Guy Edward Toh Emerson is a computational semanticist currently serving as an Academic Fellow at the Department of Computer Science & Technology and a College Lecturer in Computer Science at Gonville & Caius College, University of Cambridge. He will transition to an Assistant Professor role in Natural Language Processing at the Department of Theoretical and Applied Linguistics in October 2025, while also acting as Executive Director of Cambridge Language Sciences to advance interdisciplinary research. His educational background includes: Undergraduate studies in Mathematics at Trinity College, Cambridge (2009). Master's in Computer Science with a focus on Computational Linguistics. PhD in 2018 from the University of Cambridge under Ann Copestake. His research integrates computational linguistics, natural language processing, and artificial intelligence, emphasizing language technology, machine learning, and multilingualism. He actively supports the revitalization of the Hokkien language through interdisciplinary approaches. Additional professional activities include academic leadership and policy engagement in language sciences. Fluent in English, German, French, Hokkien, and Mandarin, with intermediate knowledge of Greek, Georgian, Swedish, Dutch, and Rhine-Franconian, he balances academic pursuits with ballroom and Latin dancing.
Prof. Dr. Andreas Obersteiner serves as Professor of Mathematics Education at the Technical University of Munich (TUM), School of Social Sciences and Technology, Department of Educational Sciences since April 2021. He leads the Chair of Mathematics Education and holds the position of Academic Program Director for the Professional Profile 'Natural Science Education – Teaching at Gymnasium' since 2022. His research centers on mathematical cognition, teacher development, and fraction learning, with significant contributions to understanding cognitive biases in numerical processing. Education Background 1. Staatsexamen for Teaching at Gymnasium (1999-2004) 2. Staatsexamen (2005-2007) PhD in Educational Sciences, TUM School of Education (2012) Habilitation, TUM School of Education (2018) Research Focus Prof. Obersteiner's work investigates mathematical cognition with emphasis on mental number representation and the natural number bias in fraction comparison. His research employs eye-tracking methodology to analyze cognitive processes in students and teachers. He develops interventions to enhance teacher diagnostic competence and mathematical learning, particularly through projects like FractAl and FracVisET that address fraction understanding. His interdisciplinary approach bridges educational psychology, cognitive science, and mathematics education. Publication Trends His recent publications (2023-2025) demonstrate consistent focus on fraction cognition, natural number bias mitigation, and teacher assessment competencies. He frequently employs mixed-methods approaches combining eye-tracking, verbal reports, and experimental interventions. Publication venues include top-tier journals like Learning and Instruction and Educational Studies in Mathematics , reflecting his impact on both theoretical and practical aspects of mathematics education. Scientific Recognition Alexander von Humboldt Fellowship (2017-2018) Academic Leadership Prof. Obersteiner supervises doctoral candidates including Dr. Sarah Huber and leads multiple funded projects (ALICE, DigiProMIN, COSIMA). As Academic Program Director, he shapes teacher education curricula. His editorial roles include Associate Editor for the Journal of Numerical Cognition (2020-2024) and coordination of EARLI's Special Interest Group 'Conceptual Change' (2019-2023). Research Infrastructure He directs the Chair of Mathematics Education at TUM, which operates within the Department of Educational Sciences and collaborates with TUM Referenzgymnasien schools. His team conducts experimental research using eye-tracking labs and digital simulations for teacher training, supported by current projects including RocketTutor and SHARP.
Steve Oney is an Associate Professor at the University of Michigan School of Information and Computer Science and Engineering (by courtesy). His research focuses on enabling and encouraging more people to write and customize computer programs by creating new programming tools and exploring usability issues in programming environments. With a strong background in Human-Computer Interaction, he bridges the gap between theoretical research and practical applications in programming education, accessibility, and developer tool design. Dr. Oney completed his Ph.D in Human-Computer Interaction at Carnegie Mellon University's Human-Computer Interaction Institute under Professor Brad Myers and Dr. Joel Brandt. He also earned an M.Eng in Computer Science and SB degrees in Computer Science and Mathematics from MIT. His research spans multiple interconnected areas with a unifying theme of making programming more accessible and understandable. Key focus areas include programming education tools that help instructors understand student code at scale, web automation systems that simplify repetitive tasks, accessibility research addressing challenges faced by visually impaired programmers, and innovative VR programming environments. His work consistently emphasizes the human aspects of programming, exploring how tools can better support diverse programming needs and contexts. Dr. Oney's research output shows a strong trajectory toward increasingly sophisticated tools that integrate AI capabilities while maintaining a focus on human-centered design principles. Recent publications demonstrate growing emphasis on inclusive design, educational applications, and the integration of generative AI in programming environments. L@S 2024 Best Paper Award for CFlow CHI 2023 Honorable Mention for VizProg UIST 2024 Best Short Paper (EdCode) VL/HCC 2019 Best Short Paper Recognition for Contribution to Diversity and Inclusion (CSCW 2021) UMSI Excellence in Instruction Award (2021) University of Michigan President's Postdoctoral Fellowship (2015) As a mentor, Oney advises multiple Ph.D. students and postdoctoral researchers, with several successful graduates including Dr. Lei Zhang (June 2024). His research has secured over $1 million in funding from the National Science Foundation, Google, and Adobe, supporting projects that address critical challenges in programming education, accessibility, and developer tool design. He leads the Programming Tools Lab at the University of Michigan, where his team develops innovative tools that help programmers work more effectively. Current projects focus on AI-enhanced programming education, web automation, accessibility for diverse user groups, and next-generation programming environments for virtual and augmented reality.
Gee Y. Lee is an Associate Professor with Tenure in the Department of Statistics and Probability and the Department of Mathematics at Michigan State University. Lee holds a PhD from the University of Wisconsin-Madison and is an Associate of the Society of Actuaries (ASA). Their research focuses on applying advanced statistical and machine learning methods to solve complex problems in actuarial science and insurance. Dr. Lee's educational background includes: PhD from the University of Wisconsin-Madison Associate (ASA) designation from the Society of Actuaries Dr. Lee's research spans several critical areas in modern actuarial science. Their primary focus includes insurance loss modeling for rate-making and loss reserving applications, optimization of multivariate insurance coverage, and dependence modeling. A significant portion of their recent work applies machine learning methods, particularly deep neural networks, to traditional actuarial problems. They are also pioneering research in analyzing unstructured data for insurance applications, which represents an emerging frontier in the field. Their work bridges theoretical statistical methods with practical insurance industry needs. Dr. Lee's publication record demonstrates a clear evolution from traditional actuarial methods toward more sophisticated and interdisciplinary approaches. Early work focused on fundamental aspects of insurance pricing and modeling, while more recent publications incorporate machine learning techniques, natural language processing, and advanced optimization methods. A notable trend is the increasing integration of unstructured data analysis into actuarial science, reflecting broader industry shifts. Their research consistently addresses both theoretical advancements and practical applications in insurance risk assessment and management. While specific awards aren't detailed in the available information, Dr. Lee's recognition includes: Associate (ASA) designation from the Society of Actuaries Michigan State University recognized by the Society of Actuaries as granting MS and PhD degrees focused on actuarial science (as of 2023) Dr. Lee actively mentors students at multiple levels, supervising undergraduate research through REU programs, directed studies (STT 490, MTH 490, MTH 491B), and graduate research for MS and PhD candidates. They have advised numerous students who have presented at UURAF (Undergraduate Research Assistant Fellowship) conferences. For graduate students, Dr. Lee supports research leading to MS degrees in Statistics, Applied Statistics, and Industrial Mathematics with actuarial science focus, as well as PhD dissertations in Statistics. Beyond direct student supervision, Dr. Lee has organized significant academic events including the Simon Conference for Young Researchers in Risk Management and Insurance (2019, 2023) and contributed to other workshops, demonstrating leadership in the actuarial research community. While specific lab names aren't mentioned, Dr. Lee appears to lead a research group focused on actuarial science and insurance analytics at Michigan State University. Their collaborative work with researchers like Scott Manski, Taps Maiti, Peng Shi, and others suggests an active research team working at the intersection of statistics, machine learning, and actuarial applications. The research group seems particularly focused on bridging traditional actuarial methods with modern data science techniques.
Peter Kazanzides is a Research Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University, where he joined the faculty in 2002. His research focuses on robotics, medical robotics, augmented reality, and computer-assisted interventions with primary applications in computer-integrated surgery. His educational background includes multiple degrees from Brown University: ScB (1983) in Electrical Engineering AB (1983) in Computer Science ScM (1985) in Electrical Engineering ScM (1987) in Applied Mathematics PhD (1988) in Electrical Engineering Kazanzides is a member of the Robotics, Vision, and Graphics research group and directs the Sensing, Manipulation, and Real-Time Systems (SMARTS) laboratory. His work spans surgical robotics, mixed reality, and systems engineering, with emphasis on computer-assisted surgery in extreme environments including minimally invasive surgery, microsurgery, and space teleoperation. The SMARTS lab develops real-time sensing systems, augmented/mixed reality interfaces using head-mounted displays, high-performance motor control, and sensor fusion technologies, with strong focus on system integration and open-source platforms like the da Vinci Research Kit (dVRK). Analysis of his recent publications (2024-2025) reveals dominant trends in surgical robotics autonomy, augmented reality navigation, force estimation, and digital twin technologies. Key themes include AI-driven task automation, haptic feedback enhancement, real-time instrument segmentation, and simulation environments for surgical training, primarily leveraging the da Vinci Research Kit framework. As director of the SMARTS lab within the Laboratory for Computational Sensing and Robotics (LCSR), Kazanzides leads a collaborative ecosystem including the Computer Integrated Interventional Systems (CIIS) Lab, Advanced Medical Instrumentation and Robotics (AMIRO) Lab, Dynamical Systems and Controls Lab (DSCL), Computer Aided Medical Procedures (CAMP) Lab, Medical UltraSound Imaging & Intervention Collaboration (MUSiiC) Lab, and Photoacoustic & ULtrasonic Systems Engineering (PULSE) Lab. His lab maintains responsibility for the development and support of the open-source da Vinci Research Kit, a critical resource for surgical robotics research worldwide.
Felicia Castro-Villarreal serves as a Professor of School Psychology within the Department of Educational Psychology at the University of Texas at San Antonio (UTSA), part of the College of Education and Human Development. She holds licensure as a Licensed Specialist in School Psychology (LSSP) and is actively pursuing Licensed Psychologist status, demonstrating her dual commitment to academic rigor and clinical practice in educational settings. Her educational trajectory includes a Bachelor’s degree in Psychology from UTSA, a Master of Science in Clinical Psychology from St. Mary’s University, and a Ph.D. in School Psychology from Oklahoma State University. She completed her doctoral internship at the APA-accredited Cypress-Fairbanks ISD Psychological Services Department, establishing foundational expertise in school-based psychological services. Dr. Castro-Villarreal’s research program centers on multicultural assessment and culturally responsive consultation practices, directly addressing systemic inequities in educational psychology. Her work examines how cultural identity influences assessment validity, develops consultation frameworks for diverse educators, and investigates barriers to mental health services for marginalized student populations—particularly focusing on autism spectrum disorders and high-stakes testing implications. Analysis of her recent publications reveals a cohesive research trajectory emphasizing social justice in school psychology. Her work consistently bridges theory and practice through mental health screening innovations, autism intervention adaptations, and critical examinations of labeling practices. Notably, her 2025 study on autistic women’s care experiences and 2023 meta-analysis on mental health screening demonstrate methodological diversity while maintaining focus on underserved populations. While specific grant details and student advisees aren't publicly documented, her extensive publication record—including systematic reviews, qualitative explorations, and intervention studies—indicates active research leadership. Her contributions to yearbook introductions and special issues reflect scholarly service to advance equity-focused discourse within educational psychology.
Dr. Sarah Wolf serves as Head of the Junior Research Group 'Mathematics for Sustainability Transitions' at Free University of Berlin's Department of Mathematics and Computer Science and as a Senior Researcher and Board Member at the Global Climate Forum (GCF). Her dual affiliation bridges rigorous mathematical modeling with real-world sustainability policy, focusing on complex socio-ecological systems through an interdisciplinary lens since joining GCF's Green Growth initiative in 2012. Wolf earned her PhD in Mathematics from Freie Universität Berlin in 2010 with the thesis 'From Vulnerability Formalization to Finitely Additive Probability Monads,' developed during interdisciplinary work at the Potsdam Institute for Climate Impact Research. Her academic foundation combines pure mathematics with applied climate impact research, establishing her unique approach to formalizing sustainability concepts. Her research centers on agent-based modeling of socio-technical systems, with core expertise in sustainability transitions , green growth mechanics , and sustainable mobility . She develops mathematical frameworks to clarify vulnerability concepts while embedding simulations in stakeholder dialogues through innovations like the 'Decision Theatre Triangle.' This work uniquely positions mathematics as both analytical tool and communication medium for climate policy. Analysis of her 15 most recent publications reveals an evolutionary trajectory from foundational vulnerability formalization (2009-2012) toward applied stakeholder-integrated modeling (2021-2023). Her work consistently bridges mathematical rigor with policy relevance, showing increasing emphasis on participatory approaches while maintaining computational sophistication in agent-based systems. No scientific awards are documented in the source material, though her leadership in the MATH+ junior research group indicates competitive funding attainment. As group head, she directs research strategy and likely mentors junior researchers, though no formal student advisees are listed. Wolf leads the 'Mathematics for Sustainability Transitions' junior research group within FU Berlin's Biocomputing Group, collaborating with institutions like the Potsdam Institute. Her team develops computational frameworks for green growth transitions, emphasizing stakeholder co-creation through platforms like the Decision Theatre while maintaining strong ties to GCF's global policy networks.
Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.
Joel Sokol is the Harold E. Smalley Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He serves as Director of the interdisciplinary Master of Science in Analytics (MSA) degree, offered both on-campus and online. His academic journey began with a Ph.D. in Operations Research from MIT (1999), followed by bachelor's degrees in Mathematics, Computer Science, and Applied Sciences in Engineering from Rutgers University (1994). Education Ph.D. in Operations Research (MIT, 1999) B.S./B.A. in Mathematics, Computer Science, Applied Sciences in Engineering (Rutgers, 1994) Dr. Sokol's research focuses on Sports Analytics , Health Informatics , and Supply Chain Optimization . He pioneered the LRMC (Logistic Regression/Markov Chain) method for NCAA basketball tournament predictions, which has become an industry standard. His work extends to organ transplantation logistics, maritime shipping networks, and semiconductor manufacturing optimization, blending machine learning with traditional operations research techniques. The articles reflect his interdisciplinary expertise: 2025 introduced a Smart Stadium Testbed for real-time sports analytics, while 2024 addressed Language Model Safety . Earlier publications (2023–2018) focused on transplant survival modeling, vaccine scheduling, and maritime logistics, showcasing his ability to apply analytics to diverse domains. Scientific Awards EURO Management Science Strategic Innovation Prize (2008) Cozzarelli Prize finalist (non-sports research) Georgia Tech's highest teaching awards (multiple years) INFORMS and IISE recognitions for curriculum development As a leader in analytics education, Sokol co-founded the INFORMS Sports Operations Research section and served as INFORMS Vice President of Education. His work has practical applications in professional sports, healthcare, and industry, with methodologies adopted by teams, medical institutions, and global logistics networks.
Karen M. Feigh is a Professor and Associate Chair for Research at Georgia Tech's Daniel Guggenheim School of Aerospace Engineering, with a courtesy appointment in the School of Interactive Computing. She directs the Georgia Tech Cognitive Engineering Center and is affiliated with the Vertical Lift Research Center of Excellence (VLRCOE) and the Institute for Robotics and Intelligent Machines (IRIM). Her work spans computational cognitive modeling, socio-technical systems, and adaptive automation. Education: B.S. in Aerospace Engineering (Georgia Tech), MPhil in Aeronautics (Cranfield University, UK), Ph.D. in Industrial and Systems Engineering (Georgia Tech) Research Focus: Feigh's research explores human-machine interaction in aviation, space operations, and robotics. She designs cognitive work support systems using field work, human-subjects studies, and mathematical modeling. Recent projects emphasize human-autonomy teaming and the human experience of machine learning across domains. Leadership & Collaborations: She leads the Cognitive Engineering Center (CEC), founded in 2005, which brings together aerospace engineers, computer scientists, and education researchers. The CEC contributes to air/space procedures, military decision-making systems, and autonomous vehicle technologies. Awards & Distinctions: David S. Lewis Professorship (2025) AIAA Wilbur and Orville Wright Graduate Award (2006) Zonta International Amelia Earhart Fellowship (2005) NSF Graduate Research Fellow (2001-2006) Marshall Scholar (2001-2003) Grants & Advisory Roles: Feigh has led and collaborated on FAA, NIA, ONR, NSF, and NASA-sponsored projects. She serves as Associate Editor for the Journal of Cognitive Engineering and Decision Making and previously chaired the Human Factor and Ergonomics Society’s Cognitive Engineering and Decision Making Technical Group.
Konstantinos Alexakos is a Professor and Program Coordinator for General Science (GSCI) and Science Education (covering biology, chemistry, physics and earth science for grades K-12) at the School of Education, Brooklyn College, City University of New York (CUNY). He also holds a professorship in The Ph.D. Program in Urban Education at the Graduate Center, CUNY. His academic work focuses on science education, teacher development, and the integration of mindfulness practices in educational settings. His educational background includes: B.S. in Physics from The City College of New York - CUNY (1989) M.A. in Education in Physics and General Science from New York University (2000) M.Phil. in Science Education from Columbia University (2004) Ph.D. in Science Education from Teachers College, Columbia University (2005) Alexakos's research interests span multiple interconnected domains within science education. He has made significant contributions to understanding mindfulness and wellness in educational contexts , particularly how contemplative practices can enhance teaching and learning environments. His work explores emotional dimensions of science education , examining how teachers and students navigate emotional landscapes in classrooms. He has pioneered research on cogenerative dialogue and coteaching as mechanisms for improving educational practice. His scholarship also addresses diversity, equity, and inclusion in science education, with particular attention to race, gender, and cultural considerations. Alexakos investigates teacher identity formation and the challenges science educators face in urban settings, while also developing frameworks for authentic inquiry that empower teachers as researchers. His recent work increasingly focuses on holistic approaches to education that integrate physical, emotional, and intellectual dimensions of learning. Analysis of his recent publications (2017-2022) reveals a clear trajectory toward integrating contemplative practices with science education. His scholarship demonstrates a consistent focus on teacher development through authentic inquiry, with increasing emphasis on wellness, mindfulness, and emotional dimensions of teaching and learning. The publications show a progression from theoretical frameworks to practical applications of heuristics and contemplative practices in educational settings. His collaborative work with Kenneth Tobin forms a substantial portion of his output, indicating a productive research partnership focused on transforming educational practices. The publications span multiple formats including books, book chapters, and journal articles, demonstrating versatility in scholarly communication. His scientific awards and recognitions include: Brooklyn College Student Technology Fee Award for "Science Lab Probes" ($12,000) PSC-CUNY Research Award (PSCREG-38-335) for "Self and Science Teacher Attrition" ($6,000; 2007-08) PSC-CUNY Research Award (PSCOOC-37-30) for "The Science Teacher, Subjective Constructs of Science Teaching, and the 'Organic Link'" ($5,990; 2006-07) Alexakos has been actively involved in mentoring students through various teaching practicums and research activities. His courses include Natural Science for Early Childhood & Childhood Education, General Science for Child & Elementary School, Physical Science for Childhood Teachers, and multiple student teaching practicum courses. He has developed the master of arts in teaching (MAT) in science education program at Brooklyn College that infuses research and practice in science teaching in urban settings. His grant activities include research projects on teacher attrition, the science teacher as the organic link, and wellness practices in educational contexts. He has also secured funding for technology implementation in science education through the Student Technology Fee Award. While not explicitly mentioned as leading a specific laboratory, Alexakos is deeply involved with the Urban Science Education Research Seminar at the CUNY Graduate Center, where he has presented and organized numerous sessions. He has collaborated extensively with colleagues including Kenneth Tobin, Maria Powietrzynska, and Leah Pride on research related to emotions, mindfulness, and wellness in educational settings. His work with the Association of Greek American Professional Women (AGAPW) on "Inspiring Women in Science, Technology, Engineering, and Mathematics (STEM)" demonstrates his commitment to supporting underrepresented groups in STEM fields.
Dr. Britnie Delinger Kane is an Associate Professor of Literacy Education at Zucker Family School of Education , The Citadel . She also serves as the Director of the Center for Literacy Excellence and Program Coordinator for Literacy Education . Prior to her work at The Citadel, she taught at the University of Colorado, Denver . Ph.D. in Literacy Education, Peabody College, Vanderbilt University M.Ed. in Secondary English Education, Peabody College, Vanderbilt University B.A. in English, University of South Carolina’s Honors College Research Interests center on equitable teacher learning , disciplinary literacy , writing instruction , and STEM education . Her work explores preservice teacher education, collaborative teacher talk, and instructional coaching. She has published in journals like American Educational Research Journal , Journal of the Learning Sciences , and Journal of Teacher Education . Recent Publications include studies on mathematics coaching, disciplinary literacies, adolescent literacy efficacy, and trauma-sensitive instruction. These works emphasize professional learning communities , content-specific coaching , and equity in instructional design .
Dr. Thomas Britz is a Senior Lecturer at the School of Mathematics and Statistics, UNSW Sydney . He is a member of the Combinatorics Research Group and serves as Chief Editor of Parabola and Managing Editor for the Australasian Journal of Combinatorics . PhD in Mathematics (Aarhus University, 2003) Research: Discrete Mathematics, Combinatorics, Graph Theory, Matroid Theory, Coding Theory, Design Theory, and applications to renewable energy, bioinformatics, criminology, and more His research has been supported by grants including an ARC Discovery Grant and international fellowships. He has supervised numerous PhD, Master’s, and Honours students across diverse projects. Recent publications focus on harmonic Tutte polynomials, group divisible designs, and closeness centrality in graphs, reflecting his expertise in foundational and applied combinatorics. Awards : Vice-Chancellor’s Award for Student Wellbeing (2021), Science Dean's Award for Excellence in Student Wellbeing (2021), Vice-Chancellor's Award for Contributions to Student Learning (2015) He contributes to outreach activities , editorial service, and university committees, including the Education Excellence Committee and Sustainability Committee at UNSW.
Jessica Young Schmidt is an Associate Teaching Professor in the Department of Computer Science at North Carolina State University, serving as ABET Coordinator since 2017 and Course Coordinator for CSC226: Discrete Mathematics. Her primary focus is undergraduate education, particularly CSC116: Introduction to Computing – Java and CSC226, where she implements flipped classroom models and comprehensive end-of-semester exercises. Her educational background includes: Ph.D. in Computer Science, North Carolina State University, 2012 M.S. in Computer Science, North Carolina State University, 2009 B.S. in Computer Science and Mathematics, Roanoke College, 2007 Dr. Schmidt's research centers on computer science education, emphasizing active learning, instructional design, and software engineering pedagogy. She has pioneered methods like collaborative software engineering exercises for CS1 materials and frameworks for assessing critical thinking, with her work on end-of-semester integration earning a SIGCSE 2020 award. Her publications reveal an evolution from privacy policy analysis (2009-2012) toward educational innovation in recent years. Her scholarly contributions demonstrate consistent focus on curriculum development and assessment, particularly in integrating software testing throughout computer science education and enhancing student comprehension through structured reflection. She has received the following recognition: Third Best Paper, SIGCSE 2020 Technical Symposium (Experience Reports and Tools Track) As ABET Coordinator, Dr. Schmidt drives curriculum assessment and continuous improvement, while her CSC226 coordination ensures pedagogical consistency across sections. Her teaching innovations directly support student success in foundational computer science concepts.
Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.
