Daniel Razansky is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, leading the Professorship for Biomedical Imaging. His research spans engineering, physics, biology, and medicine, focusing on developing advanced in vivo imaging tools like optoacoustic tomography and ultrasound neuromodulation. His recent work emphasizes multi-scale functional and molecular imaging , with applications in neuroscience , Alzheimer’s disease , and stroke diagnostics . Collaborations include National Tsing Hua University and the EU Horizon consortium SWEEPICS. Current projects target hybrid imaging systems (e.g., MRI-MSOT) and image-guided neuromodulation. Scientific awards include the IPPA James Smith Prize for his contributions. His lab has secured significant grants, including a $2.5M NIH award and SNSF funding. He mentors PhD students like Quanyu Zhou and Eva Remlova, who have received accolades for their research. The Razansky Lab at ETH Zurich’s Preclinical Imaging Center explores medical microrobotics , dynamic fluid flow imaging , and neuroimaging techniques , aiming to bridge engineering with clinical applications.
Brett Sanders is a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on developing innovative algorithms for flow and transport in river and coastal systems and integrating information technologies to create more accurate and efficient simulation tools for flood hazard assessment. His primary research interests include: Flooding and erosion hazards, particularly coastal flooding and urban flooding Surface water quality Low impact development impacts on hydrology Dam-break flooding Aerial and terrestrial lidar scanning Geographical information systems High performance computing for simulation tools Social dimensions of flood risk and adaptation behaviors Dr. Sanders' recent publications (2024-2025) reveal a comprehensive research program addressing both technical and social aspects of flood risk. His work spans computational hydrodynamics, flood hazard mapping, infrastructure vulnerability assessment, and the socioeconomic dimensions of flood risk. He has made significant contributions to understanding multi-grid modeling of urban flooding, post-fire flood hazards, satellite-based monitoring of land motion, and social inequalities in flood exposure. His research demonstrates how flood dynamics are more complex than simple bath-tub filling models suggest, with important implications for urban planning and climate adaptation. Dr. Sanders has received recognition as a Chancellor's Professor at UC Irvine, indicating distinguished scholarly achievement. His educational background includes: Ph.D. in Civil Engineering from the University of Michigan (1997) M.S. in Civil Engineering from the University of Michigan (1994) B.S. in Civil Engineering from the University of California, Berkeley (1993)
Ahmed Hammad is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, where he also serves as Director of Academic Integrity, ENG WIL, Co-op and Career Connections. With 25 years of industry experience as a Project Planning & Control Manager on global mega-projects (including Oil Sands, LNG, and Infrastructure across Canada, UAE, Australia, and Egypt), he brings extensive practical expertise to academia. Education: Doctorate of Philosophy, Construction Engineering & Management, University of Alberta (2009) Master of Science, Construction Engineering & Management, University of Alberta (1999) Master of Engineering, Construction Engineering and Management, Cairo University (1996) Bachelor of Science, Civil Engineering, Mansoura University (1989) Research Focus: Dr. Hammad's work centers on applying smart tools to achieve sustainable construction through maximizing efficiency and minimizing waste . His research employs Machine Learning , Multi-Criteria Decision Making , Knowledge-Based Decision Support Systems , and digital twin technologies to optimize project planning, resource allocation, and sustainable material selection. He investigates the integration of BIM and Augmented Reality to enhance construction processes while reducing environmental impact. Publication Trends: Recent publications (2023-2025) emphasize sustainable construction methodologies, with 60% focusing on GHG reduction, resource optimization, and decision support systems. Key themes include machine learning for labor estimation, TOPSIS/MCDM for sustainable material selection, and digital twins for production planning, reflecting his NSERC-funded projects on KBDSS and construction-oriented digital twins. Scientific Recognition: Best Paper Award at 8th International Conference on Industrial Engineering and Operations Management (2018) Best Paper Award at HBRC Green Smart Sustainable Buildings Conference (2024) Research Leadership: Dr. Hammad secures major industry-academic partnerships, including NSERC Mission Alliance Grants ($1.2M+) with 16 industry partners for GHG reduction projects and NSERC Alliance Grants with 9 partners for digital twin development. His completed projects include collaborations with the City of Edmonton and Alberta Ministry of Infrastructure on resource allocation models. Research Ecosystem: As leader of the Sustainable Construction Research Group (SCRG), he fosters industry-academia collaboration through regular workshops with construction firms and government agencies, focusing on translating research into practical tools for sustainable project delivery.
Timothy Bretl is a Professor of Aerospace Engineering at the University of Illinois at Urbana-Champaign, holding the Severns Faculty Scholar position since 2021. He also serves as Associate Head of the Aerospace Engineering department. His research focuses on robotics, control systems, rehabilitation robotics, and engineering education. Bretl earned his Ph.D. from Stanford University (2005), with prior degrees from Swarthmore College. He holds affiliate roles across multiple departments, including Neuroscience, Coordinated Science Laboratory, and Computer Science. Education: Ph.D. in Aeronautics and Astronautics, Stanford University (2005) B.A. in Mathematics and B.S. in Engineering, Swarthmore College (1999) His research spans engineering education innovations, robotic manipulation, and brain-machine interfaces. Notable awards include the NSF CAREER Award (2010), Best Manipulation Paper (2012), and multiple teaching honors like the Rose Award for Teaching Excellence (2016). Bretl’s work integrates theoretical foundations with practical applications in prosthetics, autonomous systems, and educational technology. He has advised numerous projects on robotics, control systems, and human-robot interaction. His lab explores advanced topics like elastic rod manipulation, magnetic positioning, and curriculum reform in STEM education. Collaborative projects include partnerships with industry and interdisciplinary teams at the Beckman Institute.
Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.
Stephen Bach is an Assistant Professor in the Computer Science Department at Brown University, where he leads the BATS (Bach's Awesome Team of Students) research group. His research focuses on improving how humans teach computers through programmatic weak supervision and methods for learning from fewer examples like zero-shot and few-shot learning. His primary research interests include weak supervision, data programming, probabilistic soft logic (PSL), statistical relational learning (SRL), information extraction, zero-shot learning, and few-shot learning. Bach's work often focuses on exploiting high-level, symbolic or semantically meaningful domain knowledge, with applications in information extraction, image understanding, scientific discovery, and data science. Bach's recent publications show a strong focus on language models, weak supervision techniques, and multimodal learning, particularly examining the capabilities and limitations of models like CLIP. His research has increasingly emphasized practical applications in low-resource settings and cross-lingual scenarios. Best Paper Award at NeurIPS Workshop on Socially Responsible Language Modelling Research (SoLaR) 2023 Larry S. Davis Doctoral Dissertation Award Selected for oral presentation at ICLR 2024 Best of VLDB 2018 paper selection Bach advises numerous Ph.D., Master's, and undergraduate students, many of whom have gone on to positions at leading tech companies, research institutions, and graduate programs. His research group has developed several influential frameworks including Snorkel (for weak supervision), PSL (Probabilistic Soft Logic), T0 (for zero-shot task generalization), ZSL-KG (for zero-shot learning with knowledge graphs), TAGLETS (for semi-supervised learning with auxiliary data), and WISER (for programmatic weak supervision in sequence tagging).
Dr. H.V. Jagadish is a Professor in the Computer Science and Engineering department, focusing on database systems, data science, and ethical AI. He emphasizes mentorship and independence in research, guiding students to become colleagues through collaborative growth. Research Focus His work spans Data integration and fairness in machine learning Query processing and knowledge graph analysis Reproducibility and ethical data management Advising and Research Dr. Jagadish encourages open communication, frequent feedback, and proactive student-driven research. He supports internships and conference attendance, prioritizing SIGMOD and VLDB for presentations.
Zhengjie Miao is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on data management and artificial intelligence, particularly developing algorithms and tools to enhance data science pipelines. He is part of the SFU Data Science Research Group and previously worked as a Research Scientist at Megagon Labs. Education: PhD in Computer Science from Duke University (advisors: Sudeepa Roy, Jun Yang), M.S. from Columbia University (advisor: Eugene Wu), B.S. from Peking University. Research interests span data management systems, machine learning integration in data science, and human-in-the-loop data preparation. His work emphasizes improving data usability through techniques like data lineage tracking, explainable query systems, and fairness-aware data processing. Recent publications highlight advancements in collaborative annotation with LLMs, automated data standardization, and contrastive learning frameworks for column annotation. Key contributions include the Rotom framework for meta-learned data augmentation, the Watchog system for column annotation, and CAPE for query explanation. His research bridges database theory with practical applications in education and industry.
Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.
Béla Bollobás is a renowned mathematician affiliated with the University of Memphis as the Jabie Hardin Chair of Excellence in Combinatorics and the University of Cambridge as a Fellow of Trinity College and Honorary Professor at the Centre for Mathematical Sciences. His work spans combinatorics, probability theory, and graph theory, with significant contributions to percolation and random graphs. Dr. Rer. Nat. (Budapest, 1967) Ph.D. (Cambridge, 1972) Sc.D. (Cambridge, 1984) Bollobás pioneered extremal graph theory, random graphs, and probabilistic combinatorics. He introduced novel graph polynomials and advanced bootstrap percolation models, impacting both theoretical mathematics and statistical physics. His research includes inhomogeneous random graphs and cellular automata in random environments. His selected publications reveal a focus on percolation thresholds, graph invariants, and stochastic processes. Notably, he derived sharp thresholds for bootstrap percolation and defined critical probabilities for Voronoi percolation. Senior Whitehead Prize (2007) Fellow of the Royal Society (2011) Foreign Member, Hungarian Academy of Sciences (1990) Foreign Member, Polish Academy of Sciences (2013) Honorary Doctorate, Adam Mickiewicz University (2013) Szechenyi Prize (2017) Bollobás has supervised over 50 Ph.D. students and authored over 450 publications, including 10 books. He co-founded the journal Combinatorics, Probability and Computing and served on eight editorial boards. He organized numerous conferences, including Bill Tutte and Paul Erdős events.
Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.
Dr. Sheryl Staub-French is a Professor and Head (Pro Tem) in the Department of Civil Engineering at the University of British Columbia (UBC), Faculty of Applied Science. She directs the BIM TOPiCS Lab, focusing on digital delivery methods for sustainable construction using BIM. Her research spans over 100 publications in BIM, VDC, and collaboration frameworks. She previously served as Associate Dean of Equity, Diversity, and Inclusion (EDI), advancing inclusive practices in engineering education. She holds a BS from Santa Clara University and MS/PhD from Stanford University. Her research interests include BIM implementation challenges, design coordination, 4D visualization, and sustainable construction innovation. She teaches courses like CIVL 300 (Construction Engineering), CIVL 426 (Virtual Design and Construction), and advanced BIM topics. Her lab collaborates with industry/government to develop BIM guidelines and tools. Notable projects include UBC’s Brock Commons Tall Wood Building and studies on IPD adoption barriers in Canada. Her work bridges academia and industry, emphasizing digital transformation and EDI. She has pioneered BIM-based methods for facility management and energy modeling, addressing gaps in information quality and handover processes. Her contributions span policy development, tool innovation, and educational reforms to enhance construction sector efficiency and inclusivity.
Professor Alistair Brandon-Jones (PhD, PGDip, BSc Hons) is a Full Chaired Professor of Operations and Supply Chain Management at the University of Bath's School of Management since 2014. He serves as Head of the Information, Decisions and Operations Division (2020–present) and holds Visiting Professor roles at Hult International Business School and Danish Technical University. His career spans academic leadership positions at Manchester Business School, Bath, and Warwick Business School, where he completed his PhD. Research Interests : Digitalisation of operations and supply chains, professional service operations, healthcare operations, and cybersecurity in AI systems. His work combines empirical methods with cross-sector collaborations, supported by UKRI, ESRC, EPSRC, and the Welsh Assembly Government. Key themes include telehealth integration , responsible AI deployment , and value-based procurement . Teaching & Consulting : He teaches Operations Management, Supply Chain, and Project Management across executive, MBA, and undergraduate levels, with consulting experience for Maersk, NHS, and Qatar Leadership Centre. His textbooks like Operations Management (10th ed., 2022) are globally cited. Scientific Awards : Times Higher Education Teaching Excellence Prize Association of MBAs (AMBA) Award Production Operations Management Society (POMS) Recognition Hult International Business School Teaching Prize Supervision & Projects : Currently supervising doctoral students in digital transformation, he leads two major projects: ESRC Digital Security by Design Social Science Hub (2020–2025) and Welsh Government Healthcare Integration (2009–2009). His research aligns with UN SDG 12 (Sustainable Consumption and Production) through supply chain innovations.
Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.
Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.
