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Assistant Professor
David Lindlbauer is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on advancing Mixed Reality (MR) and Extended Reality (XR) interfaces through computational interaction methods that optimize spatial, temporal, and multimodal feedback.
Professor
Nina Balcan is the Cadence Design Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, with affiliations in both the Machine Learning Department (MLD) and Computer Science Department (CSD). She maintains her office in Gates Hillman Center (GHC) 8205 and is a prominent figure in theoretical machine learning and algorithmic game theory. Her research spans multiple critical areas in computer science, with a strong focus on the theoretical foundations of machine learning, algorithm design and analysis, and computational approaches to game theory and economics. Balcan has made significant contributions to developing principled algorithms for deep learning, learning with limited supervision, representation learning, and life-long learning. Her work uniquely bridges theoretical computer science with practical applications, particularly in the analysis of complex objects and processes, including algorithmic learning and multi-agent systems. Analysis of her recent publications reveals a strong trend toward data-driven algorithm design, with particular emphasis on learning to optimize combinatorial algorithms, revenue-maximizing mechanisms, and robust learning frameworks. Her work consistently demonstrates how theoretical guarantees can inform practical algorithm development across diverse domains from optimization to economics. Major Awards and Honors: ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award (awarded to the outstanding young computer professional of the year) Winner of Outstanding Student Paper Award at UAI 2024 Winner of Exemplary Artificial Intelligence Track Paper Award at ACM EC 2019 Runner Up Best Paper Award at COLT 2012 Professor Balcan has served as Program Committee Co-chair for major conferences including NeurIPS 2020, ICML 2016, and COLT 2014, demonstrating her leadership in the machine learning community. Her teaching portfolio at CMU includes foundational courses such as 10-701 Machine Learning, 10-315 Machine Learning, and 10-715 Advanced Introduction to Machine Learning, where she has mentored numerous students in both theoretical and applied aspects of the field. Her research group focuses on developing theoretically sound yet practically applicable machine learning algorithms, with particular attention to algorithm configuration, data-driven optimization, and learning in strategic environments. Current projects involve learning to improve combinatorial algorithms, designing revenue-maximizing mechanisms, and developing robust learning frameworks that can operate effectively in challenging environments.
Lecturer
AI Xin is a Lecturer in the School of Computing at the National University of Singapore (NUS), specializing in Artificial Intelligence and Data Science. She teaches courses such as machine learning, deep learning, and data mining, including advanced modules like CS4225 and CS5425. Education: Ph.D. in Electrical and Computer Engineering from NUS; B.Eng. from Xidian University, China. Her research spans Game Theoretical Modelling , Optimization Methods , Algorithm Design , and Wireless Networks . She has contributed to multi-agent systems, algorithmic game theory, and wireless community networks, focusing on robust and distributed solutions. Her recent publications highlight trends in game theory for wireless networks , distributed coverage algorithms , and optimization for network efficiency , with a strong emphasis on theoretical and practical applications in AI and networking. Scientific Awards: Teaching Excellence Award (NUS, 2024). She has taught courses on Big Data Systems for Data Science and Computational Thinking , bridging academic rigor with industry relevance through her prior experience in risk management, supply chain, and sales at BHP Billiton Marketing Asia.
Professor
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Professor
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
Researcher
Julia Kurz is a Researcher at TU Dortmund University's Department of Sociology of Science and Technology, Faculty of Social Sciences. She leads the 'Visually Integrated Clinical Collaboration' project within the Collaborative Research Center (SFB) Media of Cooperation. Her work spans healthcare informatics, educational research, and disability studies. Education: M.A. in Sociology from the University of Siegen (2011), with prior studies at Philipps-University Marburg. Professional experience includes research roles at the University of Siegen (2012–2023) and support work with disabled populations (since 2001). Research focuses on information workflows in hospitals, collaborative data practices, and participatory technology design. Recent talks address topics like disempowerment in participation frameworks and challenges in transdisciplinary research. Her publications explore medical visualization tools, mentoring impacts, and role-playing pedagogy. Award recognition details are not explicitly listed, but her grants include SFB funding for the Media of Cooperation project. Advising activity is not documented here. She contributes to seminars on competence, knowledge theory, and educational innovation.
Associate Professor
Dr. Ralph Evins is an Associate Professor and Director of the Graduate Program in the Department of Civil Engineering at the University of Victoria. He holds affiliations with the Urban Energy Systems laboratory at Empa and ETH Zurich in Switzerland. His expertise spans building energy simulation, energy system optimization, and machine intelligence applications in sustainable design. Evins holds an MEng from Imperial College London and an EngD from the University of Bristol. His research focuses on computational problem-solving in energy systems, including surrogate modeling, optimization algorithms, and machine learning. He develops tools like the Holistic Urban Energy Simulation (HUES) platform and BESOS software framework to bridge building, district, and city-scale energy analysis. His work emphasizes holistic systems thinking, integrating energy hubs, thermal modeling, and digital twin technologies. Recent articles explore surrogate model refinement, inverse modeling for building characterization, and decarbonization strategies. He collaborates with industry to translate academic innovations into practical solutions. Evins advises students in energy systems and leads projects on net-zero building design, retrofit prioritization, and smart grid integration. His research addresses challenges in climate adaptation, energy efficiency, and sustainable urban development through interdisciplinary approaches.
Assistant Professor
Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.
Assistant Professor
Smita Ghosh is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University, part of the College of Arts and Sciences. Her research focuses on social network analysis, algorithms for information diffusion, and applications in cybersecurity, disaster management, and machine learning. She holds a B.Tech. from the West Bengal University of Technology, India, and an M.S. and Ph.D. from the University of Texas, Dallas. Her work addresses challenges in rumor containment, clickbait detection, and optimizing network models for social media content analysis. Recent publications include studies on hypergraph-based solutions for rumor blocking and stochastic models for emergency response in social networks. She also explores cross-modal topic modeling for enhancing content detection algorithms. Notable contributions include developing data-driven strategies for identifying hate speech spreaders and improving wildfire severity predictions using environmental features. Her research bridges theoretical computer science with real-world applications in public health, education, and disaster management. Her academic contributions include organizing conference proceedings like the 18th International Conference on Algorithmic Aspects in Information and Management (AAIM 2024). She actively contributes to educational initiatives such as the Classroute project, creating multilingual educational content for Punjabi and Urdu speakers.
Professor
Elham Kazemi is a Professor of Mathematics Education at the University of Washington’s College of Education, holding the Geda and Phil Condit Endowed Chair. She specializes in elementary teacher education, instructional leadership, and equity-focused mathematics pedagogy. Her research emphasizes professional learning communities, student-centered classroom practices, and disrupting inequities in education. Education: PhD in Mathematics Education, UCLA (1999) MA in Educational Psychology, UCLA (1997) BS in Psychology (Elementary Teaching Certificate), Duke University (1992) Research Interests: Kazemi’s work focuses on equitable mathematics instruction, teacher learning, and leadership development. She designs school-wide professional development models (e.g., Math Labs) and studies classroom discourse, student disciplinary identities, and organizational learning. Key projects include the Labs Project (funded by JSMF) and RMLL (NSF-funded leader learning). Publications & Grants: Over 40 peer-reviewed articles, including work in American Educational Research Journal and Journal of Teacher Education . Co-authored influential books like Choral Counting and Counting Collections and Intentional Talk . Recipient of $5.4M+ in grants from NSF, McDonnell Foundation, and others for projects on teacher leadership and equitable instruction. Awards: 2022 AERA Open Article of the Year 2019 Outstanding Faculty Award for Educational Equity 2015 Geda & Phil Condit Professorship Advising & Leadership: Kazemi co-leads initiatives like the Leading Towards Racially Just Mathematics Instruction partnership. She has supervised numerous graduate students and collaborates with schools to implement equity-driven practices. Labs & Teams: Her work includes the Labs Project (designing Learning Labs for mathematics/literacy discussions) and the Mathematics Education Project (MEP), fostering statewide collaboration among educators.
Assistant Professor
Austin Rovinski is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University’s Tandon School of Engineering. He specializes in chip design, electronic design automation (EDA), and open-source hardware methodologies. His research focuses on VLSI design, domain-specific accelerators, and chiplet-based systems. Prior to NYU, he held a postdoctoral position at Cornell University and earned all his degrees (Ph.D., M.S., and B.S.) from the University of Michigan. Education: Ph.D., Electrical Engineering, University of Michigan - Ann Arbor Master’s, Electrical Engineering, University of Michigan - Ann Arbor Bachelor’s, Electrical Engineering, University of Michigan - Ann Arbor Research Focus: Developing open-source EDA frameworks like OpenROAD Optoelectronic interconnect systems for 2.5D packaging Agile hardware design methodologies Reconfigurable sparse matrix accelerators RISC-V-based manycore processors (e.g., Celerity project) Key Contributions: Austin led the development of the OpenROAD RTL-to-GDS flow and contributed to the Sirius and Celerity projects. His work emphasizes reproducibility, democratizing chip design through open-source tools. Awards: IEEE Micro Top Picks (2015) Michigan EECS Outstanding Research Award (2016) NSF Graduate Research Fellowship Honorable Mention (2017, 2018) Advising & Grants: Actively mentors graduate students in chip design and EDA. His research is supported by NYU’s Tandon School of Engineering and collaborations with industry partners. Labs & Teams: Core contributor to the OpenROAD project, part of NYU’s hardware design and EDA initiatives, and collaborator on the Celerity manycore processor project.
Clinical Professor
Ahmed Ferhadi is a Clinical Professor of Middle Eastern & Islamic Studies at New York University (NYU), specializing in linguistics with a focus on Second Language Acquisition, Sociolinguistics, and Language Testing. He holds a PhD in Linguistics from the University of Michigan (1990), an MA in Teaching Arabic as a Foreign Language (TAFL) from the same institution, and an M.S. in Applied Linguistics from the University of Edinburgh. His teaching career spans institutions like Princeton University, Columbia University, and Kurdish universities such as Salahuddin and Suleimaniyya Universities. He pioneered video-based language performance evaluation methods during his tenure at Middlebury College in 1991, which became a standard practice. Ferhadi has also served in high-level advisory roles, including President of the Association of American Teachers of Arabic (AATA) since 2017 and Distinguished Global Scholar of Kurdish Studies at American University (2010). Research interests include Kurdish language standardization, advanced Arabic pedagogy, and the sociopolitical dimensions of language. His projects include a Kurdish standardization initiative and curriculum development for advanced Arabic learners. Ferhadi has received numerous accolades, including the Golden Teaching Award (NYU 2000) and Top Language Award (U.S. Department of State 2006). His publications span linguistic theory, pedagogical techniques, and cultural studies, with recent works addressing Arabic dialect dynamics and Kurdish language policy. Ferhadi’s work bridges academic rigor with practical applications, emphasizing technology’s role in modern language education.
Professor
Dr. David Burton is a Professor in the Department of Plant, Food, and Environmental Sciences at Dalhousie University's Faculty of Agriculture. He serves as Director of the Centre for Sustainable Soil Management and leads initiatives in soil health, greenhouse gas emissions, and sustainable agricultural practices. His teaching spans undergraduate and graduate courses in soil science, nutrient management, and climate change. Research focuses on microbial metabolism in soil, nitrogen cycling, and the environmental impacts of agricultural practices. He co-founded the Atlantic Soil Health Lab and manages the Greenhouse Gas Analysis Lab. Key affiliations include the Canadian Society of Soil Science (Fellow), Soil Conservation Council of Canada, and Fertilizer Canada's 4R Research Network. Recent work emphasizes soil's role in climate resilience, including presentations on regenerative farming and soil carbon sequestration. He collaborates with government and industry to develop climate-smart soil management policies and tools for nitrogen management optimization. Awards: Fellow of the Canadian Society of Soil Science Labs: Centre for Sustainable Soil Management, Greenhouse Gas Analysis Lab, Atlantic Soil Health Lab Grants: NSERC CREATE Climate Smart Soils
Associate Professor
Jennifer Lynes is an Associate Professor at the University of Waterloo in the Department of Environment and Business, where she co-directs the Sustainability & Financial Management program. She also serves as Chair of REEP Green Solutions and co-founded the North American Sustainable Concerts Working Group. Current roles: Associate Professor, Co-Director (Sustainability & Financial Management), REEP Green Solutions Chair Education background: Marketing and Environmental Studies Her research bridges business and environmental sustainability, focusing on: Social and community-based green marketing Youth environmental engagement programs Residential energy conservation behavior Sustainable tourism and hospitality practices Recent publications analyze: Water efficiency programs and behavioral change Electric vehicle adoption strategies Textile waste management in fashion consumption Sustainable eating behaviors in Canadian universities Scientific recognition includes: Emerald Publishing Group Highly Commended Paper (2014) She teaches courses such as: ENBUS 102: Introduction to Environment and Business ENBUS 309: Applied Social Marketing ENBUS 630: Enterprise Strategies for Social Accountability
Professor
Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.
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