Cheng Zhang is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on computer vision, machine learning, artificial intelligence, and cyber-physical systems. He holds a Ph.D. from The Ohio State University (2022), an M.S. from Beijing University of Posts and Telecommunications (2016), and a B.Eng. from Tianjin University (2013). His work emphasizes high-fidelity 3D garment generation, text-to-image systems, and addressing challenges in long-tailed data for instance segmentation. Key achievements include the 2023 Best Paper Finalist at ICCV and the 2022 Ohio State University Graduate Research Award. Selected publications span top venues like ACM SIGGRAPH Asia, ECCV, and ICCV, showcasing contributions to generative models, view synthesis, and calibration techniques in computer vision.
Benjamin Machta is an Assistant Professor of Physics at Yale University, affiliated with the Department of Physics and the QBio Institute. He holds a BS from Brown University and a PhD from Cornell University, followed by a postdoctoral fellowship at Princeton University. His research focuses on applying theoretical physics to understand biological systems, particularly leveraging statistical physics and information theory to study biological membranes near critical points and the energetic constraints of biological signaling. Education: BS in Physics (Brown University), PhD in Physics (Cornell University), Postdoc at Princeton University (Lewis-Sigler Theory Fellow). Research Interests include: membrane criticality, phase transitions in biological systems, information-theoretic limits in organism function, and energy dissipation in biological processes. His work often bridges theoretical models with experimental data, such as collaborations with Sarah Veatch’s lab on membrane phase behavior. Publications highlight themes like membrane criticality, protein phase separation, and energy constraints in signaling. His group’s current projects explore cochlear mechanics, thermodynamic control in biological systems, and the role of criticality in sensory systems. Awards: 2019 Simons Investigator Award. Lab Affiliations: QBio Institute and Department of Physics at Yale, located in YSB-C164. Group members include postdocs Isabella Graf and Michael Abbott, and graduate students Asheesh Momi, Mason Rouches, and others.
Finlay Maguire is an Assistant Professor jointly appointed in the Faculty of Computer Science and the Department of Community Health & Epidemiology at Dalhousie University. He leads the Maguire Lab, which develops data-driven methods to address health and social crises through genomic epidemiology and interdisciplinary health data science. He is also affiliated with the Shared Hospital Laboratory, Sunnybrook Research Institute, and multiple national and international public health consortia including PHA4GE, CanCOGeN, and IRIDA. PhD: University College London / Natural History Museum (2016) MA: University of Oxford (2011) Donald Hill Family Fellowship, Dalhousie University (2021) Dr. Maguire's research focuses on two main areas: genomic epidemiology of infectious diseases and interdisciplinary health data science collaborations . His work in genomic epidemiology includes developing bioinformatics and machine learning tools to study antimicrobial resistance (AMR) and SARS-CoV-2 dynamics, often in collaboration with public health agencies. His broader health data science work addresses issues such as online radicalization, healthcare access for refugees, and autism-related language use, combining computational methods with social science. His recent publications (2023–2025) reflect a strong trend in pathogen genomics , AMR , zoonotic spillover , and computational social science . He has published on novel coronaviruses in bats, SARS-CoV-2 animal models, invasive Group A Streptococcus, and sociological analyses of incel communities. Much of this work involves tool development (e.g., ArgNorm, Pathoplexus) and data standardization (e.g., PHA4GE metadata standards). Finalist, 2024 Discovery Awards (Emerging Professional) 2023 President’s Research Excellence Award for an Emerging Investigator, Dalhousie Finalist, 2023 Discovery Awards (Emerging Professional) Funding from CIHR, NSERC, Genome Canada, SSHRC, BMGF Dr. Maguire actively mentors graduate students and postdocs, including PhD candidates in Computer Science and MSc students in Community Health & Epidemiology. He has secured major training grants such as the CIHR Health Research Training Platform and the Canadian One Health Training Program for Emerging Zoonoses. He also contributes to capacity-building initiatives like MicroResearch in Ghana and Kenya. The Maguire Lab is embedded in a rich network of collaborations, including the CARD database, Public Health Agency of Canada, Canadian Food Inspection Agency, and Sunnybrook Health Sciences Centre. The lab emphasizes open science, reproducible research, and interdisciplinary training, as seen in the development of open-source tools and participation in international consortia.
Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.
Harri Lähdesmäki is an Associate Professor (tenured) at the Department of Computer Science, Aalto University, where he leads the Computational Systems Biology research group. His work focuses on probabilistic machine learning and deep generative models with applications in biomedicine and molecular biology. Key Research Interests: Probabilistic machine learning, deep generative models, computational biology, bioinformatics, longitudinal data modeling Contact: harri.lahdesmaki@aalto.fi | Konemiehentie 2, 02150 Espoo, Finland His recent publications highlight advancements in: Gaussian process priors for scalable deep generative models Single-cell analysis of immune repertoires in leukemia and diabetes Probabilistic deconvolution methods for RNA-seq data Epigenetic analysis using hidden Markov and mixed models Transformer-based survival prediction and missing data handling Harri’s work integrates mechanistic modeling with Bayesian inference, particularly applied to immunology, cancer biology, and early disease prediction.
Ian Miers serves as an Assistant Professor in the Department of Computer Science at the University of Maryland, holding a joint appointment with the University of Maryland Institute for Advanced Computer Studies (UMIACS) and serving as a core faculty member of the Maryland Cybersecurity Center (MC2). His academic home resides within the Department of Computer Science, though the overarching college/school structure is not explicitly stated in available materials. His research program centers on applied cryptography with a context-driven methodology: starting from real-world security challenges to develop deployable cryptographic protocols. Key focus areas include blockchain privacy (notably Zerocoin/Zerocash), zero-knowledge proofs, anonymous credentials, and secure messaging systems. Miers emphasizes practical implementations that address subtle security requirements in production environments, bridging theoretical cryptography with tangible system security. Analysis of his 15 most recent publications reveals dominant trends in zero-knowledge proof scalability (zkSNARKs), privacy-preserving infrastructure for blockchains, and cryptographic solutions for content moderation in encrypted messaging. His work consistently targets deployable systems, with increasing focus on balancing privacy guarantees with accountability requirements in real-world applications. Miers actively recruits PhD students for hands-on research in his small lab, emphasizing direct collaboration on applied security and blockchain problems. As a founding scientist of Aleo, Bolt Labs, and Zcash, he translates academic research into commercial products, with his work receiving coverage from major media outlets including The Washington Post, The New York Times, and Wired.
Matthias Feurer is a Thomas Bayes Fellow and interim professor at the Chair of Statistical Learning and Data Science, funded by the Munich Center for Machine Learning (MCML) at Ludwig Maximilian University of Munich. He is a member of the Department of Statistics at LMU Munich, working under Prof. Dr. Bernd Bischl. His academic background includes: PhD in Computer Science from Albert-Ludwigs-Universität Freiburg, supervised by Prof. Dr. Frank Hutter M.Sc. in Computer Science from the University of Freiburg B.Sc. in Computer Science and Media from the Media University Stuttgart Feurer's research focuses on simplifying machine learning usage through Automated Machine Learning (AutoML). His work encompasses hyperparameter optimization, meta-learning, and model selection, with increasing emphasis on multi-objective AutoML that considers factors beyond predictive performance such as interpretability, deployability, and fairness. He actively develops open-source tools to advance the field. His recent publications demonstrate a strong trajectory in practical AutoML systems, with growing attention to tabular machine learning, foundation models integration, and addressing real-world constraints in optimization. His work consistently bridges theoretical advances with practical implementations through several widely-used open-source projects. Notable achievements include: 1st place in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge Winner of the 2nd AutoML challenge Winner of the kdnuggets blog contest on AutoML Feurer is actively mentoring and teaching, having advertised PhD positions focused on AutoML, optimization, and benchmarking. He co-founded the Open Machine Learning Foundation supporting OpenML.org. His upcoming move to TU Dortmund as an assistant professor in AutoML and Optimization signals continued growth in his academic career while maintaining his research focus on making machine learning more accessible and rigorous.
Abbas Milani is a tenured Professor of Mechanical Engineering at the University of British Columbia's Okanagan campus, where he holds the Tier 1 Principal's Research Chair in Sustainable & Smart Manufacturing and serves as Director of the Materials and Manufacturing Research Institute (MMRI). He also serves as Technical Director of the Composites Research Network (CRN), Lead of the Canadian-International Biocomposites Research Network, and leads multiple major initiatives including the UBC-Pacific Economic Development Canada-Advancing Circular Economy (ACE) program and the UBC-NRC IRAP National Circular Economy CtO Program. Dr. Milani's primary research focuses on advanced modeling, simulation, and multi-criteria design optimization of composite and biocomposite materials, structures, and manufacturing processes. His expertise spans Textile Composites/Biocomposites, Materials Constitutive Relations, Finite Element Modeling, Robust Inverse Methods, Material Selection for End-of-Life Design Strategies, Multiple Criteria Decision Making, and Industry 5.0 applications. His interdisciplinary research bridges mechanical engineering, sustainable materials science, and smart manufacturing technologies. Analysis of his recent publication record reveals a strong emphasis on sustainable materials development, with particular focus on biocomposites, life cycle assessment methodologies, and optimization of manufacturing processes. His work integrates computational modeling with experimental validation across diverse application areas including medical devices, sustainable packaging, and circular economy strategies. The publications demonstrate increasing integration of artificial intelligence and machine learning approaches with traditional engineering methods. 2015 UBC Okanagan Researcher of the Year Award Killam Faculty Research Award (2016) Inducted into Royal Society of Canada - College of New Scholars (2020) Gold Medal Service Contribution Award by Academics World Reviewer Contribution Award by ASM International Multiple teaching excellence awards from UBC Dr. Milani has successfully mentored over 100 students and postdoctoral fellows who have secured positions in both industry and academia. His research program has been supported by more than $15 million in funding from government and industrial organizations. He leads the NSERC CREATE in Immersive Technologies (CITech) program and co-leads the Advanced Materials and Fabrication Core Competency within the Survive and Thrive Applied Research (STAR) program, demonstrating his commitment to training the next generation of engineers and advancing applied research.
Bryan H. Choi is an Associate Professor of Law at the University of Colorado Law School , where he bridges law and computer science to address software and AI safety. His work on software liability has influenced national cybersecurity strategy discussions. As an Adviser for the ALI Principles Project on Civil Liability for Artificial Intelligence , he shapes legal frameworks for emerging technologies. Education : JD and AB in Computer Science from Harvard University; clerkships with U.S. Court of Appeals judges Leonard I. Garth and William C. Bryson. Roles : Former joint appointment at Ohio State University Law School and Computer Science Department; Faculty Fellow at UPenn's CTIC; Director of Law and Media at Yale's ISP. Research Focus : Choi's scholarship examines software liability , AI accountability , and privacy law through interdisciplinary lenses. He critiques institutional approaches to software safety and advocates for empirical legal frameworks over participation-based models. Recent Articles address AI malpractice , NIST software standards , and forensic tool validation , reflecting trends in AI regulation and cyber-physical system liability . His 2021 NSF grant funded technical-legal methods for safety-critical systems. Awards & Grants : National Science Foundation (NSF) Grant (2021) Adviser, ALI Principles Project on Civil Liability for Artificial Intelligence Community Engagement : Active in Law and Computer Science communities , serving on committees for the ACM Symposium , Cybersecurity Law and Policy Scholars Conference , and co-organizing the AAAI Bridge Program on AI and Law .
Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .
Jianming Liang is a full professor at Arizona State University's College of Health Solutions, specializing in biomedical informatics, data science, and computer vision. His research focuses on self-supervised learning, foundation models, and improving transfer learning techniques for medical imaging applications. National Academy of Inventors Fellow (2021) ASU Faculty Innovation Award (2019) ASU Distinguished Faculty Award (2023) NIH R01 grant recipient Led lab producing FDA-approved medical imaging products His lab has developed multiple open-source frameworks like Ark , Foundation_X , and ModelsGenesis for medical image analysis. Team has received over 70 student research awards including NCWIT Collegiate and AMIA Ph.D. Dissertation honors. Key research contributions include: Anatomically consistent foundation models Domain-adaptive pretraining strategies Annotation-efficient deep learning Integrated classification/localization/segmentation frameworks 40+ US patents (50+ pending) Major publications demonstrate leadership in self-supervised learning for chest radiography, pulmonary embolism detection, and medical AI explainability.
Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .
Mani Golparvar Fard is a Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science and the Department of Civil and Environmental Engineering. He also contributes to the Technology Entrepreneur Center. His research focuses on integrating artificial intelligence, computer vision, and data analytics to advance construction management, infrastructure monitoring, and automation. Key areas include BIM integration, reality capture systems, and deep learning-based progress tracking. His work emphasizes automated construction progress monitoring through semantic segmentation, vision-language models, and UAV-based data collection. He has pioneered methods like Scan2BIM-NET for converting point clouds into BIM models and developed frameworks for worker safety analysis using machine learning. Awards: Walter L. Huber Civil Engineering Research Prize (2018) Daniel W. Halpin Award for Scholarship (2016) Advising & Grants: While no specific grant details are provided, his research is supported by collaborations with industry and government initiatives, such as the Japanese national bridge inspection project. He advises a team focused on AI-driven construction solutions and maintains active partnerships with engineering firms. Labs & Teams: Leads research groups in vision-based construction analytics, automated scheduling systems, and BIM integration. His work is disseminated through platforms like the VisualSiteDiary system and the InstaDam open-source platform for structural damage analysis.
Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
