Find Academic Supervisors

Zijin ZhangView profile

Assistant Professor

• Operations Management
• Data-Driven Decision Making
• Algorithm Design
• +4 more

Zijin Zhang is an Assistant Professor of Business Analytics at the Carroll School of Management, Boston College. She holds a Ph.D. in Technology & Operations from the University of Michigan's Ross School of Business and a B.S. in Mathematics and Statistics from Nanjing University. Her office is located in Fulton Hall 454B at Boston College. Her research centers on data-driven decision-making in operations management, organized around three pillars: Engineering value : Designing algorithms for real-time decisions under uncertainty Economic value : Optimizing data acquisition costs versus decision impact Social value : Examining ethical implications of data practices and technology regulation Applications span retail operations, AI markets, and public-sector resource allocation. Recent publications focus on algorithmic decision-making in operations management, with recurring themes of data optimization, fairness in resource allocation, and policy impact analysis. Methodologies combine optimization, game theory, and machine learning. Awards & Honors: Rackham Doctoral Intern Fellowship (2024) Michigan Ross China Research Award (2023) First Prize, National Olympiad Informatics (2013) Thomas William Leabo Fellowship (2022-2023) DSI Doctoral Research Showcase Finalist (2025) Teaching includes Operations Management (TO 313) at University of Michigan with a 4.9/5.0 evaluation score. She has received multiple research grants including Rackham Research Grant (2024) and Ross School Doctoral Grant (2022). Engages in academic service as INFORMS conference session co-chair (2024) and PhD program panels. Maintains industry connections through past internships at Oracle and CITIC Group.

• Motion Planning for Autonomous Vehicles
• Formal Methods
• Safe Reinforcement Learning
• +3 more

Xiao Wang is a research assistant and PhD student in the Cyber-Physical Systems Group at the Technical University of Munich since 2019. She holds a Master of Science in Mechanical Engineering from the same university (2018) and a Bachelor of Engineering in Vehicle Engineering from Tongji University, China. Her research focuses on Motion Planning for Autonomous Vehicles , Formal Methods , and Safe Reinforcement Learning . She has supervised multiple theses exploring topics like constrained RL, online verification, imitation learning, and safety falsification for autonomous systems. Her teaching roles include exercises and practical courses on Artificial Intelligence and Motion Planning for Autonomous Vehicles since 2018. Her publications (2020–2023) span journals like Transactions on Machine Learning Research and conferences such as ITSC and FISITA , addressing challenges in safe RL, control barrier functions, and naturalistic traffic rule violations. She has also contributed to integrating the Apollo framework with the CommonRoad motion planning environment. Key research areas: Safe Reinforcement Learning, Motion Planning, Formal Verification, Autonomous Driving, Control Barrier Functions, Trajectory Prediction

Christopher OnderView profile

Lecturer

• Control Engineering
• Mechanical Engineering
• Energy Systems
• +3 more

Christopher Onder is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where he serves as Deputy Head of the Institute for Dynamic Systems and Control. His research focuses on control engineering, energy systems, and sustainable transportation solutions. Role : Lecturer, Deputy Head of Institute Department : Mechanical and Process Engineering Institute : Dynamic Systems and Control University : ETH Zürich His research interests include: Control systems for hybrid and electric vehicles Energy management optimization Thermal comfort in public transport Co-design of mechanical and racing strategies Nonlinear control in aerospace applications Model-based calibration for diesel engines Contact: onder@idsc.mavt.ethz.ch

Charith MendisView profile

Assistant Professor

• Compilers
• Programming Languages
• Machine Learning for Systems
• +3 more

Charith Mendis is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Computer Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on the intersection of compilers, program optimization, and machine learning systems. Dr. Mendis received his educational background from prestigious institutions: Ph.D. in Computer Science from Massachusetts Institute of Technology (2020) S.M. in Computer Science from Massachusetts Institute of Technology (2015) B.Sc. in Electronics and Telecommunication Engineering from University of Moratuwa (2013) His primary research interests center around compiler technology and machine learning systems. Mendis leads the ADAPT lab at UIUC, where his team works on creating high-performance ML optimization techniques and automated compiler construction using machine learning and formal methods. His work bridges the gap between traditional compiler design and modern machine learning approaches, with applications in tensor compilers, graph neural networks, and sparse computation. He has developed novel frameworks for optimizing deep learning workloads, verification of compiler transformations, and performance modeling for emerging hardware architectures. Mendis has established himself as a leading researcher in compiler optimization for machine learning systems, with a particular focus on tensor compilers, graph neural networks, and performance modeling. His recent publications demonstrate increasing sophistication in combining formal methods with machine learning techniques to solve challenging problems in compiler optimization and verification, with multiple papers accepted at top-tier conferences including OOPSLA, PLDI, POPL, and SIGMOD. His notable scientific achievements include: Google ML and Systems Junior Faculty Award (2025) DARPA Young Faculty Award (2024) NSF CAREER Award (2024) Distinguished Paper Award at POPL (2025) William A. Martin Thesis Award for Outstanding SM thesis, MIT (2015) Multiple teaching excellence awards at UIUC (2021-2023) Dr. Mendis actively mentors students through the ADAPT lab, offering research opportunities for undergraduates, master's students, and PhD candidates interested in compiler technology and machine learning systems. His research is supported by significant funding from the ACE center (part of JUMP 2.0), National Science Foundation (NSF), DARPA, IIDAI, and industry partners including Google, Intel, Amazon, and Qualcomm. He teaches advanced courses in compiler construction and machine learning for compilers. He leads the ADAPT lab at UIUC, which focuses on developing advanced compiler technologies for modern machine learning workloads. The lab maintains active collaborations with industry partners and has established itself as a leading research group in compiler optimization for AI systems. Current projects include tensor compilers, graph neural network optimization, and automated verification of deep learning systems.

Yikun BanView profile

Associate Professor

• Reinforcement Learning
• Data Mining
• Neural Networks
• +7 more

Yikun Ban is a tenure-track Associate Professor in the School of Computer Science and Engineering at Beihang University, where he is a member of the State Key Laboratory of Software Development Environment. He earned his PhD in Computer Science from the University of Illinois Urbana-Champaign (2023), MS in Computer Science from Peking University (2019), and BS in Software Engineering from Wuhan University (2016). PhD: University of Illinois Urbana-Champaign (2023) MS: Peking University (2019) BS: Wuhan University (2016) His research focuses on principled algorithms for reinforcement learning with human feedback, neural contextual bandits, and exploration-exploitation problems. He develops frameworks combining deep learning with bandit theory for applications in recommendation systems, disinformation detection, and dynamic graph learning. Recent publications address: Robust neural contextual bandits (NeurIPS 2024) Graph neural bandits (KDD 2023) Meta-learning for bandit scheduling (NeurIPS 2023) Clustering in contextual bandits (WWW 2021, AAAI 2021) Honors include the NeurIPS Scholar Award and recognition as ICML Outstanding Reviewer . His open-source LOCB repository provides Python implementations for contextual multi-armed bandit algorithms with local clustering, supporting applications in recommendation systems and online learning.

William HobbsView profile

Assistant Professor

• Politics and Health
• Social Spillover Effects of Government Actions
• Social Networks and Adaptation
• +7 more

William Hobbs is the Lois and Mel Tukman Assistant Professor in the Department of Psychology at Cornell University , affiliated with the College of Human Ecology. His research intersects politics and health , focusing on social spillover effects of government actions and adaptation to life changes through computational social science methods. Teaches Data Science for Social Scientists I & II (HD/Psych 2930/2940) Co-teaches graduate course Text and Networks in Social Science Research (HD/Soc/Info 6610, Govt 6619) Research strengths include causal inference , representative sampling , and machine learning applications for small training sets. His work has been featured in The Atlantic , Science Magazine , and other major outlets. Current Data Science Lab projects analyze: Political polarization in social media Health behavior networks Government policy feedback Content moderation systems Lab hires Cornell undergraduates with R/Python experience for data management tasks through HD 4010 research credit.

Shweta SinghView profile

Assistant Professor

• Artificial Intelligence
• Responsible AI
• Explainable AI
• +7 more

Dr. Shweta Singh serves as an Assistant Professor of Information Systems and Management at Warwick Business School, University of Warwick. She concurrently holds prestigious appointments as a Fellow at the Warwick Institute for Global Sustainability Development (IGSD) and a Behavioral Data Science researcher at The Alan Turing Institute in London. Her academic journey includes a Ph.D. in Information and Decision Sciences from the Carlson School of Management at the University of Minnesota, complemented by dual Master's degrees in Computer Science and Applied Economics from the same institution. Ph.D. in Information and Decision Sciences, University of Minnesota Master's in Computer Science, University of Minnesota Master's in Applied Economics, University of Minnesota Dr. Singh's research centers on developing ethical and responsible artificial intelligence systems that address societal challenges. Her work specifically targets mitigating AI bias, creating explainable AI frameworks, and leveraging technology to combat societal injustice. She investigates how digital platforms, sharing economy models, and IT outsourcing create business value while ensuring these technologies promote sustainability and reduce inequalities. Her innovative approach combines technical AI expertise with deep social awareness, particularly focusing on gender equality and child protection in digital spaces. Her publication record demonstrates consistent high-impact contributions to Information Systems Research, International Conference on Information Systems, and related venues. The trajectory of her work shows increasing focus on practical applications of responsible AI, with recent projects addressing online child safety and human trafficking prevention. Her research increasingly intersects with policy development, as evidenced by her contributions to UK Parliamentary Office of Science and Technology briefs. Doctoral Dissertation Fellowship, University of Minnesota McNamara Fellowship, University of Minnesota Social Impact Project of the Year shortlist (2023) Asian Women of Achievement Award finalist (2023) British Indian Awards finalist (2019) Top 5 Women in Tech for Good Award shortlist (2022) Inspiring 50 UK recognition (2025) Dr. Singh actively mentors students and has been recognized with the Staff Social Inclusion Award (2024) for her teaching excellence. Her advisory roles extend beyond academia to include the UN Women UK delegation for the Commission on the Status of Women and the Advisory Board of AI retail company 'Love the Sales'. She serves as an external collaborator for Boston Consulting Group's Henderson Institute, bridging academic research with industry applications. Through her leadership in the ISM-Analytics (ISMA) Group at Warwick, Dr. Singh fosters interdisciplinary collaboration focused on creating socially responsible technological solutions. Her work with the IGSD specifically targets UN sustainability goals related to reducing inequalities and promoting inclusive societies through responsible AI implementation.

Zhe ZhangView profile

Assistant Professor

• Business Analytics
• Fairness and Bias in Algorithms
• Sharing Economy
• +3 more

Zhe Zhang is an Assistant Professor of Innovation, Technology, and Operations at the Rady School of Management, University of California San Diego (UCSD). He holds a Ph.D. in Information Systems and Management from Carnegie Mellon University's Heinz College and dual bachelor's degrees in Economics and Statistics from Stanford University. His research focuses on the societal and spillover impacts of information technology, including fairness in algorithmic decision-making, sharing economy dynamics, and digital transformation effects. Ph.D., Carnegie Mellon University (Heinz College) B.S. in Mathematical and Computational Sciences, Stanford University B.A. in Economics (with honors), Stanford University His work spans disciplines like machine learning, applied microeconomics, and operations management. Current research includes analyzing cashierless retail technology's operational and behavioral impacts, algorithmic bias mitigation strategies, and the economic implications of Amazon Prime adoption. Key findings highlight how digital innovations reshape consumer demand, manufacturer strategies, and algorithmic fairness outcomes. Recent publications address: Bias amplification through data imputation in healthcare Strategic overfitting in data science contests Sharing economy's effect on durable goods markets He has presented at top conferences in information systems (CIST, WISE), economics (NBER), and computer science (KDD, FAccT). Awards include runner-up for the ACM SIGMIS Doctoral Dissertation Award (2019) and a POMS 2017 Supply Chain Management best paper finalist. Prior to his current role, Zhang worked as a part-time Data Creative staff member at DataKind (NYC) and was a 2016 fellow at the Data Science for Social Good Summer Fellowship (Chicago). He has also contributed to fairness methods in AI at Facebook and nonprofit research at NRDC and Union of Concerned Scientists.

Molly CrockettView profile

Professor

• Cognitive Science
• Ethics
• Social Epistemology
• +6 more

Molly Crockett is a Professor at Princeton University's Department of Psychology and affiliated with the University Center for Human Values . Her research integrates cognitive science, social psychology, philosophy, and data science to examine how systems of power shape knowledge production, and how technologies like artificial intelligence reinforce social inequalities. Education: Ph.D. from the University of Cambridge Her lab focuses on epistemic injustice , using behavioral experiments, computational modeling, and machine learning to study moral cognition, narrative testimony, and the cultural evolution of ethics. Recent work highlights AI's role in distorting scientific understanding and explores how social norms constrain self-knowledge. Key article trends include analyses of algorithmic bias in social networks, moral outrage dynamics, and the interplay between self-perception and societal expectations. She actively mentors postdoctoral, PhD, and undergraduate researchers, emphasizing diversity and inclusion in scientific practice. Publications span topics from neurobiological markers of guilt to positive illusions in relationships , reflecting her interdisciplinary approach. Her lab avoids tech industry funding to maintain research autonomy.

Sadegh TalebiView profile

Assistant Professor

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +2 more

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Ming JinView profile

Assistant Professor

• Trustworthy AI
• Cyber-physical systems
• Machine Learning
• +4 more

Ming Jin is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a PhD from UC Berkeley and a B.Eng. from Hong Kong University of Science and Technology. His research focuses on trustworthy AI, CPS security, and energy systems, with affiliations to the Power and Energy Center and Autonomy and Robotics @ VT. Education: PhD in Electrical Engineering and Computer Science (UC Berkeley, 2017), B.Eng. (Honors) in Electronic and Computer Engineering (HKUST, 2012). Postdoc in Industrial Engineering and Operations Research at UC Berkeley. Research interests include safe reinforcement learning, foundation models, cybersecurity, and power systems. Awards include the Siebel Scholarship (2018) and first place in the 2021 CityLearn Challenge. Active in conference organization (e.g., ICML, AAAI) and tutorial development on topics like Safe RL and CPS security. Grants include NSF support for embodied optimization (2025), Amazon-VT Initiative (2023), and Commonwealth Cyber Initiative projects. Involved in labs focused on AI, robotics, and energy systems. Publications span AI safety, RL frameworks, and CPS resilience, with over 50 peer-reviewed articles since 2015.

A.S. RichterichView profile

Associate Professor

• Digital Cultures
• Hacker and Maker Communities
• Feminist Technology Activism
• +3 more

A.S. Richterich is an Associate Professor in Digital Cultures at Maastricht University’s Faculty of Arts and Social Sciences (FASoS), Department of Literature & Art. Their research focuses on social practices involving digital technology, particularly in hacker/maker communities and feminist technocultural spaces. They hold a PhD in Digital Media Studies from the University of Siegen (2012) and conducted postdoctoral research at the University of Sussex as a Marie Skłodowska-Curie Fellow (2019-2021). Key areas of expertise include hacker/makerspace dynamics, digital literacy, feminist technology activism, and ethnographic methods. Richterich has published widely in journals such as *Information, Communication & Society* and *Learning, Media, & Technology*, examining topics like data solidarity, design thinking critiques, and gendered participation in tech communities. Recent projects include investigating feminist automation ethics and the socio-political implications of hackathons. They received the Marie Skłodowska-Curie Fellowship (2019-2021) for research on experiential learning in maker cultures. Current work explores marginalized groups’ engagement with digital technologies, including women-only developer communities and feminist hackerspaces.

Alan RubelView profile

Professor

• Information Ethics
• Privacy and Security
• Surveillance
• +4 more

Alan Rubel is a Professor and Director of the Information School at the University of Wisconsin-Madison. He also serves as a member of the Department of Medical History & Bioethics and an affiliate of the UW Law School. His research focuses on information ethics, policy, and law, particularly addressing privacy, surveillance, algorithmic decision-making, and bioethics. Rubel holds a PhD in Philosophy (2006) and a JD (2003), both from the University of Wisconsin-Madison. He has held academic positions since 2010, including visiting researcher roles at the 4TU Centre for Ethics and Technology in the Netherlands. His work bridges philosophy, law, and technology, emphasizing ethical challenges in automated systems and data-driven societies. Education: PhD in Philosophy (2006), JD (2003), both from UW-Madison. Professional roles include former Director of the Center for Law, Society & Justice, and prior fellowships at Johns Hopkins and Georgetown Universities. Rubel’s interdisciplinary approach addresses contemporary issues like learning analytics, neurotechnology ethics, and digital privacy. His research highlights privacy in higher education, ethical implications of AI, and bioethical dilemmas in emerging technologies. Notable contributions include the book Algorithms and Autonomy: The Ethics of Automated Decision Systems (2021) and influential articles on algorithmic transparency, surveillance, and information fiduciaries.

• Journalism
• Communication Technology
• Social Media
• +3 more

Yee Man (Margaret) Ng is an Associate Professor in Journalism and the Institute of Communications Research at the University of Illinois. She combines data science methodologies with communication research to study social media dynamics, digital journalism, and technology adoption patterns. Her research interests include: Computational social science Social media analytics Data visualization techniques Communication technology impacts Journalism innovation Post-adoption technology behavior Ng's methodology integrates big data analysis, machine learning algorithms, and traditional survey/experimental approaches. Current work extends diffusion of innovations theory by examining social media users' discontinuance and readoption behaviors. Academic contributions span both empirical research and educational practice. She has taught courses like: JOUR 460 - Data Storytelling JOUR 410 - Data Storytelling for Journalism JOUR 425 - Multimedia Editing and Design JOUR 199 - Hands-on Media Technology Her publications analyze topics ranging from YouTube's recommendation algorithms to digital sustainability in news media, with particular focus on: Platform migration patterns Algorithmic content moderation Political communication dynamics Media technology adoption Regional web usage differences Incivility measurement techniques

David JensenView profile

Professor

• Machine Learning
• Causal Modeling
• Artificial Intelligence
• +3 more

David Jensen is a Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He directs the Knowledge Discovery Laboratory and the Computational Social Science Institute. His research focuses on machine learning, causal modeling, and analyzing large social, technological, and computational systems. Jensen's work is supported by organizations like the National Science Foundation and DARPA. Education: DSc in Engineering and Policy, Washington University in St. Louis (1992) MS in Engineering and Policy, Washington University in St. Louis (1988) BS in Mechanical Engineering, University of Nebraska (1986) Research Interests: Causal inference in relational and dynamic systems Machine learning applications in security and privacy Computational social science Large-scale network analysis Achievements: Recipient of teaching awards from UMass College of Natural Sciences (2011) and CICS (2022) 2017 IEEE INFOCOM Test of Time Paper Award Leadership roles in conferences and journals, including action editor for the Journal of Machine Learning Research Labs and Affiliations: Founder of the Knowledge Discovery Laboratory (2000) Director of the Computational Social Science Institute (2018-2022) Member of the Computing Community Consortium (CCC) Council