Hassan Khan is an Associate Professor at the University of Guelph's School of Computer Science, with research spanning security, systems, and human-computer interaction (HCI). He is a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI). Research Interests: His work focuses on improving AI-driven mobile security systems through human-in-the-loop evaluations, addressing vulnerabilities in continuous authentication, shoulder surfing, and privacy in enterprise/repair settings. He explores how users interact with security mechanisms and designs interfaces to enhance usability. Scientific Recognition: He has received the NSERC Early Career Researcher Award and a NSERC Discovery Grant, with media coverage in outlets like Time Magazine, The Globe and Mail, and New Scientist. Teaching: Khan teaches courses such as Computer Security Foundations and Advanced Penetration Testing, emphasizing practical cybersecurity and AI systems architecture.
Yoshua Bengio is a Full Professor at the Université de Montréal, affiliated with the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences. He is a pioneer of deep learning and a leading figure in AI safety. He co-founded Mila – Quebec Institute of Artificial Intelligence and serves as its scientific director. His work focuses on advancing AI technology while addressing ethical and safety challenges, including AI governance and catastrophic risk mitigation. Education: Ph.D. in Computer Science from McGill University (1991), postdoctoral studies at MIT. Research interests include deep learning, causal inference, AI ethics, and responsible AI development. He contributed to the Montreal Declaration for Responsible AI and leads the International Scientific Report on AI Safety. Recent articles emphasize AI safety frameworks, governance, and technical advancements in machine learning. Awards include the Turing Award (2018), Killam Prize (2019), and recognition as TIME's Most Influential Person (2024). He holds prestigious fellowships and is a member of the UN Scientific Advisory Board for Breakthrough Science and Technology. Affiliations include Mila, IVADO (as founding scientific director), and CIFAR programs. His work bridges academia, industry, and policy to ensure AI benefits humanity while minimizing existential risks.
Joel Greenhouse is a Professor of Statistics at Carnegie Mellon University (CMU), affiliated with the Department of Statistics & Data Science. He has been on the faculty since 1983 and held leadership roles, including serving as Associate Dean of the College of Humanities and Social Sciences from 1997 to 2002. He also holds an adjunct appointment as Professor of Epidemiology and Psychiatry at the University of Pittsburgh. His expertise spans statistical methodology, clinical trial design, and meta-analysis, with a focus on integrating data from multiple sources to address complex healthcare and public health challenges. Greenhouse earned his Ph.D. in Biostatistics from the University of Michigan and completed a postdoctoral fellowship at CMU. His research emphasizes developing statistical tools for observational studies, clinical trials, and meta-analytic frameworks, particularly in neurology, mental health, and public policy contexts. Notable contributions include analyzing the impact of media on youth suicide rates, improving aphasia classification through automated speech analysis, and evaluating highway safety through driver health data. Education: Ph.D. in Biostatistics, University of Michigan Affiliations: Adjunct Professor at University of Pittsburgh, Member of National Academy of Sciences’ committees Professional Service: Data and safety monitoring boards for NIH/VA studies, co-chair of Federal Motor Carrier Safety Administration review panels His awards include CMU’s Doherty Award for Education, Ryan Teaching Award, and E. Dunlop Smith Award for teaching excellence. His work bridges theoretical statistics with real-world applications, particularly in interdisciplinary collaborations across medicine, psychology, and public policy. Greenhouse’s recent articles highlight trends in leveraging large datasets for clinical insights (e.g., aphasiaBank), re-evaluating environmental and behavioral health associations, and advancing causal inference methods. His interdisciplinary approach ensures statistical rigor addresses societal challenges, from suicide prevention to highway safety.
Dina El-Zanfaly serves as an Assistant Professor in the School of Design at Carnegie Mellon University (CMU), where she directs the hyperSENSE: Embodied Computations Lab. Her work bridges computational design and human-centered interaction, focusing on how physicality shapes sensory experiences and cognitive processes through intelligent systems. Education: PhD in Design and Computation, Massachusetts Institute of Technology (MIT) Master of Science in Design and Computation, MIT (Fulbright scholar) Her research critically examines computational methods for augmenting sensory perception, with emphasis on embodied sense-making in hybrid environments. She investigates co-creative interactions between humans and intelligent systems, exploring how computational tools empower designers and non-designers to shape products, social spaces, and interconnected technologies. Key questions address mutual learning between humans and machines through improvisation and creative production. Analysis of her 2022-2025 publications reveals dominant themes in mixed reality interfaces, AI-augmented skill acquisition (particularly in crafts and welding), and tangible co-creation with generative AI. Her work consistently integrates physical computing with mindfulness applications and privacy-aware smart environments, demonstrating interdisciplinary reach across education, manufacturing, and therapeutic contexts. Scientific Awards: Fulbright Scholarship As lab director, El-Zanfaly mentors students in computational making and embodied interaction projects. Her research is supported through initiatives like Fab Lab Egypt and collaborations with MIT, where she co-founded the Computational Making Group. She chairs major conferences including Fab15 in Egypt and serves on the DESFORUM program committee, indicating significant leadership in maker education and design research communities. She founded and leads the hyperSENSE Lab at CMU, which investigates computational embodiment through projects like Origami Sensei and Sand-in-the-loop. Previously, she co-established the Computational Making Group at MIT and co-founded Fab Lab Egypt (the first community maker space in North Africa/Arab world), demonstrating sustained commitment to global maker ecosystems and interdisciplinary team building.
Xiaohang Li is an Associate Professor in the Department of Electrical and Computer Engineering at the King Abdullah University of Science and Technology (KAUST) , where he serves as Principal Investigator of the Advanced Semiconductor Laboratory (ASL) . He holds a Ph.D. (2015), M.S. (2011), and B.S. (2008) in Electrical Engineering and Applied Physics from Georgia Institute of Technology, Lehigh University, and Huazhong University of Science and Technology, respectively. Research Focus: Professor Li specializes in (ultra)wide bandgap semiconductors (III-nitrides, III-oxides) for next-generation devices, including LEDs, lasers, transistors, and sensors. His work spans materials growth, simulation, fabrication, and characterization, targeting applications in energy, communications, and biomedical industries. Key Trends in Publications: Recent articles highlight advancements in InGaN micro-LED fabrication, β-Ga2O3 flexible transistors, AlN MOSFETs, and machine learning-optimized plasmonic structures. Themes include wide bandgap materials , 3D integration , thermal management , and flexible electronics . Scientific Awards: Harold M. Manasevit Young Investigator Award (2018) IEEE North Jersey Section ED/CAS MTT/AP Chapters Award (2019) Georgia Tech representative, Global Young Scientist Summit (2015) IEEE Photonics Society Graduate Student Fellowship (2014) D. J. Lovell Scholarship (2013) Mentorship: Since joining KAUST, Li has advised over 100 students and leads the ASL team in pioneering semiconductor research. His group focuses on fundamental and applied studies of wide bandgap materials to drive innovations in energy and health sectors.
Maria Camila Ceballos Betancourt is an Assistant Professor (Teaching & Research) in Beef Cattle Welfare at the Faculty of Veterinary Medicine , University of Calgary . She holds a Ph.D. and M.Sc. in Animal Welfare and Behaviour from São Paulo State University (UNESP) , Brazil, and a B.Sc. in Animal Science from National University of Colombia . Her research focuses on animal welfare , human-animal interactions , and cattle temperament , with an emphasis on livestock handling practices and sustainable production systems . Education: B.Sc. in Animal Science, National University of Colombia (2010) M.Sc. in Animal Welfare and Behaviour, UNESP (2014) Ph.D. in Animal Welfare and Behaviour, UNESP (2017) She teaches VETM322: Animal Welfare and Behaviour annually since 2020. Her research integrates animal behavior , physiological and reproductive performance measures , and applied handling interventions . Recent publications address grimace scales for pain assessment , human-animal dynamics in livestock systems , and technological innovations for piglet monitoring , reflecting her interdisciplinary approach between agricultural science , veterinary medicine , and behavioral neuroscience . Her work spans continents, including internships at the Animal Welfare Science Centre (University of Melbourne, Australia) and postdoctoral research at the University of Pennsylvania (USA). While no formal awards are listed, her research has been highlighted in media outlets like Canadian Cattlemen’s The Beef Magazine and CBC Calgary .
Pedro Carlos De Barros Fernandes is an Associate Professor at Universidade Lusófona , Deputy Director of the 1st cycle in Biotechnology, and an integrated researcher at the Institute of Bioengineering and Biosciences (iBB-IST). He holds a PhD in Biotechnology (1999) and a Master in Biotechnology/Biochemical Engineering (1994) from Universidade Técnica de Lisboa (IST), along with a Chemical Engineering degree from IST (1989). A member of the Order of Engineers (ID 24667), he co-founded Biotrend, a Portuguese bioprocess development company. Education PhD in Biotechnology (1999), Universidade Técnica de Lisboa MSc in Biotechnology (1994), Instituto Superior Técnico BSc in Chemical Engineering (1989), Instituto Superior Técnico Research Interests span biocatalysis, enzyme immobilization for food and pharmaceutical applications, marine biotechnology, microfluidic device development for biosensing, and steroid bioconversions using mycobacterial systems. His work integrates process engineering principles with sustainable bioprocessing techniques. Publication Trends show a focus on microreactor technology, enzyme stabilization in non-conventional media, marine-derived biocatalysts, and food waste valorization. Key themes include biocatalytic process intensification, aqueous two-phase systems for biomolecule purification, and sustainable carbon sources for biopolymer production. Scientific Awards UTL/Santander Totta Scientific Award in Biological Engineering (2011) Advising has included supervision of 5 doctoral theses and over 32 master’s theses. His expertise extends to peer-reviewing scientific articles and evaluating R&D projects. Labs & Teams are associated with iBB-IST (Institute of Bioengineering and Biosciences) and BioRG (Universidade Lusófona), with contributions to the Ciência Viva program for science dissemination.
Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.
Professor Valentyn Panchenko is a leading academic in Economics at the UNSW Business School, specializing in advanced econometric methodologies and financial modeling. Holding a PhD from the University of Amsterdam and an MPhil from the Tinbergen Institute, his research bridges theoretical econometrics with real-world financial applications, emphasizing big data analysis, network structures, and dependence modeling in economic systems. His expertise spans financial econometrics, time series analysis, non-parametric statistics, and agent-based economic simulations. He focuses on Granger causality, model evaluation, structural economic modeling, and bounded rationality with heterogeneous agents. His work has secured significant grants including ARC Discovery Projects and DECRA fellowships, enabling cutting-edge research on market dynamics and economic interactions. Professor Panchenko's publications appear in top-tier journals like the Journal of Econometric Theory, AEJ: Micro, Journal of Economic Dynamics & Control, and Journal of Banking & Finance. His methodological contributions include novel approaches to copula-based forecasting, nonlinear causality testing, and evolutionary learning models in strategic economic environments. While specific student advising details aren't provided, his research leadership demonstrates sustained impact across econometric theory, financial markets, and experimental economics.
Khiet P. Truong is an Associate Professor affiliated with the Digital Society Institute and the Human Media Interaction group. Their research focuses on the intersection of artificial intelligence, robotics, and human-computer interaction, with particular emphasis on speech emotion recognition, conversational agents for children, and multimodal interaction analysis. Recent work includes exploring how robots can restore trust through apologies, benchmarking Dutch automatic speech recognition systems, and developing child-friendly interfaces for cultural heritage archives. Truong has also investigated physiological signals like laughter and stress markers in speech across diverse contexts. Scientific Awards : Best Functional Design Award (2019) Research Trends : Analysis of speech patterns, emotion recognition, robot-human dialogue, and multimodal behavioral cues dominate their recent publications. Key subfields include Dutch language processing, laughter classification, trust indicators in child-robot interactions, and healthcare applications of speech technology. Academic Activities : Chair of the 26th ACM International Conference on Multimodal Interaction (2024) Examiner roles for PhD defenses and research evaluations (2024–2023) Contributions to workshops on emotion representation and social signal processing
Hugh Churchill is a Professor in the Department of Physics at the University of Arkansas, College of Arts & Sciences. His research focuses on quantum materials and devices, particularly condensed matter physics with applications in 2D systems and quantum transport. Education: PhD in Physics from Harvard University, BA in Physics and BM in Music Performance from Oberlin College Recent research trends include studies on 2D materials like transition metal dichalcogenides and black phosphorus, investigating quantum transport phenomena, supercurrent tuning, strain engineering for exciton control, and applications of machine learning in quantum material discovery. His work also explores THz emission mechanisms and quantum noise mitigation strategies. Arkansas Research Alliance Fellow Presidential Early Career Award for Scientists and Engineers NSF CAREER Award ORAU Powe Junior Faculty Award AFOSR Young Investigator Connor Faculty Fellowship Hugh teaches graduate and undergraduate courses in quantum mechanics, modern physics, and 2D materials, including PHYS 5413 Quantum Mechanics I and PHYS 6713 Condensed Matter Physics II.
Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.
Dr. Richard Y. Zhao is a tenured Professor in the Department of Pathology and Microbiology-Immunology at the University of Maryland School of Medicine. His research combines molecular biology, fission yeast genetics, mammalian biology, and virology to study virus-host interactions, particularly for HIV and Zika virus. He previously held academic positions at Northwestern University and Columbia University and has contributed to over 120 peer-reviewed articles. B.S., China Oceanography University (1981) M.S., Oregon State University (1995) Ph.D., Oregon State University (1991) Postdoctoral Training, Columbia University (1991-1992) Dr. Zhao's research focuses on: Virus-host interactions and pathogenicity High-throughput drug screening for antivirals Role of viral proteins in neuroinflammation and cancer Translational genomics in precision medicine His recent publications highlight SARS-CoV-2 ORF3a, Zika envelope proteins, and HIV protease inhibitors, emphasizing host-pathogen mechanisms across species. He has served on NIH panels and editorial boards for journals like Cell Research and Retrovirology . Scientific awards include: Fellow, American Academy of Microbiology (2019) Bernard L Mirkin Endowed Chair (2001-2004) Honorary Director, Shandong Gallo Institute (2009) Distinguished Service from SCBA (2015) Outstanding Service from CBA-USA (2016) Dr. Zhao also contributes to clinical diagnostics and personalized medicine through molecular testing and pharmacogenetics programs.
Karen S. Anderson, M.D., Ph.D. is a Professor of Medicine at Mayo Clinic in Phoenix, Arizona, where she serves as a Contract Physician in the Division of Hematology/Oncology within the Department of Internal Medicine. Her clinical practice focuses on breast cancer, and she is affiliated with the Mayo Clinic Comprehensive Cancer Center and the Breast Clinic. As an active researcher, Dr. Anderson leads clinical trials in breast cancer immunotherapy and biomarker development. Dr. Anderson's educational background includes: Medical Scientist Training Program (MD), Duke University School of Medicine (1994) Ph.D. in Microbiology and Immunology, Duke University (1994) BA in Chemistry, University of Virginia (1986) Internship and Residency in Internal Medicine, Brigham and Women's Hospital, Boston Fellowship in Adult Hematology and Oncology, Dana Farber Cancer Institute Dr. Anderson's research focuses on cancer immunology with particular emphasis on breast cancer biomarkers, ovarian cancer biomarkers, pancreatic cancer biomarkers, cancer vaccines, and HPV-related cancers. Her work spans from basic immunology to clinical applications, with a strong focus on translating laboratory findings into clinical practice. She has developed innovative approaches for early cancer detection and has been instrumental in advancing breast cancer immunotherapy through clinical trials. Analysis of Dr. Anderson's recent publications reveals a consistent focus on breast cancer immunology, biomarker discovery, and HPV-related cancers. Her work integrates molecular biology, immunology, and clinical oncology to develop novel diagnostic and therapeutic approaches. Notably, her research has expanded to include computational approaches for neoantigen prediction and has addressed public health challenges including HPV-related cancer screening and even COVID-19 vaccine strategies. Dr. Anderson has received several prestigious awards: Outstanding Faculty Mentor Arizona State University Faculty Women's Association (2020) Chief Resident West Roxbury VA Hospital (1996) Phi Beta Kappa University of Virginia (1986) Merck Scholar (1986) Alpha Omega Alpha Honor Medical Society (1986) Echols Scholar University of Virginia (1982) Dr. Anderson has been actively involved in mentoring students and early-career researchers, serving on numerous thesis committees at Arizona State University across multiple departments including Molecular and Cellular Biology, Chemistry, and the Barrett Honors College. Her research is supported by multiple grants from the National Cancer Institute, including her role as Co-Chair of the Breast and Gynecologic Cancers Collaborative Group within the Early Detection Research Network. Dr. Anderson is a key member of the Arizona Biomarker Alliance Executive Committee and has established collaborative research teams focused on cancer biomarker discovery and validation. Her laboratory work integrates protein microarray technology, immunology, and cancer genomics to develop novel diagnostic and therapeutic approaches for breast and HPV-related cancers.
Dr. Mohamed Hassan is an Assistant Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on Cyber-Physical Systems-on-Chip (iCPSoCs) , emphasizing design, analysis, and deployment for critical domains like Unmanned Aerial Vehicles (UAVs), Autonomous Cars, and healthcare systems. Key research areas include hardware/software codesign, real-time systems, embedded systems, and security. He teaches courses such as COMPENG 4DM4 (Computer Architecture) and COMPENG 4DS4 (Embedded Systems) . His work bridges foundational theories (e.g., scheduling, AI) with infrastructure-level innovations (e.g., compilers, memory systems). The Fanos Research Lab he leads explores interdisciplinary solutions for efficient CPS-on-Chip, addressing challenges in multicore predictability, memory latency, and edge computing. Recent contributions include frameworks for explainable memory-centric workloads and techniques to accelerate TinyML inference. Dr. Hassan serves on Technical Program Committees for conferences like RTAS and OSPERT, highlighting his role in advancing real-time embedded systems research.
