSC under Professor Phil Crews. Postdoctoral training included work under Professors Roger Linington (UCSC) and Pieter Dorrestein (UC San Diego as an NIH IRACDA Fellow). Her research integrates advanced mass spectrometry techniques with biological systems to study small molecule communication in microbial communities and cancer metabolism. Research Interests : Specialization in imaging mass spectrometry (MALDI-TIMS-MS2), microbial metabolomics, and development of novel techniques like SICRIT (Soft Ionization by Chemical Reaction in-Transfer). Her lab investigates how microbial interactions influence metabolite production and explores metabolomic signatures in ovarian cancer progression. Recent work includes spatial quantification of signaling molecules (e.g., c-di-GMP in biofilms) and the role of neurotransmitters like norepinephrine in cancer cell survival. Awards : K12 BIRCWH Scholar (2016–2017) 2019 UIC Rising Star in the Life Sciences 2022 ACS Infectious Diseases Young Investigator Award 2022 American Society for Pharmacognosy Matt Suffness Young Investigator Award Grants & Collaboration : Developed the Natural Products Atlas (open-access knowledge base) and contributed to workflows like TIMSCONVERT. Collaborations span microbiology, oncology, and bioinformatics, with a focus on translational research in women’s health. Current projects include analyzing fallopian tube-ovary cross-talk in high-grade serous ovarian cancer and optimizing mass spectrometry for high-throughput screening. Labs & Teams : Directs an interdisciplinary lab at UCSC, emphasizing open-source method development and collaborative microbiome research. Her team includes graduate students and postdocs working on microbial communication, cancer metabolomics, and imaging technology innovation.
Dr. Nidhi Hegde is an Associate Professor in the Department of Computing Science at the University of Alberta and a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). Her research focuses on privacy-preserving machine learning, algorithmic fairness, and robust algorithm design for networked systems. Dr. Hegde's current research investigates differential privacy in bandit algorithms, debiasing frameworks for language models, and long-term fairness guarantees for minority groups. Her work combines theoretical foundations with practical applications in distributed systems and multi-agent learning environments. Recent publications address covariate shift effects in optimization, private matroid optimization, and reinforcement learning with functional noise. She teaches graduate courses on Responsible AI and Ethical Issues in Data Analytics, covering topics including data privacy, fairness in algorithms, interpretability, and accountability. Dr. Hegde maintains active research collaborations and previously led privacy research at Borealis AI (RBC's research institute).
Joseph Ramsey is a Researcher in the Department of Philosophy at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . He serves as Director of Research Computing and has been instrumental in developing computational infrastructure and algorithms for causal inference. Core projects: Tetrad (causal search algorithms), AProS (proof generator for logic), Causality Lab , and Laboratory for Symbolic and Educational Computing . His research spans causal modeling, algorithm design, and applications in neuroscience, bioinformatics, and education. He has contributed to software tools like Causal-learn and Py-Tetrad , enabling scalable causal discovery in high-dimensional datasets. He has received funding from NASA, NSF, and the University of Pittsburgh for projects ranging from Martian rover software to glaucoma detection models. His work integrates philosophy, computer science, and applied statistics.
Celia Reina is an Associate Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania’s School of Engineering and Applied Science (SEAS). Her research focuses on multiscale modeling of materials, bridging statistical mechanics, thermodynamics, and machine learning. She develops novel frameworks for predicting non-equilibrium material behavior using data-driven methods and uncertainty quantification. Her work emphasizes integrating computational tools like neural networks (Stat-PINNs, VONNs) with physical principles to model dissipative systems, phase transitions, and mesoscale dynamics. Key areas include coarse-graining techniques, epistemic uncertainty analysis, and predictive modeling of complex materials under dynamic loading. Recent publications highlight advancements in stochastic systems, resonant metamaterials, and the derivation of thermodynamic models from particle-level fluctuations. She leads efforts in experimental-simulation co-design to enhance predictive capabilities in materials science.
Joshua Loftus is a Professor of Statistics and Data Science at the London School of Economics (LSE), Department of Statistics. His research focuses on improving data science practices to reduce bias and enhance fairness in algorithms, particularly addressing social harms and scientific reproducibility. He develops methods for statistical inference post-model selection and uses causality to analyze algorithm fairness and interpretability. His work bridges high-dimensional statistics, causal inference, and ethical AI, with a strong emphasis on practical applications using R in data science education. Before joining LSE, Loftus earned his PhD in Statistics at Stanford University, served as a Research Fellow at the Alan Turing Institute (affiliated with the University of Cambridge), and was an Assistant Professor at New York University (2017–2020). His research interests extend to the societal implications of technology, advocating for systems that prioritize human values over technical efficiency. Key research themes include counterfactual fairness, causal reasoning in algorithmic systems, and disaggregated interventions to reduce inequality. His recent work explores temporal aspects of fairness, model-agnostic auditing, and the integration of ethical frameworks into machine learning pipelines. While no scientific awards are explicitly listed, his contributions to foundational AI ethics and statistical methodology are widely recognized in academic circles. Advising and grant details are not provided in the source text, but his leadership in interdisciplinary research collaborations, such as the Turing Institute affiliation, highlights active engagement in research networks. Loftus is part of the LSE’s vibrant data science community, contributing to both theoretical advancements and applied solutions for equitable technology deployment.
Tom Beucler is a Conditional Pre-Tenure Assistant Professor in Geo-Environmental Data Science at the University of Lausanne’s Institute for Earth Surface Dynamics (IDYST). He holds a Master’s degree in Science and Mechanics from École Polytechnique (2014) and a PhD in Atmospheric Science from MIT (2019). Postdoctoral research at Columbia University and UC Irvine focused on machine learning applications in climate science under Professors Pierre Gentine and Michael Pritchard. Research Interests: Climate informatics, atmospheric physics, fluid dynamics, tropical meteorology, and integrating machine learning into climate models for extreme weather prediction and hydrological cycle modeling. Collaborations: Works with environmental scientists and computer engineers to improve climate models using neural networks and causal discovery methods. Initiatives: Organizes weekly brainstorming sessions to promote machine learning adoption in environmental sciences. Publications span climate-invariant machine learning, data-driven parameterizations, and hybrid AI-climate modeling frameworks like ClimSim. His work emphasizes causal consistency and generalizability across climate conditions.
Bernadette Bucher is an Assistant Professor in the Robotics Department (primary) and Computer Science and Engineering Department at the University of Michigan. Her research focuses on embodied AI, vision-language grounding, and mobile manipulation, with an emphasis on interpretable visual representations and uncertainty estimation for robotics tasks. She previously worked at Boston Dynamics AI Institute, NVIDIA Research, and Lockheed Martin Corporation. Her academic background includes a PhD in Computer Science from the University of Pennsylvania (GRASP Lab) under advisors Kostas Daniilidis and Nikolai Matni, alongside MA degrees in Mathematics and Economics from the University of Alabama (2014). Research interests include robotics, computer vision, and machine learning intersections, particularly autonomous mobile manipulation. Her work emphasizes uncertainty-aware systems and deployable learning-based methods. Notable achievements include the Best Paper in Cognitive Robotics at ICRA 2024. Her research spans projects like EVORA for off-road autonomy and ASHiTA for hierarchical task analysis. She has contributed to open-source projects like RoboNet and actively publishes in top conferences (CVPR, CoRL, ICRA). Key projects: EVORA, ASHiTA, Vision-Language Frontier Maps (VLFM) Grants and funding: Honda Research Institute (Curious Minded Machines project) Labs/Teams: Active participation in robotics labs at University of Michigan and prior collaborations with industry partners
